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1Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
Original article
Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
Julia Unger,1 Polina Putrik,2 Frank Buttgereit,3 Daniel Aletaha,4 Gerolamo Bianchi,5 Johannes W J Bijlsma,6 Annelies Boonen,2 Nada Cikes,7 João Madruga Dias,8 Louise Falzon,9 Axel Finckh,10 Laure Gossec,11 Tore K Kvien,12 Eric L Matteson,13 Francisca Sivera,14 Tanja A Stamm,15 Zoltan Szekanecz,16 Dieter Wiek,17 Angela Zink,3,18 Christian Dejaco,19,20 Sofia Ramiro21
To cite: Unger J, Putrik P, Buttgereit F, et al. Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the eUlar points to consider. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
► Prepublication history and additional material for this paper are available online. to view these files, please visit the journal online (http:// dx. doi. org/ 10. 1136/ rmdopen- 2018- 000756).
JU and PP contributed equally.
received 1 July 2018revised 8 September 2018accepted 15 October 2018
For numbered affiliations see end of article.
Correspondence toDr christian Dejaco; christian. dejaco@ gmx. net
Epidemiology
© author(s) (or their employer(s)) 2018. re-use permitted under cc BY-nc. no commercial re-use. See rights and permissions. Published by BMJ.
AbstrActObjective to summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology.Methods a systematic literature review (Slr) was performed in key electronic databases (1946–2017) comprising an update of an Slr in rheumatology and a hierarchical Slr in other medical fields. Data on the type of workforce prediction models and the factors considered in the models were extracted. Key general as well as specific need/demand and supply factors for workforce calculation in rheumatology were identified. the workforce prediction risk of bias tool was developed and applied to existing workforce studies in rheumatology.Results in total, 14 studies in rheumatology and 10 studies in other medical fields were included. Studies used a variety of prediction models based on a heterogeneous set of need/demand and/or supply factors. Only two studies attempted empirical validation of the prediction quality of the model. Based on evidence and consensus, the newly developed risk of bias tool includes 21 factors (general, need/demand and supply). the majority of studies revealed high or moderate risk of bias for most of the factors.Conclusions the existing evidence on workforce prediction in rheumatology is scarce, heterogeneous and at moderate or high risk of bias. the new risk of bias tool should enable future evaluation of workforce prediction studies. this review informs the european league against rheumatism points to consider for the conduction of workforce requirement studies in rheumatology.
InTROduCTIOnRheumatic and musculoskeletal diseases (RMDs) are highly prevalent and, according to the burden of disease report, present a
major cause of disability-adjusted life years worldwide.1 Due to population growth, ageing and improved diagnosis, the prev-alence of RMDs in developed countries increased by 60% from 1990 to 2010.2 While expert consensus exists with respect to how best manage RMDs in order to prevent adverse long-term consequences,3–5 inad-equate manpower documented in many countries hinders implementation of these recommendations.6 7 Workforce planning in
Key messages
What is already known about this subject? ► the projections from existing workforce studies in rheumatology vary by a factor of five, largely due to methodological heterogeneity.
What does this study add? ► this study provides a synthesis of information of the workforce prediction literature in rheumatology and other medical fields about general aspects, supply, need and demand factors considered in workforce models.
► We hereby use a self-developed workforce predic-tion risk of bias tool to guide and assess the quality of workforce studies in rheumatology.
How might this impact clinical practice? ► the developed tool is meant to improve the meth-odological quality of future workforce studies and ultimately to lead to better workforce planning in rheumatology.
► this review informs the european league against rheumatism points to consider for the conduction of workforce requirement studies in rheumatology.
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RMD OpenRMD OpenRMD Open
healthcare is further challenging due to time and costs involved in training of new physicians. Methodologically sound workforce planning should guide policy decisions on the number of students entering into education and medical training programmes.8
A recent systematic literature review (SLR) on work-force projection models in rheumatology from Western countries identified a large heterogeneity in methods for projecting the rheumatology workforce needs.9 Notably, published studies covered only a handful of Western countries, and the resulting projections from available studies varied by a factor of five9 and are thus not a reli-able basis for political decisions. Therefore, the develop-ment and implementation of a sound approach to health workforce planning is needed to ensure access of the population to best practice disease management.
The need for an agreed-on methodology for work-force predictions is discussed not only in the field of rheumatology. A number of workforce prediction studies have also been conducted in other medical fields.10–12 It is likely that major principles of workforce modelling are common to other specialties in medicine. To date, however, insufficient attention has been given to synthe-sise the existing evidence on methodologies used for workforce predictions. To our knowledge, there has been no attempt so far to agree on a standard methodology for the conduction of workforce studies, nor has there been any attempt to appraise such studies for methodological quality and risk of bias.
The overarching aim of this SLR was to inform the European League Against Rheumatism (EULAR) task force working on ‘points to consider’ for the conduction of workforce requirement studies in rheumatology.13 The specific objectives of the present work were (1) to perform an update of the published SLR on workforce prediction in rheumatology,9 (2) to conduct a hierarchical SLR (overview of reviews) of workforce prediction models in other medical fields, and (3) using available data to develop a workforce prediction risk of bias tool and to apply it to existing workforce studies in rheumatology.
MeTHOdsdesign of the systematic literature searchWe conducted two SLRs, including an update of an SLR of workforce requirement studies in rheumatology9 and a hierarchical SLR (which is an overview of systematic reviews) of workforce prediction studies in other medical fields (including all medical specialties, but also related areas like nursing, physiotherapy and pharmacy) in Western countries.
search strategy and eligibility criteriaThe EULAR task force to develop ‘points to consider’ for the conduction of workforce requirement studies in rheumatology outlined the scope of the literature search according to the PICO (Population, Interven-tion, Comparator, Outcomes) format. The population was
defined as (1) adult rheumatologists (for the update of the recent SLR in rheumatology) and (2) other medical fields, namely medical specialists and other health professionals (for the hierarchical search). The scope of the update did not include paediatric rheumatologists. The intervention was defined as (1) the type of workforce model, (2) the factors used to build up the model or (3) the empirical data used for the calculation of work-force requirements. The comparator could not be defined for this review question. The outcome was defined as the number of rheumatologists/other specialists needed to serve the (general) population. Studies with any time frame for predictions, including those making calculations for baseline only (ie, calculations referring to the year when prediction has been made), were included.
For the update search in rheumatology, we used the same search strategy and eligibility criteria as in Dejaco et al.9 MEDLINE, Embase, PubMed, CINAHL and the Cochrane Library were searched (Search Strategy in the Online Supplementary Text S1) between 1 November 2015 (date of the original search) and 6 October 2017. The search strategy for the hierarchical SLR was designed by an experienced librarian (LF). First, using known studies on workforce prediction in other fields, a number of searches were run in PubMed applying special features to find similar articles and/or SLRs where these studies were included, followed by a cited reference search on Web of Science. Further, using a set of search terms (online supplementary text S2), we conducted a search in MEDLINE and Cochrane library (1946 to 29 September 2017), PubMed Clinical Queries and PubMed Health (both limited to SLRs and to 2017).
In order to get a full scope of practices in workforce prediction in rheumatology and other medical fields, we also searched for grey literature including screening homepages of 37 societies of rheumatology and other medical associations between May and September 2017 (online supplementary table S1). The following search terms were used: ‘workforce models’, ‘workforce’, ‘fore-casting’, ‘workforce forecasting’, ‘calculating workforce’, ‘workforce planning’, ‘workforce supply’ and ‘workforce demand’. Additionally, we emailed national societies of rheumatology to enquire about how the rheuma-tology workforce calculation was done at a national level (online supplementary table S2). Furthermore, authors of the studies retrieved by the original SLR were inquired whether any post-evaluations of the published model quality and accuracy had been performed.
study selection and data extractionFor both searches, references and abstracts were imported into the reference management software Endnote V.X7.0.2. Duplicates were removed. Two researchers (JU and PP) independently screened all abstracts and titles. Next, full texts were reviewed to determine eligibility. Disagreements were resolved by discussion, and if neces-sary, a third author (SR) was involved to make a final decision.
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ecember 2018. D
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3Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
EpidemiologyEpidemiologyEpidemiology
For both searches, study details and results of eligible studies were retrieved using a standardised data extraction sheet. For the SLR in rheumatology, we extracted data on the same parameters as in the original SLR,9 mainly on factors related to demand/need and supply of rheu-matologists, as well as country, year, total number of rheumatologists required to serve the population and type of model. Additionally, information about regional heterogeneity, uncertainty analyses, application of any weighting of included factors, stakeholder involvement, role of other health professionals as well as employment trends were extracted from the 11 studies in the original SLR9 and the newly identified papers.
For the SLR in other medical fields, the following data were extracted: (1) study characteristics, including information about authors, year, medical field, design, objective, numbers of studies reviewed and sponsor/grants; and (2) content of the study, including informa-tion about type of workforce models, country, number of studies using the specific model, advantages and disadvantages of models, factors related to supply, need and demand, regional heterogeneity, uncertainty anal-yses, stakeholder involvement, prediction quality and others.
The quality of the SLRs was not assessed as we were mostly interested in reviewing which models and under-lying factors have been used in other fields and not in the prediction results of these studies.
development of a rheumatology workforce prediction risk of bias toolBased on the results of both literature reviews, key factors for workforce prediction models were identified. These included general factors (eg, type of the model, stake-holder involvement) as well as factors specifically related to the prediction of the workforce need/demand (eg, percentage of referrals to rheumatologist, epidemiology of diseases) and supply (eg, time spent on rheumatolog-ical care, entry and exit from the profession). Three risk of bias levels (low, moderate or high) were distinguished for each factor, with a clear description of which evidence would correspond to each of the levels. High risk of bias indicates that the factor was not or was only insufficiently considered in the workforce prediction model (without reasonable justification); low risk of bias corresponds to a well-considered factor in sufficient level of detail and based on reliable evidence. A moderate risk of bias reflects that the factor was partially described but without full level of detail. The decisions were driven by available evidence in rheumatology and other medical fields as well as task force expertise, with a few informal rounds to define the number of factors, shape and optimise the wording. We developed this workforce prediction risk of bias tool in order to use it for evaluating the risk of bias of the existing workforce modelling studies in rheuma-tology.
ResulTsFor the SLR in rheumatology, the literature search yielded 3221 hits. Screening of homepages (online supplementary table S1), contacting national rheuma-tology societies (37/49 answered; online supplementary table S2) and hand searches yielded seven additional records. After removing duplicates, a total of 2453 arti-cles remained. After a formal assessment, three studies were included and added to the existing 11, so in total there were 14 studies in rheumatology chosen for analysis (flowchart in online supplementary figure S1). The SLR in other medical fields yielded 4649 articles, of which 10 articles met the inclusion criteria (flowchart in online supplementary figure S2).
General characteristics of workforce prediction studies in rheumatologyGeneral characteristics of the 14 workforce predic-tion studies in rheumatology are presented in table 1. Studies were performed for the USA (n=4),14–17 Canada (n=3),18–20 Germany (n=3),7 21 22 UK (n=2),23 24 Spain (n=1)25 and one study covered USA and Canada (n=1).26 Most studies (n=8)7 14 15 17 19 21 22 25 used some form of an integrated model, which included demand, need and supply factors, and four studies considered the existing imbalance between demand and supply at baseline.14 17 19 24 Half of the studies (n=9) provided predictions for the future (as opposed to limiting predic-tions to study time),7 14–17 19 20 25 26 with a time horizon varying between 10 and 20 years. An assessment of the model performance was attempted by a total of four studies,7 15–17 with two studies having done an update of an earlier prediction.7 17 Both studies reported inaccura-cies in the previous prediction, due to underestimating the retirement tendencies and employment patterns (part-time work) of female rheumatologists17 or changes in the life expectancy and demographic characteristics of the population.7 While more than half of the studies did not perform uncertainty analyses, a few reported some form of uncertainty analyses by considering variation in one or several parameters (eg, population growth, insur-ance coverage, income growth).14 15 17 25 Three studies took regional heterogeneity into account.7 16 17 Involving stakeholders from multiple disciplines was not common practice as it was only done in a few studies performed by large study groups.7 15 17 Detailed information about the three newly included studies is depicted in online supple-mentary table S3–S5.
