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. 2019 Jul 24;4(2):361–380. doi: 10.1007/s41669-019-0167-7

A Nationwide Study of Prevalence Rates and Characteristics of 199 Chronic Conditions in Denmark

Michael Falk Hvidberg 1,✉, Soeren Paaske Johnsen 2, Michael Davidsen 3, Lars Ehlers 1
PMCID: PMC7248158  PMID: 31342402

Abstract

Background

Real-world data of disease prevalence represents an important but underutilised source of evidence for health economic modelling.

Aims

The aim of this study was to estimate nationwide prevalence rates and summarise the characteristics of 199 chronic conditions using Danish population-based health registers, to provide an off-the-shelf tool for decision makers and researchers.

Methods

The study population comprised all Danish residents aged 16 years or above on 1 January 2013 (n = 4,555,439). The study was based on the linkage of national registers covering hospital contacts, contacts with primary care (including general practitioners) and filled-in out-of-hospital prescriptions.

Results

A total of 65.6% had one or more chronic condition. The ten conditions with the highest degree of prevalence were hypertension (23.3%), respiratory allergy (18.5%), disorders of lipoprotein metabolism (14.3%), depression (10.0%), bronchitis (9.2%), asthma (7.9%), type 2 diabetes (5.3%), chronic obstructive lung disease (4.7%), osteoarthritis of the knee (3.9%) and finally osteoporosis (3.5%) and ulcers (3.5%) in joint tenth place. Characteristics by gender, age and national geographical differences were also presented.

Conclusions

A nationwide catalogue of the prevalence rates and characteristics of patients with chronic conditions based on a nationwide population is provided. The prevalence rates of the 199 conditions provide important information on the burden of disease for use in healthcare planning, as well as for economic, aetiological and other research.

Electronic supplementary material

The online version of this article (10.1007/s41669-019-0167-7) contains supplementary material, which is available to authorized users.

Key Points for Decision Makers

Real-world evidence of disease prevalence is important for estimating the burden of disease, cost of illness and budget impact of new health technologies.
The Danish civil registration number provides a unique opportunity to link different types of register data for an individual patient, thus providing the best possible information on the actual treatment of chronic diseases.
Nationwide register-based prevalence statistics for 199 chronic diseases show, for most disease areas, a higher current treatment level than that expected from epidemiological research. In 2013, almost two-thirds of the entire Danish population aged 16 years or above either had a hospital diagnosis or had been in medical treatment for one or more chronic condition.
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Introduction

Worldwide, the financial pressures on healthcare providers are increasing. To control the rising cost of healthcare, decision makers need access to real-world evidence of current treatment patterns [1, 2]. Real-world evidence of disease prevalence is important for estimating the burden of disease, cost of illness, and budget impact of new health technologies [3, 4]. In addition, it is important to obtain unbiased, independent documentation of disease burden, as there may be concerns with the cost-of-illness studies funded by companies [5].

The burden of chronic diseases is increasing rapidly in most countries. In Denmark, approximately 30–50% of the adult population have one or more chronic condition or long-standing illness [6–10]. Moreover, the burdens of chronic conditions are increasing [11–20]. The numbers and expenditure are growing along with an ageing population, and up to 80% of total healthcare costs can be attributed to chronic conditions [21–24]. Thus, the need for reliable and affordable estimates of the prevalence and disease burden of chronic conditions to guide decision-making in healthcare is increasing [24–26].

There are different ways to measure the prevalence of chronic conditions, varying from community-based health surveys and screening investigations to register-based studies. The choice of definitions and methods naturally affects which patients are included and hence the prevalence [27, 28].

The Scandinavian countries have an established tradition of documenting the diseases and hospital treatments of the entire population in registers; however, the registers have primarily been used to study individual conditions rather than to assess the total burden of chronic conditions [7, 24, 26, 29–34], or to forecast drug spending to help with decision-making [35, 36].

The aim of the present study was to estimate the national prevalence rates and to summarise the characteristics of 199 chronic conditions using the complete Danish population aged 16 and above. To the best of the authors’ knowledge, the current study is the most comprehensive, independent register-based attempt to estimate the full-population prevalence-based disease burden of chronic diseases.

