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J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158102 www.haemnet.com
The impact of factor infusion frequency
on health-related quality of life in people
with haemophilia
CLINICAL RESEARCH
Gabriel Pedra, Pia Christoersen, Kate Khair, Xin Ying Lee, Sonia O’Hara, Jamie O’Hara, John Pasi
Background: Some studies suggest that people with
haemophilia (PwH) who use prophylaxis value low
frequency of clotting factor administration more than
a lower risk of bleeding. However, more frequent
infusions oer the potential of reducing joint disease
and pain, which in turn may improve functioning and
quality of life. Aims: To explore the impact on health-
related quality of life (HRQoL) aspects of haemophilia
associated with adherence and annual infusion rate
in the context of factors influencing treatment that
are important to patients, including prophylaxis,
chronic pain, concomitant conditions and hospital
admission. Materials and methods: HRQoL was
assessed in participants with severe haemophilia in
the ‘Cost of Haemophilia in Europe: a Socioeconomic
Survey’ (CHESS) study who were using prophylaxis.
Patients using on-demand treatment were excluded.
This multivariate analysis examined the interaction
between factors potentially influencing treatment
and HRQoL, and minor and major bleeds. Results:
From the total CHESS population (n=1,285), 338
(26%) participants provided responses for major and
minor bleeds and target joints, and 145 (11%) provided
EQ-5D-3L responses. Major and minor bleeds were
associated with pain. Patients with severe chronic pain
reported a substantial negative impact on HRQoL;
but this was significantly improved by increases in
the annual infusion rate. This was not apparent in
GABRIEL PEDRA
HCD Economics, Daresbury, UK. Email: gabriel.pedra@
hcdeconomics.com
PIA CHRISTOFFERSEN
Novo Nordisk A/S, Søborg, Denmark
KATE KHAIR
Centre for Outcomes and Experience Research in
Children’s Health Illness and Disability (ORCHID), Great
Ormond Street Hospital, London; Haemnet, UK
XIN YING LEE
Novo Nordisk A/S, Søborg, Denmark
SONIA O’HARA
HCD Economics, Daresbury, UK
JAMIE O’HARA
Faculty of Health and Social Care, University of Chester, UK
JOHN PASI
Haemophilia Centre, Royal London Hospital, UK
The impact of infusion frequency on health-related quality of
life in people with haemophilia is variable, with influencing
factors including haemophilia severity and experience of pain.
A recent study indicates a need to balance burden of treatment
with protection against bleeds.
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J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158 www.haemnet.com 103
participants with mild or moderate pain. Conclusion:
Increasing the frequency of prophylaxis infusions is
associated with improved quality of life in PwH who
have severe chronic pain. However, increasing the
number of infusions per week in those with mild or
moderate chronic pain with the intention of improving
prophylactic eect may not have the same eect.
Keywords: Haemophilia, quality of life, infusion
frequency, chronic pain, CHESS study
Recommendations from the World Federation for
Hemophilia for the management of haemophilia
state that prophylaxis with factor replacement
therapy should maintain trough factor levels of
>1%, with the aim of minimising bleeds and reducing the
long-term risk of joint damage due to haemarthrosis [1].
However, the advent of extended half-life factors and
non-factor products such as emicizumab has raised
expectations among clinicians of improved quality of
life through a reduction in infusion frequency, greater
convenience and better tailored treatment [2].
The infusion frequency required to achieve trough
factor VIII (FVIII) levels of >1% ranges from once a
week to alternate days among most people with
haemophiliaA. However, even these dose regimens
do not prevent all spontaneous bleeds despite high
adherence [3], and the residual joint arthropathy continues
to develop, so that young men in their 20s and 30s
report increased pain and limitation of function [4,5]. Some
European countries are now seeing as many as 14–16%
of patients using daily intravenous infusions [6].