Factors related to need/demand for rheumatology careTable 2 provides an overview of factors that influence the need/demand for rheumatology care. Large heter-ogeneity was observed with regard to the scope of the diseases covered by rheumatologists, even within the same countries.7 14–17 19 21–25 Most of these studies have also estimated the percentage of patients referred to rheumatologists.7 14 15 17 19 21 23 24 Rheumatologist work-load in terms of numbers of visits per year was included
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RMD OpenRMD OpenRMD Open
Tab
le 1
G
ener
al fa
ctor
s us
ed in
rhe
umat
olog
y w
orkf
orce
stu
die
s
Aut
hor,
year
Co
untr
yM
od
el1
Tim
e ho
rizo
n2U
pd
ate
of
the
mo
del
3A
sses
smen
t o
f m
od
el
per
form
ance
4U
ncer
tain
ty a
naly
ses5
Reg
iona
l het
ero
gen
eity
6S
take
hold
er
invo
lvem
ent7
Ogr
yzlo
, 197
526U
SA
Can
ada
Nee
ds
bas
e d
5 ye
ars
N
o up
dat
e
No
asse
ssm
ent
N
ot p
erfo
rmed
O
utly
ing
com
mun
ities
and
m
any
urb
an c
entr
es (w
ith
pop
ulat
ion
exce
edin
g 10
0 00
0) d
o no
t ha
ve e
noug
h rh
eum
atol
ogis
ts
Not
sta
ted
Mar
der
et
al, 1
99114
US
AN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
≠sup
ply
at
bas
elin
e
10 a
nd 2
0 ye
ars
N
o up
dat
e
No
asse
ssm
ent
M
ost
cons
erva
tive
estim
ate
calc
ulat
ed
bas
ed o
n (1
) sim
ulta
neity
ad
just
men
t (1
.25)
; (2)
p
rod
uctiv
ity fa
ctor
(5
000
visi
ts/y
ear)
; (3)
d
ecre
ase
in n
eed
of o
ther
m
edic
al v
isits
. Res
ult:
tw
ice
as h
igh
need
of
rheu
mat
olog
ists
Not
sta
ted
N
ot s
tate
d
Dea
l et
al, 2
00715
US
AN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
=su
pp
ly a
t b
asel
ine
20 y
ears
with
p
red
ictio
ns fo
r 5-
year
inte
rval
Up
dat
e p
erfo
rmed
in
2015
4
Ass
essm
ent
per
form
ed in
th
e up
dat
e of
201
5
Test
ed d
eclin
e in
peo
ple
w
ithou
t in
sura
nce
and
a
high
er in
crea
se in
in
com
e
Not
sta
ted
In
volv
ed a
n ad
viso
ry
pan
el in
clud
ing
phy
sici
ans
and
hea
lth
pr o
fess
iona
ls
Zum
mer
and
H
end
erso
n, 2
00018
Can
ada
Nee
d a
nd s
upp
ly
bas
ed
Bas
elin
e on
ly
No
upd
ate
N
o as
sess
men
t
Not
per
form
ed
Not
sta
ted
N
ot s
tate
d
Ed
wor
thy,
200
019C
anad
aN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
≠sup
ply
at
bas
elin
e
10 y
ears
N
o up
dat
e
No
asse
ssm
ent
N
ot p
erfo
rmed
N
ot s
tate
d
Not
sta
ted
Han
ly, 2
00120
Can
ada
Nee
d a
nd s
upp
ly
bas
ed
25 y
ears
with
p
red
ictio
ns fo
r 5-
year
inte
rval
No
upd
ate
N
o as
sess
men
t
Not
per
form
ed
Not
sta
ted
N
ot s
tate
d
Ras
pe,
199
522G
erm
any
Nee
d, d
eman
d
and
sup
ply
b
ased
, ass
umed
d
eman
d=
sup
ply
at
bas
elin
e
Bas
elin
e on
ly
No
upd
ate
N
o as
sess
men
t
Not
per
form
ed
Not
sta
ted
N
ot s
tate
d
Ger
man
Soc
iety
fo
r R
heum
atol
ogy,
C
omm
ittee
for
Car
e,
2008
21
Ger
man
yN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
=su
pp
ly a
t b
asel
ine
Bas
elin
e on
ly
Up
dat
e p
erfo
rmed
in
201 7
No
asse
ssm
ent
N
ot p
erfo
rmed
N
ot s
tate
d
Not
sta
ted
Con
tinue
d
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5Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
EpidemiologyEpidemiologyEpidemiology
Aut
hor,
year
Co
untr
yM
od
el1
Tim
e ho
rizo
n2U
pd
ate
of
the
mo
del
3A
sses
smen
t o
f m
od
el
per
form
ance
4U
ncer
tain
ty a
naly
ses5
Reg
iona
l het
ero
gen
eity
6S
take
hold
er
invo
lvem
ent7
Làza
ro y
De
Mer
cad
o et
al,
2013
25S
pai
nN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
=su
pp
ly a
t b
asel
ine
11 y
ear s
N
o up
dat
e
No
asse
ssm
ent
B
ase
scen
ario
: Inc
reas
ed
dem
and
(15%
) due
to
pop
ulat
ion
grow
th a
nd
incr
ease
d d
eman
d in
car
eB
est
scen
ario
: inc
reas
e in
dem
and
onl
y d
ue t
o p
opul
atio
n gr
owth
Wor
se s
cena
rio: i
ncre
ase
in d
eman
d (3
0%) d
ue t
o p
opul
atio
n gr
owth
and
in
crea
sed
dem
and
for
heal
thca
re
Not
sta
ted
N
ot s
tate
d
Com
mitt
ee o
f R
heum
atol
ogy ,
19
8823
UK
Nee
d a
nd s
upp
ly
bas
ed
Bas
elin
e on
l y
No
upd
ate
N
o as
sess
men
t
Not
per
form
ed
Man
y co
untie
s of
the
UK
ar
e la
ckin
g rh
eum
atol
ogic
al
serv
ice
Not
sta
ted
Row
e et
al,
2013
24U
KN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
≠sup
ply
at
bas
elin
e
Bas
elin
e on
l y
No
upd
ate
N
o as
sess
men
t
Not
per
form
ed
Inp
ut d
ata
will
cha
nge
bas
ed o
n re
gion
al v
aria
tions
in
pat
ient
dem
ogra
phi
cs
and
mod
els
of c
are
Not
sta
ted
Am
eric
an C
olle
ge
of R
heum
atol
ogy ,
20
1538
US
AN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
≠sup
ply
at
bas
elin
e
15 y
ears
with
p
red
ictio
ns fo
r 5-
year
inte
rval
NA
, too
rec
ent
Ass
esse
d a
gain
st s
tud
y of
20
0515
Bes
t-w
orse
sce
nario
:M
ale-
fem
ale
ratio
in
wor
kfor
ceR
etire
men
t p
roje
ctio
nsFu
ll- a
nd p
art-
time
pro
ject
ions
Aca
dem
ic v
s no
n-ac
adem
ic s
ettin
gN
umb
er o
f new
gra
dua
tes
Num
ber
of n
on-p
hysi
cian
p
rovi
der
s (N
P a
nd P
A)
Num
ber
of p
atie
nts
with
OA
see
n b
y rh
eum
atol
ogis
ts
Is a
sses
sed
at
bas
elin
e (2
015)
for
10 r
egio
ns o
f U
SA
, and
sep
arat
ely
for
the
10 la
rges
t m
etro
pol
itan
area
sN
o ch
ange
in g
eogr
aphi
c se
rvic
es in
the
nex
t 10
ye
ars
is a
ssum
edP
hysi
cian
s p
ract
icin
g in
m
etro
pol
itan
stat
istic
al
area
wor
k on
ave
rage
15%
le
ss h
ours
tha
n th
ose
not
wor
king
in t
hese
are
as
Mul
tidis
cip
linar
y ex
per
t gr
oup
: eig
ht
core
mem
ber
s an
d
add
ition
al e
xper
t lia
ison
s m
ade
up o
f va
rious
affi
liatio
ns a
nd
dis
cip
lines
to
ensu
re
a w
ide-
rang
e of
idea
s an
d e
xper
ienc
es in
the
fie
ld o
f rhe
umat
olog
y;
focu
s gr
oup
s w
ith
sele
ct s
take
hold
ers
(not
sta
ted
whi
ch)
HR
SA
Hea
lth
Wor
kfor
ce, 2
01516
US
AN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
=su
pp
ly a
t b
asel
ine
12 y
ears
N
A, t
oo r
ecen
tFa
ce v
alid
ity b
y ex
per
ts,
inte
rnal
val
idat
ion
(ver
ifica
tion,
incl
udin
g ‘s
tres
s te
st’ f
or e
xtre
me
valu
es),
exte
rnal
and
p
red
ictiv
e va
lidat
ion
agai
nst
othe
r (n
ot u
sed
in
mod
ellin
g) d
ata
sour
ces,
b
etw
een
mod
el v
alid
atio
n (w
ith r
esul
ts o
f oth
er
mod
els)
Not
per
form
ed
Sep
arat
e es
timat
es fo
r fo
ur r
egio
ns, b
asel
ine
sup
ply
≠to
bas
elin
e d
eman
d
in r
egio
ns
Not
sta
ted
Tab
le 1
C
ontin
ued
Con
tinue
d
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ecember 2018. D
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RMD OpenRMD OpenRMD Open
Aut
hor,
year
Co
untr
yM
od
el1
Tim
e ho
rizo
n2U
pd
ate
of
the
mo
del
3A
sses
smen
t o
f m
od
el
per
form
ance
4U
ncer
tain
ty a
naly
ses5
Reg
iona
l het
ero
gen
eity
6S
take
hold
er
invo
lvem
ent7
Ger
man
Soc
iety
for
Rhe
umat
olog
y, 2
0177
Ger
man
yN
eed
, dem
and
an
d s
upp
ly
bas
ed, a
ssum
ed
dem
and
=su
pp
ly a
t b
asel
ine
Tim
e ho
rizon
not
p
rovi
ded
for
all
asp
ects
NA
, too
rec
ent
Ass
esse
d a
gain
st s
tud
y of
20
089
N
ot p
erfo
rmed
G
ener
al r
egio
nal d
efici
t of
0–
1, 2
rhe
umat
olog
ists
/100
00
0 in
hab
itant
s
The
stud
y gr
oup
co
nsis
ted
of
rheu
mat
olog
ists
(a
mb
ulan
t/in
pat
ient
, re
hab
ilita
tive
sett
ing)
, ep
idem
iolo
gist
s an
d m
emb
ers
of t
he G
erm
an
Rhe
umat
olog
y S
ocie
ty
The
risk
of b
ias
scor
es: r
ed d
ot (
)=hi
gh r
isk
of b
ias,
ind
icat
ing
that
the
fact
or h
as n
ot b
een
cons
ider
ed o
r co
nsid
ered
in a
n in
adeq
uate
way
, in
wor
kfor
ce p
red
ictio
n m
odel
; ora
nge
dot
()=
mod
erat
e ris
k of
bia
s, w
hen
a fa
ctor
has
bee
n co
nsid
ered
with
lim
itatio
ns; g
reen
dot
()=
low
ris
k of
bia
s an
d c
orre
spon
ds
to a
wel
l-co
nsid
ered
fact
or in
suf
ficie
nt le
vel o
f det
ail a
nd b
ased
on
a re
liab
le e
vid
ence
. Det
aile
d d
escr
iptio
n of
gra
din
g sy
stem
is
pre
sent
ed in
onl
ine
sup
ple
men
tary
tab
le S
7.(1
) For
the
mos
t ac
cura
te p
red
ictio
n, a
mod
el s
houl
d c
onsi
der
sup
ply
, nee
d a
nd d
eman
d fa
ctor
s an
d n
ot a
ssum
e th
at d
eman
d is
eq
ual s
upp
ly a
t th
e b
asel
ine.
(2) P
red
ictio
ns b
etw
een
5 an
d 1
5 ye
ars
seem
to
be
the
mos
t ad
equa
te t
ime
horiz
on fo
r w
orkf
orce
cal
cula
tion
in r
heum
atol
ogy.