Methods

Study Population

The nationwide study population and cohort consisted of 4,555,439 Danish residents who were alive and aged 16 years or above on 1 January 2013, of which 49.2% were men.

The Registers

In Scandinavian countries, general practitioners (GPs) and hospitals have a long-standing tradition of reporting diseases, treatments, medications and other treatment-related information. This is done at the micro level for national health registers. Register data are collected mostly for public administration such as claims and management, surveillance and control functions [37]. The comprehensiveness, scope and population completeness are unique to Denmark and other Scandinavian countries, enabling individual linkage across registers by means of the individual personal identification number assigned to each person [38]. The main register used was the Danish National Patient Register (NPR) [39], including the Danish Psychiatric Central Research Register [40], containing treating-physician-reported International Statistical Classification of Diseases, 10th Revision (ICD-10) hospital diagnoses. Moreover, to ensure the inclusion of patients not treated in hospitals, the National Health Service Register (NHSR) [41] and National Prescription Registry (TNPR) [42] were included in the study, since the NPR did not include diagnosis data from private specialist doctors or GPs. The NHSR contains data collected primarily for administrative purposes from health contractors in primary healthcare. It includes information about citizens, providers and health services, but minimal clinical information. Furthermore, the TNPR, comprising all prescribed and distributed medicines outside hospitals, was included to ensure the best possible identification of conditions and the representativeness thereof by clinical recommendation. All registers had a unique civil registration number for each person; furthermore, birth date, gender and other information were derived from the Danish Civil Registration System [43]. The registers used are described in more detail in other studies [44, 45]. Table 1 summarises the details of the registers used.

Table 1.

The registers used and characteristics of the selected population in summary

Registry Years of registry use Population Contains
The National Patient Register [39] 1994–2012 All somatic hospital-treated in/outpatients. Primary, secondary and additional diagnosis for patients aged 16 years or above ICD-10 diagnosis codes for all public and private hospital-treated patients for every contact and treatment for the entire population as well as a civil registration number. Furthermore, data from all hospital treatments/procedures/operations for all hospital-treated patients as well as a civil registration number
The Danish Psychiatric Central Research Register [40] 1995–2012 All psychiatric hospital-treated in/outpatients. Primary, secondary and additional diagnosis for patients aged 16 years or above ICD-10 diagnosis codes for all public hospital-treated patients for every contact and treatment for the entire population as well as a civil registration number. No private psychiatric hospital exists
The National Health Service Register [41] 2000–2012 All patients in primary care aged 16 years or above All GP services for whole population and every consultation based on civil registration number. The register does not contain information on diagnosis, but many services are disease-specific and can thus be used for identifying chronic conditions
The Danish National Prescription Registry [42] 1995–2012 All patients in primary care with a prescription who are aged 16 years or above All Danish medicine prescriptions sold for the entire population using 6-digit ATC codes as well as a civil registration number
The Danish Civil Registration System [43] 2013 Whole population aged 16 years or above resident in Denmark on 1 January 2014 Data regarding birth date, age, gender, etc. for the entire population as well as a civil registration number
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ATC Anatomical Therapeutic Chemical Classification System, GP general practitioner, ICD-10 International Statistical Classification of Diseases, 10th Revision

The Definition of a ‘Chronic Condition’, Clinical Ratification and Review

A thorough description of the distinct phases and methods used are provided elsewhere [44–46]. In short, a ‘chronic condition’ was defined in line with previous studies, i.e. the ‘condition had lasted or was expected to last twelve or more months and resulted in functional limitations and/or the need for functional limitations and/or the need for ongoing medical care’ [47–49]. An expert panel consisting of professors, medical specialists and other experts from Aalborg University, the Clinical Institute of Aalborg University at Aalborg University Hospital, the Department of Clinical Epidemiology at Aarhus University Hospital, and others was consulted using the Delphi method in order to identify which of the approximately 22,000 ICD-10 codes and conditions could be considered ‘chronic’ based on the definition [44]. The ICD-10 codes were aggregated to 199 conditions, yet several conditions included subgroups of ICD-10 codes; thus, some consequently contained multiple conditions within the same disease area. Subsequently, all ICD-10 conditions considered chronic by definition were included in the study in pursuit of comprising the full-population burden of chronic conditions. Consequently, the 199 conditions consisted of several ICD-10 codes and thus groups of illnesses.

