Social Science & Medicine 198 (2018) 148–156
Contents lists available at ScienceDirect
Social Science & Medicine
journal homepage: www.elsevier.com/locate/socscimed
Health equity monitoring for healthcare quality assurance
a,∗
a
b
c
b
R. Cookson , M. Asaria , S. Ali , R. Shaw , T. Doran , P. Goldblatt
T
d
a
Centre for Health Economics, University of York, York YO10 5DD, England, United Kingdom
Department of Health Sciences, University of York, England, United Kingdom
Analytical Insight Resource Unit, NHS England, England, United Kingdom
d
Institute for Health Equity, University College London, England, United Kingdom
b
c
A R T I C L E I N F O
A B S T R A C T
Keywords:
Health equity
Quality indicators
Health care
Small-area analysis
Socioeconomic factors
Population-wide health equity monitoring remains isolated from mainstream healthcare quality assurance. As a
result, healthcare organizations remain ill-informed about the health equity impacts of their decisions – despite
becoming increasingly well-informed about quality of care for the average patient. We present a new and improved analytical approach to integrating health equity into mainstream healthcare quality assurance, illustrate
how this approach has been applied in the English National Health Service, and discuss how it could be applied
in other countries. We illustrate the approach using a key quality indicator that is widely used to assess how well
healthcare is co-ordinated between primary, community and acute settings: emergency inpatient hospital admissions for ambulatory care sensitive chronic conditions (“potentially avoidable emergency admissions”, for
short). Whole-population data for 2015 on potentially avoidable emergency admissions in England were linked
with neighborhood deprivation indices. Inequality within the populations served by 209 clinical commissioning
groups (CCGs: care purchasing organizations with mean population 272,000) was compared against two
benchmarks – national inequality and inequality within ten similar populations – using neighborhood-level
models to simulate the gap in indirectly standardized admissions between most and least deprived neighborhoods. The modelled inequality gap for England was 927 potentially avoidable emergency admissions per
100,000 people, implying 263,894 excess hospitalizations associated with inequality. Against this national
benchmark, 17% of CCGs had significantly worse-than-benchmark equity, and 23% significantly better. The
corresponding figures were 11% and 12% respectively against the similar populations benchmark. Deprivationrelated inequality in potentially avoidable emergency admissions varies substantially between English CCGs
serving similar populations, beyond expected statistical variation. Administrative data on inequality in healthcare quality within similar populations served by different healthcare organizations can provide useful information for healthcare quality assurance.
1. Introduction
determinants of health rather than healthcare delivery (World Health
Organization, 2014). Due to this parallel development, quality improvement agencies (for example, the Organisation for Economic Cooperation and Development's (OECD) Health Care Quality Indicators
project) (Raleigh and Foot, 2010) and quality improvement frameworks
(for example, the Quality and Outcomes Framework in the UK (NHS
Digital, 2017b) and accountable care organizations (ACOs) in the US
(Centers for Medicare and Medicaid Services, 2017) often overlook
equity. Because quality targets tend to be more difficult to achieve for
socially disadvantaged populations, there are concerns that quality
frameworks penalise providers serving these populations (Delgadillo
et al., 2016; Doran et al., 2016; Yasaitis et al., 2016) potentially exacerbating existing disparities in the quality of care (Buntin and
Ayanian, 2017). Adjustment for social risk factors is now being
Quality of care and health equity have become two of the key issues
on policy agendas worldwide. However, despite the inclusion of equity
dimensions in foundational works on healthcare quality (Donabedian,
2002; Institute of Medicine, 2001) and efforts by organisations such as
the Institute for Healthcare Improvement (Institute for Healthcare
Improvement, 2017) and the English National Health Service (NHS)
(NHS England, 2017b) to integrate equity and quality, responses to
these issues have often progressed along separate lines. Efforts to improve quality have focused on safety and cost-effectiveness, with improvements in equity largely a by-product of reducing variation in
performance between providers (Doran et al., 2008), whereas policy
responses to health equity have focused on the wider social
∗
Corresponding author.
E-mail address: richard.cookson@york.ac.uk (R. Cookson).
https://doi.org/10.1016/j.socscimed.2018.01.004
Received 17 September 2017; Received in revised form 1 December 2017; Accepted 4 January 2018
Available online 06 January 2018
0277-9536/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Social Science & Medicine 198 (2018) 148–156
R. Cookson et al.
2. Methods
advocated (Fiscella et al., 2014; Joynt et al., 2017; National Academies
of Sciences and Medicine, 2016) but this falls short of providing useful
information about equity of care for vulnerable populations, which
requires stratification by social risk factors. And whilst there have been
isolated examples of quality improvement programs that have explicitly
addressed equity (Badrick et al., 2014; Blustein et al., 2011) most are
not designed to address this issue.
