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Modeling the need for hip and knee replacement surgery. Part 1. A two-stage cross-cohort approach

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 61, No. 12, December 15, 2009, pp 1657–1666
DOI 10.1002/art.24892
© 2009, American College of Rheumatology
ORIGINAL ARTICLE
Modeling the Need for Hip and Knee Replacement
Surgery. Part 1. A Two-Stage Cross-Cohort
Approach
ANDY JUDGE,1 NICKY J. WELTON,1 JAT SANDHU,2
AND
YOAV BEN-SHLOMO1
Objective. To explore inequalities in the need for hip/knee replacement surgery using a 2-stage cross-cohort approach.
Methods. In the first stage, a small-area population-based survey, the Somerset and Avon Survey of Health, was used to
provide a high-quality measure of need for hip/knee replacement using the New Zealand (NZ) score. Receiver operating
characteristic curve analyses were used to validate a simplified NZ score, excluding information from clinical examination. In the second stage, a nationally representative population-based survey, the English Longitudinal Study of
Ageing, was used to explore inequalities in need for hip/knee replacement using the simplified NZ score. Multilevel
Poisson regression modeling was used to estimate rates of need for surgery. Exposures considered were age, sex, social
class, ethnicity, obesity, Index of Multiple Deprivation 2004 deprivation quintiles, rurality, and ethnic mix of area.
Results. Rates of need for hip/knee replacement increase with age and are lower in men than in women (rate ratio [RR]
0.7, 95% confidence interval [95% CI] 0.6 – 0.9 for hips; RR 0.8, 95% CI 0.7–1.0 for knees). Those of lowest social class have
greater need. Need was greatest for people living in more deprived areas. Individual ethnic group did not predict the need
for surgery. For hip replacement, there was no rurality effect; for knee replacement, those in town and fringe areas had
greater need. Obesity was a strong predictor of need for surgery (RR 2.3, 95% CI 1.9 –2.8 for hips; RR 2.4, 95% CI 2.0 –2.8
for knees).
Conclusion. This study provides evidence of greater variations of inequalities in need for hip/knee replacement than
previous studies. Further research should explore geographic variation and produce small-area estimates of need to
inform local health planning. It is important to complement data on need with willingness to undergo surgery.
Joint replacements make a substantial contribution to public health and are among the most common elective procedures. They are cost effective (1,2), with good prosthesis
survival rates (3,4), reduced pain, increased mobility, and
improved quality of life (5–12). However, health needs
will not be the same across different areas of a country and
will vary according to the demographic characteristics of
the area. In the UK, the Musculoskeletal Services Framework produced by the Department of Health recognizes
this, describing needs assessment in the context of understanding the prevalence and incidence of musculoskeletal
disorders, where patients are, and their use of services
(13). Priority action must be taken to distribute resources
The opinions expressed by the authors are theirs alone
and do not represent the opinions of supporting organizations. The developers and funders of the English Longitudinal Study of Ageing and the UK Data Archive do not bear
any responsibility for the analyses or interpretations presented here.
Supported by the Medical Research Council/Health Services Research Collaboration at the Department of Social
Medicine, University of Bristol. Dr. Welton’s work was supported by the UK Medical Research Council. Dr. Sandhu’s
work was supported by a National Coordinating Centre for
Research Capacity Development Department of Health Public Health Initiative 2003. The data are from the English
Longitudinal Study of Ageing, which receives funding from
the US National Institute on Aging and a consortium of UK
government departments coordinated by the Office for National Statistics, and the Somerset and Avon Survey of
Health, which receives funding from the Department of
Health and the South and West National Health Service
Research and Development Directorate.
1
Andy Judge, PhD (current address: University of Oxford,
Oxford, UK), Nicky J. Welton, PhD, Yoav Ben-Shlomo,
MBBS, PhD: University of Bristol, Bristol, UK; 2Jat Sandhu,
PhD: University of Bristol, Bristol, UK, and University of
British Columbia, Vancouver, British Columbia, Canada.
Address correspondence to Andy Judge, PhD, Botnar
Research Centre, Institute of Musculoskeletal Sciences,
Nuffield Department of Orthopaedics, Rheumatology and
Musculoskeletal Sciences, University of Oxford, Windmill
Road, Headington, Oxford, OX3 7LD, UK. E-mail: andrew.
judge@ndorms.ox.ac.uk.
Submitted for publication January 27, 2009; accepted in
revised form July 30, 2009.
INTRODUCTION
1657
1658
relative to health need; otherwise, inequities in accessing
services occur that may lead to health inequalities.