Data Collection: The Basis of the Data Algorithms

Since many chronic conditions last longer than the defined 12 months, but do not last for a lifetime, the varying ‘chronicity’ of conditions was divided into four groups of severity [44]:

  • Category I: stationary to progressive chronic conditions (no time limit equals inclusion time going back from the time of interest for as long as valid data were available. In the current study, this starting point was defined by the introduction of the ICD-10 diagnosis coding in Denmark, in 1994).

  • Category II: stationary to diminishing chronic conditions (10 years from register inclusion time to the time of interest).

  • Category III: diminishing chronic conditions (5 years from register inclusion time to the time of interest).

  • Category IV: borderline chronic conditions (2 years from register inclusion time to the time of interest).

The above four categories were designed to include the different chronic conditions when registers covered several years and still have the best possible clinical certainty that the conditions were still existing at the fixed time point of 1 January 2013. All 199 chronic conditions were divided into one of the four categories by medical specialists and experts. An algorithm was created for data collection based on the four categories, as seen in Fig. 1 for all 199 conditions and coherent ICD-10 codes. However, 35 of the 199 chronic conditions were not considered by experts to be properly representative using solely NPR diagnosis data. Thus, they created more complex algorithms using several registers besides diagnosis data, ranging from medicine and hospital treatments to GP services. For example, medicine and coherent indication codes were used to identify people with depression without a hospital diagnosis, and only when the indication codes identified the medicine used for depression, and not others such as pain treatment, etc. The same applied to GP services indicating, for example, diabetes or chronic obstructive lung disease (COPD) treatment, etc., where no hospital diagnosis was found. The details of all 199 unique definitions, their categorisation into one of the four categories and algorithms for replication can be found elsewhere [44, 45].

Fig. 1.

Fig. 1

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The four categories of chronicity and the inclusion time periods

Statistical Analysis

Prevalence estimates were calculated in both per cent and per 1000 subjects; the proportion was calculated as the number of conditions identified divided by the total number of residents aged 16 years or above alive on 1 January 2013 (n =4,555,439) multiplied by either 100 or 1000. Thus, prevalence was calculated from a specific point in time, based on the above inclusion time periods back in time for each condition. See Fig. 1 or further details in the literature [44].

The prevalence proportions for all conditions were stratified and presented by age and sex for use by, for example, local authorities, health planners and national researchers.

Direct standardisation of age, gender and education based on the national average (i.e. using Denmark as the standard/reference population for age, gender and education) [50] was applied to illustrate differences free of basic socio-economic effects (presented in brackets in Tables 2 and 3 and the electronic supplementary material). The gender and age (10-year intervals) variables were obtained from the Danish Civil Registration System [43], and the educational variables were obtained from the Population Education Register using Danish Education Nomenclature (DUN) classification [51].

Finally, tables were stratified geographically in the five national regions, and the mean age and standard deviation (SD) for each condition were calculated, but due to size and relevance for the international reader, geographical regions, mean age and SDs are presented in the electronic supplementary material.

All data management and data analysis were performed in SAS 9.4 (from Statistics Denmark’s research servers).

Results

The Prevalence Rates

The population’s full burden of chronic disease, with all the chronic conditions summarised, was 65.6% (see Table 2 or Table 3). The ten most prevalent conditions were hypertension (23.3%), respiratory allergy (18.5%), disorders of lipoprotein metabolism (14.3%), depression (10.0%), bronchitis (9.2%), asthma (7.9%), type 2 diabetes (5.3%), COPD (4.7%), osteoarthritis of the knee (3.9%) and osteoporosis (3.5%)/ulcer (3.5%); see the overview of conditions solely by overall disease groups in Table 2 and all 199 conditions in Table 3.

Table 2.