(3) F
req
uent
up
dat
es o
f the
mod
el (1
-yea
r to
4-y
ear
inte
rval
) sho
uld
be
don
e in
ord
er t
o ta
ke in
to a
ccou
nt t
he v
aria
bili
ty o
f ass
ump
tions
.(4
) At
leas
t tw
o ki
nds
of q
ualit
y as
sess
men
t fo
r b
asel
ine
calc
ulat
ions
and
/or
for
futu
re p
red
ictio
ns a
re r
ecom
men
ded
.(5
) Unc
erta
inty
ana
lyse
s w
ith m
ore
than
tw
o p
aram
eter
s ar
e re
com
men
ded
in o
rder
to
det
ect
assu
mp
tions
tha
t m
ay v
ary
due
cha
nges
.(6
) Pre
dic
tions
sho
uld
con
sid
er t
he r
elev
ant
regi
onal
pro
file
of t
he c
ount
ry.
(7) T
he in
volv
emen
t of
mor
e th
an o
ne g
roup
of s
take
hold
ers
is h
ighl
y re
leva
nt fo
r al
l sta
ges
of t
he p
red
ictio
n.H
RS
A, H
ealth
Res
ourc
es a
nd S
ervi
ces
Ad
min
istr
atio
n; N
A, n
ot a
pp
licab
le; N
P, n
urse
pra
ctiti
oner
; OA
, ost
eoar
thrit
is; P
A, p
hysi
cian
ass
ista
nt.
Tab
le 1
C
ontin
ued
in six studies.7 14 19 21 22 24 Projections of population ageing were considered by most of the studies7 14–18 20 25; however, epidemiological developments17 or economic factors7 15–17 such as insurance or household income were rarely included in the predictions. The potential of medical development to modify the demand and need for care has been acknowledged in a number of studies; however, it not actually modelled into the predictions because of difficulty in making robust assumptions.
Factors related to supply of rheumatologistsTable 3 shows supply-based factors considered by the studies. Most of the studies (n=11)7 14 16 17 19–25 described the clinical setting, with few studies making their predictions for multiple settings, for example, private and public. Time spent on rheumatological care (as opposed to teaching or administrative tasks) was consid-ered in 10 out of 14 models7 14 15 17 19–22 24 25; however, it should be noted that the data used for calculations were frequently based on authors’ assumptions. Effects of task shifting between professionals (eg, increasing role of nurse professionals in care) was another diffi-cult to estimate factor, with only few studies making an attempt to put this into numbers.14 15 17 23 24 Predictions of the entry to (eg, training) and exit (eg, emigration, illness) from the profession were considered.14–19 25 26 Workforce demographic trends comprised an important part of the future workforce prediction. Estimation of the number of physicians projected to retire and/or gender structure of future workforce was incorporated in 8 of 14 models.7 14–18 20 25 An important trend of more women entering the profession has been observed in a few studies,15–17 20 25 and, given that women are more likely to work part-time, this had important implications for the number of physicians to be trained. Studies typi-cally presented the results of prediction in headcounts (ie, number of rheumatologists). Four studies (three of which were found in update search) also presented full-time equivalents (FTEs).7 16 17 20
Manpower requirements in other medical fieldsThe 10 SLRs from the second search (overview of system-atic reviews) covered a heterogeneous scope of areas, including nurses (n=2),27 28 pharmacists (n=1),29 paedi-atric specialties (n=1),30 public health (n=1)31 and studies that were not limited to any specialty (n=3)32–34 or consid-ered a mix of specialties (n=2)35 36 (online supplementary table S6).
Of the 10, only two reviews35 36 actually provided a summary of the workforce projections, and none has provided an assessment of the model performance. The remaining reviews synthesised models from a method-ological and theoretical point of view, describing which models were used and which need, demand and supply factors should be considered.
While most of the SLRs acknowledged the relevance of regional heterogeneity, only one considered it by making different predictions according to the region or
on June 19, 2020 by guest. Protected by copyright.
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j.com/
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D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
7Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
EpidemiologyEpidemiologyEpidemiology
Tab
le 2
N
eed
/dem
and
fact
ors
used
in r
heum
atol
ogy
wor
kfor
ce s
tud
ies
Aut
hor,
year
Sco
pe
of
dis
ease
s co
vere
d b
y rh
eum
ato
log
y sp
ecia
lty8
Dis
ease
d
efini
tio
n9
So
urce
of
pre
vale
nce
dat
a*V
isit
s/ye
ar p
er
pat
ient
10%
pat
ient
s re
ferr
ed
to r
heum
ato
log
ist11
Pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t12
So
urce
use
d
for
pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t*
Pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses13
So
urce
use
d f
or
pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses*
Eff
ects
of
med
ical
d
evel
op
men
t14
Nat
iona
l ec
ono
mic
in
dic
ato
rs15
Ogr
yzlo
, 197
526N
ot s
tate
d
Not
sta
ted
A
utho
r’s
estim
ate16
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Not
sta
ted
N
ot s
tate
d
Mar
der
et
al,
1991
1420
con
diti
ons
and
fib
rom
yalg
ia
and
ost
eop
oros
is
Mod
ified
Gra
dua
te
Med
ical
Ed
ucat
ion
Nat
iona
l Ad
viso
ry
Com
mitt
ee
(GM
EN
AC
) lis
t17
ICD
9-C
M
Nat
iona
l A
rthr
itis
Dat
a w
ork
grou
p
(NA
DW
)
2–4
visi
ts/y
ear
per
p
atie
nt18
Est
imat
ed fo
r ea
ch d
isea
se
sep
arat
ely
Age
U
nite
d S
tate
s B
urea
u of
the
C
ensu
s p
opul
atio
n (U
S C
ensu
s p
roje
ctio
ns)
Not
sta
ted
N
ot s
tate
dR
egul
ar r
efer
ral
pat
tern
s an
d a
vera
ge
num
ber
of v
isits
may
ch
ange
due
to
med
ical
d
evel
opm
ents
, but
too
lit
tle in
fo w
as a
vaila
ble
to
est
imat
e
Not
sta
ted
Dea
l et
al,
2007
158
dis
ease
s19P
artia
lly c
ited
20N
AD
W 5
and
up
dat
esN
ot s
tate
d
Est
imat
ed fo
r ea
ch
dis
ease
sep
arat
ely21
Age
U
S C
ensu
s p
roje
ctio
nsN
ot s
tate
d
Not
sta
ted
Dis
cuss
es e
ffect
of
med
ical
dev
elop
men
t an
d c
hang
e in
pra
ctic
e or
gani
satio
n, d
ifficu
lt to
q
uant
ify
Per
cap
ita
inco
me
and
in
sura
nce
stat
us
Zum
mer
and
H
end
erso
n,
2000
18
Not
sta
ted
N
ot s
tate
d
Aut
hor’ s
es
timat
e22N
ot s
tate
d
Not
sta
ted
A
g e
Not
sta
ted
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Ed
wor
thy ,
20
0019
7 d
isea
se(s
) gr
oup
s23N
ot s
tate
d
Aut
hor’ s
es
timat
e24Ti
me
cons
umed
b
y p
atie
nt/y
ear
with
ran
ge 0
.7–3
ho
urs
Est
imat
ed fo
r so
me
dis
ease
gro
ups
N
ot s
tate
d
Not
sta
ted
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Han
ly, 2
00120
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
Not
sta
ted
N
ot s
tate
d
Age
S
tatis
tics
Can
ada
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Ras
pe,
199
5226
dis
ease
gro
ups25
Par
tially
ci
ted
6
Aut
hor’s
es
timat
e[26
Four
vis
its/y
ear
per
pat
ient
N
ot s
tate
d
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Not
sta
ted
N
ot s
tate
d
Ger
man
S
ocie
ty fo
r R
heum
atol
ogy ,
C
omm
ittee
for
Car
e, 2
00821
5 in
flam
mat
ory
dis
ease
gro
ups27
an
d 5
oth
er
dis
ease
gro
ups28
Not
sta
ted
A
utho
r’s
estim
ate29
Num
ber
of v
isits
d
iffer
from
typ
e of
d
isea
se: a
vera
ge
of 4
vis
its/y
ear
per
pat
ient
30
Est
imat
ed 1
00%
in
flam
mat
ory,
12%
of
oth
er d
isea
ses
Not
sta
ted
N
ot s
tate
dA
ssum
ed n
ot t
o ch
ange
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Làza
ro y
De
Mer
cad
o,
2013
25
12 d
isea
se
grou
ps31
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
A
ge
Nat
iona
l Ins
titut
e of
S
tatis
tics
Not
sta
ted
N
ot s
tate
dIm
pr o
vem
ent
of m
edic
al
tech
nolo
gies
incr
ease
s m
anp
ower
nee
d
Not
sta
ted
Com
mitt
ee o
f R
heum
atol
ogy ,
19
8823
5 d
isea
se g
roup
s32N
ot s
tate
d
Aut
hor’ s
es
timat
e33N
ot s
tate
d
Infla
mm
ator
y 10
0%,
12%
of o
ther
d
isea
ses
Not
sta
ted
N
ot s
tate
dA
ssum
ed n
ot t
o ch
ang e
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Row
e et
al,
2013
2412
dis
ease
(s)
grou
ps34
Par
tially
ci
ted
6
Sev
eral
U
K a
nd
inte
r nat
iona
l st
udie
s
As
per
NIC
E
guid
elin
es,
dis
tingu
ishe
s b
etw
een
first
vi
sit
(30
min
) and
fo
llow
-up
vis
it (1
0–15
min
)
Con
sid
ered
but
no
det
ails
pro
vid
ed
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Dis
cuss
es w
orkl
oad
in
crea
se d
ue t
o m
ore
freq
uent
use
of t
oxic
d
rugs
Not
sta
ted
Con
tinue
d
on June 19, 2020 by guest. Protected by copyright.
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D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
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RMD OpenRMD OpenRMD Open
Aut
hor,
year
Sco
pe
of
dis
ease
s co
vere
d b
y rh
eum
ato
log
y sp
ecia
lty8
Dis
ease
d
efini
tio
n9
So
urce
of
pre
vale
nce
dat
a*V
isit
s/ye
ar p
er
pat
ient
10%
pat
ient
s re
ferr
ed
to r
heum
ato
log
ist11
Pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t12
So
urce
use
d
for
pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t*
Pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses13
So
urce
use
d f
or
pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses*
Eff
ects
of
med
ical
d
evel
op
men
t14
Nat
iona
l ec
ono
mic
in
dic
ato
rs15
Am
eric
an
Col
lege
of
Rhe
umat
olog
y,
2015
38
10 d
isea
ses35
Sel
f-re
por
ted
: p
hysi
cian
-d
iagn
osed
an
d s
elf-
dia
gnos
ed
Nat
iona
l H
ealth
In
form
atio
n S
yste
ms
Sur
veill
ance
st
atis
tics,
C
ente
rs fo
r D
isea
se
Con
trol
and
P
reve
ntio
n36
Not
sta
ted
A
sses
sed
num
ber
of
visi
ts in
the
pat
ient
p
opul
atio
n (p
roxy
to
% o
f pat
ient
s re
ferr
ed),
spec
ific
assu
mp
tions
for
OA
ar
e gi
ven37
Age
and
sex
U
S C
ensu
s p
roje
ctio
nsD
iscu
ssed
in
crea
sed
nu
mb
ers
due
to
obes
ity t
rend
s
Dat
a (o
f RA
) bas
ed
on t
he R
oche
ster
E
pid
emio
logy
P
roje
ct in
M
inne
sota
and
d
iffer
ent
stud
ies
Dis
cuss
ed c
hang
es in
co
st o
f dru
g s
Hou
seho
ld
annu
al
inco
me
and
so
cioe
cono
mic
co
nditi
ons
HR
SA
Hea
lth
Wor
kfor
ce,
2015
16
Dis
ease
s of
the
m
uscu
losk
elet
al
syst
em a
nd
conn
ectiv
e tis
sue38
ICD
9 (c
odes
72
5–72
9)
U.S
. Cen
ters
fo
r M
edic
are
and
Med
icai
d
Ser
vice
s
Not
sta
ted
N
ot s
tate
d
Age
and
sex
A
CS
, BR
FSS
, N
NH
S, C
ensu
s B
urea
u
Hea
lth s
tatu
s fo
r p
red
ictio
n of
the
use
of
heal
thca
r e
Not
sta
ted
Ass
umed
hea
lthca
re
del
iver
y w
ill n
ot c
hang
e su
bst
antia
lly fr
om t
he
bas
e ye
a r
Hou
seho
ld
anua
l in
com
e an
d
soci
oeco
nom
ic
stat
u s
Ger
man
S
ocie
ty fo
r R
heum
atol
ogy ,
20
177
Infla
mm
ator
y d
isea
ses39
and
au
toin
flam
mat
ory
dis
ease
s
Not
sta
ted
B
ased
on
Zin
k et
al,
2016
7
Est
imat
ed a
mou
nt
and
tim
e fo
r p
reva
lent
(4×
20
min
) and
inci
den
t ca
ses
(1.5
×40
m
in)
Ass
ump
tions
for
co-c
onsu
ltatio
n fo
r os
teoa
rthr
itis,
os
teop
oros
is a
nd
pai
n sy
ndro
mes
are
gi
ven41
Age
N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
Dis
cuss
es t
hat
dig
ital
dev
elop
men
ts a
nd o
ther
he
alth
per
sonn
el m
ay
have
an
influ
ence
on
wor
kloa
d
Am
ount
of
insu
ranc
e se
rvic
es is
d
iscu
sse d
Tab
le 2
C
ontin
ued
Con
tinue
d
on June 19, 2020 by guest. Protected by copyright.
