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R E S E A R C H A R T I C L E Open Access
Calculating incidence rates and prevalence
proportions: not as simple as it seems
Inge Spronk
1,2*
, Joke C. Korevaar
1
, René Poos
3
, Rodrigo Davids
1
, Henk Hilderink
3
, François G. Schellevis
1,4
,
Robert A. Verheij
1
and Mark M. J. Nielen
1,3
Abstract
Background: Incidence rates and prevalence proportions are commonly used to express the populations health
status. Since there are several methods used to calculate these epidemiological measures, good comparison between
studies and countries is difficult. This study investigates the impact of different operational definitions of numerators
and denominators on incidence rates and prevalence proportions.
Methods: Data from routine electronic health records of general practices contributing to NIVEL Primary Care Database
was used. Incidence rates were calculated using different denominators (person-years at-risk, person-years and midterm
population). Three different prevalence proportions were determined: 1 year period prevalence proportions, point-
prevalence proportions and contact prevalence proportions.
Results: One year period prevalence proportions were substantially higher than point-prevalence (58.3 - 206.6%) for
long-lasting diseases, and one year period prevalence proportions were higher than contact prevalence proportions
(26.2 - 79.7%). For incidence rates, the use of different denominators resulted in small differences between the different
calculation methods (-1.3 - 14.8%). Using person-years at-risk or a midterm population resulted in higher rates compared
to using person-years.
Conclusions: All different operational definitions affect incidence rates and prevalence proportions to some extent.
Therefore, it is important that the terminology and methodology is well described by sources reporting these
epidemiological measures. When comparing incidence rates and prevalence proportions from different sources, it is
important to be aware of the operational definitions applied and their impact.
Keywords: Incidence rate, Prevalence proportion, General practice, Electronic health record
Background
Incidence rates and prevalence proportions of symptoms
and diseases in the general population are important indica-
tors of a population’s health status [1]. These epidemiological
measures of disease frequency are the foundation to monitor
diseases, formulate and evaluate healthcare policy and con-
duct scientific research [2]. The comparison of incidence
rates and prevalence proportions between studies and coun-
tries, and determining factors explaining differences, results
in increased knowledge on both prevention and aetiology of
diseases [3]. However, fair comparisons between data sources
are difficult to make due to differences induced by the use of
different numerators and denominators.
From epidemiological handbooks, the definitions of inci-
dence rates and prevalence proportions are not unambigu-
ous. The incidence rate ‘represents the frequency of new
occurrences of a medical disorders in the studied popula-
tion at risk of the medical disorder arising in a given period
of time’and the prevalence proportion is ‘the part (per-
centage or proportion) of a defined population affected by
a particular medical disorder at a given point in time, or
over a specified period of time’[4,5]. Incidence is a rate of
occurrence and thus related to a longitudinal design,
whereas prevalence is the frequency of occurrence at a
given point in time and connects to a cross-sectional
sample [6]. However, further operationalisation of these
definitions requires a number of decisions for both the
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* Correspondence: i.spronk@nivel.nl
1
Nivel, Netherlands Institute for Health Services Research, P.O. Box 1568,
3500BN, Utrecht, The Netherlands
2
Department of Public Health, Erasmus MC, University Medical Center
Rotterdam, Rotterdam, The Netherlands
Full list of author information is available at the end of the article
Spronk et al. BMC Public Health (2019) 19:512
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denominator and numerator. In general, there is low level
of consensus on which operationalisations are best and
various methods are applied. Besides, in some circum-
stances the available information does not allow us to
choose between different definitions [7]. Moreover, what
was already highlighted by Elandt-Johnson in 1975 and
which is still true nowadays, is that there is a lack of preci-
sion and ambiguity in terminology within the field of
epidemiology [8]. Especially round the term ‘rate’which is
interchangeably used with the term proportion and some-
times with the term ratio [8,9]. As a consequence, the
comparability of incidence rates and prevalence propor-
tions between different sources is challenging.