Extended half-life (EHL) products have already
changed clinical practice in economically developed
countries, but standard half-life products still dominate
prescribing elsewhere. EHL FVIII can maintain target
trough levels with a reduced infusion frequency
whereas EHL factor IX (FIX) oers higher trough levels
and a lower frequency of infusions [7]. It is clear that
maintenance of higher trough levels reduces the
risk of bleeds [8]. The choice between fewer infusions
or higher trough levels is likely to be influenced by
patient preference, available resources and cost, and
may not be exclusively one pathway or the other but
a compromise representing a balance somewhere in
between. When considering the best outcome from
the patient’s perspective, it is important to understand
the likely impact on clinical endpoints and quality of
life. Patient preference studies suggest that patients
value low frequency of administration more than a
lower risk of bleeding [9,10] and this was the commonest
reason cited for considering a switch from a traditional
standard half-life (SHL) product to an EHL factor [11].
However, joint disease and pain also impair health-
related quality of life (HRQoL) and daily functioning [12,13],
and these studies may not fully represent the potential
benefits available to patients from maintaining higher
trough factor levels.
The aim of this study was to test the hypothesis that
HRQoL in people with haemophilia (PwH) is influenced
by factors that are important to them, including
prophylaxis, chronic pain, concomitant conditions and
hospital admission.
MATERIALS AND METHODS
The CHESS study
The ‘Cost of Haemophilia in Europe: a Socioeconomic
Survey’ (CHESS) study was a cross-sectional,
retrospective study carried out in 2015, where patients
aged ≥18 years with severe haemophilia in five European
countries (France, Germany, Italy, Spain and the UK) were
invited to participate [14]. 1,285 patients were recruited
by 139 haematologists and haemophilia care providers
based in hospitals and clinics. Data were collected
using two questionnaire forms: the patient record form
(PRF) was completed by physicians and a Patient and
Public Involvement and Engagement form (PPIE) was
completed by individual patients. Full details of the
methodology for data collection have been published [14].
Physicians reported the number of comorbidities
diagnosed at the time of consultation, hospital
admissions in the previous year, the presence of a target
joint (binary variable), level of adherence to prescribed
factor replacement therapy (high or low/medium, based
on the physician’s records) and the severity of chronic
pain (according to clinical assessment). In CHESS,
primary prophylaxis was defined as receiving prophylaxis
since the start of treatment for haemophilia; secondary
prophylaxis was defined as switching to prophylaxis
from on-demand treatment[15]. We included only
patients taking a prophylaxis dose of ≥10 IU/kg, which is
the minimum eective dose [16]. Patients currently
receiving on-demand treatment were not included due
to their infrequent use and dierent requirements for
adherence. Patients with a history of inhibitors or
currently with inhibitors were also excluded.
Additionally, patients were excluded when the ratio of
factor dose to body weight exceeded 1.2 (calculated as
FVIII: IU per infusion/body weight x 2; FIX: IU per
infusion/body weight) because this suggested the
possibility of an increased risk of thrombosis or of error
in completing the questionnaire.
J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158104 www.haemnet.com
Joint bleeds were defined as episodes of bleeding
into joints diagnosed by the physician which were
recorded as major and minor bleeds. Minor bleeds
were defined as those with mild pain, minimal swelling,
minimal restrictions of motion and resolution within 24
hours of treatment. Major bleeds were defined as pain,
eusion, limitation of motion and failure to respond to
treatment within 24 hours. The presence and number
of target joints was defined as joints aected by chronic
synovitis diagnosed by the physician. This definition,
originally proposed in 2004 [17], was adopted following
discussion with an expert advisory group.
All participants in the CHESS study provided informed
consent. The CHESS protocol was approved by the
Research Ethics Sub Committee of the Faculty of Health
and Social Care within the University of Chester. The
approval stipulated that the study was to be carried out
in correspondence with regional and relevant guidelines.
