Improving equitable healthcare resource use: Developing a neighbourhood district nurse needs index for staffing allocation

doi:10.21203/rs.3.rs-4431791/v1

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Research Article

Improving equitable healthcare resource use: Developing a neighbourhood district nurse needs index for staffing allocation

https://doi.org/10.21203/rs.3.rs-4431791/v1

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Journal Publication

published 08 Nov, 2024

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Background

Allocating health care resources to local areas in proportion to need is an important element of many universal health care systems, aiming to provide equal access for equal need. The UK National Health Service allocates resources to relatively large areas in proportion to need, using needs-weighted capitation formulae. However, within those planning areas, local providers and commissioners also require robust methods for allocating resources to neighbourhoods in proportion to need to ensure equitable access. We therefore developed a local resource allocation formula for NHS district nursing services for a City in the North West of England, demonstrating a novel approach for equitable resource allocation to small areas.

Methods

Using linked data from community health services, primary care, secondary care and social care, we used a zero-inflated Poisson regression to model the number of district nursing services contacts for each individual based on predictors of need, while including the supply of district nurses per head to account for historical supply induced patterns. Individual need was estimated based on the predictions from this model, keeping supply fixed at the average. We then compared the distribution of district nurses between neighbourhoods, based on our formula, to the current service staffing distribution.

Results

Key predictors of need for district nursing services were age, deprivation, chronic diseases such as, cardiovascular disease, chronic liver disease, neurological disease, mental ill health, learning disability living in a nursing home, living alone, and receiving palliative care. Need for district nursing services was highly weighted towards older and more deprived populations. The current distribution of staff was, however, more correlated with age than deprivation. Moving to a needs-based staffing distribution would shift staff from less deprived areas to more deprived areas potentially reducing inequalities.

Conclusion

A neighbourhood-level model for needs for district nursing is a useful tool that can potentially improve the allocation of resources, addressing unmet need and inequalities.

District nurses provide key community services to help people with health care needs live independently.¹ These services are commonly delivered through collaborations between health care and social care teams, including General Practitioners (GPs), other community and mental health nurses, therapists, other health professionals and social care workers.¹ These teams are usually organised around neighbourhoods or groups of GP practices. To ensure equitable access to these services differences in staffing levels need to match differences in need across neighbourhoods. Whilst there have been some previous studies developing tools to support the management of community nursing workloads, enabling safe staffing levels,² there have been no previous models, to our knowledge, developed to inform the needs based-distribution of district nurse staffing levels between geographical neighbourhoods.

In the UK, as in many other countries, the NHS uses weighted capitation formulae to distribute health care funding to health care planning regions called Integrated Care Boards (ICBs). These are relatively large areas with an average population of 1.5 million people.³ The formulae are applied to separate service areas (e.g., general and acute, mental health, prescribing and primary care services) that along with adjustment for differences in the cost of providing services in each area are used to compute need-based shares for each ICB. The general approach is to use a regression model with utilisation as the outcome and a large set of need (e.g., age, sex, long-term conditions, deprivation) and supply (e.g., distance to hospitals, waiting times) related factors as independent variables.^4,5 Need is then estimated as predicted utilisation whilst holding the supply variables constant, therefore estimating patterns of utilisation that are driven by need and not differences in supply. ^6–8 In 2019 a national formula was developed for community services (including district nursing).⁹ This formula only included a limited number of needs variables (age, sex and area deprivation) and was modelled using data from one region in the South East of England, as national data was not available.

The methods used for national allocation of resources between regions, can also be applied within regions to inform allocation of resources, such as staffing levels, between neighbourhoods. Increasingly regional linked data resources¹⁰ are becoming available for health care planning and these can be used to provide rich information on patterns of need, enabling needs-based workforce allocation.

In this study, we examined the district nursing services in Liverpool, a city in the North West of England with 16 neighbourhood teams. The staffing levels of these teams have not previously been set based on any formal assessment of need. We used newly available linked data across primary, secondary care and community services to develop a needs-weighted capitation measure for district nursing across the city in analysis and assess how staffing levels would need to change to better reflect patterns of need.