http://rmdopen.bm
j.com/
RM
D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
9Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
EpidemiologyEpidemiologyEpidemiology
Aut
hor,
year
Sco
pe
of
dis
ease
s co
vere
d b
y rh
eum
ato
log
y sp
ecia
lty8
Dis
ease
d
efini
tio
n9
So
urce
of
pre
vale
nce
dat
a*V
isit
s/ye
ar p
er
pat
ient
10%
pat
ient
s re
ferr
ed
to r
heum
ato
log
ist11
Pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t12
So
urce
use
d
for
pro
ject
ion
of
po
pul
atio
n d
evel
op
men
t*
Pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses13
So
urce
use
d f
or
pro
ject
ion
of
epid
emio
log
y o
f d
isea
ses*
Eff
ects
of
med
ical
d
evel
op
men
t14
Nat
iona
l ec
ono
mic
in
dic
ato
rs15
The
risk
of b
ias
scor
es: r
ed d
ot (
)=hi
gh r
isk
of b
ias,
ind
icat
ing
that
the
fact
or h
as n
ot b
een
cons
ider
ed o
r co
nsid
ered
in a
n in
adeq
uate
way
, in
wor
kfor
ce p
red
ictio
n m
odel
; ora
nge
dot
()=
mod
erat
e ris
k of
bia
s, w
hen
a fa
ctor
has
bee
n co
nsid
ered
with
lim
itatio
ns; g
reen
dot
()=
low
ris
k of
bia
s an
d c
orre
spon
ds
to a
wel
l-co
nsid
ered
fact
or in
suf
ficie
nt le
vel o
f det
ail a
nd b
ased
on
a re
liab
le e
vid
ence
. Det
aile
d d
escr
iptio
n of
gra
din
g sy
stem
is p
rese
nted
in o
nlin
e su
pp
lem
enta
ry t
able
S7.
(1) T
he s
cop
e of
dis
ease
s co
vere
d b
y rh
eum
atol
ogy
spec
ialty
is d
efine
d a
nd t
he p
rob
abili
ty t
hat
it is
rep
rese
ntat
ive
is h
igh.
(2) A
crit
eria
-sta
ted
dis
ease
defi
nitio
n th
at r
elie
s on
phy
sici
an-r
epor
ted
dia
gnos
es a
nd u
sing
mor
e th
an o
ne s
ourc
e is
rec
omm
end
ed.
(3) S
epar
ate
estim
atio
ns fo
r th
e ty
pe
of d
isea
ses,
the
dis
ease
pha
se o
r th
e ty
pe
of v
isits
sho
uld
be
don
e.(4
) It
is r
ecom
men
ded
to
cons
ider
sep
arat
e es
timat
ions
of t
he p
erce
ntag
e of
ref
erra
ls p
er d
isea
se g
roup
.(5
) For
the
con
sid
erat
ion
of t
he d
evel
opm
ent
of t
he p
opul
atio
n, w
orkf
orce
cal
cula
tions
sho
uld
inco
rpor
ate
age
and
/or
sex
stru
ctur
e an
d/o
r ot
her
fact
ors,
rel
ying
on
mor
e th
an o
ne d
ata
sour
ce.
(6) T
he in
volv
emen
t of
mor
e th
an t
wo
fact
ors
that
influ
ence
the
ep
idem
iolo
gy o
f dis
ease
s, u
sing
mor
e th
an o
ne d
ata
sour
ce, s
houl
d b
e co
nsid
ered
in t
he p
red
ictio
ns.
(7) W
orkf
orce
cal
cula
tions
sho
uld
con
sid
er t
he e
ffect
s of
med
ical
dev
elop
men
t, e
ither
bas
ed o
n fo
rmal
dat
a or
exp
ert
cons
ensu
s.(8
) For
a g
ood
fore
cast
ing
mod
el, t
he c
onsi
der
atio
n of
mor
e th
an o
ne e
cono
mic
fact
ors
for
the
natio
nal e
cono
mic
dev
elop
men
t of
a c
ount
ry is
rec
omm
end
ed.
(9) N
o p
ublis
hed
dat
a re
fere
nced
; aut
hor
assu
mes
tot
al p
reva
lenc
e of
rhe
umat
ic d
isea
ses=
pre
vale
nce
of r
heum
atoi
d a
rthr
itis×
5.(1
0) T
he fo
llow
ing
cond
ition
s w
ere
sum
mar
ised
in t
he M
odifi
ed G
rad
uate
Med
ical
Ed
ucat
ion
Nat
iona
l Ad
viso
ry C
omm
ittee
(GM
EN
AC
) lis
t: g
onoc
occa
l inf
ectio
n of
join
t, c
ryst
allin
e ar
thrit
is, p
soria
tic a
rthr
opat
hy, p
yoge
nic
arth
ritis
, acu
te n
on-p
yoge
nica
rthr
itis,
rhe
umat
oid
art
hriti
s,
anky
losi
ng s
pon
dyl
itis,
ost
eoar
thrit
is, r
esid
ual a
rthr
itid
es, fi
bro
mya
lgia
, ost
eom
yelit
is, P
aget
’s d
isea
se, o
steo
por
osis
, dis
c d
isp
lace
men
t, n
eck
and
bac
k p
ain,
inte
rnal
join
t d
eran
gem
ent,
bur
sitis
and
ten
din
itis,
con
nect
ive
tissu
e d
isea
se, o
ther
mus
culo
skel
etal
dis
ord
ers.
(11)
Ass
umed
a h
ighe
r nu
mb
er o
f nee
ded
vis
its fo
r p
soria
tic a
rthr
itis,
pyo
geni
c ar
thrit
is, R
A, fi
bro
mya
lgia
and
con
nect
ive
tissu
e d
isea
se; c
onsi
der
ed s
ever
ity o
f dis
ease
.(1
2) R
heum
atoi
d a
rthr
itis,
ost
eoar
thrit
is, s
pon
dyl
oart
hriti
s, p
olym
yalg
ia r
heum
atic
a, lu
pus
, low
bac
k p
ain,
gou
t, o
steo
por
osis
.(1
3) P
artia
lly c
ited
mea
ns t
hat
som
etim
es p
ublis
hed
crit
eria
wer
e ci
ted
and
som
etim
es n
ot.
(14)
Est
imat
ed a
ccor
din
g to
the
Nat
iona
l Am
bul
ator
y M
edic
al C
are
Sur
vey
(NA
MC
S):
RA
52.
0%, O
A 7
.0%
, sp
ond
yloa
rthr
itis
77.3
%, p
olym
yalg
ia r
heum
atic
a 48
.3%
, lup
us 2
9.9%
, low
bac
k p
ain
2.9%
, gou
t 11
.7%
, ost
eop
oros
is 5
.1%
.(1
5) N
o p
ublis
hed
dat
a re
fere
nced
; aut
hor
assu
mes
a t
otal
pre
vale
nce
of a
rthr
itis
to b
e 19
% in
wom
en a
nd 1
1% in
men
.(1
6) P
olya
rthr
itis,
cry
stal
art
hrop
athi
es, c
onne
ctiv
e tis
sue
dis
ease
s, v
ascu
litis
, sof
t-tis
sue
dis
ease
s, d
egen
erat
ive
mus
culo
skel
etal
dis
ease
s, o
steo
por
osis
.(1
7) N
o p
ublis
hed
dat
a re
fere
nced
; aut
hor
assu
mes
a t
otal
pre
vale
nce
of p
olya
rthr
itis
of 1
%, c
ryst
al a
rtho
pat
hies
0.1
%, c
onne
ctiv
e tis
sue
dis
ease
s 0.
1%, v
ascu
litis
0.0
5%, s
oft-
tissu
e d
isea
ses
5% a
nd d
egen
erat
ive
mus
culo
skel
etal
dis
ease
s 10
%.
(18)
Rhe
umat
oid
art
hriti
s, s
pon
dyl
oart
hriti
s, c
onne
ctiv
e tis
sue
dis
ease
, vas
culit
is, p
olya
rtic
ular
sec
ond
ary
oste
oart
hriti
s, g
ener
alis
ed p
ain
synd
rom
es.
(19)
Aut
hor
assu
mes
tot
al p
reva
lenc
e of
rhe
umat
ic d
isea
ses
to b
e 4%
—es
timat
e su
pp
orte
d b
y se
vera
l ref
eren
ces
rang
ing
from
loca
l Ger
man
stu
die
s to
larg
e st
udie
s fr
om t
he U
SA
.(2
0) U
ndiff
eren
tiate
d a
rthr
itis,
rhe
umat
oid
art
hriti
s, s
pon
dyl
oart
hriti
s, c
onne
ctiv
e tis
sue
dis
ease
s, v
ascu
litis
.(2
1) O
steo
arth
ritis
, cry
stal
art
hrop
athi
es, s
usp
ecte
d in
flam
mat
ory
bac
k p
ain,
fib
rom
yalg
ia, b
one
dis
ease
s.(2
2) N
o p
ublis
hed
dat
a re
fere
nced
; aut
hor
assu
mes
tot
al p
reva
lenc
e of
2%
for
infla
mm
ator
y rh
eum
atic
dis
ease
s an
d 1
0% fo
r th
e ot
her
cond
ition
s d
escr
ibed
.(2
3) E
stim
ated
am
ount
and
tim
e fo
r p
reva
lent
(4 v
isits
×20
min
) and
inci
den
t ca
ses
(1.5
vis
its×
40 m
in) a
nd a
lso
for
co-c
onsu
ltatio
n fo
r ot
her
dis
ease
s. F
or t
he c
o-co
nsul
tatio
n, t
hey
assu
med
10%
of 2
6 00
0 se
vere
cas
es p
er 1
00 0
00 in
hab
itant
s fo
r co
-con
sulta
tion
(260
0 ca
ses×
15m
in).
(24)
Rhe
umat
oid
art
hriti
s, s
pon
dyl
oart
hriti
s, o
steo
arth
ritis
, oth
er m
etab
olic
bon
e d
isea
ses,
sys
tem
ic a
utoi
mm
une
dis
ease
s, s
oft-
tissu
e d
isea
ses,
nec
k an
d b
ack
pai
n, fi
bro
mya
lgia
, cry
stal
art
hrop
athi
es, p
aed
iatr
ic r
heum
atol
ogy,
tum
our
and
infe
ctio
us p
atho
logi
es, o
ther
p
atho
logi
es.
(25)
Rhe
umat
oid
art
hriti
s, o
steo
arth
ritis
, bac
kach
e, c
onne
ctiv
e tis
sue
dis
ease
s, o
ther
rhe
umat
ic d
isor
der
s.(2
6) N
o p
ublis
hed
dat
a re
fere
nced
; aut
hor
assu
mes
tot
al p
reva
lenc
e of
~2.
7% fo
r d
isea
ses.
(27)
Mus
culo
skel
etal
con
diti
ons,
ost
eoar
thrit
is-r
elat
ed jo
int
pai
n, o
steo
por
osis
, bac
k p
ain,
rhe
umat
oid
art
hriti
s, a
nkyl
osin
g sp
ond
yliti
s, s
yste
mic
lup
us e
ryth
emat
osus
, scl
erod
erm
a, g
out,
reg
iona
l pai
n sy
ndro
mes
, chr
onic
wid
esp
read
pai
n, ju
veni
le id
iop
athi
c ar
thrit
is.