A number of population-based prevalence studies have
attempted to explore inequalities in need for hip and knee
replacement. They found that need increased with age and
is greater in women than in men (14 –21). Poor people
were more likely to need joint replacement (18,19), and
need was associated with less education and lower income
(22). There was no evidence that rurality was associated
with need for hip and knee replacement (18,19). However,
the majority of previous studies were conducted in geographically small areas, and estimates of need may vary
geographically. Second, studies exclude people from the
estimate of need based on a list of comorbidities that could
potentially make them unfit for surgery. Having a listed
comorbidity does not necessarily mean a patient is ineligible for surgery. Third, to our knowledge, no studies have
looked at whether need for joint replacement varies by
ethnic group. Finally, studies only look at a few sociodemographic domains, such as age and sex (23). In this
study, we explore all relevant sociodemographic variables
and risk factors in a multivariable regression analysis that
considers the effects of interactions, overdispersion, and
geographic variation.
The advantage of small-area population-based studies is
that they are specifically designed to estimate the population requirement for joint replacement and have a highquality measure of need. For example, in the Somerset and
Avon Survey of Health (16,17) and Ontario studies (20),
the need for surgery is confirmed radiographically and
through clinical examination. However, small-area studies
are limited in terms of their generalizability. On the other
hand, large nationally representative population surveys
are more generalizable, but are often not designed to examine a specific health problem and rarely have detailed
clinical data and/or radiography.
One possible solution that combines the strengths of
these 2 types of study is a 2-stage cross-cohort approach
whereby in the first stage, a small-area population-based
study is obtained with a high-quality measure of need
(scoring tool) to identify people requiring hip and knee
replacement. A simpler measure of need (shorter version
of the scoring tool) is then created using information available in a national survey, which would typically exclude
data from clinical examinations and radiographs. Receiver
operating characteristic (ROC) curve analyses are then performed to validate the simpler measure of need against the
gold standard in the small-area survey. In the second stage,
the nationally representative survey can be used to identify people in need of hip and knee replacement using the
simpler scoring tool. We illustrate this approach with an
example of joint replacement from the UK. However, the
methods are general and applicable in other settings and
conditions where health care services need to make provision decisions based on the estimated need.
MATERIALS AND METHODS
Stage 1. We obtained access to the Somerset and Avon
Survey of Health, a small-area population-based survey
Judge et al
conducted in 1994 –1995, used to estimate need for hip
(17) and knee (16) replacement. The sample was obtained
using a multistage sampling strategy (24) of 28,080 people
age ⱖ35 years from 40 general practices in the former UK
counties of Avon and Somerset. Questionnaires were completed on symptoms of hip/knee pain and stiffness, comorbidity, limitations to activities of daily living, previous use
of health services, preferences and priorities for treatment,
and indicators of socioeconomic status. A full clinical
examination of the hip, knee, and lower back was carried
out by a medically trained researcher and a team of nurses
with orthopaedic experience who had undergone a standard training program and who followed an examination
schedule. If clinically significant hip or knee disease was
discovered at the clinic, study participants were invited
for a radiograph of the joint. Because the aim was to assess
the need for primary joint replacement, respondents reporting a previous hip/knee replacement on the same side
as the reported symptoms were excluded.
For people with hip/knee pain, the severity of their joint
disease was assessed using the New Zealand (NZ) score
(25) (Table 1), which is a continuous score from 0 to 100
comprised of 4 main components: degree and occurrence
of pain, functional limitations, pain on clinical examination and other abnormal findings, and involvement of
other joints and the degree to which independence was
threatened. Higher scores reflect more severe disease. A
cutoff of ⱖ55 was previously used to identify those in need
of surgery (16,17). The score was developed through professional consensus in New Zealand to determine access to
and priority for joint replacement, with a recent study
confirming the validity of the score (26). In the UK, some
primary care trusts have begun using the NZ score as a tool
to determine access to an orthopaedic surgeon (27) to
ensure that referrals to a consultant are appropriate.
Within this study, the radiographic findings were not incorporated into the NZ score because of the well-known
uncertainty about the relationship between radiographic
findings and symptoms, and uncertainty as to how best to
incorporate radiographic findings into the NZ criteria.