Health-related quality of life assessment
HRQoL was assessed using the validated tool
EQ-5D-3L[18,19]. This involves patient self-reporting
of their health status in five dimensions: mobility,
self-care, usual activities, pain and discomfort,
anxiety and depression. EQ-5D-3L index values are
normally confined to the range -0.594 to 1.0, where
1.0 represents ‘perfect health’, zero represents ‘dead’,
and values less than zero are health states ‘worse
than dead’. We were unable to utilise predictive
methodologies confined to a 0–1 range (e.g. logistic
regression, beta regression) without transforming
the data EQ-5D-3L values in the sample at zero. The
distribution of EQ-5D-3L values was therefore shifted
so that, in this analysis, EQ-5D-3L values ranges from
zero to 1.594 (as zero meaning worse than dead and
1.594 perfect health). Shifting EQ-5D-3L values does
not change their distribution and will facilitate the use
of a more appropriated statistical method given the
complex distribution. Other techniques have been
proposed, such as adding a threshold in the upper level
of the EQ-5D-3L scale range (Tobit models), but the
values obtained from this study are very close to the
threshold which would bias the results [20].
Statistical analysis
Descriptive analysis was conducted to summarise
patient characteristics. Means were used to describe
continuous variables and frequency and proportions
to describe categorical variables. Independent sample
t-tests and chi squared for independence tests were
conducted to test for between-group dierences.
Multivariate analysis was carried out using
generalised linear regression models. The analyses
were performed using R software and the function
glm(). Three models were generated with a single
set of candidate covariates: the first examined the
eect of the covariates on quality of life, the others
examined their eect on minor and major bleeds. A
gamma distribution with log link function was assumed
for a quality of life model whereas a Poisson with
log link function was used for the major and minor
bleeds regression models. For each model, a selection
approach was applied to identify the best model given
its complexity and goodness of fit. Candidate covariates
included all potentially important determinants of
the outcomes. The model selection was based on a
top-down selection, which consists of having a model
with all candidate covariates and removing each
covariate sequentially to see the improvement of the
fit. Likelihood ratio test was the test used in the model
selection process.
The set of candidate covariates were:
y = age + target joint + subtype + treatment
strategy + number of hospital admissions + number
of comorbidities + chronic pain + adherence +
infusion +(infusion rate * chronic pain)
where y is the dependent variable for each model.
Correlation matrix was obtained between explanatory
variables in order to identify any issues regarding
multicollinearity of the variables.
RESULTS
Patient characteristics
Patient characteristics are summarised in Table 1.
From the CHESS population (n=1,285), excluding
58 with a current inhibitor, of 1,227 patients with
severe haemophilia, 338 (26%) provided responses
for major and minor bleeds and target joints and 145
(11%) provided an EQ-5D-3L response. Most patients
had haemophilia A. The other participants were not
included in the analysis as described in the materials
and methods section.
The majority of participants were young adults
(<30 years old) and the mean level of comorbidity
was correspondingly low, though there was marked
variation in the number of concomitant conditions;
this was matched by variation in admission frequency.
Prophylaxis was secondary in almost two thirds of
patients; the number of infusions weekly ranged
J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158 www.haemnet.com 105
Table 1. Demographics of the study group (n=48)
SUBSET N=338 CHESS DATA 1,285
Age mean (SD)
median (range)
30.86 (12.07)
27 (18–67)
35.94 (14.70)
32 (18–88)
Haemophilia type A
B
283 (84%)
55 (16%)
996 (77.5%)
289 (22.5%)
Number of patients with target joint Yes
No
200 (59.2%)
138 (40.8%)
768 (59.8%)
517 (40.2%)
Number of comorbidities mean (SD)
median
0.80 (1.28)
0 (0–12)
0.94 (1.33)
1 (0–12)
Number of admissions mean (SD)
median
0.61 (1.43)
0 (0–15)
0.76 (1.46)
0 (0–15)
Treatment strategy primary PPX†
secondary PPX†
123 (36%)
215 (64%)
217 (16.9%)
1068 (83.1%)
Number of prescribed infusions per year mean (SD)
median
134.45 (42.40)
156 (52–364)
124.30 (50.05)
136.71 (52–364)
Weekly infusion rate†† 1
1.4
2
3
3.5
5
6
7
30 (9%)
6 (2%)
97 (29%)
179 (53%)
19 (6%)
5 (1%)
1 (<1%)
1 (<1%)
145 (21%)
10 (1%)
198 (29%)
276 (40%)
35 (5%)
20 (3%)
4 (1%)
1 (<1%)
Adherence high
low
117 (35%)
221 (65%)
773 (60.2%)
512 (39.8%)
Chronic pain rating none
mild
moderate
high
121 (36%)
139 (41%)
71 (21%)
7 (2%)
461 (35.9%)
474 (36.9%)
301 (23.4%)
49 (3.8%)
† PPX Prophylaxis (see Materials and Methods for definition).