(28)
Rhe
umat
oid
art
hriti
s, s
pon
dyl
oart
hriti
s, s
yste
mic
lup
us e
ryth
emat
osus
, sys
tem
ic s
cler
osis
, Sjo
gren
’s s
ynd
rom
e, o
steo
arth
ritis
, pol
ymya
lgia
rhe
umat
ica,
gia
nt c
ell a
rter
itis,
gou
t, fi
bro
mya
lgia
.(2
9) R
epor
t b
ased
on
surv
eys
and
ano
ther
tw
o su
rvey
-bas
ed p
ublic
atio
ns.
(30)
Ass
umed
tha
t 25
% o
f pat
ient
s w
ents
with
OA
are
see
n b
y a
rheu
mat
olog
ist.
(31)
No
furt
her
spec
ifica
tion.
(32)
Rhe
umat
oid
art
hriti
s, s
pon
dyl
oart
hriti
s, c
ryst
al a
rthr
opat
hies
, col
lage
nosi
s, v
ascu
litis
.(3
3) Z
ink
A, A
lbre
cht
K (2
016)
. Wie
häu
fig s
ind
mus
kulo
skel
etal
e E
rkra
nkun
gen
in D
euts
chla
nd?
Z R
heum
atol
75:
346–
353.
(34)
Ass
umed
10%
of 1
8 m
illio
n p
eop
le (2
600×
15 m
in).
*Ris
k of
bia
s re
late
d t
o th
e d
ata
sour
ce is
tak
en in
to a
ccou
nt in
sco
ring
of t
he r
esp
ectiv
e fa
ctor
AC
S, A
mer
ican
Com
mun
ity S
ervi
ce; B
RFS
S, B
ehav
iora
l Ris
k Fa
ctor
Sur
veill
ance
Sys
tem
; HR
SA
, Hea
lth R
esou
rces
and
Ser
vice
s A
dm
inis
trat
ion;
ICD
9-C
M, I
nter
natio
nal C
lass
ifica
tion
of D
isea
ses,
Nin
th R
evis
ion—
Clin
ical
Mod
ifica
tion;
NA
, not
ap
plic
able
; NIC
E, N
atio
nal I
nstit
ute
for
Hea
lth a
nd C
are
Exc
elle
nce;
NN
HS
, Nat
iona
l Nur
sing
Hom
e S
urve
y; O
A, o
steo
arth
ritis
; RA
, rhe
umat
oid
art
hriti
s.
Tab
le 2
C
ontin
ued
on June 19, 2020 by guest. Protected by copyright.
http://rmdopen.bm
j.com/
RM
D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
10 Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
RMD OpenRMD OpenRMD Open
Tab
le 3
S
upp
ly fa
ctor
s us
ed in
rhe
umat
olog
y w
orkf
orce
stu
die
s
Aut
hor,
year
Clin
ical
set
ting
42
Tim
e sp
ent
on
clin
ical
(r
heum
ato
log
ical
) ca
re43
So
urce
of
dat
a fo
r es
tim
atin
g o
f %
o
f p
atie
nt c
are
in
rheu
mat
olo
gy*
Task
s d
eleg
ated
to
oth
er h
ealt
h p
rofe
ssio
nals
in
rheu
mat
olo
gy
(HP
)44D
emo
gra
phi
c tr
end
s in
wo
rkfo
rce45
Ent
ry a
nd e
xit
fro
m
the
pro
fess
ion46
So
urce
of
info
rmat
ion
for
in-
and
out
flo
w o
f m
edic
al g
rad
uate
s*
Res
ult
pre
sent
ed
in n
umb
er o
f rh
eum
ato
log
ists
an
d/o
r cl
inic
al
FTE
s47
Ogr
yzlo
, 197
526N
ot s
tate
d
Not
sta
ted
48N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
A
ttrit
ion
rate
of
trai
ning
pro
gram
me
Not
sta
ted
Num
ber
of
rheu
mat
olog
ists
Mar
der
et
al, 1
99114
Am
bul
ator
y an
d
hosp
ital (
outp
atie
nt
only
)
~80
%–8
5% o
f w
orki
ng t
ime49
Not
sta
ted
Per
mor
bid
ity
ind
icat
ed t
he
exp
ecte
d n
umb
er
of v
isits
del
egat
ed
to a
non
-phy
sici
an
mem
ber
of t
he o
ffice
st
aff:
PsA
, RA
, Sp
A,
OA
, OP
5%
–15%
of
visi
ts
Ret
irem
ent
and
dea
th
due
to
age
P
roje
cted
num
ber
of
new
ent
rant
s
His
toric
al t
rend
sN
umb
er o
f rh
eum
atol
ogis
ts
Dea
l et
al, 2
00715
Not
sta
ted
~
90%
of
rheu
mat
olog
ists
see
p
atie
nts
Not
sta
ted
Ab
out
25%
of
rheu
mat
olog
ists
are
w
orki
ng w
ith a
NP
or
PA
Fem
ale
and
old
er
rheu
mat
olog
ists
hav
e le
ss v
isits
, you
nger
d
octo
rs t
end
to
wor
k le
ss h
ours
Num
ber
and
fill
rate
of
rhe
umat
olog
y p
ositi
ons,
incl
udin
g fo
reig
n st
uden
ts
Cou
ncil
of G
rad
uate
M
edic
al E
duc
atio
nN
umb
er o
f rh
eum
atol
ogis
ts
Zum
mer
and
H
end
erso
n, 2
00018
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
Not
sta
ted
O
ver
50%
of
rheu
mat
olog
ists
are
>
50, a
nd 1
5% w
ill r
etire
in
nex
t 10
yea
rs
Num
ber
of t
rain
ees
in r
elat
ion
to c
urre
nt
vaca
ncie
s, n
umb
er o
f gr
adua
ted
sp
ecia
lists
th
at w
ill p
ract
ice
out
of C
anad
a
Sur
vey
by
the
Eco
nom
ics
and
Man
pow
er
Com
mitt
ee o
f th
e C
anad
ian
Rhe
umat
olog
y A
ssoc
iatio
n
Num
ber
of
rheu
mat
olog
ists
Ed
wor
thy ,
200
019C
omm
unity
, ac
adem
ic,
adm
inis
trat
or
5%–8
0% o
f wor
king
tim
e50N
ot s
tate
dN
ot s
tate
d
Not
sta
ted
A
ttrit
ion
rate
in
clud
ing
illne
ss,
emig
ratio
n (e
stim
ated
at
10%
), nu
mb
er
of n
ew g
rad
uate
s en
terin
g th
e m
arke
t N
ot s
tate
dN
umb
er o
f rh
eum
atol
ogis
ts
Han
ly, 2
00120
Aca
dem
ic
~50
%–6
0% o
f w
orki
ng t
ime
N
ot s
tate
dN
ot s
tate
d
The
‘gre
ying
’ of t
he
phy
sici
ans,
cha
ngin
g lif
esty
les
and
ex
pec
tatio
ns o
f you
ng
phy
sici
ans,
incr
easi
ng
pro
por
tion
of w
omen
Not
sta
ted
N
ot s
tate
dN
umb
er o
f rh
eum
atol
ogis
ts a
nd
clin
ical
FTE
Ras
pe,
199
522H
osp
ital,
priv
ate
pra
ctic
e, c
entr
es
of e
xcel
lenc
e (o
utp
atie
nt o
nly)
45 h
ours
/wee
k
Not
sta
ted
Prim
ary
care
sp
ecia
list51
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
Num
ber
of
rheu
mat
olog
ists
Ger
man
Soc
iety
fo
r R
heum
atol
ogy,
C
omm
ittee
for
Car
e,
2008
21
Out
pat
ient
clin
ic
75%
of w
orki
ng t
ime52
Not
sta
ted
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
N
ot s
tate
dN
umb
er o
f rh
eum
atol
ogis
t s
Con
tinue
d
on June 19, 2020 by guest. Protected by copyright.
http://rmdopen.bm
j.com/
RM
D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
11Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
EpidemiologyEpidemiologyEpidemiology
Aut
hor,
year
Clin
ical
set
ting
42
Tim
e sp
ent
on
clin
ical
(r
heum
ato
log
ical
) ca
re43
So
urce
of
dat
a fo
r es
tim
atin
g o
f %
o
f p
atie
nt c
are
in
rheu
mat
olo
gy*
Task
s d
eleg
ated
to
oth
er h
ealt
h p
rofe
ssio
nals
in
rheu
mat
olo
gy
(HP
)44D
emo
gra
phi
c tr
end
s in
wo
rkfo
rce45
Ent
ry a
nd e
xit
fro
m
the
pro
fess
ion46
So
urce
of
info
rmat
ion
for
in-
and
out
flo
w o
f m
edic
al g
rad
uate
s*
Res
ult
pre
sent
ed
in n
umb
er o
f rh
eum
ato
log
ists
an
d/o
r cl
inic
al
FTE
s47
Làza
ro y
De
Mer
cad
o, 2
01325
Aca
dem
ic, n
on-
acad
emic
, priv
ate
pra
ctic
e
78.4
% o
f wor
king
tim
e53S
urve
y am
ong
rheu
mat
olog
ists
Not
sta
ted
A
ge a
nd g
end
er o
f cu
rren
t an
d fu
ture
w
orkf
orce
tak
en in
to
acco
unt
Num
ber
of r
esid
ents
th
at g
rad
uate
s ea
ch
year
Not
sta
ted
Num
ber
of
rheu
mat
olog
ists
Com
mitt
ee o
f R
heum
atol
ogy,
19
8823
Gen
eral
hos
pita
l
Not
sta
ted
54N
ot s
tate
dJu
nior
med
ical
sta
ff H
ouse
offi
cer:
0.5
FT
E p
er c
onsu
ltant
, se
cret
aria
l and
ad
min
istr
ativ
e su
pp
ort
1 FT
E p
er
cons
ulta
nt
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
Num
ber
of
rheu
mat
olog
ists
Row
e et
al,
2013
24C
omm
unity
(r
heum
atol
ogis
t,
rheu
mat
olog
ist
with
G
IM),
acad
emic
25%
–65%
55P
rogr
amm
ed
activ
ities
bas
ed
on B
ritis
h S
ocie
ty
of R
heum
atol
ogy
reco
mm
end
atio
ns
Sha
red
car
e b
etw
een
prim
ary
and
sec
ond
ary
care
nec
essa
ry
but
dep
end
ent
on
the
exis
tenc
e of
in
term
edia
te c
are56
Not
sta
ted
N
ot s
tate
d
Not
sta
ted
Num
ber
of
rheu
mat
olog
ists
Am
eric
an C
olle
ge
of R
heum
atol
ogy,
20
1538
Aca
dem
ic (8
0%)
and
non
-aca
dem
ic
(20%
)
Aca
dem
ic s
ettin
g 1
doc
tor=
0.5
clin
ical
FT
EN
on-a
cad
emic
set
ting
1 d
octo
r=1
FTE
Exp
ert
cons
ensu
sIn
clud
e nu
mb
er o
f N
P a
nd P
A in
the
m
odel
ling
Wor
kfor
ce is
age
ing;
w
omen
wor
k 7
hour
s le
ss p
er w
eek
and
see
30
% le
ss p
atie
nts.