We acquired data from the English Longitudinal Study
of Ageing, a nationally representative population-based
sample of 11,392 people age ⱖ50 years living in private
households in the UK and developed by a team of researchers based at the National Centre for Social Research,
University College London, and the Institute for Fiscal
Studies (28). The sample was drawn from the households
that previously responded to the Health Survey for England in 1998, 1999, or 2001 (the 1999 survey included a
boost sample that represented ethnic minorities). Several
waves of data are available from the Economic and Social
Data Service: wave 0 contains data for the English Longitudinal Study of Ageing participants from the Health Survey for England data sets, and wave 1 was conducted in
2002–2003 and contains information from individual interviews and self-completion questionnaires. The health
module contains information on the severity of hip/knee
pain and activities of daily living. A weighting variable is
included with the data set. The aim of weighting is to take
account of any bias from nonresponse in order to make the
Inequalities in the Need for Hip and Knee Replacement
1659
Table 1. The New Zealand Priority Criteria for Major Joint Replacement Surgery (maximum score 100)
Clinical features
Pain (40%)
Degree (patient must be on the maximum medical therapy at the time of rating)
None
Mild: slight or occasional pain; patient has not altered patterns of activity or work
Mild to moderate: moderate or frequent pain; patient has not altered patterns of activity or work
Moderate: patient is active but has had to modify or give up some activities because of pain
Moderate to severe: fairly severe pain with substantially limited activities
Severe: major pain and serious limitation
Occurrence
None or with first steps only
Only after long walks (30 minutes)
With all walking, mostly day pain
Significant, regular night pain
Functional activity (20%)
Time walked
Unlimited
31–60 minutes (e.g., longer shopping trips to the mall)
11–30 minutes (e.g., gardening, grocery shopping)
2–10 minutes (e.g., trip to the letter box)
⬍2 minutes or indoors only (more or less housebound)
Unable to walk
Other functional limitations (e.g., putting on shoes, managing stairs, sitting to standing, sexual activity, recreation
or hobbies, walking aids needed)
None
Mild
Moderate
Severe
Movement and deformity (20%)
Pain on examination (overall results are both active and passive range of motion)
None
Mild
Moderate
Severe
Other abnormal findings (limited to orthopaedic problems, e.g., reduced range of motion, deformity, limp, instability,
progressive radiograph findings)
None
Mild
Moderate
Severe
Other factors (20%)
Multiple joint disease
No, single joint
Yes, each affected joint mild to moderate in severity
Yes, severe involvement (e.g., severe rheumatoid arthritis)
Ability to work, give care to dependants, live independently (difficulty must be related to affected joint)
Not threatened or difficult
Not threatened but more difficult
Threatened but not immediately
Immediately threatened
Total
respondent sample more representative of the population.
Calibration weighting was used, which attaches an estimated probability of response to each household that explains the discrepancy between the survey and the distribution of age and sex in the population.
We went back to the original Somerset and Avon Survey
of Health questionnaires and determined how the information they contained was used to create an NZ score.
Questions used were then compared with those in the
English Longitudinal Study of Ageing and matched as
Score
0
4
6
9
14
20
0
4
10
20
0
2
4
6
8
10
0
2
4
10
0
2
5
10
0
2
5
10
0
4
10
0
4
6
10
100
closely as possible, enabling a simpler (proxy) NZ score to
be created (see Supplementary Table 1, available in the
online version of this article at http://www3.interscience.
wiley.com/journal/77005015/home). Patients in the English Longitudinal Study of Ageing were assigned an NZ
score if they had had a limiting long-term illness over a
period of time, were often troubled with pain, and had
pain in either their hips or knees. Because a clinical examination was not performed, it was not possible to complete the section on “pain on examination and other ab-
1660
Judge et al
normal findings” of the NZ score; hence, a simpler score
out of 80 was used.
Stage 2. The nationally representative survey (English
Longitudinal Study of Ageing) was used to explore inequalities in the need for hip and knee replacement, using
as an outcome variable the simpler NZ score out of 80 with
a cutoff of 48 to create a binary variable of whether or not
a person was in need of surgery. The individual and ecologic exposure variables were explored.
Individual. The following patient-level information was
extracted: age (50 –54, 55–59, 60 – 64, 65– 69, 70 –74, 75–
79, 80 – 84, and ⱖ85 years), sex, occupational social class
(I ⫽ professional, II ⫽ managerial and technical, IIIN ⫽
skilled nonmanual, IIIM ⫽ skilled manual, IV ⫽ partly
skilled, and V ⫽ unskilled), ethnicity (white and nonwhite), and obesity (body mass index ⬍30 kg/m2 and ⱖ30
kg/m2; measured by an interviewer who took height/
weight measurements).
Ecologic. Geographic information was not readily available in the archived English Longitudinal Study of Ageing
data set on the Economic and Social Data Service Web site.
An application was made to the National Centre for Social
Research to obtain additional geographic data. The Census
Area Statistics ward (anonymized) and the district the
patient lives in allowed multilevel modeling to be used to
explore geographic variation and control for clustering in
the data. The following ecologic data have been linked to
the census ward a patient lives in: the Index of Multiple
Deprivation 2004 deprivation quintiles (weighted to the
ward population because each census ward varies in size:
1 [least deprived], 2, 3, 4, and 5 [most deprived]) (29),
rurality (urban [population ⱖ10,000], town and fringe, and
village/isolated), and ethnic mix of the area (white [ⱖ10%
white and ⱕ0.5% African American, Asian, and other] and
nonwhite [all remaining groups]).