A major obstacle to improving equity in healthcare has been a lack
of appropriate analytical tools. Performance measures in healthcare
focus on a mythical “average” patient, providing insufficient information about differences in quality and outcomes that are considered
unfair (Cookson et al., 2016; Fiscella et al., 2000). Periodic reports on
healthcare inequalities are produced in some countries (Agency for
Healthcare Research and Quality, 2016; Harvey et al., 2016; Moy et al.,
2005) but these typically focus on large geographical regions (Mayberry
et al., 2006) or local government areas without specific responsibility
for healthcare (Remington et al., 2015) and lack the more specific
equity metrics and benchmarks needed for assessing and improving the
quality of healthcare organizations. To hold healthcare decision makers
accountable for the equity dimension of quality, new metrics are
needed which (1) speak directly to organizations with direct responsibility for healthcare purchasing, planning and delivery, and (2) are
responsive to short-term changes in healthcare delivery. Only then will
health equity metrics be incorporated into quality assurance dashboards commanding the attention of senior healthcare executives.
To address this challenge, in 2016 the English NHS introduced a
new approach to health equity monitoring for internal quality assurance and external public accountability purposes (NHS England, 2016a,
2016b). The initial NHS focus was on equity indicators based on rates of
potentially avoidable emergency hospitalization at the neighborhood
level, one of which we illustrate in this article, and consideration is
being given to adding further indicators in due course. The new approach can be used to construct equity indicators based on many
standard indicators of healthcare structure, process and outcome
quality including – but not limited to – primary care supply, primary
care process quality, hospital waiting times, hospital re-admissions,
hospital mortality, and mortality considered amenable to health care
(Cookson et al., 2016).
The NHS chose to focus initially on potentially avoidable emergency
admissions for two reasons. First, average rates of these admissions are
responsive to short-term changes in health care delivery (Harrison
et al., 2014; Huntley et al., 2014; Purdy and Huntley, 2013). Second,
they rise steeply with neighborhood deprivation, raising concern not
only about equity of access to preventive and co-ordinated healthcare
(Asaria et al., 2016a) but also about cost pressures on the healthcare
system as a whole (Asaria, et al., 2016b). Under the new approach,
inequality in potentially avoidable emergency admissions was measured within the populations of “clinical commissioning groups” (CCGs)
– care organizations in England with responsibility for purchasing and
planning healthcare for patients enrolled with local NHS family practices. Equity within the CCG's enrolled population was then compared
against two benchmarks: the national average level of inequality and
the average level of inequality within ten CCG populations that are
comparable in terms of deprivation, age profile, ethnic mix and rurality
(NHS England, 2017a). In this article we illustrate the NHS equity indicator based on the sub-set of potentially avoidable emergency admissions for chronic ambulatory care sensitive conditions. This is an
indicator of the quality of ambulatory care services in managing longterm conditions (Herrin et al., 2015; Purdy et al., 2009; Torio and
Andrew, 2014) and the equity version of this indicator is intended to
provide quality assurance information about the NHS duty to consider
reducing inequalities in both access and outcomes of healthcare (Health
and Social Care Act, 2012). In this paper, we use this indicator to illustrate the general analytical approach and discuss its potential application to healthcare quality assurance in other countries.
2.1. Data
2.1.1. Organizational geography
In England in 2015 there were 209 clinical commissioning groups
(CCGs) – each serving a mean of 272,000 NHS patients registered with a
local family practice (range 73,000 to 913,000). CCGs are responsible
for purchasing and planning healthcare for the vast majority of their
resident populations. However, the registered and resident populations
do not fully overlap because residents can choose to register with a
practice in a neighboouring CCG. We used registered population data
from practice registers, rather than resident population data from the
census, to match the legal responsibility of the CCG and to illustrate
how the approach can be applied to ACOs in the US and other settings
where the enrolled population does not coincide with the resident population. CCGs were introduced in April 2013. There were 211 CCGs
initially, falling to 209 in 2015. Before that, there were 152 “Primary
Care Trusts” (PCTs). Despite this numerical change, however, there was
stability in most areas with 180 of the 211 CCGs being formed from a
single PCT or part of a single PCT, and the opening and closing of
practices to accommodate local population change does not cause
substantial change in CCG boundaries.