Overview of disease prevalence in Denmark: number of patients, prevalence rate (per 1000), age and gender (per cent within gender) of disease groups of conditions and chosen medicine for Denmark at 1 January 2013

Name of condition ICD-10 code/definition Number and prevalence
Denmark Men Age 16–44 (per 1000) Age 45–74 (per 1000) Age 75 + (per 1000)
N Per 1000 (standardised) Per cent
B—Viral hepatitis and human immunodeficiency virus (HIV) disease B18, B20–B24 8813 1.9 (2.0) 65.3 1.8 2.4 0.3
C—Malignant neoplasms C00–C99; D32–D33; D35.2–D35.4; D42–D44 229,331 50.3 (50.4) 43.3 10.2 69.4 155.8
D—In situ and benign neoplasms, and neoplasms of uncertain or unknown behaviour and diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism D00–D09; D55–D59; D60–D67; D80–D89 116,560 25.6 (25.7) 36.3 13.2 27.3 80.1
E—Endocrine, nutritional and metabolic diseases E00–E14; E20–E29; E31–35; E70–E78; E84–E85; E88–E89 877,433 192.6 (192.7) 45.6 43.5 279.6 501.4
G—Diseases of the nervous system G00–G14; G20–G32; G35–G37; G40–47; G50–64; G70–73; G80–G83; G90–G99 561,054 123.2 (123.5) 40.1 70.6 162.2 188.6
H—Diseases of the eye and adnexa and diseases of the ear and mastoid process H02–H06; H17–H18; H25–H28; H31–H32; H34–H36; H40–55; H57; H80, H810; H93, H90–H93 448,176 98.4 (98.6) 47.5 25.6 112.6 394.4
I—Diseases of the circulatory system I05–I06; I10–28; I30–33; I36–141; I44–I52; I60–I88; I90–I94; I96–I99 1,254,427 275.4 (275.5) 45.3 73.3 381.5 753.9
J—Diseases of the respiratory system J30.1; J40–J47; J60–J84; J95, J97–J99 1,210,598 265.7 (266.3) 42.1 209.3 298.8 381.9
K—Diseases of the digestive system K25–K27; K40, K43, K50–52; K58–K59; K71–K77; K86–K87 329,337 72.3 (72.6) 44.7 41.3 86.3 157.4
L—Diseases of the skin and subcutaneous tissue L40 65,469 14.4 (14.5) 47.8 7.9 19.3 21.7
M—Diseases of the musculoskeletal system and connective tissue M01–M25; M30–M36; M40–M54; M60.1–M99 1,032,808 226.7 (227.1) 42.2 113.2 291.2 470.5
N—Diseases of the genitourinary system N18 20,162 4.4 (4.5) 59.4 1.0 4.8 20.0
Q—Congenital malformations, deformations and chromosomal abnormalities Q00–Q56; Q60–Q99 124,898 27.4 (27.5) 41.7 33.9 23.2 16.2
F—Mental and behavioural disorders F00–99 683,194 150.0 (150.7) 41.0 135.2 150.2 223.7
Having one or more chronic condition 2,989,441 656.2 (657.2) 45.5 480.5 771.5 953.9
Mean number of chronic conditions (std. dev) 2.2 (2.8) – – 2.0 (2.6) 1.1 (1.6) 2.7 (2.8) 5.3 (3.6)
 Depression medicinec,** ATC: N06A 529,918 116.3 (116.7) 36.3 88.7 126.9 201.9
 Antipsychotic medicinec,** ATC: N05A 138,625 30.4 (30.6) 45.7 26.1 31.9 44.8
 Indication prescribed anxiety medicinec,** All prescriptions with either indication code 163 (for anxiety) or 371 (for anxiety, addictive) 102,568 22.5 (22.6) 34.5 19.9 23.7 30.1
 Heart failure medicationc,** ATC: C01AA05, C03, C07 or C09A with indication code 430 (for heart failure) 7468 1.6 (1.7) 64.6 0.1 2.0 7.5
 Ischaemic heart medicationc,** ATC: C01A, C01B, C01D, C01E 129,484 28.4 (28.5) 51.8 1.4 32.0 147.3
All of the five types of medicine above 688,006 151.0 (151.6) 40.4 100.2 166.1 331.1
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Standardised rates or standard devitions in brackets

See table with 10-year age intervals in Supplementary Material 2 in the electronic supplementary matertial

Conditions marked ‘A’ overlap with other conditions and are thus not counted twice [44]

ATC Anatomical Therapeutic Chemical Classification System, ICD-10 International Statistical Classification of Diseases, 10th Revision

cComplex defined conditions; see reference for further details [44]

**Two-year prevalence

Table 3.