†† Numbers of patients using prophylaxis and % of total using prophylaxis
from1–7. Physicians reported that almost two thirds
had a rating of low adherence (on a Likert scale of
1–2 vs 3–4) to their prophylaxis regimen. Physicians
reported pain in 64% of patients. This was rated mild in
most cases but considered to be moderate in one in
five patients, with few having severe pain.
Compared with the CHESS population as a whole,
the subset included in this analysis were slightly
younger, more likely to be using primary prophylaxis,
and fewer had high adherence. However, both had
more people on three times weekly administration and
the distribution of pain severity was similar.
Infusion rate on major bleeds
The coecients from the generalised models were
exponentiated (exp{β}) to represent the proportional
change in the response variable (EQ-5D-3L score,
number of major and minor bleeds). The generalised
model for major bleeds showed that using primary
(rather than secondary) prophylaxis, number of
comorbidities, infusion rate and having mild or severe
chronic pain were not statistically significant variables
(Table 2). Similarly, the interaction between infusion
rate and pain was not significant. In the model of best
fit, major bleeds were positively associated with age
(i.e. higher number of bleeds were more frequent with
higher age), having a target joint and hospital admissions
whereas high prophylaxis adherence was associated with
less events of major bleeds. Pain at every level of severity
was positively associated with number of major bleeds.
Infusion rate and minor bleeds
In the case of minor bleeds, the generalised model
also showed no significant association with primary
prophylaxis vs secondary prophylaxis, comorbidities,
mild pain, the interactions of infusion rate and pain,
J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158106 www.haemnet.com
and also with age or haemophilia subtype. In the
model of best fit, the presence of target joints and
admissions were positively associated with minor
bleeds whereas high adherence and higher infusion rate
were associated with fewer minor bleed events. The
association was also positive for chronic moderate and
severe pain, but not mild pain, and for the interaction
between infusion rate and severe pain.
Health-related quality of life
There was no significant association in the generalised
model between HRQoL and age, target joints,
comorbidities, haemophilia subtype, adherence,
infusion rate, or mild or moderate pain. The model of
best fit showed that HRQoL was positively associated
with using lifelong prophylaxis but negatively associated
with number of hospital admissions. Patients with
severe chronic pain reported a substantial negative
impact on HRQoL; this was slightly but significantly
improved by increases in the annual infusion rate.
Analysis of the impact of infusion rate on HRQoL
shows a trend to incremental decrease in EQ-5D-3L
score with each additional infusion per week (equivalent
to 52 per year) by approximately 2%, increasing to 10%
reduction with daily infusions.
DISCUSSION
Prophylactic clotting factor replacement reduces
major or minor bleeding, with patients on primary
prophylaxis reporting significantly increased HRQoL
[21]. There is a clear relationship between bleeding
and hospital admission: patients with higher bleeding
frequency are more likely to be admitted, with greater
impact on their day-to-day life and a reduced HRQoL.