2.1.2. Small area geography
Our basic unit of analysis was the “CCG-LSOA” – a block of CCG
registered population residing within a neighbourhood census unit
called a “lower super output area” (LSOA). Each patient has a neighbourhood or “Lower Super Output Area” (LSOA) in which they live.
Each LSOA has a deprivation score. Patients register with a GP practice
and these practices belong to CCGs responsible for their hospital care.
To calculate the inequality in a CCG, we include everyone who is registered with that CCG's GP practices based on the LSOA where they
live. Effectively we split each LSOA into CCG blocks, as illustrated in
Fig. 1.
We include all the shaded blocks for each CCG, taking the deprivation score of the LSOA in which they are located. LSOAs have a mean
population of 1650 (range 1000 to 3000), while CCG-LSOAs have a
mean population of 636 (range 1–2536 from 1st to 99th percentile).
Our CCG-LSOA population estimate was based on the fraction of the
relevant NHS practice list attributed to the LSOA. The resulting mean
number of CCG-LSOAs per CCG was 428 (range 95 to 1972). CCGLSOAs with smaller-than-resident populations arise near CCG boundaries, where residents of an LSOA are registered in more than one CCG
with such LSOAs appearing in the analysis for more than one CCG.
However, most LSOAs have a majority of their population registered
with a single CCG (95.4% on average). Even among LSOAs whose populations are registered with multiple CCGs, the largest proportion
Fig. 1. How CCG-LSOAs are constructed – fictional example.
Note: The 3 shaded areas are CCGs, the 25 (5*5) cells are LSOAs, and the 30 shaded
blocks within the cells are CCG-LSOAs.
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tends to be registered with a single CCG – for example, 1748 LSOAs
have population registered with five different CCGs, but among this
group on average 90% of the population are registered with a single
CCG.
health event requiring emergency hospital treatment is influenced by
individual risk factors (e.g. age, morbidities) and behaviors (e.g. diet,
smoking) which in turn are influenced by cumulative long-term environmental risk factors (e.g. childhood circumstances; living and
working conditions; and access to resources for investing in health). A
residual social gradient in potentially avoidable emergency hospitalization would therefore remain even if the CCG achieved perfect equity
by providing equal access to high quality preventive and co-ordinated
care.
It is not possible to adjust for all risk factors since detailed information may not be available in administrative data, especially for
individuals who have limited contact with health providers. In addition,
there is a danger of over-adjustment for risk factors that are highly
correlated with deprivation and amenable to modification over time by
healthcare services. The danger of over-adjustment increases with the
breadth of policy responsibility: the broader the policy toolbox, the
greater the ability to modify risk factors. Preventive and long-term care
can modify individual risk factors, and wider public health and social
policies can modify environmental risk factors which in turn will influence individual risk factors and behaviors.
The appropriate quality assurance benchmark is therefore not zero
inequality, but the residual degree of inequality expected for a similar
population with a similar social patterning of unobserved risk factors.
Two key benchmarks are the national gradient and the similar population gradient, which assume that the social patterning of unobserved
risk factors in a CCGs is the same as, respectively, the country as a
whole or as a cluster of CCGs serving similar populations. We selected
ten similar populations based upon a standard analysis by the NHS of
CCG population “similarity” in terms of twelve variables reflecting
deprivation, health, population size and age profile, population density
and ethnicity (NHS England, 2016c). The monitoring of time trends in
relation to these benchmarks allows an assessment of how equity performance is responding to healthcare initiatives.
We measured the slope of the gradient using the “absolute gradient
index” (AGI). This is the coefficient from the population-weighted
linear regression of age-sex standardized avoidable hospitalization rates
against fractional deprivation rank on a scale of 0–1, using all neighborhoods registered to the CCG. It is the same as the conventional slope
index of inequality except that the AGI indices use the national deprivation rank rather than the local deprivation rank. This difference allowed us to compare CCG inequality on a like-for-like basis with the
national inequality benchmark and the similar population benchmark,
even though different CCG registered populations can have different
deprivation profiles. For a CCG serving a relatively affluent population,
for example, the most deprived fifth of neighborhoods might all be
fairly affluent in national terms. A low rate of hospitalization in these
neighborhoods would then not reflect the same equity achievement as a
low rate among nationally deprived neighborhoods. The AGI can be
interpreted as the simulated gap in potentially avoidable emergency
hospitalization between the most and least deprived neighborhood in
England, allowing for the gradient in between, if England had the same
gradient as the registered population of the CCG.