2.1. Data

We use anonymised linked data for all adults (age18+) living in Liverpool and registered with a GP in 2021. This included primary care data, extracted from GP practice clinical systems, linked to data on use of community services from the community services dataset (CSDS)¹¹, information on secondary care usage (hospital admissions and A&E attendances) from the secondary users dataset.¹² All data was linked at the individual level by the NHS Data Services for Commissioners Regional Office and made available as an anonymised extract to the regional relevant authority, NHS Cheshire and Merseyside Integrated Care Board (ICB). We further link these two datasets with the index of multiple deprivation (IMD),¹³ which categorizes small areas of England according to their level of deprivation. The variables used in the analysis included age, gender, Index of Multiple Deprivation (IMD)¹³, whether died during the year, days alive during the year (logged), being in a care home, living alone, receiving palliative care, having a learning disability, dementia, cancer, cardiovascular disease, chronic liver disease, chronic obstructive pulmonary disease (COPD), neurological disease, mental health problems, number of visits to accident and emergency in the year, elective and emergency admissions to hospital in the year, number of general practitioners (GP) consultations, and number of district nurses in the team serving the individual as a proportion of the catchment population of the team. The codes used to extract these diagnostic variables were based on nationally agreed algorithms that are used to provide financial compensation to GPs through the national Quality and Outcomes Framework.¹⁴ Our outcome measure was the number of contacts with district nurses within the year as recorded within the CSDS data.

2.2 Analysis

We used the following model to generate the coefficients associated with each predicting variable:

$${Contacts}_{i}=\alpha +{{\beta }_{1}Age}_{i}+{{\beta }_{2}Sex}_{i}+{{\beta }_{3}IMD}_{i}+ {{{\beta }_{4}Age}_{i}\times Gender}_{i}+ {{{\beta }_{5}Age}_{i}\times IMD}_{i}+ {\gamma X}_{i}+{\delta H}_{i}+\rho {U}_{i}+{\theta DN}_{i}+ {\epsilon }_{i}$$

Where ${Contacts}_{i}$ was the number of times individual $i$ used a district nursing service, in the year in analysis.

${Age}_{i}$ was a vector containing five age intervals: 18–29, 30–49, 50–69, 70–89 and 90 + years old.

${Sex}_{i}$ was a variable taking value 1 if the individual’s phenotypical sex was male and 0 if female.

${IMD}_{i}$ was a vector containing IMD quintiles.

${X}_{i}$ was a vector containing other sociodemographic variables (e.g., ethnicity, living in a nursing home).

${H}_{i}$ was a vector containing chronic health conditions (e.g., dementia, use of palliative care, learning disabilities, dead).

${U}_{i}$ includes variables of utilization of health care services (e.g., emergency care, GP consultations)

${DN}_{i}$ corresponds to the proportion of district nurses to the population in the neighbourhood where individual $i$ lives.

We used a zero inflated Poisson model,¹⁵ to account for the count nature of the activity data with a high proportion of people with zero utilisation.¹⁶ This model is composed by two parts: (1) The first stage accounted for the probability of using district nursing, (2) The second stage accounted for the number of times the individual used district nursing conditional on being predicted to use it at least once.

After estimating the utilisation model, we computed our estimate of need for each individual in the population as the predictions from this model using the estimated model coefficients. Supply side effects (i.e., proportion of district nurses in a neighbourhood) were sterilized, by setting this level to be equal to the mean level of the entire city.^17,18 Following the procedure used on national NHS resource allocation formula¹⁹, where we find that being form a minority ethnic group is associated with lower utilisation (conditional on other measures of need), we assume that this is due to lower service access and also sterilise this from the prediction of need, so that need is not under estimated due to poor access in this group.

We then derived the needs index for two type of sub-populations, (1) the catchment populations for the 16 current district nursing teams and (2) small neighbourhoods called Lower Super Output Areas (LSOA). LSOA are standardised small geographical areas covering the whole of England, each containing a population of around 1500 people. To derive the local needs index for sub populations we standardise the predicted utilisation levels by dividing the mean of individual predictions for each sub population by the mean predictions for all individuals in Liverpool. Results were also displayed comparing the needs index by age group gender and IMD quintile. We then estimated the needs-based staffing levels by distributing the total number of full-time equivalent district nurses in the city to each of the 16 district nursing teams in proportion to the estimated need and compared that to the actual distribution of district nurses between these teams in 2021.