First, decisions are needed to establish the denominator.
There are two main approaches used to define the patient
population for the denominator, including the whole popu-
lation in a year [10,11], and the population at one specific
point in time [12,13]. For the calculation of incidence rates
an at-risk population in a year is used as a third approach
[14,15]. Using person-years at risk is the correct method
to calculate incidence rates according to the definition of
incidence [4,5,16], however it is not always possible to ad-
equately determine this population on the available infor-
mation [7] and therefore also other denominators are used.
Second, for prevalence proportions, the definition of
the prevalence proportion needs to be specified, which
affects both the denominator and numerator. There are
three definitions used: 1) a point-prevalence, the propor-
tion of the population that has a disease at a specific
point in time [17–19], 2) a 1 year period prevalence, the
proportion of the population that has a disease at some
time during a year [10,20,21] and 3) a contact preva-
lence, the proportion of the population with at least one
encounter with a health care professional for a disease
during a year [22–25].
These operational definitions will affect incidence rates
and prevalence proportions but their impact is un-
known. Therefore, the purpose of the current study is to
investigate the impact of different operational definitions
on incidence rates and prevalence proportions based on
general practice data.
Methods
NIVEL primary care database
Data were derived from electronic health records (EHRs)
of general practices contributing to NIVEL Primary Care
Database (https://www.nivel.nl/en/nivel-primary-care-
database). Data included consultations, morbidity, diag-
nostic tests, and drug prescriptions of all patients en-
listed in these practices. Diagnoses were recorded and
classified by general practitioners (GPs) according to the
International Classification of Primary Care 1 (ICPC-1)
[26]. Data from 2010 to 2012 including 408 general
practices (reference date of extraction of the database:
October 20, 2014) were used to calculate incidence rates
and prevalence proportions for 2012. To ensure com-
pleteness and good quality of data, only data from prac-
tices meeting quality criteria were used [27].
Denominator
Dutch inhabitants are obligatory linked to a general
practice, including those persons who do not visit their
associated GP. Therefore, the size, and age and gender
distribution of the population can be determined from
patient lists and the listed practice population represents
the general population [2,28].
Numerator
The numerator of incidence rates and prevalence pro-
portions represents the number of persons with a par-
ticular symptom or disease. For determining the number
of incident and prevalent cases, GP recorded diagnostic
information was used. In their EHRs, GPs can link diag-
nostic information to encounters or so-called episodes
of care, defined as the period between the first and last
encounter for a certain health problem. However, for
calculating incidence rates and prevalence proportions,
episode of illness, which ‘extends from the onset of
symptoms to their complete resolution’, are needed [29].
With data from NIVEL Primary Care Database, an algo-
rithm was developed to construct episodes of illness
based on recorded diagnoses of encounters and episodes
of care [27]. The input for the algorithm consisted of
raw data from EHRs over the period 2010–2012, includ-
ing encounters recorded in episodes of care, single
diagnosis-coded encounters and date of diagnosis for all
chronic diseases that started before January 1st 2010.
The first step of the development of the algorithm, was
categorising all ICPC-1 codes in non-chronic (reversible)
and chronic (non-reversible) diseases by a group of ex-
perts including researchers, epidemiologists, GPs and
medical informaticians. For the analyses in this paper we
only used the episodes of illness of 109 chronic diseases
and 155 long-lasting non-chronic diseases. To estimate
the number of incident and prevalent chronic cases in
2012, we used all encounters in the period 2010–2012 and
the date of diagnosis that started before January 1st 2010
of recorded episodes of care. The start date of the episode
is either the start date of the episode of care or the first
encounter for this health problem in the period 2010–
2012. For chronic diseases, no end date of the episode of
illness is defined, since chronic diseases are considered ir-
reversible. For the long-lasting non-chronic diseases, we
used all recorded encounters and episodes of care in the
period 2010–2012 to estimate incident and prevalent
cases in 2012. To make a distinction between two con-
secutive episodes of illness for the same non-chronic dis-
ease, a minimum contact-free interval, i.e. a period in
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whichitislikelythatapatientdoesnotvisittheGP
again if a disease is over, of 52 weeks was defined, de-
pending on the assumed length of the disease epi-
sode. After this period of time, a new episode of
illness may occur. The end date of the episode of ill-
ness was estimated as half of the contact-free interval
(26 weeks) after the last encounter, since the patient
is recovered between the date of the last encounter
and a maximum of 52 weeks.