To help decision makers interpret the AGI and assess the scale of
their inequality challenge, we also derived an approximate estimate of
the excess hospitalizations associated with inequality, drawing on the
epidemiological concept of population attributable risk. This concept
represents the number of emergency hospital admissions that would
hypothetically be avoided if all neighborhoods had the same admission
rate as the most affluent. We estimated this using the AGI multiplied by
the relevant population and divided by two. This formula is a simple
approximation, based on the assumptions of a linear relationship between deprivation and admissions and an evenly distributed population
across the deprivation spectrum (Asaria et al., 2016b).
The analysis was carried out using R statistical software. (version
3.2.4). Full analysis code can be found at: https://github.com/
miqdadasaria/ccg_equity.
2.1.3. Hospital admissions
Data on emergency admissions were taken from NHS Hospital
Episode Statistics (HES), a data warehouse containing details of all
admissions at NHS hospitals in England. We extracted data for 1
October 2014 to 30 September 2015 and calculated the indirectly agesex standardized admission rate for each CCG-LSOA. Admissions by
people with unknown age, sex, LSOA or CCG were excluded, including
individuals not registered with a family practice. Approximately 0.2%
of admissions had no recorded age or sex, 0.7% had no LSOA (given
valid age and sex) and 0.6% had no CCG (given valid age, sex and
LSOA). Approximately 1.4% of admissions were excluded.
2.2. Measures
2.2.1. Potentially avoidable emergency hospitalization
We used emergency admissions for chronic ambulatory care sensitive conditions as defined in existing NHS indicators (NHS Digital,
2017a). This includes diagnoses defined using ICD-10 codes for conditions such as asthma, bronchitis, diabetes, dementia and heart disease.
2.2.2. Neighborhood deprivation
The measure of deprivation we used was the Index of Multiple
Deprivation 2015, a multi-domain index of deprivation (McLennan
et al., 2011). This combines domains of deprivation including low income, unemployment, poor housing and crime.
2.2.3. Similar CCGs
The list of similar CCGs was created by the NHS to aid benchmarking of CCG level information, based on 10 CCGs with the lowest
sum of squared differencesce on 12 indicators including age profiles,
deprivation, population density and ethnicity, after first normalising
the indicators by subtracting the mean and dividing by the inter-decile
difference (NHS England, 2017a).
2.3. Analytic approach
Fig. 2 illustrates the general analytic approach to health equity
monitoring against national and similar population benchmarks. The
solid line is based on a linear regression weighted by population, and
illustrates the positive association between the care quality indicator –
in this case, potentially avoidable emergency hospitalization – and
neighborhood deprivation within the registered population of the selected CCG. The slope of this line represents the CCG inequality “gradient” by level of deprivation within the registered population: the
steeper the slope, the larger the degree of deprivation-related inequality
in potentially avoidable emergency hospitalization. The dashed line is
the gradient within England as a whole, and the dotted line is the
gradient within a sub-set of England comprising the registered population of this CCG along with ten other CCGs with similar populations.
These benchmark gradients are also based on linear regressions. In this
example, the CCG gradient is less steep (better) than both the national
and similar population gradients to an extent that is statistically significant, so we can conclude that this CCG is relatively equitable on
both benchmarks. We can then monitor change over time against national and similar population benchmarks in response to actions taken
by healthcare decision makers.
The benchmark gradients play a crucial role in quality assuring the
equity performance of healthcare organizations. Healthcare organizations can be expected to address poor quality and equity of healthcare,
but cannot address wider social determinants of the gradient in
healthcare outcomes on their own (see Fig. 3). The risk of an acute ill150
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Fig. 2. Healthcare equity against national and similar population benchmarks – hypothetical scatterplot showing
potentially avoidable emergency hospitalization and deprivation for all neighborhoods within a clinical commissioning
group.
*Neighborhood rate of potentially avoidable emergency
hospitalization, indirectly standardized for age and sex.
**Neighborhood national deprivation rank from the Index of
Multiple Deprivation 2015, converted into a fraction between 0 (least deprived) and 1 (most deprived).