Statistical methods. The outcome of interest was a binary/dichotomous variable of whether or not the patient
was in need of hip or knee replacement. Exposure variables consisted of age, sex, social class, ethnicity, obesity,
Index of Multiple Deprivation 2004 deprivation quintiles,
rurality, and ethnic mix of the area. A univariable Poisson
regression model was fitted in Stata statistical software
(StataCorp, College Station, TX) to examine the association
between the rates of need for joint replacement and each of
the sociodemographic variables. We also fitted a multivariable model controlling for all variables. Analyses were
weighted to control for bias from nonrandom nonresponse
in the English Longitudinal Study of Ageing sample. Wald
tests were used to explore linear trends by fitting models
with the variable as a score. To assess for nonlinear trend,
likelihood ratio tests were used, comparing a model with a
categorical variable with a model with the variable as a
score. Effect modification was considered by using likelihood ratio tests for interaction between each of the sociodemographic variables. Separate models were fitted for hip
and knee replacement.
The hierarchical structure of the data consisted of
11,392 individuals nested within 2,913 wards within 348
Figure 1. Receiver operating characteristic (ROC) curve for hips.
districts. Multilevel Poisson regression models were fitted
in the statistical software MLwiN (University of Bristol,
Bristol, UK). For the hip model, the intraclass correlation
coefficient was 0.032; hence, 99.0% of the variation in
rates of need for hip replacement was at the individual
level (for the knee model, 99.8% of the variation was at the
individual level). There was no evidence of clustering
across either wards (P ⫽ 1.00 for hip and knee models) or
districts (P ⫽ 0.56 hip, P ⫽ 0.90 knee); hence, the simpler
fixed-effects regression model was adequate.
RESULTS
We conducted an ROC curve analysis on the Somerset and
Avon Survey of Health data set, taking the NZ score out of
100 with a cutoff of ⱖ55 to be the gold standard to determine a threshold for the simpler score out of 80. For both
hip and knee replacement, the area under the curve was
maximized using a threshold of 44 (providing the best
tradeoff between sensitivity and specificity); hence, the
simpler score can reliably be used. However, a threshold of
48 was chosen for analysis because this correctly classifies
the greatest number of people as to whether they are in
need of surgery (sensitivity 89.5%, specificity 98.8%, cor-
Figure 2. Receiver operating characteristic (ROC) curve for knees.
Inequalities in the Need for Hip and Knee Replacement
1661
Table 2. Rates of need for hip replacement by sociodemographic groups*
Individual-level variables
Age groups, years
50–54
55–59
60–64
65–69
70–74
75–79
80–84
ⱖ85
P linear trend
P nonlinear trend†
Sex
Female
Male
Social class
I. professional
II. managerial and technical
IIIN. skilled nonmanual
IIIM. skilled manual
IV. partly skilled
V. unskilled
P linear trend