Incidence rates and prevalence proportions
EHRs provide information about the number of quarters
patients were registered in a general practice in a year.
The number of quarters registered is used to calculate
the denominators. Most patients were registered for a
whole year (90%), but due to moving, changing GP,
death or birth, patients could be registered less than four
quarters. Therefore, the term ‘person-year’was used,
which was defined as the number of quarters of the year
that a patient was registered in a general practice.
Incidence rates were calculated as the sum of all new
episodes of illness of a certain disease in 2012 divided by
the size of the population. The size of the population
was defined in three ways: 1) the total population in a
year in person-years, 2) the midterm population, defined
as the size of the population on July 1st, 3) the number
of patient years of the population at-risk in a year
(Table 1). The at-risk period is the period that a patient
was not recorded having a specific disease, i.e. the time
that the patient is at-risk for getting that disease. Preva-
lent cases are thus not included in the population
at-risk. When the population in a year or the population
at one point time is used, the denominator is the same
for each diagnose, whereas the denominator was calcu-
lated for each diagnose separately if the at-risk popula-
tion was used.
Year and point-prevalence proportions were calculated
as the sum of all patients with a particular episode of ill-
ness divided by the population (Table 1). We used
person-years as the denominator for 1 year period preva-
lence proportions and the size of the population on
December 31th 2012 was used for point-prevalence pro-
portions. The numerator for 1 year period prevalence pro-
portions included all patients with an episode of illness in
2012, for point-prevalence proportions the numerator was
the sum of patients with an on-going episode of illness on
December 31th 2012. We also calculated contact preva-
lence proportions. These were calculated as the sum of all
patients with at least one encounter with a general practi-
tioner for a particular disease in 2012 divided by
person-years. Incidence rates and prevalence proportions
were calculated per 1000 persons or per 1000 person-
years, whichever was appropriate. The ten highest incident
and prevalent cases were tabulated. All calculations were
performed using Stata 13.0.
Results
Population characteristics
After exclusion of practices that did not satisfy the qual-
ity criteria, the study population consisted of 312 general
practices (76%) (Table 2) which were geographically
evenly distributed over the Netherlands and formed a
representative sample of Dutch general practices accord-
ing to urbanization level of the practice location. The
total number of registered patients was 1,223,818 repre-
senting 1,145,726 person-years. The mean age of the
population was 40.0 ± 22.8 years and consisted of slightly
more females (50.7%) than males. Population character-
istics were representative for the Dutch population with
respect to age and sex [30]. The population on July 1st,
2012 (the midterm population) consisted of 1,130,532
patients and on December 31th of 1,105,536 patients.
Incidence rates
Incidence rates of the ten highest incident diagnoses
were calculated based on three different defined popula-
tions (Table 3). The use of person-years at-risk as de-
nominator resulted in slightly higher rates compared to
the use of person-years (0.9 - 14.8%). The differences
were higher in chronic diagnoses than in long-lasting
diagnoses.
Table 1 Definitions of Numerators and Denominators
Numerator Denominator
Incidence rate
Incidence rate: person-years Sum of all new episodes of illness in 2012 Person-years
Incidence rate: person-years at-risk Sum of all new episodes of illness in 2012 Person-years at-risk
Incidence rate: midterm population Sum of all new episodes of illness in 2012 Midterm population
Prevalence proportion
Point-prevalence proportion Episodes on December 31th Population on December 31th
1 year period prevalence proportion Episodes in 2012 Person-years
Contact prevalence proportion Number of persons with ≥1 contact in 2012 Person-years
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Comparing the use of person-years at-risk with the mid-
term population, incidence rates are for some diseases
higher when the population at-risk is used. For other dis-
eases, rates are higher when the midterm population was
used. Differences ranged from −0.8 to 13.3%.