Note: Dots represent neighbourhoods registered to the clinical commissioning group. This example shows a clinical
commissioning group with an inequality gradient that is
shallower than both the national benchmark gradient and
the similar area benchmark gradient, indicating better-thanbenchmark equity.
benchmark of 927; the similar ten benchmark is not shown as it varies
by CCG. Against the national benchmark, 17% of areas (35 of 209)
exhibited equity that was statistically worse-than-benchmark at a 95%
confidence level – i.e. CCG inequality was larger than national inequality – and 23% (48 of 209) exhibited significantly better-thanbenchmark equity. Against the similar population benchmark, the
corresponding figures were 11% worse-than-benchmark and 12%
better-than-benchmark. Against both benchmarks, 9% of areas show
worse-than-benchmark equity performance and 10% show better-thanbenchmark equity performance.
There was moderate negative correlation between the average deprivation of a CCG and its equity performance against the relevant similar population benchmark, as measured by the similar population
inequality gap minus the CCG inequality gap (Pearson's r −0.57). This
means that English CCGs serving relatively deprived populations generally performed worse on health equity than those serving relatively
affluent populations, and that about one third of the variation in CCG
equity performance (Pearson's r squared 0.32) was associated with
average deprivation. This correlation reduced but persisted when using
relative rather than absolute measures of inequality, such as the absolute gap as a proportion of the CCG modelled mean for an individual
with the national average level of deprivation.
Fig. 5 illustrates healthcare equity in six selected CCGs. We have
selected pairs of organizations serving populations with different
average levels of deprivation, with each pair illustrating better-thanbenchmark versus worse-than-benchmark equity (“Horsham and Mid
Sussex” versus “Windsor, Ascot and Maidenhead” with low deprivation,
“Ashford” versus “North Lincolnshire” with medium deprivation, and
“Brent” versus “Liverpool” with high deprivation). In five of these six
examples both benchmark comparisons were statistically significant.
However, the comparison for “Windsor, Ascot and Maidenhead” was
not statistically significant against either national or similar population
benchmarks – in this dataset, there was no example of an CCG serving a
low deprivation population that had significantly worse-than-
Fig. 3. The healthcare and non-healthcare determinants of emergency hospitalization.
Note: Long-term care can include various medical and non-medical services for people
with chronic mental or physical illness or disability who cannot care for themselves for
long periods, including help with normal daily tasks like dressing, feeding and housekeeping.
3. Results
The mean indirectly age-sex standardized rate of potentially
avoidable emergency hospitalization in England was 792 per 100,000
people. The national absolute gradient index (AGI) – the estimated gap
between the most and least deprived neighborhoods in England – was
927 (95% confidence interval 912 to 942), or 117% of the mean
neighborhood level CCG-LSOA rate. The modelled rates for the most
and least deprived neighborhoods were 1261 and 334, respectively.
Fig. 4 shows healthcare equity in 2015 for all 209 CCGs in England,
with 95% confidence limits. The horizontal line is the national AGI
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Fig. 4. Variation in equity between clinical commissioning groups in 2015–Absolute Gradient Index (AGI)
of inequality in potentially avoidable emergency hospitalization.
Notes
a) The caterpillar legs show AGI estimates with 95%
confidence intervals for 209 clinical commissioning
groups in England.
b) The dotted horizontal line is the national benchmark
AGI.
c) The clinical commissioning group highlighted in
black, illustrating better-than-average equity against the
national benchmark, is Ashford.
equity metrics into mainstream quality assurance processes for organizations with direct responsibility for healthcare purchasing, planning
and delivery for enrolled populations as small as 100,000 people; (ii) to
assess the equity dimension of quality against two relevant benchmarks:
national inequality and inequality within similar enrolled populations;
and (iii) to assess the scale of the health inequality challenge facing the
organization using the epidemiological concept of population attributable risk. Particular strengths of potentially avoidable emergency
hospitalization as a key equity indicator include the ability to address
the equity dimension of a high profile quality issue with substantial cost
implications, and to incorporate data on the quality of care for disadvantaged people who are relatively unlikely to participate in
household surveys but relatively likely to suffer emergency hospital
admission.
This approach can also be used to monitor how equity changes over
time in response to short-term changes in healthcare delivery by particular care organizations (Sheringham et al., 2016). For example, Liverpool has recently introduced policies to improve the coordination of
care and reduce avoidable emergency hospitalization, including integrated primary and long-term care services, “step down” hospital
beds for non-acute care, and tele-monitoring in the home (Devlin et al.,
2016). Monitoring how equity indicators respond to policy changes of
this kind may help to refine policy implementation and learn lessons
about more and less cost-effective ways of reducing health inequality.