P nonlinear trend†
Ethnicity
White
Nonwhite
Ecologic variables
IMD 2004
1 (least deprived)
2
3
4
5 (most deprived)
P linear trend
P nonlinear trend†
Rurality
Urban (population ⱖ10,000)
Town and fringe
Village/isolated
P linear trend
P nonlinear trend†
Ethnic mix of area
White
Nonwhite
Risk factors
Obesity
BMI ⬍30 kg/m2
BMI ⱖ30 kg/m2
Number
(%)
Crude rate of
need per 1,000
(95% CI)
1,981 (17.4)
2,185 (19.2)
1,688 (14.8)
1,711 (15.0)
1,471 (12.9)
1,094 (9.6)
806 (7.1)
456 (4.0)
Crude RR
(95% CI)
Adjusted RR
(95% CI)
29.0 (22.6–37.3)
39.7 (32.2–48.8)
41.2 (32.8–51.7)
43.5 (34.9–54.3)
51.7 (41.4–64.4)
61.6 (48.9–77.7)
77.0 (60.7–97.8)
71.2 (50.9–99.4)
1.00
1.37 (0.99–1.89)
1.42 (1.01–1.99)
1.50 (1.07–2.09)
1.78 (1.27–2.48)
2.12 (1.51–2.99)
2.65 (1.88–3.75)
2.45 (1.61–3.72)
⬍ 0.001
0.84
1.00
1.33 (0.94–1.89)
1.15 (0.79–1.67)
1.23 (0.85–1.77)
1.37 (0.94–1.99)
1.95 (1.34–2.83)
2.44 (1.65–3.61)
2.84 (1.75–4.59)
⬍ 0.001
0.30
6,205 (54.5)
5,187 (45.5)
54.6 (49.2–60.6)
36.5 (31.7–41.9)
1.00
0.67 (0.56–0.80)
1.00
0.72 (0.58–0.90)
497 (4.4)
2,997 (26.3)
2,618 (23.0)
2,218 (19.5)
1,779 (15.6)
785 (6.9)
7.5 (2.8–20.0)
29.3 (24.0–35.9)
43.0 (35.9–51.5)
57.7 (48.8–68.3)
66.1 (55.6–78.7)
60.1 (45.4–79.6)
1.00
3.91 (1.44–10.62)
5.73 (2.12–15.51)
7.69 (2.85–20.77)
8.81 (3.26–23.84)
8.01 (2.89–22.20)
⬍ 0.001
0.003
1.00
2.51 (0.92–6.84)
2.98 (1.09–8.18)
4.40 (1.62–11.92)
3.72 (1.36–10.21)
3.03 (1.06–8.65)
0.002
0.006
10,996 (96.5)
320 (2.8)
44.8 (41.1–48.9)
81.4 (56.4–117.4)
1.00
1.81 (1.24–2.64)
1.00
1.43 (0.86–2.37)
2,573 (22.6)
2,530 (22.2)
2,348 (20.6)
2,161 (19.0)
1,779 (15.6)
25.6 (20.1–32.5)
35.1 (28.5–43.1)
47.9 (40.1–57.4)
57.1 (48.1–67.9)
75.6 (64.3–88.9)
1.00
1.37 (1.00–1.88)
1.87 (1.39–2.53)
2.23 (1.66–3.00)
2.96 (2.21–3.95)
⬍ 0.001
0.89
1.00
1.22 (0.85–1.75)
1.67 (1.19–2.35)
2.08 (1.48–2.93)
2.39 (1.68–3.40)
⬍ 0.001
0.88
8,606 (75.5)
1,393 (12.2)
1,392 (12.2)
48.9 (44.5–53.7)
44.2 (34.6–56.4)
31.2 (23.3–41.9)
1.00
0.90 (0.69–1.17)
0.64 (0.47–0.87)
0.004
0.42
1.00
1.24 (0.92–1.67)
1.00 (0.69–1.45)
0.57
0.22
2,163 (19.0)
9,228 (81.0)
45.3 (37.3–55.1)
46.4 (42.3–50.9)
1.00
1.02 (0.83–1.27)
–‡
–‡
7,556 (66.3)
2,566 (22.5)
31.3 (27.6–35.6)
72.9 (63.6–83.7)
1.00
2.33 (1.93–2.81)
1.00
2.28 (1.88–2.76)
* 95% CI ⫽ 95% confidence interval; RR ⫽ rate ratio; IMD ⫽ Index of Multiple Deprivation; BMI ⫽ body mass index.
† To assess for nonlinear trend, likelihood ratio tests were used, comparing a model with a categorical variable with a model with the variable as a
score.
‡ Variable excluded from fully adjusted model because no evidence was associated with rates of need for hip replacement.
rectly classified 97.9% for the hip; sensitivity 85.9%, specificity 97.6%, correctly classified 96.3% for the knee) (Figures 1 and 2). When screening individuals for surgery, we
wanted to reduce the risk of false-positive results (operating on someone that does not need surgery). By favoring
increased specificity, an individual considered not in need
of surgery using the gold standard is less likely to be
considered in need using the simpler score.