S
hare
of w
omen
in
crea
sing
Num
ber
and
fill
rate
of
rhe
umat
olog
y p
ositi
ons,
dro
p-o
ut,
num
ber
of t
hose
who
w
ill p
ract
ise
outs
ide
US
A
Sur
vey
and
dat
a fr
om
Am
eric
an M
edic
al
Ass
ocia
tion
(AM
A)
Num
ber
of
rheu
mat
olog
ists
and
cl
inic
al F
TE
HR
SA
Hea
lth
Wor
kfor
ce, 2
01516
7 se
ttin
gs:
Em
erge
ncy
room
s,
hosp
itals
, pro
vid
er
offic
es, o
utp
atie
nt
dep
artm
ents
, hom
e he
alth
, nur
sing
ho
mes
, res
iden
tial
faci
litie
s
Not
sta
ted
N
ot s
tate
dN
ot s
tate
d
Age
and
gen
der
d
istr
ibut
ion
of t
he
wor
kfor
ce t
aken
into
ac
coun
t57
Num
ber
of n
ewly
tr
aine
d d
octo
rs
ente
ring
the
mar
ket
AM
A M
aste
rfile
fo
r p
hysi
cian
s,
the
Ass
ocia
tion
of
Am
eric
an M
edic
al
Col
lege
s (A
AM
C)
2012
–201
3 G
rad
uate
M
edic
al E
duc
atio
n C
ensu
s, P
hysi
cian
A
ssis
tant
Ed
ucat
ion
Ass
ocia
tion
surv
ey
Num
ber
of
rheu
mat
olog
ists
and
cl
inic
al F
TE
Ger
man
Soc
iety
for
Rhe
umat
olog
y, 2
0177
Hos
pita
l, p
rivat
e p
ract
ice,
re
hab
ilita
tion
cent
res
Of a
tot
al o
f 54
hour
s/p
er w
eek,
38
hour
s p
atie
nt w
ork58
Sou
rce
are
give
n fo
r th
e d
efini
tion
of t
he n
umb
er o
f w
orki
ng h
ours
/w
eek
and
the
tim
e d
edic
ated
to
rheu
mat
olog
y ca
re
Not
sta
ted
R
heum
atol
ogis
ts a
re
agei
ng a
nd m
any
will
re
tire
soon
Not
sta
ted
N
ot s
tate
dN
umb
er o
f rh
eum
atol
ogis
ts a
nd
clin
ical
FTE
Tab
le 3
C
ontin
ued
Con
tinue
d
on June 19, 2020 by guest. Protected by copyright.
http://rmdopen.bm
j.com/
RM
D O
pen: first published as 10.1136/rmdopen-2018-000756 on 5 D
ecember 2018. D
ownloaded from
12 Unger J, et al. RMD Open 2018;4:e000756. doi:10.1136/rmdopen-2018-000756
RMD OpenRMD OpenRMD Open
Aut
hor,
year
Clin
ical
set
ting
42
Tim
e sp
ent
on
clin
ical
(r
heum
ato
log
ical
) ca
re43
So
urce
of
dat
a fo
r es
tim
atin
g o
f %
o
f p
atie
nt c
are
in
rheu
mat
olo
gy*
Task
s d
eleg
ated
to
oth
er h
ealt
h p
rofe
ssio
nals
in
rheu
mat
olo
gy
(HP
)44D
emo
gra
phi
c tr
end
s in
wo
rkfo
rce45
Ent
ry a
nd e
xit
fro
m
the
pro
fess
ion46
So
urce
of
info
rmat
ion
for
in-
and
out
flo
w o
f m
edic
al g
rad
uate
s*
Res
ult
pre
sent
ed
in n
umb
er o
f rh
eum
ato
log
ists
an
d/o
r cl
inic
al
FTE
s47
The
risk
of b
ias
scor
es: r
ed d
ot (
)=hi
gh r
isk
of b
ias,
ind
icat
ing
that
the
fact
or h
as n
ot b
een
cons
ider
ed o
r co
nsid
ered
in a
n in
adeq
uate
way
, in
wor
kfor
ce p
red
ictio
n m
odel
; ora
nge
dot
()=
mod
erat
e ris
k of
bia
s, w
hen
a fa
ctor
has
bee
n co
nsid
ered
with
lim
itatio
ns; g
reen
dot
()=
low
ris
k of
bia
s an
d c
orre
spon
ds
to a
wel
l-co
nsid
ered
fact
or in
suf
ficie
nt le
vel o
f det
ail a
nd b
ased
on
a re
liab
le e
vid
ence
. Det
aile
d d
escr
iptio
n of
gra
din
g sy
stem
is
pre
sent
ed in
onl
ine
sup
ple
men
tary
tab
le S
7.(1
) Con
sid
erin
g m
ore
than
one
leve
l of s
ettin
g fo
r th
e ca
lcul
atio
n of
wor
kfor
ce s
upp
ly im
pro
ves
the
accu
racy
of t
he p
roje
ctio
ns.
(2) A
ccur
ate
pro
ject
ions
req
uire
the
per
cent
age
of t
ime
spen
t on
clin
ical
car
e b
y m
akin
g es
timat
ions
for
the
num
ber
, dur
atio
ns a
nd t
ypes
of v
isits
, usi
ng m
ore
than
one
dat
a so
urce
.(3
) Pos
sib
le t
ask
shift
ing
with
HP
is r
elev
ant
for
wor
kfor
ce c
alcu
latio
n an
d c
an r
ely
on d
ata
or fo
rmal
exp
ert
cons
ensu
s.(4
) Mor
e th
an o
ne d
emog
rap
hic
tren
ds
like
agei
ng a
nd m
illen
nial
tre
nd s
houl
d b
e co
nsid
ered
for
fore
cast
ing.
(5) T
he a
ccur
acy
of t
he m
odel
can
be
incr
ease
d b
y co
nsid
erin
g m
ore
than
one
ent
ry a
nd e
xit
fact
or, u
sing
mor
e th
an o
ne d
ata
sour
ce.
(6) P
roje
cted
num
ber
of r
heum
atol
ogis
ts a
nd c
linic
al F
TEs
shou
ld b
e ex
plic
it fr
om t
he c
alcu
latio
ns.
(7) A
ccor
din
g to
aut
hor’s
sta
tem
ent
calc
ulat
ion
adju
sted
for
clin
ical
car
e, r
esea
rch
and
tea
chin
g; 2
000
rheu
mat
olog
ists
in U
SA
from
whi
ch 1
700
are
pra
ctis
ing,
300
are
tea
chin
g/re
sear
cher
s; s
ame
pro
por
tions
are
ass
umed
fo
r C
anad
a.(8
) Aut
hors
est
imat
e a
~15
%–2
0% e
xtra
num
ber
of r
heum
atol
ogis
t to
com
pen
sate
for
‘oth
er a
ctiv
ities
’ inc
lud
ing
rese
arch
and
ed
ucat
ion.
(9) A
utho
rs a
ssum
e th
at c
omm
unity
bas
ed r
heum
atol
ogis
ts u
se 8
0% o
f a 5
5-ho
ur w
eek
(=44
hou
rs) f
or c
linic
al v
isits
, 20%
for
adm
inis
trat
ive
wor
k an
d e
duc
atio
n; a
cad
emic
rhe
umat
olog
ists
use
25%
of a
60-
hour
wee
k (=
15
hour
s) fo
r cl
inic
al v
isits
and
75%
for
adm
inis
trat
ion,
res
earc
h an
d t
rain
ing;
ad
min
istr
ator
s us
e 5%
of a
60-
hour
wee
k (=
3 ho
urs)
for
clin
ical
vis
its a
nd 9
5% fo
r ad
min
istr
ativ
e w
ork
and
wor
k w
ith c
omp
lex
med
ical
sys
tem
s an
d p
rovi
ncia
l org
anis
atio
ns. A
tot
al o
f 46
wor
king
wee
ks/y
ear
is a
ssum
ed (5
-wee
k va
catio
n, 1
wee
k co
nfer
ence
).(1
0) A
utho
rs p
rovi
de
a d
iagr
am o
n p
atie
nts’
flow
from
prim
ary
to s
pec
ialis
t ca
re a
nd v
ice
vers
a; h
owev
er, t
he e
ffect
of t
his
dia
gram
on
the
num
ber
of v
isits
/rhe
umat
olog
ists
req
uire
d w
as n
ot p
rovi
ded
.(1
1) A
utho
rs e
stim
ate
that
out
of a
10-
hour
wor
king
day
, 7.5
hou
rs w
ill b
e av
aila
ble
for
clin
ical
vis
its.
(12)
Acc
ord
ing
to t
he s
urve
y p
erfo
rmed
the
follo
win
g ac
tiviti
es r
educ
e th
e tim
e fo
r cl
inic
al v
isits
: res
earc
h, t
each
ing,
sci
entifi
c se
ssio
ns, t
rain
ing,
con
gres
ses,
inst
itutio
nal p
artic
ipat
ion
and
oth
er a
ctiv
ities
.(1
3) A
ll rh
eum
atol
ogis
ts s
pen
d t
ime
on d
evel
opm
ent
and
mai
nten
ance
of e
duc
atio
nal p
rogr
amm
es fo
r co
ntin
uing
ed
ucat
ion
of g
ener
al p
ract
ition
ers
and
col
leag
ues
in o
ther
sp
ecia
lties
and
for
othe
r he
alth
pro
fess
iona
ls.
(14)
Com
mun
ity-b
ased
rhe
umat
olog
ist:
55%
of w
orki
ng t
ime
for
clin
ics,
10%
war
d w
ork,
inp
atie
nt r
efer
rals
, day
uni
t an
d m
ultid
isci
plin
ary
team
mee
ting
(MD
T) s
upp
ort,
10%
ad
min
istr
ativ
e w
ork,
25%
sup
por
ting
pro
fess
iona
l act
iviti
es (t
each
ing,
tra
inin
g, a
pp
rais
al, a
udit,
clin
ical
gov
erna
nce,
CP
D (c
ontin
uing
pro
fess
iona
l dev
elop
men
t), r
eval
idat
ion,
res
earc
h, d
epar
tmen
tal m
anag
emen
t an
d s
ervi
ce, d
evel
opm
ent);
com
mun
ity-b
ased
rh
eum
atol
ogis
t w
ith g
ener
al in
tern
al m
edic
ine:
45%
of w
orki
ng t
ime
for
clin
ics,
18%
for
GIM
and
sp
ecia
lty w
ard
rou
nd, i
npat
ient
ref
erra
ls, d
ay u
nit
and
MD
T su
pp
ort,
9%
for
pat
ient
-rel
ated
ad
min
istr
atio
n, r
elat
ives
and
co
ntac
t, 9
% fo
r p
eri-
take
and
pos
t-ta
ke w
ard
rou
nds
wee
kday
s an
d w
eeke
nds,
19%
for
teac
hing
, tra
inin
g, a
pp
rais
al, a
udit,
clin
ical
gov
erna
nce,
rev
alid
atio
n, r
esea
rch,
dep
artm
enta
l man
agem
ent
and
ser
vice
dev
elop
men
t;
acad
emic
rhe
umat
olog
ist:
15%
sp
ecia
l clin
ics,
10%
inp
atie
nt r
efer
ral a
nd w
ard
wor
k, 5
0% fu
ll ac
adem
ic s
essi
ons,
25%
sup
por
ting
pro
fess
iona
l act
iviti
es; a
20%
–25%
red
uctio
n of
pat
ient
s p
er c
linic
is s
ugge
sted
in c
ase
a co
nsul
tant
is in
volv
ed in
tea
chin
g ju
nior
sta
ff, s
tud
ents
or
sup
ervi
sing
nur
se c
linic
s.(1
5) L
ocal
CAT
S (i
nter
med
iate
ser
vice
s b
etw
een
prim
ary
and
sec
ond
ary
care
kno
wn
as C
linic
al A
sses
smen
t an
d T
reat
men
t se
rvic
es) a
nd t
he p
ossi
bili
ty t
o in
volv
e ge
nera
l pra
ctiti
oner
s, t
he in
trod
uctio
n of
nur
se-l
ed c
linic
s,
tele
pho
ne fo
llow
-up
clin
ics
or e
lect
roni
c ad
vice
to
gene
ral p
ract
ition
ers.
(16)
Ass
umed
tha
t cu
rren
t ra
tes
of w
orkf
orce
par
ticip
atio
n w
ill r
emai
n st
able
into
the
futu
re (2
025)
.(1
7) C
onsi
der
ed t
he n
umb
er o
f wor
king
hou
rs/w
eek
and
the
per
cent
age
of r
heum
atol
ogis
ts w
ho a
re w
orki
ng in
the
hos
pita
l or
as fr
eela
ncer
.*R
isk
of b
ias
rela
ted
to
the
dat
a so
urce
is t
aken
into
acc
ount
in s
corin
g of
the
res
pec
tive
fact
or.
CP
D, c
ontin
uing
pro
fess
iona
l dev
elop
men
t; F
TE, f
ull-
time
equi
vale
nt; G
IM, g
ener
al in
tern
al m
edic
ine;
NP,
nur
se p
ract
ition
er; O
A, o
steo
arth
ritis
; OP,
Ost
eop
oros
is; P
A, p
hysi
cian
ass
ista
nt; P
sA, p
soria
tic a
rthr
itis;
RA
, R
heum
atoi
d a
rthr
itis;
Sp
A, S
pon
dyl
oart
hriti
s.