The same can be said for the impact of bleeding on
increasing levels of pain and HRQoL. The absence
of an association between minor bleeding and the
use of primary vs secondary prophylaxis appears
counter-intuitive; possible explanatory factors include
the relatively low age of CHESS participants, a lower
perceived relative impact of minor bleeds by adults,
and a small dierence in eect size between primary
and secondary prophylaxis.
As shown in Table 2, a high adherence rate is
significantly associated with fewer major and minor
bleeds, with a corresponding decreasing trend in
HRQoL. However, adherence to a lifelong prophylaxis
regimen is associated with a treatment burden that
imposes reductions in HRQoL. This study shows that
a trend toward increasing infusion frequency per
week to increase protection from bleeding may have
some ceiling eects and may be associated with some
decreases in HRQoL scores. It is informative to consider
the additional annual treatment burden arising from
two to seven infusions per week: with a median infusion
frequency of three per week, this would entail up to
an extra 208 infusions per year (or higher for patients
currently using fewer infusions per week). It is this
substantial increase in the number of infusions annually
that underlies the impact on HRQoL. This dilemma
could be resolved with EHL factors, which have the
potential to increase trough levels with no increase or
even a reduction in weekly infusions [22].
The regression analysis further shows that an
increase in the annual infusion rate in patients with
severe pain is associated with a significant improvement
in HRQoL score. This possibly reflects higher trough
levels preventing frequent re-bleeding in damaged
joints, and subsequent joint deterioration and chronic
pain. This was not the case for mild or moderate levels
of pain, for which the reduction in HRQoL score had
little eect. This may suggest that, in PwH with more
severe pain, having higher trough levels may have a
greater benefit than those with less chronic pain and
could be considered as part of the balance of risk and
benefit of treatment.
The current generation of PwH in the CHESS study
who have received lifelong prophylaxis are ageing
with mild or moderate pain rather than the severe pain
associated with less intense therapy options in older
regimens [23]. This study shows that, in patients with mild
or moderate pain, as with those with severe chronic
pain, increasing infusion rate and adherence do not
have the same impact on HRQoL or bleeds.
This study suggests that having fewer weekly
infusions is more than just ‘a convenience’. The burden
of prophylaxis has a day-to-day impact on HRQoL.
Clinical management depends on raising trough factor
levels to provide greater protection against bleeds and
arthropathy. These findings show that increasing the
treatment burden (infusion frequency) is associated
with a negative impact on HRQoL; further research is
needed to determine whether it is also associated with
lower adherence and compromised treatment gains.
We relied on physician reporting of pain. There
is evidence that physician- and patient-reported
assessments and treatment of haemophilia-related
pain may not correlate well, and physicians tend to
underestimate pain compared with patients [24-26].
In this study, physicians reported that almost two
thirds of patients had pain, which was moderate in
21% and severe in 2%; there were seven patients with
J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158 www.haemnet.com 107
severe pain. For comparison, the HERO study found
the prevalence of haemophilia-related pain was 27%
(n=230) when reported by patients in an online survey,
whereas six reported extreme pain/discomfort [12,27]. This
suggests that, although pain is an important challenge
for PwH, the prevalence of severe pain is low and it is
dicult to recruit large numbers of aected patients
in a population-based study. However, the regression
analysis showed a statistically strong relationship
between chronic severe pain and major bleeds. Further
study in a larger group of patients is therefore warranted.
Adherence was assessed from physicians’ records.
Self-reported assessment has been shown to correlate
well with objective measures of adherence such as
pharmacy records [28], though there is also evidence
that patients may overestimate their adherence[29].
Assessment by a health provider has also been shown to
correlate significantly with self assessment, though slightly
less well with a patient-completed questionnaire [30].