When comparing the use of person-years with the mid-
term population, higher rates were found when the mid-
term population (difference −1.3%). Absolute differences
were low; ranging from −0.05/1000 per year in chronic
diseases to −0.45/1000 per year in long-lasting diseases.
For all three comparisons, differences were larger in high
frequent diagnoses and smaller in low frequent diagnoses
(results not shown).
Prevalence proportions
Comparing 1 year period prevalence proportions with
point-prevalence proportions on December 31th, substan-
tially higher proportions were found for 1 year period
prevalence proportions of long-lasting diseases (differences:
58.3–206.6%) (Table 4). On the contrary, point-prevalence
proportions resulted in slightly higher rates (difference
3.5%) in chronic diagnoses. Absolute differences ranged
from −5.04/1000 per year in chronic diseases to 33.72/
1000 per year in long-lasting diseases.
When 1 year period prevalence proportions were com-
pared to contact prevalence proportions, largest differences
were found for prevalence proportions of chronic diseases.
These differed from 15.1% to 418.4% for high frequent
chronic diseases. Also differences in long-lasting diseases
were relevant. 1 year period prevalence proportions were
26.2–79.7% higher. Absolute differences ranged from 4.64/
1000 per year in long-lasting diseases to 56.05/1000 per
year in chronic diseases.
Finally, point-prevalence proportions were compared
to contact prevalence proportions. Contact prevalence
proportions were higher for long-lasting diseases (17.5–
44.2%), whereas point-prevalence proportions were
higher for chronic diseases (19.3–436.9%). Absolute dif-
ferences ranged from -16.63/1000 per year in long-last-
ing diseases to 58.91/1000 per year in chronic diseases.
For all three comparisons, differences were larger in low
frequent diagnoses and smaller in high frequent diagno-
ses (results not shown).
Discussion
This study investigated to what extent different oper-
ational definitions of the numerator and denominator in-
fluence incidence rates and prevalence proportions.
Different definitions to define the population denominator
have a small effect on incidence rates. However, the use of
an 1 year period prevalence proportion instead of a
point-prevalence or contact prevalence results in large dif-
ferences. Authors should therefore thoroughly report how
they have calculated their presented epidemiological num-
bers. Besides, to ensure comparability of point-prevalence
proportions from different studies, the time point used in
the study should be reported.
Valid incidence rates and prevalence proportions are
important as they are the foundation to monitor
diseases and they are used to formulate and reflect on
healthcare policy [2]. Comparison of these epidemio-
logical measures between different sources, like be-
tween different countries, is important as well as
investigation on factors explaining differences lead to
Table 2 Characteristics of the Study Population
Number of cases (n) Percentage (%)
Population characteristics
Patients 1,223,818
Person-years 1,145,726
Gender
Male 603,179 49.3
Female 620,639 50.7
Age
a
(year)
0–4 63,969 5.2
5–17 190,197 15.5
18–44 432,438 35.3
45–64 338,904 27.7
65–74 111,286 9.1
75–84 62,395 5.1
≥85 24,629 2.0
General practice characteristics
Number 312
Patients (mean ± SD) 3923 ± 2449
Person-years (mean ± SD) 3672 ± 2289
Mode of practice
b
Solo 133 42.6
Duo 79 25.3
Group 76 24.4
Unknown 24 7.7
Degree of urbanization
c
Extremely urbanised 65 20.8
Strongly urbanised 71 22.8
Moderately urbanised 57 18.3
Hardly urbanised 60 19.2
Not urbanised 48 15.4
Unknown 11 3.5
a
The age of patients on the last day of the year was used for the
total population
b
In a solo practice, one GP is working. In a duo practice two GPs are employed
and in a group practice three or more GPs are engaged with the practice
c
Extremely urbanized comprised of ≥2500 addresses/ km
2
; strongly urbanized
of ≥1500–2500 addresses/ km
2
; moderately urbanised of ≥1000–1500
addresses/ km
2
; hardly urbanised of ≥500–1000 addresses/ km
2
; not urbanised
of < 500 addresses/km
2
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increased knowledge on both aetiology and prevention
of diseases [3]. Operational definitions of the numer-
ator and denominator to calculate incidence rates and
prevalence proportions are of influence to the actual
rates and proportions and therefore it is important to
be aware of these influences in order to make fair
comparisons.