Tab
le 3
C
ontin
ued
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EpidemiologyEpidemiologyEpidemiology
Table 4 Need/demand and supply factors identified from systematic literature reviews of workforce studies in other medical fields than rheumatology
Factors of need/demand and supply that were discussed in relation to workforce modelling processStudies discussing the factor
Demand/need factors
Use patterns, market factors (eg, access to services and preferences of health consumers), insurance coverage
6 studies27 29 31 32 34 35
Morbidity, mortality, incidence and severity, degree of need (dependency-acuity method) 6 studies27 28 32–35
Population growth, ageing 7 studies27 30–35
Desirable service volume (estimated demand for care), in relation to population health referral volume 2 studies27 30
Changes in guidelines that can help to anticipate increase or decrease in need/demand 1 study27
Income and education level, deprivation 2 studies28 34
Geographical distribution, travel distances 2 studies28 30
Adjustments for market inefficiencies1 1 study32
Technology development, increased complexity of care 4 studies29 32 34 35
Supply factors
Age structure, mortality, retirement, millennial and feminisation trends, full-time and part-time unemployment, manpower work pattern
9 studies27–35
Substitution rates, entry into practice and attrition, foreign medical graduates 6 studies27 29–33
Clinical FTE or % of non-clinical activities (research, teaching, travelling time, time out, time invested in education)
6 studies28–30 32 34 35
Mobility patterns and practice style, migration 3 studies27 29 35
Increasing no of support staff, task shifting, skill mix, expansion in roles 3 studies27 29 35
General labour market regulations (eg, Working Time Directive), economic and political factors, unemployment
6 studies27 30–34
Productivity rates, caseload, referrals 4 studies27 28 30 31
Practice organisation, staffing norms, skill mix 2 studies27 35
Payment methods, incentives 2 studies27 35
Job satisfaction factors 2 studies29 31
Spouse’s employment status 1 study31
(1) Authors of the included studies have adjusted for known US health market inefficiencies, eg, that FFS (fee-for-service) practices require 56% more physicians compared with HMO (health maintenance organisations).
metropolitan area.30 36 Three reviews reported uncertainty analyses by summarising different scenarios or results of simulation models.27 35 36 The quality of prediction was discussed by more than half of the reviews (n=6),27 28 32–35 without doing a formal quality appraisal, stating that quality improves when more parameters are considered in the model. On the other hand, poor quality of data has been acknowledged to have a profound impact on prediction results. Only two SLRs27 35 recognised the importance of involving stakeholders as they form the background for decisions.
Factors related to need/demand and supply in other medical fieldsTable 4 shows need/demand and supply-based factors considered in workforce prediction studies in other medical fields. Care use patterns and market factors (eg, access to services, preferences of health consumers, insur-ance coverage) were described but not always included in the workforce calculations.27 29 31 32 34 35 Population growth and ageing, morbidity and mortality statistics was another
group of commonly mentioned factors.27 30–35 Factors like income and educational level (n=2),28 34 geographical distributions (n=2)28 30 or service and referral volume (n=1)30 were less frequently discussed, and real examples of how these could be modelled in the workforce predic-tion were absent.
Workforce supply–related variables like workforce age, mortality, retirement, millennial (persons who entered workforce in the new millennia) and gender trends, full-time and part-time employment were considered (at least in part) by most of the reviews (n=9).27–35 Six reviews27 29–33 also took substitution rates (eg, replace-ment of retiring physicians) and entry into practice into account. Factors related to time spent on clinical work or the percentage of non-clinical activities, time out (eg, career breaks) or time invested in education were covered by 6 of 10ten SLRs.29 30 32 34 35 37 Fewer reviews considered mobility patterns and practice styles as well as migration (n=3)27 29 35 and task shifting to other health professionals (n=3)27 29 35 in their models.
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Most of the types of models and factors used were in line with the workforce prediction literature in rheumatology.
development of the workforce prediction risk of bias toolBased on the results of the literature review, 21 key factors for a workforce prediction model (see online supplementary figure S3) were identified. These factors were divided into three groups, namely, general factors, need/demand factors and supply-based factors.13 A short overview of the factors and the proposed grading system is depicted in table 5. A full description of the grading tool with the underlying rationale is given in online supplementary figure S7. Figure 1 summarises the envis-aged structure of the potential comprehensive workforce prediction model that includes the factors outlined in the risk of bias tool.
Application of the workforce prediction risk of bias toolWe applied our workforce prediction risk of bias tool to 14 workforce studies in rheumatology. An overview of this assessment is provided in tables 1 and 2 and in online supplementary table S8–S10. No single study scored with a low risk of bias on all 21 factors, rather the majority of studies had high or moderate bias in several items. Quality of data sources, incorporated in some of the gradings, was one of the most important reasons for increasing the risk of bias. For example, if a workforce prediction study included task-shifting between professionals but calculations were based only on author’s assumptions, it was graded as moderate, as opposed to when authors have obtained empirical data or a more formal expert consensus. In assessment of performance of general factors, several studies performed well in the choice of the model,14 17 19 24 time horizon14–17 19 20 25 and stake-holder involvement.7 15 17 Highest risk of bias was found concerning the regular update of models and the assess-ment of model accuracy, both of which have rarely been done. Most studies failed to adequately consider regional heterogeneity and uncertainty analyses. Among demand/need factors, reporting the scope of the diseases covered by rheumatologists was the only item in which most of the studies performed well. No single study achieved the lowest risk of bias score on disease definition, population projections and effects of medical developments. Among supply factors, the definition of clinical setting and demo-graphic trends in workforce were adequately addressed in most studies, whereas task shifting, time dedicated to clinical care, or measuring the entry to and exit from the profession were frequently of low quality.
dIsCussIOnThis study had three closely linked objectives, namely summarising the review of workforce prediction studies in rheumatology and other medical fields, as well as the development of a tool for the assessment of risk of bias of workforce studies and its subsequent application in rheu-matology studies.
The review of workforce studies in rheumatology was an update of an earlier SLR.9 We have identified three new studies, two of them7 17 representing an update of the previously conducted workforce predictions in the USA and Germany. The updates of workforce calcula-tions provide an important source of information for the assessment and validation of the models. Major conclu-sions of these updates referred to underestimations in the supply side of the models due to retirement patterns or gender trends (more women) in the rheumatology workforce resulting in a greater need for rheumatologists than previously predicted in order to cover the existing and expected future demand for care.17 38 Other sources of inaccuracy were forecasts around life expectancy and demographic developments,7 also resulting in a higher predicted need for care.
While methods and models used in the newly included studies were as heterogeneous as in older studies, in the most recent literature there was a tendency towards the use of integrated models with a wide range of relevant supply, need and demand factors. Two of the three new studies involved a multidisciplinary group and multiple stakeholders,7 17 which seems appropriate given the complexity of the topic and the different users of the results. Another trend more commonly seen in recent studies was the expression of results in headcounts and FTEs acknowledging the increment in part-time work. Increasing efforts in workforce predictions from different countries and a growing body of evidence underline the need and timeliness of synthesising the literature into a more solid methodological basis for future studies in the area.
The overview of SLRs in other medical fields has led to several important insights. First, the need for accu-rate workforce prediction has also been voiced across different medical specialities. Second, no standardised approaches for workforce prediction exist in other medical fields, leading to a similar heterogeneity of methods and predictions as in rheumatology. Third, studies in other fields have taken into consideration workforce supply, demand and need factors similar to studies in rheumatology. Finally, workforce prediction in other fields faces challenges similar to those in rheu-matology. These include accuracy and validation of the models, data quality, uncertainty around assumptions and to some extent stakeholder involvement and consid-eration of regional imbalances in larger countries. It is important to note that none of the systematic reviews in other medical fields reported an empirical evaluation of the workforce prediction model; hence, it remains unknown whether one can rely on the theoretical and conceptual assumptions provided and to what extent the suggested parameters improve model performance.
We have identified 21 key factors relevant for rheuma-tology workforce prediction, categorised into general factors and workforce need/demand and supply factors. Making use of these key factors, we developed a tool that can be applied for the assessment of the risk of bias of
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EpidemiologyEpidemiologyEpidemiology
Tab
le 5
W
orkf
orce
pre
dic
tion
risk
of b
ias
tool
*
Fact
or
Ris
k o
f b
ias
Gen
eral
fact
ors
Ty
pe
of m
odel
H
igh:
mod
el w
as o
nly
bas
ed o
n d
eman
d o
r ne
ed o
r su
pp
ly fa
ctor
s
Mod
erat
e: in
tegr
ated
mod
el t
hat
cons
ider
ed d
eman
d, n
eed
and
sup
ply
with
sup
ply
=d
eman
d a
t b
asel
ine
Lo
w: i
nteg
rate
d m
odel
tha
t co
nsid
ered
dem
and
, nee
d a
nd s
upp
ly w
ith s
upp
ly≠d
eman
d a
t b
asel
ine
Ti
me
horiz
on
Hig
h: p
red
ictio
ns >
30 y
ears
M
oder
ate:
pre
dic
tions
<5
or b
etw
een
16 a
nd 3
0 ye
ars
Lo
w: p
red
ictio
ns b
etw
een
5 an
d 1
5 ye
ars
U
pd
ate
of t
he m
odel
H
igh:
no
upd
ate
was
per
form
ed
Mod
erat
e: a
ny k
ind
of u
pd
ate
was
per
form
ed, b
ut n
ot w
ithin
1–4
yea
rs’ i
nter
val
Lo
w: f
req
uent
up
dat
es w
ere
per
form
ed (1
–4 y
ears
’ int
erva
l)
A
sses
smen
t of
mod
el p
erfo
rman
ce
Hig
h: n
o as
sess
men
t w
as d
one
M
oder
ate:
one
kin
d o
f qua
lity
asse
ssm
ent
was
don
e to
ens
ure
the
rigou
r an
d a
ccur
acy
of t
he m
odel
Lo
w: m
ore
than
one
ass
essm
ent
was
don
e
U
ncer
tain
ty a
naly
ses
H
igh:
no
unce
rtai
nty
anal
ysis
was
per
form
ed
Mod
erat
e: o
ne o
r tw
o un
cert
aint
y an
alys
es w
ere
per
form
ed, w
ithou
t cl
ear
just
ifica
tion
of t
he c
hoic
e
Low
: mor
e th
an t
wo
unce
rtai
nty
anal
yses
wer
e p
erfo
rmed
, cho
ices
and
ana
lyse
s w
ell j
ustifi
ed
R
egio
nal h
eter
ogen
eity
H
igh:
reg
iona
l het
erog
enei
ty w
as n
ot c
onsi
der
ed
Mod
erat
e: c
alcu
latio
ns w
ere
per
form
ed o
n na
tiona
l lev
el b
ut a
ntic
ipat
ed r
egio
nal d
iscr
epan
cies
are
dis
cuss
ed
Low
: cal
cula
tions
too
k in
to a
ccou
nt r
elev
ant
regi
onal
pro
file
of t
he c
ount
ry
S
take
hold
er in
volv
emen
t
Hig
h: s
take
hold
ers
wer
e no
t in
volv
ed in
the
wor
kfor
ce p
red
ictio
n
Mod
erat
e: o
ne g