Disease prevalence of 199 chronic conditions: number of patients treated, prevalence rate (per 1000), age and gender (per cent within gender) of all conditions and chosen medicine for Denmark at 1 January 2013

No. Name of condition ICD-10 code/definition Number and prevalence
Denmark Men Age 16–44 (per 1000) Age 45–74 (per 1000) Age 75+ (per 1000)
N Per 1000 (standardised) Per cent
B—Viral hepatitis and human immunodeficiency virus [HIV] disease B18, B20–B24 8813 1.9 (2.0) 65.3 1.8 2.4 0.3
1 Chronic viral hepatitis B18 4584 1.0 (1.0) 60.1 1.0 1.2 0.1
2 Human immunodeficiency virus [HIV] disease B20–24 4229 0.9 (0.9) 71.0 0.8 1.2 0.1
C—Malignant neoplasms C00–C99; D32–D33; D35.2–D35.4; D42–D44 229,331 50.3 (50.4) 43.3 10.2 69.4 155.8
3 Malignant neoplasms of other and unspecified localizations C00–C14; C30–C33; C37–C42; C45–C49; C69; C73–74; C754–C759 20,557 4.5 (4.6) 53.5 1.3 6.6 10.1
4 Malignant neoplasms of digestive organs C15–C17; C22–C26 4839 1.1 (1.1) 59.5 0.1 1.6 3.4
5 Malignant neoplasm of colon C18 18,826 4.1 (4.1) 47.0 0.2 4.9 20.3
6 Malignant neoplasms of rectosigmoid junction, rectum, anus and anal canal C19–C21 10,680 2.3 (2.3) 55.4 0.1 3.2 9.5
7 Malignant neoplasm of bronchus and lung C34 14,762 3.2 (3.3) 49.4 0.2 4.8 10.6
8 Malignant melanoma of skin C43 19,636 4.3 (4.3) 42.6 2.0 5.7 9.0
9 Other malignant neoplasms of skin C44 15,597 3.4 (3.4) 51.2 0.3 3.9 16.7
10 Malignant neoplasm of breast C50 50,687 11.1 (11.1) 0.7 1.0 17.6 29.5
11 Malignant neoplasms of female genital organs C51–C52; C56–C58 7245 1.6 (1.6) – 0.3 2.4 3.9
12 Malignant neoplasm of cervix uteri, corpus uteri and part unspecified C53–C55 11,608 2.5 (2.5) 0.0 0.7 3.5 7.1
13 Malignant tumour of male genitalia C60, C62–C63 5194 1.1 (1.1) 99.9 1.2 1.2 0.7
14 Malignant neoplasm of prostate C61 26,697 5.9 (5.9) 100.0 0.0 7.5 27.0
15 Malignant neoplasms of urinary tract C64–C68 10,319 2.3 (2.3) 71.2 0.1 2.9 9.9
16 Brain cancerc C71, C75.1–C75.3, D33.0–D33.2, D35.2–D35.4, D43.0–D43.2, D44.3–D44.5 (brain). C70, D32, D42 (brain membrane). C72, D33.3–D33.9, D43.3–D43.9 (cranial nerve, spinal cord) 15,310 3.4 (3.4) 52.9 1.4 4.7 6.2
17 Malignant neoplasms of ill-defined, secondary and unspecified sites, and of independent (primary) multiple sites C76–C80, C97 25,619 5.6 (5.6) 43.0 1.2 8.4 14.0
18 Malignant neoplasms, stated or presumed to be primary, of lymphoid, haematopoietic and related tissue C81–C96 19,712 4.3 (4.3) 54.4 1.5 5.6 12.4
D—In situ and benign neoplasms, and neoplasms of uncertain or unknown behaviour and diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism D00–D09; D55–D59; D60–D67; D80–D89 116,560 25.6 (25.7) 36.3 13.2 27.3 80.1
19 In situ neoplasms D00–D09 19,810 4.3 (4.4) 20.0 2.5 5.5 7.9
20 Haemolytic anaemias D55–D59 3055 0.7 (0.7) 33.7 0.7 0.6 1.2
21 Aplastic and other anaemias D60–D63 14,918 3.3 (3.3) 40.2 0.9 3.3 15.0
22 Other anaemias D64 46,613 10.2 (10.3) 38.7 2.3 9.7 53.1