The total number of contacts in 2021 that patients made with district nursing services was 307,783 contacts from 19,648 individuals, meaning an average of 15.7 contacts per person who had some contact with district nursing. Table 1 displays the summary statistics for all variables used in the analysis, for the full sample, and the sub-sample of people who have used community services at least once in 2021.

Table 1

– Descriptive Statistics
Variable	Full Cohort	District Nursing Users
Number of observations	359774	19648
Number of contacts	307567	307567
Mean contacts per person	0.85 (10.81)	15.65 (43.68)
Age Group
18–29 years	84631 (23.5%)	1163 (5.9%)
30–49 years	122776 (34.1%)	2611 (13.3%)
50–69 years	105359 (29.3%)	6195 (31.5%)
70–89 years	44764 (12.4%)	8624 (43.9%)
90 + years	2244 (0.6%)	1055 (5.4%)
Gender
Female	183207 (50.9%)	10471 (53.3%)
Male	176567 (49.1%)	9177 (46.7%)
Other characteristics
IMD	43.02 (20.62)	43.28 (20.64)
Black, Asian or Minority Ethnicity	57073 (16%)	1336 (7%)
Died within year	3875 (1%)	1837 (9%)
Log (time alive)	5.9 (0.09)	5.87 (0.22)
Nursing home	3858 (1%)	1895 (1%)
Living alone	54703 (15%)	4905 (25%)
Palliative care	2381 (1%)	1425 (7%)
Learning disability	4181 (1%)	516 (3%)
Dementia	4165 (1%)	1830 (9%)
Cancer	7496 (2%)	2247 (11%)
CVD	30921 (9%)	6020 (31%)
CLD	10287 (3%)	1059 (5%)
COPD	12187 (3%)	1850 (9%)
Neurological condition	1511 (0.4%)	430 (2%)
Any mental health problem	106564 (30%)	9894 (50%)
Accident and emergency attendances	0.36 (1.06)	1.03 (1.94)
Elective Admissions	0.02 (0.19)	0.1 (0.39)
Emergency Admissions	0.14 (0.56)	0.71 (1.3)
GP consultations	21.41 (19.85)	43.40 (28.48)
District nurses per 10,000	4.17 (1.58)	4.5 (1.46)
Note: The table displays means and standard errors, for continuous variables, or number of occurrences and percentages, for factor variables.

People using district nursing services tended to be older and slightly more likely to be female (53.3%), compared to the general population. Regarding ethnicity, the distribution of district nursing users were more likely to be “White” when compared to the overall sample (84% vs 93%). All health-related variables (chronic conditions and use of secondary care services) were more prevalent in the subgroup using district nursing services. This group also used more accident and emergency, and GP consultations and were more likely to have died during the year (9% vs 1%). They were also more likely to be served by teams with higher district nursing staff levels.

3.1.1 Regression results

Table 2 shows the results of the first stage zero inflation model of the zero inflated Poisson regression. The first stage regression indicates the association between each variable with not having contact with district nursing. Or to put it another way, having an odds ratio under 1 indicates that characteristics were associated with an increased chance of having some contact with district nurses.

Table 2

Zero-inflation model. Odds Ratios showing factors associated with **not** having any contact with district nursing.
Variable	Odds Ratio	95% CI		p-value
Age (z-score)	0.44	0.46	0.43	< 0.001
IMD (z-score)	0.98	1.00	0.96	0.114
Female gender	1.15	1.21	1.10	< 0.001
Age*Female	0.99	1.02	0.95	0.547
Age*IMD	1.01	1.02	0.99	0.541
Died during year	0.49	0.53	0.44	< 0.001
Black, Asian or other Minority ethnic group.	1.36	1.45	1.28	< 0.001
Days alive during the year (logged)	1.19	1.31	1.08	< 0.001
Nursing care home	0.45	0.49	0.42	< 0.001
Living alone	0.94	0.98	0.90	0.001
Palliative care	0.46	0.51	0.41	< 0.001
Learning disability	0.36	0.40	0.32	< 0.001
Dementia	0.65	0.71	0.60	< 0.001
Cancer	0.49	0.53	0.46	< 0.001
Cardiovascular disease	0.88	0.91	0.84	< 0.001
Chronic liver disease	1.13	1.22	1.05	0.001
COPD	1.02	1.09	0.96	0.438
Neurological condition	0.44	0.51	0.39	< 0.001
Any mental health problem	0.85	0.88	0.82	< 0.001
Accident and emergency attendances	1.00	1.02	0.99	0.721
Elective Admissions	0.53	0.56	0.50	< 0.001
Emergency Admissions	0.72	0.74	0.71	< 0.001
GP consultations	0.98	0.98	0.98	< 0.001
District nurses per 10,000 population	0.95	0.96	0.94	< 0.001