The mean number of infusions prescribed was
approximately three per week, with a range of once
weekly to once daily, but the actual number of
infusions received by patients could be lower due
to the level of adherence to treatment. We cannot
exclude the possibility that the inverse relationship
between HRQoL and infusion frequency may be due
to inverse causality – i.e. that worse HRQoL may be
the deciding factor when considering the option of
a higher infusion frequency for a person with severe
pain. However, this does not alter the observation
that increasing infusion frequency does not appear to
benefit people with mild or moderate pain (in terms of
HRQoL). It appears that patients are willing to accept
chronic mild pain (which has a negative impact on
HRQOL) rather than increase infusion frequency.
Therefore, infusion frequency may have both a direct
and indirect eect on HRQoL. This underlines the
point that optimising management is a process of
balancing the burden of treatment with protection
against bleeds and quality of life.
CONCLUSION
This study strengthens the evidence that pain,
availability of prophylaxis and bleeding rate influence
HRQoL in people with severe haemophilia. It shows
that the impact of severe pain on HRQoL is mitigated
by increasing protection through raising trough factor
levels and subsequently requiring increased weekly
infusion rates. However, improvement in HRQoL in
patients with mild or moderate chronic pain may
be masked by the negative impact of the increased
treatment burden. Further studies are needed to
evaluate the implications for factor usage, cost-benefit
relationship and impact on HRQoL of new treatment
strategies that oer a lower infusion frequency while
maintaining higher factor levels.
ACKNOWLEDGMENTS
The study reported in this paper was funded by Novo
Nordisk.
GP, KK, JO, PC, XYL and SO contributed to the
analysis, preparation and interpretation of the data.
JP contributed to insights and interpretation of
the data. The authors would like to thank Declan
Noone (HCD Economics) for his contribution to the
manuscript preparation and Steve Chaplin (Haemnet)
for drafting the paper and incorporating authors’
comments.
Informed consent has been obtained from
participants in the CHESS study, on which the study
reported in this paper is based.
Declaration of interests
SO is a trustee of the Haemophilia Society and currently
consulting for World Federation of Haemophilia
(financial support). PC and XYL are currently employed
by Novo Nordisk. JP has received grants, honoraria and
non-financial support from Alnylam, Biomarin, Catalyst
Bio, SOBI, Shire/Takeda, Octapharma, Sanofi and Pfizer
and honoraria from Apcintex, Bayer, Novo Nordisk and
Roche. KK is an employee of Haemnet and a trustee
of the Haemophilia Society, and has received research
funding and honoraria from Bayer, CSL Behring, Novo
Nordisk, Pfizer, Roche, Sobi and Takeda. JO is a Trustee
of Haemnet.
ORCID
Gabriel Pedra https://orcid.org/0000-0002-2023-5224
Pia Christoersen https://orcid.org/0000-0002-9584-8922
Kate Khair https://orcid.org/0000-0003-2001-5958
Xin Ying Lee https://orcid.org/0000-0002-1102-3756
Sonia O’Hara https://orcid.org/0000-0002-9119-8336
Jamie O’Hara https://orcid.org/0000-0001-8262-034X
John Pasi https://orcid.org/0000-0003-3394-2099
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HOW TO CITE THIS ARTICLE:
Pedra G, Christoersen P, Khair K, Lee XY, O’Hara S, O’Hara
J, Pasi J. The impact of factor infusion frequency on
health-related quality of life in people with haemophilia.
JHaem Pract 2020; 7(1): 102-109. doi: 10.17225/jhp00158.