23 Coagulation defects, purpura and other haemorrhagic conditions D65–D69 25,376 5.6 (5.6) 37.4 5.2 5.7 7.1
24 Other diseases of blood and blood-forming organs D70–D77 8896 2.0 (2.0) 43.5 1.0 2.6 3.6
25 Certain disorders involving the immune mechanism D80–D89 7660 1.7 (1.7) 50.8 1.4 2.0 1.3
E—Endocrine, nutritional and metabolic diseases E00–E14; E20–E29; E31–35; E70–E78; E84–E85; E88–E89 877,433 192.6 (192.7) 45.6 43.5 279.6 501.4
26 Diseases of the thyroidc E00–E04, E06, E07 131,908 29.0 (29.0) 15.9 11.3 39.1 66.2
27 Thyrotoxicosisc E05 41,374 9.1 (9.0) 18.3 3.8 10.8 26.7
28 Diabetes type 1c E10 23,062 5.1 (5.1) 57.9 4.9 5.6 3.5
29 Diabetes type 2c E11 242,177 53.2 (53.3) 53.0 8.5 77.9 152.1
30 Diabetes othersc E12–E14 1117 0.2 (0.2) 48.6 0.2 0.3 0.5
31 Disorders of other endocrine glands E20–E35, except E30 28,650 6.3 (6.4) 28.6 6.5 5.6 8.9
32 Metabolic disorders E70–E77; E79–E83; E85, E88–E89; 23,690 5.2 (5.2) 36.6 3.7 6.1 8.2
33 Disturbances in lipoprotein circulation and other lipidsc E78 652,242 143.2 (143.1) 51.1 11.8 221.2 408.1
34 Cystic fibrosisc E84 947 0.2 (0.2) 41.9 0.3 0.1 0.1
G—Diseases of the nervous system G00–G14; G20–G32; G35–G37; G40–47; G50–64; G70–73; G80–G83; G90–G99 561,054 123.2 (123.5) 40.1 70.6 162.2 188.6
35 Inflammatory diseases of the central nervous system G00–G09 7642 1.7 (1.7) 50.1 1.3 2.0 2.2
36 Systemic atrophies primarily affecting the central nervous system and other degenerative diseases G10–G14, G30–G32 10,401 2.3 (2.3) 46.8 0.4 2.2 12.1
37 Parkinson’s diseasec G20, G21, G22, F02.3 57,583 12.6 (12.6) 43.7 4.1 16.3 37.3
38 Extrapyramidal and movement disorders G23–G26 10,837 2.4 (2.4) 41.8 1.1 2.9 6.4
39 Sclerosis G35 13,284 2.9 (2.9) 30.6 1.9 4.2 1.5
40 Demyelinating diseases of the central nervous system G36–G37 4571 1.0 (1.0) 34.7 0.9 1.3 0.4
41 Epilepsyc G40–G41 61,695 13.5 (13.6) 48.2 9.7 16.0 20.3
42 Migrainec G43 149,866 32.9 (33.0) 18.6 24.9 44.1 15.6
43 Other headache syndromes G44 16,469 3.6 (3.6) 35.9 4.0 3.6 1.8
44 Transient cerebral ischaemic attacks and related syndromes and vascular syndromes of brain in cerebrovascular diseases G45–G46 43,977 9.7 (9.7) 53.5 1.1 12.7 37.6
45 Sleep disorders G47 36,806 8.1 (8.1) 73.5 4.1 12.6 5.0
46 Disorders of trigeminal nerve and facial nerve disorders G50–G51 21,488 4.7 (4.7) 43.0 2.8 6.0 7.4
47 Disorders of other cranial nerves, cranial nerve disorders in diseases classified elsewhere, nerve root and plexus disorders and nerve root and plexus compressions in diseases classified elsewhere G52–G55 12,429 2.7 (2.7) 48.9 1.3 4.0 3.6
48 Mononeuropathies of upper limb G56 122,395 26.9 (26.9) 37.2 12.6 39.1 36.5
49 Mononeuropathies of lower limb, other mononeuropathies and mononeuropathy in diseases classified elsewhere G57–G59 18,627 4.1 (4.1) 43.8 1.9 6.1 5.0
50 Polyneuropathies and other disorders of the peripheral nervous system G60–G64 30,289 6.6 (6.7) 56.4 1.7 9.3 18.2
51 Diseases of myoneural junction and muscle G70–G73 5758 1.3 (1.3) 47.7 0.9 1.5 1.6