Our approach can also be used to examine which neighborhoods in
which parts of the gradient see the biggest impacts, providing insights
into who gains most from the initiatives and why.
benchmark equity.
Fig. 6 illustrates how similar-ten benchmarking works and can be
presented to decision makers. It shows a CCG – Liverpool – with worsethan-benchmark equity performance. It shows the AGI for Liverpool
and its ten similar CCG populations, along with the average AGI pooled
across all eleven populations. Inequality is significantly worse than the
similar population benchmark in Liverpool and in one other CCG (South
Manchester), and significantly better in two CCGs (Brighton & Hove
and Sheffield). The AGI in Liverpool was 1523 compared with a similar
population benchmark AGI of 1177. This equates to 3840 excess hospitalizations a year associated with inequality among the Liverpool
population.
Full results for every clinical commissioning group can be found
online in our interactive inequality explorer: http://www.ccginequalities.co.uk/
4. Discussion
4.1. Summary of findings
We have illustrated the new analytical approach to health equity
equity monitoring for healthcare quality assurance introduced in
England in 2016, using the example of potentially avoidable emergency
hospitalization. The approach aims to provide healthcare purchasing
and planning organizations – in this case, English CCGs – with detailed,
up-to-date information on the equity dimension of healthcare quality
within their enrolled populations. It measures inequality in key indicators of healthcare quality within the enrolled population and then
assesses equity against two benchmarks – national inequality, and inequality within a group of care organizations with similar populations.
Inequality is measured on a comparable basis using populationweighted models of the neighborhood-level relationship between
healthcare quality and deprivation, allowing for differences between
neighborhoods in their demographic makeup.
Using data for 2015 on potentially avoidable emergency hospitalization, we found that 9% of the 209 CCGs in England showed significantly worse-than-benchmark equity against both national and similar population benchmarks, and 10% showed significantly betterthan-benchmark equity. This is considerably more than the 5% in each
category expected due to chance.
4.3. Limitations of the approach
Our indicator is designed for quality assurance and public accountability purposes, and for use in future research, rather than for
performance pay. Our finding that CCGs with more deprived populations tend to score worse on the equity indicator suggests that linking
remuneration to equity indicator scores could systematically disadvantage these CCGs. We would therefore caution against attaching
financial incentives to this indicator until more is known about costeffective ways for healthcare organisations to reduce health inequality
as measured by this indicator. We do not yet have evidence about
whether or how this new equity indicator is responsive to short-term
changes in health care delivery, and further research using quasi-experimental designs is needed to provide this evidence.
Another limitation is the reliance on administrative data, which are
prone to measurement error. In particular, although hospital data in
4.2. Strengths of the approach
The strengths of this approach include the ability (i) to incorporate
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Fig. 5. Healthcare equity in 2015 within six illustrative clinical commissioning groups, showing neighborhood level scatterplots of potentially avoidable emergency hospitalization versus
deprivation.
Note: The dots show standardized neighborhood rates of potentially avoidable emergency hospitalization within each clinical commissioning group.
Given the long-term cumulative impacts of social factors on gradients in
health risk, particular caution is required when comparing populations
with similar current sociodemographic characteristics but importantly
different historical patterns of deprivation. For example, a population
drawn from a region that has suffered from decades of industrial decline
might have a steeper unmeasured risk factor gradient than an otherwise
similar population. Liverpool, for example, has a high prevalence of
drug misuse, resulting in England's highest rate of hospital admissions
with a diagnosis of drug-related mental health or behavioral disorder –
a rate of 419 per 100,000 general population in 2015/16 (Office for
National Statistics, 2017). This unmeasured risk factor may partly explain why Liverpool's gradient is steeper than some of its similar areas,
such as Brighton and Hove with a drug-related admissions rate of 194
per 100,000. More research is needed on heterogeneity in unmeasured
risk factors between CCGs and their “similar areas”, and where large
and important differences are found it may not be appropriate to hold
England offers reasonably complete coverage of all patients admitted
for emergency inpatient treatment, population denominator data are
less complete due to gaps in NHS patient registration. This is a bigger
problem in areas that are more deprived and have more recent immigrants and homeless people, some but by no means all of whom are
not registered with the NHS, leading to inflation of admission rates in
those areas and thus potentially exaggerating the gradient. The
Department of Communities and Local Government estimated the
number of “rough sleepers” as 4134 in autumn 2016 (Department for
Communities and Local Government, 2016). This was less than 0.01%
of the England population. The number of people classed as “statutory
homeless” is larger but most of these are in temporary accommodation
and would therefore be registered and included within our data.