Age, having died during the year, receiving palliative care, living in nursing home or living alone, having been admitted to hospital, were all associated with an increased chance of having some contact with district nurses. Most of the diseases included were associated with an increased chance of having some contact with district nurses, except for chronic liver disease which was associated with a lower chance. Being female, having been alive for a greater proportion of the year and being from a Black or ethnic minority group, was associated with a lower probability of having received some contact with district nursing services. Greater availability of district nurses per capita increased the probability of having at least one contact.

Table 3 shows the results of the second stage regression, indicating the relative association of each factor with the number of contacts with district nursing, conditional on having at least 1 contact.

Table 3

Count model. Rate ratios showing factors associated increased number of district nursing contacts amongst people who have had some contact with district nursing,.
Variable	Rate Ratio	95% CI		p-value
Age (z-score)	2.81	3.10	2.54	< 0.001
IMD (z-score)	1.19	1.20	1.19	< 0.001
Female gender	1.03	1.04	1.03	< 0.001
Age*Female	0.92	0.93	0.91	< 0.001
Age*IMD	1.02	1.03	1.01	< 0.001
Died during year	1.01	1.01	1.01	< 0.001
Black, Asian or other minority ethnic group.	1.45	1.46	1.43	< 0.001
Days alive during the year (logged)	1.08	1.10	1.07	< 0.001
Nursing care home	1.08	1.10	1.06	< 0.001
Living alone	1.19	1.20	1.17	< 0.001
Palliative care	1.29	1.30	1.28	< 0.001
Learning disability	1.54	1.56	1.52	< 0.001
Dementia	2.21	2.25	2.17	< 0.001
Cancer	0.96	0.97	0.95	< 0.001
Cardiovascular disease	0.87	0.88	0.86	< 0.001
Chronic liver disease	1.28	1.29	1.27	< 0.001
COPD	1.11	1.13	1.10	< 0.001
Neurological condition	0.78	0.79	0.77	< 0.001
Any mental health problem	1.45	1.48	1.43	< 0.001
Accident and emergency attendances	1.36	1.38	1.35	< 0.001
Elective Admissions	0.97	0.97	0.97	< 0.001
Emergency Admissions	0.98	0.99	0.97	< 0.001
GP consultations	1.11	1.11	1.11	< 0.001
District nurses per 10,000 population	1.01	1.01	1.01	< 0.001

The second stage shows that age, deprivation, having died during the year, receiving palliative care, number of days alive during the year, living in a nursing home or living alone, being from Black, Asian, and Minority Ethnics group, having had an emergency admission and GP consultations were associated with an

increased number of contacts with the district nursing services. There were important interactions between age, gender and deprivation. Age has a greater effect on number of contacts in more deprived groups and amongst men. Living in a nursing home, living alone, and having used palliative care increased the number of contacts with district nursing services. Long-term conditions such as learning disability, cardiovascular disease, chronic liver disease, neurological disease and mental ill health increased the number of contacts. Conversely dementia, cancer and COPD decreased the number of contacts. Visits to accident and emergency and elective admissions predicted a lower number of contacts. The number of district nurses as a proportion of the population was associated with more contacts.

As we find that in the first stage (zero inflation) regression, being from a minority ethnic group is associated with reduced service uptake, we sterilise this from the prediction of need in the first stage, so that need is not underestimated due to poor access in this group. Figure 1 displays the needs index categorized by age groups and IMD quintiles. These figures represent the extent to which each group's requirement exceeds the overall mean. Essentially, a score of 5 indicates that the need is five times greater than the norm. Age seems is the predominant factor driving need, as older age groups had indexes as high as 11 (eleven times more need than the average population). While needs index was relatively less sensitive to IMD, there was still a positive gradient with people living in more deprived areas having greater needs. The IMD gradient became stronger with age. Our formula resulted in similar predictions between men and women. Yet, it predicted slightly higher needs weights for men in the third deprivation quintile of the 90 + age group.