Theoretically, the use of person-years results in a
more reliable denominator for incidence rates than the
midterm population. Incidence rates include a time
Table 3 Incidence rates based on different denominators
Incidence rate /1,000 Numerator: All new episodes
in 2012
All new episodes
in 2012
All new episodes
in 2012
Difference
person-
years at-
risk –
person-
years
Difference
person-
years at-risk
–
population
on 1 July
Difference
person-
years –
population
on 1 July
Denominator: Person-years at–
risk
a
Person-years
b
Population on 1
July
c
Long-lasting diagnosis
*
ICPC Mean Mean Mean Mean (%) Mean (%) Mean (%)
Contact dermatitis/ allergic
eczema
S88 34.78 33.83 34.28 0.95 (2.8%) 0.50 (1.4%) -0.45
(-1.3%)
Hayfever/allergic rhinitis R97 24.14 23.43 23.74 0.70 (3.0%) 0.39 (1.7%) -0.31
(-1.3%)
Constipation D12 20.75 20.36 20.63 0.39 (1.9%) 0.11 (0.6%) -0.27
(-1.3%)
Naevus/mole S82 16.11 15.96 16.17 0.15 (0.9%) -0.06
(-0.8%)
-0.21
(-1.3%)
Lumbar disc lesion, back pain with
radiating pain
L86 15.13 14.92 15.12 0.21 (1.4%) 0.01 (0.1%) -0.20
(-1.3%)
Vitamin deficiency/other
nutritional disorder
T91 12.90 12.74 12.91 0.16 (1.2%) -0.01
(-0.1%)
-0.17
(-1.3%)
Shoulder syndrome L92 11.97 11.86 12.01 0.11 (0.9%) -0.05
(-0.4%)
-0.16
(-1.3%)
Depressive disorder P76 10.70 10.51 10.65 0.20 (1.9%) 0.06 (0.5%) -0.14
(-1.3%)
Allergy/allergic reaction A12 10.28 10.19 10.33 0.09 (0.9%) -0.05
(-0.4%)
-0.14
(-1.3%)
Cataract F92 9.83 9.76 9.89 0.07 (0.7%) -0.06
(-0.6%)
-0.13
(-1.3%)
Chronic diagnosis
*
ICPC
Uncomplicated hypertension K86 12.59 10.96 11.11 1.63
(14.8%)
1.48
(13.3%)
-0.15
(-1.3%)
Atopic dermatitis/other eczema S87 11.75 10.91 11.06 0.84 (7.7%) 0.69 (6.3%) -0.15
(-1.3%)
Lipid metabolism disorder T93 8.50 7.97 8.08 0.52 (6.5%) 0.42 (5.1%) -0.11
(-1.3%)
Asthma R96 8.14 7.49 7.59 0.65 (8.7%) 0.55 (7.2%) -0.10
(-1.3%)
Refractive errors F91 4.99 4.88 4.95 0.10 (2.1%) 0.04 (0.1%) -0.06
(-1.3%)
Diabetes mellitus T90 4.78 4.50 4.56 0.28 (6.3%) 0.22 (4.9%) -0.06
(-1.3%)
Acquired deformity of limbs L98 4.39 4.27 4.33 0.12 (2.8%) 0.06 (1.5%) -0.06
(-1.3%)
Atherosclerosis (excl. K76,K90) K91 4.18 4.15 4.20 0.04 (0.9%) -0.02
(-0.5%)
-0.06
(-1.3%)
Malignant neoplasm of skin S77 3.82 3.74 3.78 0.09 (2.3%) 0.04 (1.0%) -0.05
(-1.3%)
Osteoarthritis knee L90 3.72 3.63 3.68 0.09 (2.6%) 0.04 (1.2%) -0.05
(-1.3%)
*
The ten highest incident long-lasting and chronic diagnoses are displayed
a
Number is per 1,000 person-years at-risk,
b
Number is per 1,000 person-years,
c
Number is per 1,000 persons
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Table 4 Comparison of prevalence proportions calculated with different methods
Prevalence proportion /1.000 1 year period
prevalence
a
Point
prevalence
b
Contact
prevalence
a
Numerator: Existing
episodes
in 2012
Existing episodes
on 31 Dec 2012
Number of persons
with ≥1 contact
in 2012
Difference 1 year
period prevalence
point prevalence
Difference 1 year
period prevalence
contact prevalence
Difference point
prevalence
contact
prevalence
Denominator: Person-years Population on