A third limitation is the assumption that organizations serving apparently “similar” populations are comparable in terms of their levels
and gradients in unmeasured individual risk factors and behaviors.
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therefore, we cannot tell whether equity performance reflects organization-wide performance across all disease areas, or particular issues in
particular disease areas. It is possible to make equity comparisons using
disease-specific hospitalization rates for broad disease categories such
as cardiovascular disease (Vanasse et al., 2014). However, we would
caution against doing so unless there are sufficiently large counts of
events within each neighborhood to make statistically valid comparisons, which will typically require pooling data over several years. Our
approach therefore provides an overall indicator of health equity for
accountable care organizations which can be compared over time and
then supplemented by disease-specific analysis of pooled years to pinpoint long-term problems within specific disease areas and specialties.
A final limitation is that we used straightforward linear regression
methods to facilitate communication of our findings to decision makers.
We performed sensitivity analysis using more sophisticated methods,
including non-linear models and empirical Bayes estimation of confidence intervals, but found this makes little difference to the identification of better-than-benchmark and worse-than-benchmark equity
(Cookson et al., 2016). The gradient in potentially avoidable emergency
hospitalization is in fact curved rather than linear, with an increasing
slope towards the most deprived end of the spectrum. A single-parameter power transform prior to estimation fits better than either a
linear or exponential model, and results in higher estimates of the gap
between most and least deprived neighborhoods. This is because the
linear approach flattens out the “uptick” in hospitalization towards the
most deprived end of the spectrum. However, this does not substantially alter the pattern of equity comparisons. Different inequality
indices use different assumptions and value judgements and we would
recommend sensitivity analysis using alternative indices – in particular,
the Relative Gradient Index, which measures the corresponding relative
concept of inequality. Use of both absolute and relative indices is particularly important in time series equity comparisons when the mean is
changing, since in those cases relative and absolute inequality can move
in different directions (Kjellsson et al., 2015).
Fig. 6. Equity assessment against the similar population benchmark for Liverpool in
2015–Absolute Gradient Index (AGI) of inequality in potentially avoidable emergency
hospitalization.
Notes
a)The vertical lines show AGI inequality estimates with 95% confidence intervals for
Liverpool and ten similar clinical commissioning groups in England.
b)The horizontal line shows the similar populations inequality benchmark, based on
pooling the registered populations within this group of clinical comissioning groups with
similar populations.
health care managers accountable for their baseline equity score. In
such cases, however, it may still be appropriate to hold health care
managers accountable for changes in their equity score over time. This
is because recent trends in unmeasured risk factors may be similar
between the CCG and its currently similar areas, even if the baseline
levels differ for long-term historical reasons. Research is therefore
needed to find better methods of risk adjustment by making creative
use of administrative data on particular risk factors, for example using
historical data on ICD-10 codes for neighborhood level hospital admissions. Until more accurate forms of risk adjustment are found,
therefore, the most important use of these equity indicators may lie not
so much in spotting poor equity performance in cross section as in
identifying systematically improving and worsening equity performance over time.
Another potential issue relates to the application of these indicators
in more geographically fragmented health systems like those in the
USA, where no accountable care organization provides anything close
to universal coverage for their local population. In such settings, accountable care organizations may potentially “cream skim” enrollees
who are healthier in terms of unmeasured risk factors than suggested by
their age, sex and neighborhood deprivation characteristics. Further
research is needed in such countries to assess the potential for “gaming”
of equity indicators through cream skimming of relatively healthy deprived individuals, and to find solutions such as additional risk adjustment and population imatching techniques to ensure that benchmark gradients from apparently similar populations are genuinely
comparable.