roup
of s
take
hold
ers
was
invo
lved
in t
he w
orkf
orce
pre
dic
tion
Lo
w: m
ore
than
one
gro
up o
f sta
keho
lder
s w
as in
volv
ed in
the
wor
kfor
ce p
red
ictio
n
Dem
and
/nee
d fa
ctor
s
S
cop
e of
dis
ease
s co
vere
d b
y rh
eum
atol
ogy
spec
ialty
H
igh:
eith
er n
ot li
sted
or
not
dee
med
rep
rese
ntat
ive
(eg,
insu
ffici
ent
num
ber
of d
isea
se g
roup
s, u
njus
tified
aut
hor’s
est
imat
e et
c)
Mod
erat
e: s
tate
d b
ut t
he p
rob
abili
ty t
hat
they
are
rep
rese
ntat
ive
is li
mite
d
Low
: sta
ted
and
the
pro
bab
ility
tha
t th
ey a
re r
epre
sent
ativ
e is
hig
h
D
isea
se d
efini
tion
H
igh:
not
sta
ted
M
oder
ate:
unc
lear
crit
eria
, sel
f-re
por
ted
dia
gnos
es o
r IC
D c
odes
from
the
reg
istr
y (s
ingl
e or
mul
tiple
dat
a so
urce
s) o
r cr
iteria
st
ated
, rel
ying
on
phy
sici
an-r
epor
ted
dia
gnos
es u
sing
sin
gle
sour
ce o
f dat
a
Low
: crit
eria
sta
ted
, rel
ying
on
phy
sici
an-r
epor
ted
dia
gnos
es a
nd u
sing
mor
e th
an o
ne s
ourc
e, in
clud
ing
at le
ast
dat
a fr
om
pop
ulat
ion
bas
ed d
atab
ase
N
o an
d le
ngth
of v
isits
/yea
r p
er p
atie
nt
Hig
h: n
ot c
onsi
der
ed
Mod
erat
e: c
onsi
der
ed, b
ut s
epar
ate
estim
atio
ns d
one
for
at le
ast
one
asp
ect
Lo
w: c
onsi
der
ed, i
nclu
din
g se
par
ate
estim
atio
n fo
r m
ore
than
tw
o as
pec
ts (t
ype
of d
isea
se, d
isea
se p
hase
, typ
e of
vis
it)
%
pat
ient
s re
ferr
ed t
o rh
eum
atol
ogis
t
Hig
h: n
ot c
onsi
der
ed
Mod
erat
e: c
onsi
der
ed w
ithou
t d
istin
guis
hing
bet
wee
n d
isea
ses
Lo
w: c
onsi
der
ed, i
nclu
din
g se
par
ate
estim
atio
n p
er d
isea
se g
roup
Con
tinue
d
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RMD OpenRMD OpenRMD Open
Fact
or
Ris
k o
f b
ias
P
roje
ctio
n of
pop
ulat
ion
dev
elop
men
t
Hig
h: n
ot c
onsi
der
ed o
r on
ly s
ize
of p
opul
atio
n is
incl
uded
M
oder
ate:
age
or/
and
sex
str
uctu
re a
nd/o
r ot
her
fact
ors
incl
uded
but
usi
ng s
ingl
e d
ata
sour
ce
Low
: age
or/
and
sex
str
uctu
re a
nd/o
r ot
her
fact
ors
incl
uded
usi
ng m
ore
than
one
sou
rce
and
rel
ying
on
stat
istic
s or
nat
iona
l p
opul
atio
n p
roje
ctio
ns
P
roje
ctio
n of
ep
idem
iolo
gy o
f dis
ease
s
Hig
h: n
ot c
onsi
der
ed
Mod
erat
e: o
ne o
r m
ultip
le fa
ctor
s in
fluen
cing
ep
idem
iolo
gy (i
ncid
ence
/pre
vale
nce)
con
sid
ered
but
usi
ng s
ingl
e so
urce
of d
ata
Lo
w: m
ore
than
tw
o fa
ctor
s co
nsid
ered
, usi
ng m
ore
than
one
dat
a so
urce
E
f fect
of m
edic
al d
evel
opm
ent
H
igh:
effe
cts
of m
edic
al d
evel
opm
ent
not
cons
ider
ed
Mod
erat
e: e
ffect
s of
med
ical
dev
elop
men
t co
nsid
ered
bas
ed o
n au
thor
’s e
stim
ates
Lo
w: e
ffect
s of
med
ical
dev
elop
men
t co
nsid
ered
bas
ed o
n fo
rmal
dat
a or
exp
ert
cons
ensu
s
N
atio
nal e
cono
mic
dev
elop
men
t
Hig
h: n
ot c
onsi
der
ed
Mod
erat
e: o
ne e
cono
mic
fact
or in
fluen
cing
eco
nom
ic d
evel
opm
ent
(eg,
per
cap
ita in
com
e) c
onsi
der
edLo
w: m
ore
than
one
eco
nom
ic fa
ctor
con
sid
ered
Sup
ply
-bas
ed fa
ctor
s
C
linic
al s
ettin
g
Hig
h: n
ot c
onsi
der
ed in
cal
cula
tion
M
oder
ate:
one
typ
e of
set
tings
con
sid
ered
in t
he c
alcu
latio
n
Low
: mor
e th
an o
ne t
ype
of s
ettin
gs c
onsi
der
ed in
the
cal
cula
tion
Ti
me
spen
t on
clin
ical
(rhe
umat
olog
ic) c
are
H
igh:
not
con
sid
ered
in c
alcu
latio
n of
sup
ply
M
oder
ate:
% o
f tim
e d
edic
ated
to
clin
ical
dut
ies
defi
ned
with
out
det
aile
d e
stim
atio
n of
num
ber
, dur
atio
n, a
nd t
ype
of v
isit
(sin
gle
or m
ultip
le d
ata
sour
ces)
or
% o
f tim
e d
edic
ated
to
clin
ical
dut
ies
calc
ulat
ed t
hrou
gh e
stim
atin
g th
e nu
mb
er, d
urat
ion
and
typ
e of
vis
its, b
ut u
sing
sin
gle
dat
a so
urce
Lo
w: %
of t
ime
ded
icat
ed t
o cl
inic
al d
utie
s ca
lcul
ated
thr
ough
est
imat
ing
the
num
ber
, dur
atio
n an
d t
ype
of v
isits
, usi
ng m
ore
than
one
dat
a so
urce
Ta
sks
del
egat
ed t
o ot
her
heal
th p
rofe
ssio
nals
in
rhe
umat
olog
y (H
P)
H
igh:
invo
lvem
ent
of o
ther
HP
in c
are
for
rheu
mat
olog
ical
pat
ient
s no
t co
nsid
ered
M
oder
ate:
invo
lvem
ent
of o
ther
HP
con
sid
ered
bas
ed o
n au
thor
’s e
stim
ates
Lo
w: i
nvol
vem
ent
of o
ther
HP
con
sid
ered
in t
he w
orkf
orce
cal
cula
tion
bas
ed o
n d
ata
or fo
rmal
exp
ert
cons
ensu
s
D
emog
rap
hic
tren
ds
in w
orkf
orce
H
igh:
not
con
sid
ered
M
oder
ate:
one
dem
ogra
phi
c tr
end
(eg,
age
ing,
fem
inis
atio
n, m
illen
nial
tre
nd) c
onsi
der
ed
Low
: mor
e th
an o
ne d
emog
rap
hic
tren
ds
cons
ider
ed
E
ntry
and
exi
t to
pr o
fess
ion
(not
rel
ated
to
dem
ogra
phi
c ch
ange
s of
wor
kflow
)
Hig
h: n
ot c
onsi
der
ed
Mod
erat
e: o
ne o
r m
ultip
le e
ntry
and
exi
t fa
ctor
s co
nsid
ered
but
usi
ng s
ingl
e d
ata
sour
ce
Low
: mor
e th
an o
ne e
ntry
and
exi
t fa
ctor
s co
nsid
ered
, usi
ng m
ore
than
one
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Figure 1 Structure of comprehensive workforce prediction studies. The figure illustrates the logic of workforce prediction planning and the factors that should be considered in a low risk of bias model. Planning should adopt an integrated model that includes a number demand/need and supply factors. Prediction should be optimally made for 5–15 years’ horizon, with regular updates and performance assessment. Baseline imbalance between need/demand and supply should be taken into account. Uncertainty analyses should be done to test the critical assumptions. Relevant stakeholders should be consulted throughout the process. Results of the prediction should be convertible to headcounts and full-time equivalents (FTEs) to facilitate decision-making process at different levels.
other workforce prediction studies. The appraisal of existing models in rheumatology revealed that none of the studies had low risk of bias scores for all items; rather, the majority of studies had moderate to high bias in several categories. For several parameters, such as the effects of medical developments on future workforce need, none of the studies scored with a low risk of bias; nonetheless, we feel that meeting requirements for a low risk of bias for these factors is realistic and should be the target of future studies.
Our study has several limitations. First, the studies included in the two literature searches were limited to published literature and over several decades. Although we used a sensitive approach to identify workforce studies in rheumatology as well as SLRs in other medical fields, we cannot exclude that some relevant papers were missed. In countries with highly centralised health-care planning, prediction models may not have been published and medical societies (which were contacted to retrieve unpublished literature) may not have been involved in these exercises and thus not aware of existing studies. Nonetheless, the grey literature search identified reports about supranational efforts (ie, EU and OECD) which summarised workforce prediction practices in healthcare planning in different countries.8 12 These reports from respected agencies, while having different focuses and thus not meeting the inclusion criteria of any of our searches, were reviewed, reassuring the task force that it is unlikely that any substantial parameters have
been missed. However, most of the research has been done in the USA and Canada, which present only one part of the health systems of the Western world. Next, this review had a limited focus on prediction of the require-ment of rheumatologists and left beyond the scope detailed review of workforce planning for other health professionals involved in care for patients with RMDs. Other limitations refer to the subjective character of the risk of bias tool and the absence of reliable methods for external validation of the quality of workforce studies. Future workforce prediction should thus pay more attention to the validation and assessment of the model performance in order to identify the key threats to model validity and the parameters with the highest priority. It should be recognised that certain factors affecting work-force requirement cannot be foreseen at time of model conduction (eg, social media were unknown in the last millennium but may affect demand today and in future), hence a regular update of the model is essential in order to increase the validity of predictions.
While workforce planning is not an exact science, it has an important role in the dialogue between different stakeholders to guide the decisions around workforce training and more general organisation of healthcare in order to cover the expected future demand of the popu-lation.12 The current study provides an important and novel synthesis of contemporary workforce prediction practices. The existing evidence on workforce prediction in rheumatology and other fields is scarce, heterogeneous
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and of low to moderate quality. The workforce predic-tion risk of bias tool should facilitate future evaluation of workforce prediction studies.
Author affiliations1Department of Health Studies, institute of Occupational therapy, FH JOanneUM University of applied Sciences, Bad gleichenberg, austria2Department of internal Medicine, Division of rheumatology, Maastricht University Medical center and caPHri research institute, Maastricht, the netherlands3Department of rheumatology and clinical immunology, charitè University Medicine, Berlin, germany4Division of rheumatology, Medical University Vienna, Vienna, austria5Division of rheumatology, aSl3-azienda Sanitaria genovese, genova, italy6Department of rheumatology & clinical immunology, University Medical center Utrecht, Utrecht, the netherlands7Division of clinical immunology & rheumatology, University of Zagreb School of Medicine, Zagreb, croatia8Department of rheumatology, centro Hospitalar Médio tejo, torres novas, Portugal9columbia University Medical center, new York city, new York, USa10Division of rheumatology, University Hospital of geneva, geneva, Switzerland11rheumatology Department, Sorbonne Université, Paris, and Pitié Salpêtrière Hhospital aPHP, Paris, France12Department of rheumatology, Diakonhjemmet Hospital, Oslo, norway13Division of rheumatology and Department of Health Sciences research, Mayo clinic college of Medicine, rochester, Minnesota, USa14Department of rheumatology, Hospital general Universitario de elda, elda, Spain15Section for Outcomes research, Medical Unversity Viennna, center for Medical Statistics, informatics, and intelligent Systems, Vienna, austria16Faculty of Medicine, Department of internal Medicine, Division of rheumatology, University of Debrecen, Debrecen, Hungary17eUlar Standing committee of Pare, Zurich, Switzerland18Deutsches rheuma-Forschungszentrum, Berlin, germany19Department of rheumatology, Hospital of Bruneck, Bruneck, italy20Department of rheumatology and immunology, Medical University graz, graz, Styria, austria21Department of rheumatology, leiden University Medical centre, leiden, and Zuyderland Medical center, Heerlen, the netherlands
Acknowledgements We thank the societies of rheumatology that answered our request for rheumatology workforce calculations at the national level, as well as authors of previous studies who provided information about post-evaluations of the model. Furthermore, we want to thank the eUlar executive assistant, Ms Patrizia Jud, who supported our inquiries to the national societies of rheumatology.
Contributors all coauthors contributed to the development of the study design and outline. lF has developed and run the library searches. JU, PP, FB, cD and Sr have analysed and synthesised the data. JU and PP have drafted the first version of the manuscript, and all authors have critically reviewed and agreed with the final version of the manuscript.
Funding this study is part of the eUlar ‘points to consider’ for the conduction of workforce requirement studies in rheumatology funded by the european league against rheumatism (eUlar), grant number ePi016.
Competing interests none declared.
Patient consent not required.
Provenance and peer review not commissioned; externally peer reviewed.
data sharing statement no additional data are available.
Open access this is an open access article distributed in accordance with the creative commons attribution non commercial (cc BY-nc 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4.0
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