52 Cerebral palsy and other paralytic syndromes G80–G83 14,410 3.2 (3.2) 53.9 2.9 3.5 3.2
53 Other disorders of the nervous system G90–G99 44,394 9.7 (9.8) 46.4 5.9 12.1 17.0
H—Diseases of the eye and adnexa and diseases of the ear and mastoid process H02–H06; H17–H18; H25–H28; H31–H32; H34–H36; H40–55; H57; H80, H810; H93, H90–H93 448,176 98.4 (98.6) 47.5 25.6 112.6 394.4
54 Disorders of eyelid, lacrimal system and orbit H02–H06 13,191 2.9 (2.9) 37.3 0.9 4.0 7.5
55 Corneal scars and opacities H17 2173 0.5 (0.5) 58.1 0.2 0.5 1.3
56 Other disorders of cornea H18 9473 2.1 (2.1) 43.5 1.0 2.1 7.4
57 Diseases of the eye lens (cataracts) H25–H28 68,009 14.9 (15.1) 40.5 0.5 15.6 84.8
58 Disorders of the choroid and retina H31–H32 1900 0.4 (0.4) 48.3 0.2 0.5 1.1
59 Retinal vascular occlusions H34 10,358 2.3 (2.3) 50.8 0.2 2.6 11.4
60 Other retinal disorders H35 68,485 15.0 (15.1) 40.2 1.6 13.0 93.7
61 Retinal disorders in diseases classified elsewhere H36 19,279 4.2 (4.3) 58.7 1.7 6.1 7.4
62 Glaucomac H40–H42 67,310 14.8 (14.9) 43.5 1.2 16.2 76.4
63 Disorders of the vitreous body and globe H43–H45 7572 1.7 (1.7) 44.6 0.7 2.4 3.1
64 Disorders of optic nerve and visual pathways H46–H48 6184 1.4 (1.4) 39.0 1.2 1.6 1.4
65 Disorders of ocular muscles, binocular movement, accommodation and refraction H49–H52 18,247 4.0 (4.0) 45.4 4.3 3.9 2.8
66 Visual disturbances H53 22,232 4.9 (4.9) 45.7 2.6 5.9 11.1
67 Blindness and partial sight H54 6614 1.5 (1.5) 44.4 0.6 1.5 5.6
68 Nystagmus and other irregular eye movements and other disorders of eye and adnexa H55, H57 11,133 2.4 (2.5) 40.3 1.7 3.0 3.7
69 Otosclerosis H80 10,360 2.3 (2.3) 35.7 0.8 3.1 5.4
70 Ménière’s diseasec H810 10,003 2.2 (2.2) 43.0 0.4 3.0 7.3
71 Other diseases of the inner ear H83 29,865 6.6 (6.3) 91.8 0.6 9.0 24.5
72 Conductive and sensorineural hearing loss H90 43,238 9.5 (9.6) 48.7 3.7 11.3 29.5
73 Other hearing loss and other disorders of ear, not elsewhere classified H910, H912, H913, H918, H930, H932, H933 8306 1.8 (1.8) 53.0 0.7 2.3 5.2
74 Presbycusis (age-related hearing loss) H911 80,659 17.7 (17.6) 48.9 0.4 9.6 147.4
75 Hearing loss, unspecified H919 87,806 19.3 (19.3) 55.3 3.0 24.5 74.5
76 Tinnitus H931 40,124 8.8 (8.7) 58.4 2.5 13.4 17.5
77 Other specified disorders of ear H938 20,537 4.5 (4.4) 48.1 0.8 5.9 16.1
I—Diseases of the circulatory system I05–I06; I10–28; I30–33;I36–141; I44–I52; I60–I88; I90–I94; I96–I99 1,254,427 275.4 (275.5) 45.3 73.3 381.5 753.9
78 Aortic and mitral valve diseasec I05, I06, I34, I35 30,123 6.6 (6.6) 50.9 0.7 6.4 37.7
79 Hypertensive diseasesc I10–I15 1,060,046 232.7 (232.7) 44.6 41.8 330.8 695.8
80 Heart failurec I11.0, I13.0, I13.2, I42.0, I42.6, I42.7, I42.9, I50.0, I50.1, I50.9 37,540 8.2 (8.3) 63.5 0.7 9.5 40.3
80A Ischaemic heart diseases I20–I25 139,173 30.6 (30.7) 60.2 2.6 41.8 114.5
81 Angina pectoris I20 78,476 17.2 (17.3) 58.4 1.6 25.7 53.0
82