A limitation of using potentially avoidable emergency hospitalization as an equity indicator is that aggregating emergency admissions for
many different chronic conditions may mask varying influences of
different conditions. Without further disease-specific analysis,
4.4. Communicating the equity dimension of quality to healthcare managers
It is important to find ways of communicating equity information
clearly to healthcare decision makers. In consultations with healthcare
managers and policymakers we found strong support for one-page
“equity dashboards” combining information on quality and equity for a
suite of key quality indicators (Cookson et al., 2016). Dashboards help
decision makers visualize complex underlying inequality patterns, place
equity information in context, identify how far particular quality problems have an equity dimension, and avoid the problem of equity indicators being isolated from mainstream quality indicators. Examples of
equity dashboards and visualization tools developed by the authors are
available at: http://www.york.ac.uk/che/research/equity/monitoring/
4.5. Applicability to other countries
The NHS approach to equity monitoring could be adapted for use in
other countries with well-developed data systems for civil registration
and healthcare quality assurance, in which data on healthcare access
and outcomes can be disaggregated to small area or individual levels. In
principle, inequality in avoidable hospitalization can be measured for
any group of healthcare purchasing and planning organizations within
any jurisdiction with data on hospitalization and socioeconomic disadvantage, so long as (i) each of the care organizations serves a sufficiently large numbers of individuals with a sufficiently broad range of
socioeconomic backgrounds for reliable statistical estimation of the
gradient, and (ii) the marker of disadvantage is sufficiently reliable (for
example, the small areas are sufficiently small and homogeneous in
terms of socioeconomic composition for meaningful comparisons). In
our study the 209 clinical commissioning groups served 272,000 registered patients on average (range 73,000 to 913,000), residing within
154
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R. Cookson et al.
to thank the other methods of the project team for their contributions to
that underpinning research: Brian Ferguson, Robert Fleetcroft, Maria
Goddard, Mauro Laudicella and Rosalind Raine. Hospital episode statistics data were obtained under license from NHS England. The views
expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or
NHS England.
an average of 428 neighborhoods (range 95 to 1972). What counts as a
sufficient number of individuals and neighborhoods will vary from one
setting to another, since statistical reliability and the accuracy of area
deprivation markers depend upon contextual factors including the
prevailing rates of avoidable hospitalization and the degree of residential segregation.
Although administrative health data in low-income countries are
often incomplete (World Health Organization, 2013), our approach
should be applicable in many middle- and high-income countries with
well-developed administrative data systems capable of disaggregating
data on social factors to small and homogenous neighborhoods.
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5. Conclusion
The production of equity indicators for organizations with direct
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first step for policy makers who are serious about reducing social inequalities in healthcare access and outcomes. The next step is to use
these indicators to evaluate organization-wide initiatives and help decision makers learn how to reduce costly emergency admissions associated with deprived populations. As evidence accumulates on the most
cost-effective ways of improving health equity, policy makers can then
start encouraging healthcare organizations to scale-up the best approaches.
Acknowledgements
Richard Cookson and Miqdad Asaria are supported by the NIHR
(Senior Research Fellowship, Dr Richard Cookson, SRF-2013-06-015).
This work builds on research funded by the UK National Institute for
Health Research (NIHR) Health Services and Delivery Research
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Assignment Overview
One of the leading continuous quality indicators that U.S. health care managers struggle with is
population-wide health equity monitoring and improvement. This indicator seems to unfortunately remain
isolated from the "mainstream" process of continuous quality improvement. Because of this, health care
managers remain relatively uninformed about
the health equity impacts of organizational decisions, despite the ease of data gathering and assessment
brought about by the advent of electronic health records.
Please read the following article, then answer the Case Assignment questions.
Cookson, R., Asaria, M., & Ali, S. (2018). Health equity monitoring for health care quality assurance.
Social Science & Medicine, Vol. 198, 148-156. Retrieved from the Trident Online Library.
Case Assignment
1. How can the monitoring process suggested in the article help in revising the processes used inside the
United States? It's working in England-so could it work in the United States? Why or why not? Justify
your answer using credible, peer-reviewed sources.
2. What are five of the current inequities in the United States health care market that should be
addressed? Document each inequity using credible, peer-reviewed sources.
3. How can each of these inequities be best resolved to the satisfaction of all stakeholders? Explain your
ideas for resolving the five inequities to your stakeholders in a few concise paragraphs.
Assignment Expectations
1. Conduct additional research to gather sufficient information to support your analysis.
2. Provide a response of 3-5 pages, not including title page and references. It is required that you show
the formulas and calculations performed to arrive at your answers.
3. As we have multiple required items to be addressed herein, please use subheadings to show where
you're responding to each required item and to ensure that none are omitted.
4. Support your paper with peer-reviewed articles and reliable sources. Use at least three peer-reviewed
sources. For additional information on how to recognize peer-reviewed journals, see:
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