Figure 2 shows the comparison between current allocation of district nursing staff (on the right) and the needs index based on our formula (on the left). Whilst the current allocation of staffing levels broadly match patterns of need, there are some neighbourhoods that are underserved relative to need.

In Fig. 3, we show how number of full-time equivalent staff would need to vary to better reflect the needs of the population. We can see that variations ranged from approximately minus 6.5 to plus 5 full-time equivalent district nurses per 10,000 inhabitants in the regions with higher variation. On average, needs based staff allocation, in Liverpool, would shift staff from teams that serve populations with relatively lower needs to teams that serve populations with relatively higher needs. But this is not consistent across teams. The team that currently serves the population with the highest needs, would gain an additional 4.5 staff per 10,000 population, if needs-based allocation was applied, whilst the team with the 2nd highest level of need would lose 6.5 WTE per 10,000 population.

We show how population linked health care data can be used to estimate neighbourhood need for district nursing services, highlighting those factors that objectively contribute to increased service need. Whilst this shows that the current allocation of staff to neighbourhood district nursing team in Liverpool does broadly reflect need, it identifies some mismatch between resources and need at a more granular level and some substantive reallocation of staff between teams would be needed, to ensure a more equitable distribution of resources. On average this would shift staff to teams with higher overall levels of need.

Our approach has a number of advantages. The availability of rich data enables the creation of a utilisation formula that accounts for a broader range of factors that contribute to need. Factors not accounted for in the national model, such as ethnicity, long-term conditions, and household circumstances are particularly useful in predicting need and likely to enhance models.²⁰ This is an advance on and a more statistically complete approach than the national model.²⁰ Furthermore, by "sterilizing" variables that may contain supply effects, we can generate predictions based on a more equitable hypothesis where every individual has equal opportunities to access healthcare.^6–8 Ultimately, this approach can help ensure that community services are allocated fairly and efficiently based on local needs.

Despite its benefits, there are potential drawbacks to our approach relying on observable data.^21,22 While our predictions make an important effort to incorporate available information regarding socio-economic characteristics and health conditions, it's important to recognize that utilisation approaches can only predict what is observable. This means that individuals who are not registered with a GP or who have undiagnosed conditions may be excluded from the analysis or not properly accounted for. Also, if there are systematic differences in unmet needs that are not sufficiently accounted for in our measures of supply, our measure would not adequately reflect patterns of total needs. Furthermore, if the observables used in the model are associated with unmet needs,⁶ then our predictions may underestimate need. For example, if individuals with physical disabilities are receiving more district nursing services than the average person but still not enough to meet all their needs, their coefficients will be lower than what they should have been, and their predicted needs will be underestimated. This highlights the importance of considering the limitations of observable data when making predictions and the need to develop complementary approaches to identify and address unmet needs.²³ Additionally, the approach to allocation we outline aims to allocate resources in proportion to need, where need is defined simply as the amount of district nurse contacts a population would expect to use if the supply was set to the mean, i.e. aiming to achieve equal access for equal need. This principle of proportionality is not the only objective of resource allocation. As we are not taking account of or estimating the benefit accrued to these population from the allocation of district nursing resources, we cannot say that this proportional distribution would necessarily be more efficient (i.e. lead to more benefit per unit of cost) or decrease the gap in outcomes between more and less socially disadvantaged groups. ^{24 25,26 27,28}

Ethnicity's inclusion is particularly important given the documented disparities in health outcomes and access to healthcare services among different ethnic groups. However alternative interpretations of the coefficients, to those we present, are plausible. We found that being from a Black, Asian, or other Minority Ethnic group reduced the chances of having any contact with district nurses (conditional of other indicators of need) but increased the numbers of contacts when people did have contact. We interpret this as indicating lower initial service access in these groups, potentially due to either service or patient related factors (e.g. help seeking behaviour), but that when they do have access, they then actually have higher needs. As we think the coefficient on this variable in the initial regression, reflects unequal access, rather lower need, we sterilise this from the prediction of need. It could however be the case that this does reflect lower need for “any contact” in this population.