31 Dec 2012
Person-years
Long-lasting
diagnosis
c
ICPC Mean Mean Mean Mean (%) Mean (%) Mean (%)
Contact
dermatitis/
allergic eczema
S88 58.41 24.68 41.31 33.72 (136.6%) 17.09 (41.4%) −16.63 (−40.3%)
Hayfever/allergic
rhinitis
R97 47.96 21.77 35.28 26.19 (120.3%) 12.68 (35.9%) −13.51 (−38.3%)
Constipation D12 37.94 18.28 25.57 19.66 (107.5%) 12.37 (48.4%) −7.29 (−28.5%)
Lumbar disc
lesion. Back
pain with
radiating pain
L86 28.31 13.63 20.50 14.68 (107.7%) 7.81 (38.1%) −6.87 (−33.5%)
Depressive
disorder
P76 28.25 17.84 21.62 10.41 (58.3%) 6.62 (30.6%) −3.78 (−17.5%)
Naevus/mole S82 24.73 9.51 17.04 15.22 (160.1%) 7.69 (45.1%) −7.53 (−44.2%)
Vitamin
deficiency/other
nutritional disorder
T91 22.33 12.55 17.70 9.78 (77.9%) 4.64 (26.2%) −5.14 (−39.1%)
Shoulder
syndrome
L92 20.81 8.79 14.62 12.02 (136.7%) 6.19 (42.4%) −5.83 (−39.9%)
Tobacco abuse P17 18.52 6.04 10.30 12.48 (206.6%) 8.21 (79.7%) −4.26 (−41.4%)
Allergy/allergic
reaction
A12 18.47 8.13 12.31 12.31 (127.2%) 6.16 (50.0%) −4.18 (−34.0%)
Chronic diagnosis
c
ICPC
Uncomplicated
hypertension
K86 139.92 144.96 94.78 −5.04 (−3.5%) 45.14 (47.6%) 50.18 (52.9%)
Asthma R96 87.46 90.60 39.23 −3.15 (−3.5%) 48.23 (122.9%) 51.37 (130.9%)
Atopic dermatitis/
other eczema
S87 79.82 82.68 23.77 −2.86 (−3.5%) 56.05 (235.8%) 58.91 (247.8%)
Lipid
metabolism
disorder
T93 67.81 70.25 30.41 −2.44 (−3.5%) 37.40 (123.0%) 39.84 (131.0%)
Diabetes mellitus T90 64.12 66.43 55.70 −2.32 (−3.5%) 8.41 (15.1%) 10.73 (19.3%)
Acquired
deformity
of limbs
L98 31.07 32.18 5.99 −1.11 (−3.5%) 25.07 (418.4%) 26.19 (436.9%)
Emphysema/
chronic
obstructive
pulmonary
disease
R95 29.72 30.79 20.47 −1.07 (−3.5%) 9.25 (45.2%) 10.32 (50.4%)
Osteoarthritis
knee
L90 27.90 28.91 9.64 −1.00 (−3.5%) 18.26 (189.4%) 19.26 (199.8%)
Malignant
neoplasm
of skin
S77 25.71 26.64 8.98 −0.92 (−3.5%) 16.73 (186.3%) 17.66 (196.6%)
Angina pectoris K74 25.39 26.30 12.31 −0.91 (−3.5%) 13.08 (106.3%) 14.00 (113.7%)
a
Number is per 1000 person-years,
b
Number is per 1000 persons
c
The ten highest prevalent long-lasting and chronic diagnosis are displayed
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component which is not incorporated in a fixed popula-
tion, and therefore, a population at one point in time is
not appropriate. Furthermore, person-years take into
account incomplete follow-up and results thereby in a
more precise denominator. However, the number of
person-years at-risk is the only correct reliable denom-
inator as it corresponds best to the definition of inci-
dence rates [4,5,16]. It is the only denominator that
takes into account the time that a person suffers from a
specific disease. This time should not be included in
the denominator as the person is not at-risk of develop-
ing that disease during that time [4,5,16]. In fact,
when using another definition of the denominator than
person-years at-risk, it should be called an incidence
proportion instead of an incidence rate [8]. However,
all three used denominators in this study are used in
general practice-based epidemiological research. In