The overall rate of need for hip replacement in the UK,
adjusted for all of the variables in the multivariable model,
1662
Judge et al
Table 3. Rates of need for knee replacement by sociodemographic groups*
Individual-level variables
Age groups, years
50–54
55–59
60–64
65–69
70–74
75–79
80–84
ⱖ85
P linear trend
P nonlinear trend†
Sex
Female
Male
Social class
I. professional
II. managerial and technical
IIIN. skilled nonmanual
IIIM. skilled manual
IV. partly skilled
V. unskilled
P linear trend
P nonlinear trend†
Ethnicity
White
Nonwhite
Ecologic variables
IMD 2004
1 (least deprived)
2
3
4
5 (most deprived)
P linear trend
P nonlinear trend†
Rurality
Urban (population ⱖ10,000)
Town and fringe
Village/isolated
P linear trend
P nonlinear trend†
Ethnic mix of area
White
Nonwhite
Risk factors
Obesity
BMI ⬍30 kg/m2
BMI ⱖ30 kg/m2
Number
(%)
Crude rate of
need per 1,000
(95% CI)
1,981 (17.4)
2,185 (19.2)
1,688 (14.8)
1,711 (15.0)
1,471 (12.9)
1,094 (9.6)
806 (7.1)
456 (4.0)
Crude RR
(95% CI)
Adjusted RR
(95% CI)
38.9 (31.3–48.3)
48.0 (39.8–58.0)
54.1 (44.4–66.1)
59.7 (49.5–71.9)
66.9 (55.2–81.2)
88.1 (72.8–106.5)
104.1 (85.0–127.4)
91.9 (68.5–123.2)
1.00
1.24 (0.93–1.65)
1.39 (1.04–1.87)
1.53 (1.15–2.04)
1.72 (1.29–2.30)
2.26 (1.70–3.02)
2.67 (1.99–3.60)
2.36 (1.64–3.40)
⬍ 0.001
0.74
1.00
1.26 (0.92–1.71)
1.23 (0.89–1.70)
1.24 (0.91–1.70)
1.42 (1.03–1.96)
2.17 (1.58–2.98)
2.45 (1.74–3.45)
2.86 (1.87–4.36)
⬍ 0.001
0.31
6,205 (54.5)
5,187 (45.5)
70.2 (64.0–77.0)
50.7 (45.1–57.1)
1.00
0.72 (0.62–0.84)
1.00
0.78 (0.65–0.95)
497 (4.4)
2,997 (26.3)
2,618 (23.0)
2,218 (19.5)
1,779 (15.6)
785 (6.9)
11.3 (5.1–25.2)
38.9 (32.6–46.4)
56.1 (47.9–65.7)
75.5 (65.3–87.3)
84.8 (72.8–98.7)
88.5 (70.6–110.8)
1.00
3.43 (1.52–7.76)
4.94 (2.19–11.15)
6.65 (2.96–14.97)
7.47 (3.32–16.83)
7.80 (3.40–17.86)
⬍ 0.001
0.005
1.00
2.61 (1.07–6.37)
3.15 (1.28–7.72)
4.30 (1.77–10.45)
4.06 (1.66–9.95)
3.77 (1.51–9.42)
⬍ 0.001
0.02
10,996 (96.5)
320 (2.8)
59.7 (55.4–64.3)
108.9 (79.5–149.4)
1.00
1.82 (1.32–2.52)
1.00
1.17 (0.74–1.86)
2,573 (22.6)
2,530 (22.2)
2,348 (20.6)
2,161 (19.0)
1,779 (15.6)
33.7 (27.4–41.5)
44.5 (37.1–53.5)
58.3 (49.5–68.6)
77.1 (66.6–89.3)
108.2 (94.6–123.6)
1.00
1.32 (1.00–1.74)
1.73 (1.33–2.25)
2.29 (1.77–2.95)
3.21 (2.51–4.11)
⬍ 0.001
0.97
1.00
1.21 (0.89–1.66)
1.55 (1.15–2.08)
2.14 (1.60–2.87)
2.70 (2.01–3.64)
⬍ 0.001
0.94
8,606 (75.5)
1,393 (12.2)
1,392 (12.2)
64.3 (59.3–69.8)
60.5 (49.1–74.4)
42.2 (32.9–54.2)
1.00
0.94 (0.75–1.17)
0.66 (0.50–0.85)
0.002
0.26
1.00
1.36 (1.05–1.75)
1.11 (0.81–1.52)
0.14
0.09
2,163 (19.0)
9,228 (81.0)
63.1 (53.6–74.3)
60.7 (56.0–65.8)
1.00
0.96 (0.80–1.15)
–‡
–‡
7,556 (66.3)
2,566 (22.5)
39.6 (35.3–44.3)
99.3 (88.3–111.6)
1
2.51 (2.13–2.95)
1
2.41 (2.04–2.84)
* 95% CI ⫽ 95% confidence interval; RR ⫽ rate ratio; IMD ⫽ Index of Multiple Deprivation; BMI ⫽ body mass index.
† To assess for nonlinear trend, likelihood ratio tests were used, comparing a model with a categorical variable with a model with the variable as a
score.
‡ Variable excluded from fully adjusted model because no evidence was associated with rates of need for knee replacement.
was 31.9 per 1,000 (95% confidence interval [95% CI]
28.4 –35.8), and for knee replacement was 41.0 per 1,000
(95% CI 37.1– 45.4). In univariable analysis, rates of need
for hip and knee replacement increase with age before
falling slightly in those age ⱖ85 years (Tables 2 and 3).
However, multivariable adjustment for other variables,
predominantly obesity, strengthened the effect of those age
ⱖ85 years such that they have the greatest need. For both
hips and knees, women have greater need than men, although the effect was attenuated somewhat in multivari-
Inequalities in the Need for Hip and Knee Replacement
able analyses. For both joints, there was a strong linear
effect of individual social class, with those of lowest social
class having the greatest need. In multivariable models,
the effect of individual social class was attenuated, mainly
by adjustment for area deprivation and obesity. For hip
and knee models, univariable analyses suggested that people of nonwhite ethnicity had greater need for joint replacement, but this was due to confounding by area deprivation and social class.