J Haem Pract 2020; 7(1). doi: 10.17225/jhp00158 www.haemnet.com 109
Table 2. Results of the regression analysis (coecients, 95% confidence intervals)
Values >1.0 represent positive associations (e.g. having a target joint is strongly associated with major bleeds); values <1.0 represent
negative associations (e.g. having haemophilia B is negatively associated with major bleeds)
EQ5D3L MINOR BLEEDS MAJOR BLEEDS
FULL MODEL BEST MODEL FULL MODEL BEST MODEL FULL MODEL BEST MODEL
(Intercept) 1.6536***
(1.3891,
1,9794)
1.5907***
(1.4018,
1.808)
2.4910***
(1.5097,
4.0477
2.4330***
(1.5911,
3.6499)
0.2292*
(0.0524,
0.8669)
0.2959*
(0.1195,
0.7238)
Age 0.9981
(0.9956,
1.0006)
— 0.9984
(0.9927,
1.004)
— 1.0199***
(1.0088,
1.0309)
1.0214***
(1.0110,
1.0317)
Having a target joint 0.9679
(0.9147,
1.0242)
— 1.6486***
(1.3995,
1.9484)
1.6573***
(1.4130,
1.9506)
2.2315***
(1.5382,
2.2098)
2.3001***
(1.59.88,
3.3808)
Primary prophylaxis
(vssecondary)
1.0497*
(0.9979,
1.1039)
1.0447*
(0.9943,
1.0973)
0.9601
(0.8439,
1.0938)
— 0.9992
(0.7624,
1.3190)
—
Number of concomitant
diseases
1.0051
(0.9790,
1.0324)
— 1.0298
(0.9740,
1.0873)
— 1.0356
(0.9330,
1.1451)
—
Number of
hospitalisations
0.9647*
(0.9329,
0.9977)
0.9612*
(0.9306,
0.9932)
1.0712***
(1.0357,
1.1064)
1.0683***
(1.0377,
1.0976)
1.0588
(0.9920,
1.1266)
1.0541
(0.9901,
1.1181)
High adherence 0.9917
(0.9446,
1.0411)
— 0.8554*
(0.7512,
0.9753)
0.8463*
(0.7444,
0.9631)
0.6817**
(0.5198,
0.8955)
0.6723**
(0.5141,
0.8804)
Having haemophilia B 0.9996
(0.9275,
1.0790)
— 1.0222
(0.853,
1.2179)
— 0.6977
(0.4408,
1.0620)
0.6877
(0.4345,
1.0435)
Increasing infusion rate/
year
0.9999
(0.9989,
1.0009)
0.9997
(0.9988,
1.0007)
0.9968*
(0.9936,
1.0000)
0.9967*
(0.9936,
0.9999)
0.9977
((0.9885,
1.0073)
0.9955*
(0.9921,
0.9989)
Mild chronic pain 0.9271
(0.7602,
1.1201)
0.8824
(0.7349,
1.0577)
1.0762
(0.6296,
1.9593)
1.0988
(0.6300,
1.9316)
2.6814
(0.6259,
12.7098)
2.1879***
(1.4188,
3.4933)
Moderate chronic pain 0.8994
(0.6869,
1.1756)
0.8344
(0.6590,
1.0556)
1.8156**
(1.0213,
3.2642)
1.8877**
(1.0723,
3.3572)
4.2172*
(0.9855,
20.1387)
3.308***
(2.0707,
5.4543)
Severe chronic pain 0.1826***
(0.0969,
0.3437)
0.1788***
(0.1217,
0.2664)
2.5249
(0.6292,
9.6216)
3.6750*
(1.1677,
10.6275)
4.3910
(0.4313,
45.1077)
3.8228***
(1.9093,
7.545)
Infusion rate/year * mild
chronic pain
1.0000
(0.9987,
1.0014)
1.0002
(0.9989,
1.0016)
1.0023
(0.9981,
1.0065)
1.0024
(0.9982,
1.0065)
0.9983
(0.9872,
1.0094)
—
Infusion rate/year *
moderate chronic pain
0.9996
(0.998,
1.0012)
0.9999
(0.9985,
1.0014)
0.9968
(0.9968,
1.0048)
1.0007
(0.9967,
10046)
1.0007
(0.9967,
10046)
—
Infusion rate/year *
severe chronic pain
1.0045*
(1.0003,
1.0087)
1.0045**
(1.0015,
1.0075)
0.9950
(0.9853,
1.0046)
0.9928*
(0.9841,
1.0014)
0.9978
(0.9819,
1.10135)
—
* P <0.05, ** P<0.01, *** P<0.001