studies based on data from general practices in coun-
tries without a patient list, a population at one point in
time is often used, as it is hard to define a reliable de-
nominator in these countries [7]. Studies from general
practices in countries with a patient list are not consist-
ent in defining the denominator and use either
person-years [21,31–33] or person-years at-risk [34–
36]. Based on the results of this study, it can be con-
cluded that using different definitions of the population
(i.e. different denominators) results in relevant differ-
ences in incident rates, especially in frequent and in
highly frequent diseases.
In general practice-based epidemiological research, 1
year period prevalence proportions, point-prevalence
proportions as well as contact prevalence proportions
are reported. Our results show clear differences between
these three types of prevalence proportions. The most
striking impact for long-lasting diagnoses was the decision
for 1 year period prevalence proportions instead of
point-prevalence proportions; 1 year period prevalence
proportions were more than twice as high. Among preva-
lence proportions of chronic diagnoses, the largest differ-
ences were seen when a 1 year period prevalence
proportion was calculated instead of a contact prevalence
proportion.
One year period prevalence proportions are most often
used in general practice research. The major differences be-
tween 1 year period prevalence proportions and point-
prevalence proportions on December 31th are caused by
the number of persons with an ending episode in the
course of a year for long-lasting diseases. When calculating
an 1 year period prevalence proportion, all existing episodes
in a year contribute to the numerator. Whereas in a
point-prevalence the existing episodes on an indicated date
are summed. The number of persons with an existing epi-
sode in a year is substantially higher than the number of
persons with an existing episode on December 31th,
explaining the large differences in prevalence proportions
for long-lasting diseases. For chronic diseases, this does not
apply as chronic diseases are non-reversible. The numer-
ator only slightly differs through people that are deceased
or moved. And as the number of people registered during
the year in person-years are higher than the number of
people registered on December 31th, point-prevalence pro-
portions are slightly higher than 1 year period prevalence
proportions for chronic diseases.
The substantially higher 1 year period prevalence
proportions compared to contact prevalence propor-
tions are caused by the numerator, since for both
prevalence proportions the denominator is the number
of person-years. For 1 year period prevalence propor-
tions, existing and new episodes are summed in the nu-
merator, whereas for contact prevalence proportions,
the number of persons with a contact for a specific dis-
easearesummed.Thedifferenceiscausedbyepisodes
of illness without an encounter in the forthcoming year.