There was a clear effect of area deprivation, where the
need for hip and knee replacement was greatest for people
living in the most deprived areas. This was not as strong as
the effect of individual social class. For hip replacement,
multivariable models suggest no rurality effect, this being
attenuated by area deprivation. For knee replacement,
there was some evidence that those in town and fringe
areas had greater need. The ethnic mix of the area people
live in was not associated with need for hip or knee replacement. Obesity was a strong predictor of need for hip
and knee replacement, where obese people have more than
twice the need compared with those who are not obese.
For the knee model, a number of interactions were observed (see Supplementary Figure 1 and Supplementary
Table 2, available in the online version of this article at
http://www3.interscience.wiley.com/journal/77005015/
home). There was evidence of an interaction between age
and sex (P ⫽ 0.002). Although the need for knee replacement increases with age, the association is weaker in men.
The effect of sex is stronger in those age ⱖ70 years than in
the younger age groups. There was an interaction between
age and deprivation (P ⫽ 0.002) where the age gradient
was weaker in the most deprived areas.
Sensitivity analyses. In our analysis, we used a cutoff of
48 to classify people as being in need of joint replacement.
Different choices of threshold will classify those with
more/less severe disease as being in need of surgery. Regression analyses were therefore repeated using both lower
and higher choices of threshold (scores of 43 and 53,
respectively) as a sensitivity analysis. Our conclusions
remained unchanged (results not shown); evidence of inequalities in the need for joint replacement by various
sociodemographic groups was consistent regardless of the
cutoff used.
DISCUSSION
This study has demonstrated evidence of inequalities in
need for hip and knee replacement across different sociodemographic groups. We have shown how a 2-stage approach can be used to identify patients in need of surgery
in large-scale nationally representative studies where cost
is an issue. Although we have used 2 independent surveys,
it would be better if in the first stage, a subsample of
patients was selected from the main nationally representative survey. These patients can then undergo a detailed
clinical assessment of their need for surgery using the full
version of a scoring system. ROC curve analyses can then
be used to validate a simpler version of the scoring tool
against the detailed version. Then in the second stage, the
1663
simpler scoring system can be applied to all of the patients
in the national study. The 2-stage approach could be used
to identify patients in need of hip and knee replacement
using nationally representative surveys in other countries,
and can also be applied to other important clinical indicators.
Previous research has found that the need for hip and
knee replacement increased with age and is greater in
women than in men (14 –21), which is consistent with our
findings. A Canadian study observed an interaction where
there were no differences between men and women with
low socioeconomic status, but in people with high socioeconomic status, women had greater need (22). No such
interaction was observed in this study. Research suggests
that poor people were more likely to be in need of joint
replacement (18,19) and that need was associated with less
education and lower income (22). We also found that need
was greater in more deprived areas and in people of lower
social class. In line with other studies, we found no evidence that rurality was associated with need for hip replacement (18,19), but did find evidence that those in the
town and fringe areas may have greater need of knee surgery. A population-based study by Ang et al found that in
a sample of elderly male veterans with moderate to severe
hip and knee pain, ethnicity was not a significant predictor
of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain or function (30). Our findings
also suggest that individual ethnic group is not associated
with the need for joint replacement.
A previous study by Steel et al used the English Longitudinal Study of Ageing to identify patients in need of
joint replacement and explore inequalities in the need for
surgery (21). They used a simpler measure of need based
on the National Institutes of Health method that only uses
information on the degree of hip and knee pain. Applying
the questions used to assess need in this earlier study to
the NZ criteria would create a score out of 20. Repeating
ROC curve analyses using the score out of 20 shows that
the area under the curve is smaller (see Supplementary
Figures 2 and 3, available in the online version of this
article at http://www3.interscience.wiley.com/journal/
77005015/home), and hence there is a weaker measure of
need. The added value of this study is that by using a
2-stage approach, we were able to create a more detailed
score out of 80, including additional information on occurrence of pain and activities of daily living recorded in
the questionnaires. With a weaker measure of need, there
may be a greater degree of misclassification and therefore
underestimation of the effects of covariates such as age,
sex, and social class (31). When comparing the findings of
Steel et al with the results of our study, they found no
association with age, women had greater need than men, a
weak nonsignificant effect of social class was observed
where lower social classes have greater need, and need
was greater in obese people and in areas of poorest wealth.