Implications for policy.

With the increasing availability of linked regional health and social care datasets, for example through the current NHS programme of establishing regional Secure Data Environments, ²⁹ there is the opportunity to apply the methods outlined here for developing effective local formula for estimating service needs. Local approaches for neighbourhood level resource allocation have been underutilised as an approach for addressing unmet needs and health care inequalities. The NHS in England has made addressing inequalities and reducing unmet need a priority.³⁰ The approach we outline provides a powerful tool for local health and care systems to meet these obligations and redistribute district nursing staff according to localised need profiles, which can significantly improve the match between healthcare services and patient requirements. This approach not only aims to ensure that resources are used where they are most needed but also contributes to a more sustainable healthcare system by optimising the use of limited staffing resources.³¹

Furthermore, since this approach relies on the availability of linked, cross-sectoral and rich data of health and care indicators, it is paramount to develop a culture of better, civically oriented and trustworthy sharing of data and robust methods to ensure rigorous curation and quality assurance of data resources, especially enhancing the processes available in the recording of routinely collected data.

Community services are a core part of England’s Integrated Care Systems (ICSs), which need tools for allocating resources proportionate to their residents’ needs for these services by neighbourhood. Our research presents a novel district nurse staffing allocation model designed to distribute healthcare resources more accurately and equitably at the neighbourhood level. By prioritising areas of higher need and ensuring that resource allocation is closely aligned with local demographic and health profiles, we offer a path towards more equitable healthcare service delivery.

Ethical approval

The results in this manuscript are informed by the analysis of anonymised secondary data held by NHS Cheshire & Merseyside and approved by their Data Access and Asset Group. Only anonymised secondary data were used. No personal data was used in this study and as such, there is no requirement for research ethics committee review.

Competing Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Wes Baker and Joe Rafferty are employed by Mersey Care NHS foundation Trust, the provider of district nursing services in Liverpool. They were involved in providing information about the nature of district nursing services and staffing in Liverpool, facilitating access to data, as well as providing contextual information for the interpretation of the results, reviewing drafts of the manuscript. They had no role in the analysis or presentation of the results. The views expressed in this article are those of the authors and do not represent the views of Mersey Care NHS foundation Trust. There are no other relationships or activities that could appear to have influenced the submitted work.

Data sharing

Data are currently available under license from NHS England. Statistical code is available on request from the authors.

Funding

This research was funded by the NIHR Policy Research Programme (RESTORE; Award ID, NIHR202484), the NIHR Applied Research Collaboration Northwest Coast (NWC ARC), the Health Foundation, United Kingdom - The Network Data Lab, the NIHR Three Research Schools Fellowship in Mental Health Research - n. MHF106 and the NIHR senior investigator award NIHR205131. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

Author Contribution

L.F. and B.B. designed the work, analysed the data, prepared figures and tables and wrote the manuscript.R.P. linked and cleaned datasets, provided contextual information for the interpretation of the results and reviewed drafts of the manuscript.W.B. provided important contributions for work design, contextual information for the interpretation of the results and reviewed drafts of the manuscriptJ.R., and I.B. provided contextual information for the interpretation of the results and reviewed drafts of the manuscript.

Acknowledgement

The authors wish to thank NHS Cheshire & Merseyside Integrated Care Board for supporting this study, and Laura Anselmi (University of Manchester), for providing methodological advice.

Data Availability

Data are currently available under license from NHS England. Statistical code is available on request from the authors.

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Competing interest reported. The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Wes Baker and Joe Rafferty are employed by Mersey Care NHS foundation Trust, the provider of district nursing services in Liverpool. They were involved in providing information about the nature of district nursing services and staffing in Liverpool, facilitating access to data, as well as providing contextual information for the interpretation of the results, reviewing drafts of the manuscript. They had no role in the analysis or presentation of the results. The views expressed in this article are those of the authors and do not represent the views of Mersey Care NHS foundation Trust. There are no other relationships or activities that could appear to have influenced the submitted work.

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Improving equitable healthcare resource use: Developing a neighbourhood district nurse needs index for staffing allocation

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