Differences were in particular higher for chronic dis-
eases. This is caused by the fact that chronic diseases
have a life-long history and people may not visit their
GP for a while. People may not suffer that much to visit
the GP in a particular year, or they are solely visiting
secondary care for their chronic disease. This is how
using contact prevalence proportions can introduce
errors. Especially for chronic diseases, the contact
prevalence proportion can largely differ from that of
other prevalence proportions because the contact
prevalence depends on the condition and on the
amount of care a patient needs. Some conditions in-
crease utilization of GP care while others do not. This
is important to keep in mind when considering the use
of contact prevalence proportions.
Next to the importance of differences in incidence
rates and prevalence proportions calculation, also dif-
ferences in the studied population (for example in age,
sex, socio-economic class, ethnic background etc.)
could result in large differences in presented incidence
rates and prevalence proportions. Which also make
comparisons across studies harder. Standardization of
rates to age and sex will help to overcome this issue.
Astrength of current study is that we were able to
apply all different operational definitions of incidence
rates and prevalence proportions on the same dataset.
Therefore, other causes contributing to differences in
rates and proportions, like differences between data-
bases and between populations [37,38], did not influ-
ence the epidemiological measures. A limitation is the
focus on long-lasting and chronic diseases. Operational
definitions for incidence rates could also been investi-
gated for acute diagnoses, but as 1 year prevalence
proportions and contact prevalence proportions are
comparable due to the short minimum contact-free
Spronk et al. BMC Public Health (2019) 19:512 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
interval of acute diagnosis this comparison is less inter-
esting. Besides, point-prevalence proportions are less
interesting as well through the seasonal influences of
acute diagnosis. Another limitation is the fact that the
used general practice data is not 100% complete. Only
data from practices meeting quality criteria were used
in present study. This ensures good quality of data, but
it does not guarantee completeness of data. We do not
think that this limitation influenced our results as we
studied differences between incidence rate and preva-
lence proportions; we did not focus on the incidence
rates or prevalence proportions of specific diagnosis.
Another limitation is the possible bias introduced by
using quarters of a year to define the denominator.
However, our patient population can only be defined by
health care claims by the GP. For each patient, a GP
claims a certain amount of money each quarter. We do
not think this has a large impact on our findings, as
around 90% of the population is registered the
complete year in a practice.
Conclusion
Operational definitions of denominators and numera-
tors to calculate incidence rates and prevalence propor-
tions influence these epidemiological measures to some
extent and thereby affect the comparability of studies.
Using different denominators accounts for only slight
differences in incidence rates. In contrast, the decision
for the type of prevalence has high impact on preva-
lence proportions. It is therefore important that both
the terminology and methodology is well described by
sources reporting these epidemiological measures.
When comparing incidence rates and prevalence pro-
portions from different sources, it is very important to
be aware of the operational definitions applied and
their impact.
Abbreviations
EHRs: Electronic health records; GP: General practitioner; ICPC-1: International
Classification of Primary Care 1
Acknowledgements
Not applicable.
Funding
None.
Availability of data and materials
The dataset used and/or analysed during the current study is available from
the corresponding author on reasonable request.
Authors’contributions
All authors conceptualized the study and defined the analysis. RD created the
dataset. IS analyzed the data. IS, JK, MN interpreted the data and drafted the
manuscript. RP, RD, HH, FG, RV contributed to the drafting and revising of the
article. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Ethical approval according to the Medical Research (Human Subjects) Act
(WMO), formal approval for this research project by a medical ethics
committee was not required. The NIVEL Primary Care Database extracts data
according to strict guidelines for the privacy protection of patients and GPs.
In addition, we sought and obtained permission for this work from the
board of the NIVEL network.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Author details
1
Nivel, Netherlands Institute for Health Services Research, P.O. Box 1568,
3500BN, Utrecht, The Netherlands.
2
Department of Public Health, Erasmus
MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
3
Centre for Health and Society, National Institute for Public Health and the
Environment (RIVM), Bilthoven, The Netherlands.
4
Department of General
Practice & Elderly Care Medicine/EMGO Institute for health and care research,
VU University Medical Center, Amsterdam, The Netherlands.
Received: 27 February 2019 Accepted: 15 April 2019
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