In our study, the associations were all in the same direction, but the effects of age, sex, social class, and obesity
were far stronger, suggesting that using a strong measure of
need provides a better picture of inequalities. However,
there are other differences between the studies that could
explain the discrepancy in the strength of inequalities
1664
observed. In the prior study, people age ⬍60 years were
excluded, broad age categories were not used, and they
looked at the overall need for hip or knee replacement
rather than considering both joints separately.
The benefit of using the English Longitudinal Study of
Ageing is that it is a large nationally representative sample
with data on both individual and ecologic sociodemographic variables. However, it was not specifically designed to identify those in need of joint replacement. Because self-reported questions contain information on the
severity of hip and knee pain and activities of daily living,
it was possible to assign people a simpler version of the NZ
score to measure disease severity without the need for a
detailed clinical assessment. However, it was not possible
to estimate side-specific scores to right and left hips and
knees because this information is not available. Nor can
we restrict the estimate of need to be for primary operations only. Although there is information on previous operations for those age ⬍60 years, we do not know which
side the operation relates to; for example, if the operation
was on the left hip, they may still need an operation on the
right hip. By using a 2-stage approach, we have been able
to describe evidence of inequalities in the need for hip and
knee replacement using self-reported data from a nationally representative survey, which does not contain data
from radiographs or a detailed clinical examination. This
is set in the context of providing information at an aggregate level to inform health planning, and although the
relationship between self-reported symptoms and radiographic severity has been questioned (32,33), at the individual level, a clinician would use additional information
from clinical examination and radiographs regarding the
decision for surgery.
A limitation of our analysis and of all such studies
estimating the need for joint replacement is the different
methods used to assess the severity of joint disease, such
as the WOMAC score (34), the NZ score (25), or the Lequesne Index (35). Authors derive an arbitrary cutoff based
on these measures to determine whether patients require
joint replacement. Clearly, different choices of cutoff will
lead to different estimates of need for surgery, but sensitivity analyses can be used, repeating the analysis with
different choices of cutoff to see if patterns of inequality
remain unchanged. There appears to be a general lack of
consensus about which indicator or threshold of disease
severity to use (36), and until this is resolved, it will
remain a limitation of future studies.
Previous studies have adjusted their estimates of need
according to a patient’s willingness and fitness for having
surgery. If adjusting for such factors attenuates inequalities, it may help explain why they exist. Older people
are less willing to seek or want joint replacement
(16,20,22,37– 40), considering the symptoms of arthritis a
normal part of aging and adapting their lifestyles to cope
(41– 45), whereas in younger people, the impact on work
and social lives is greater so they seek surgery to get back
to normal (16,37,41,45,46). Women may be less willing to
have joint surgery (others suggest they are equally willing)
(16,20,22,37,38), as are people of lower socioeconomic
status (37) and African Americans (47). The common explanation is that these groups are less positive about the
Judge et al
benefits and outcomes of surgery, as largely influenced by
friends and family and those they know who had surgery
(41,43,44). We have no information on willingness, and
although data on comorbidities are available, it is unclear
what would make a patient an unsuitable candidate for
surgery, given improvements in modern anesthesia, surgical techniques, and prosthesis survival. Local health planners may argue that such estimates of need should be
adjusted for these factors so that they can determine the
level of provision for their area based only on those who
are willing and fit for surgery. Although this may be of use
in resource allocation, willingness and fitness for surgery
are likely determinants of why inequities exist and planning services in such a way will not ensure fairness in
accessing care, because these factors may change over
time.
In the UK, local health planners (primary care trusts) are
responsible for the planning, commissioning, and delivery
of National Health Service services (48). They must assess
the health needs of people in their local area, ensuring that
services are available to and can be accessed by everyone
who needs them. The results of this study describe inequalities in the need for hip and knee replacement, but
such data are of no use on their own. Local health planners
require small-area estimates of need to plan an appropriate
level of surgical provision. The second part of this study
takes this work forward, producing estimates of need for
joint replacement across small areas of the UK to inform
local health planning.
ACKNOWLEDGMENTS
We would like to thank Dr. Mary Shaw at the Department
of Social Medicine, University of Bristol, for support and
advice throughout the project, and Professor Kelvyn Jones
at the Department of Geographical Sciences, University of
Bristol, for statistical advice on multilevel modeling. We
would also like to thank all of the study participants and
the partners and practice staff of participating general
practices. We are indebted to the entire Somerset and
Avon Survey of Health research team.
AUTHOR CONTRIBUTIONS
All authors were involved in drafting the article or revising it
critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Judge
had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data
analysis.
Study conception and design. Judge, Sandhu, Ben-Shlomo.
Acquisition of data. Judge, Sandhu, Ben-Shlomo.
Analysis and interpretation of data. Judge, Welton, Ben-Shlomo.
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