Acute myocardial infarction and

subsequent myocardial infarction

 I21–I22 36,654 8.0 (8.1) 66.6 0.7 11.1 29.8
83 AMI complex/other I23–I24 2969 0.7 (0.7) 61.3 0.1 0.9 2.3
84 Chronic ischaemic heart disease I25 84,592 18.6 (18.6) 64.7 0.8 23.8 81.4
85 Pulmonary heart disease and diseases of pulmonary circulation I26–I28 15,352 3.4 (3.4) 44.8 1.0 3.8 13.0
86 Acute pericarditis I30 5563 1.2 (1.2) 73.1 1.0 1.4 1.3
87 Other forms of heart disease I31–I43, except I34–I35 and I42 8,119 1.8 (1.8) 60.5 0.7 2.1 5.4
88 Atrioventricular and left bundle branch block I44 14,604 3.2 (3.2) 58.7 0.4 2.7 20.0
89 Other conduction disorders I45–46 11,823 2.6 (2.6) 59.6 1.0 2.8 9.5
90 Paroxysmal tachycardia I47 39,510 8.7 (8.7) 48.1 3.3 11.0 23.9
91 Atrial fibrillation and flutter I48 112,342 24.7 (24.7) 57.2 1.7 26.5 132.0
92 Other cardiac arrhythmias I49 34,418 7.6 (7.6) 47.9 2.2 8.7 28.8
93 Complications and ill-defined descriptions of heart disease and other heart disorders in diseases classified elsewhere I51–52 7337 1.6 (1.6) 50.3 0.6 1.8 5.7
94 Stroke I60, I61,I63–I64, Z501 (rehabilitation) 72,606 15.9 (16.0) 54.2 1.6 19.8 68.7
95 Cerebrovascular diseases I62, I65–I68 17,308 3.8 (3.8) 51.1 0.8 4.9 13.3
96 Sequelae of cerebrovascular disease I69 50,952 11.2 (11.2) 52.5 0.8 12.7 55.9
97 Atherosclerosis I70 32,064 7.0 (7.0) 53.6 0.4 8.3 34.2
98 Aortic aneurysm and aortic dissection I71 10,296 2.3 (2.3) 72.2 0.1 2.6 11.2
99 Diseases of arteries, arterioles and capillaries I72, I74, I77–I79 11,830 2.6 (2.6) 45.6 1.0 3.4 6.2
100 Other peripheral vascular diseases I73 28,508 6.3 (6.3) 54.8 0.7 8.3 24.4
101 Phlebitis, thrombosis of the portal vein and others I80–I82 37,388 8.2 (8.3) 45.5 3.5 10.2 22.2
102 Varicose veins of lower extremities I83 23,530 5.2 (5.2) 30.6 3.2 6.8 6.6
103 Haemorrhoidsc I84 74,285 16.3 (16.3) 40.1 14.5 17.5 19.0
104 Oesophageal varices (chronic), varicose veins of other sites, other disorders of veins, non-specific lymphadenitis, other non-infective disorders of lymphatic vessels and lymph nodes and other and unspecified disorders of the circulatory system I85–I99, except I89 and I95 15,194 3.3 (3.3) 52.6 2.2 3.9 6.1
J—Diseases of the respiratory system J30.1; J40–J47; J60–J84; J95, J97–J99 1,210,598 265.7 (266.3) 42.1 209.3 298.8 381.9
105 Respiratory allergyc J30, except J30.0 841,685 184.8 (185.2) 41.0 154.0 204.3 240.3
105A Chronic lower respiratory diseasesc J40–J43, J47 418,120 91.8 (92.0) 39.8 57.8 112.8 156.3
106

Bronchitis, not specified as acute

or chronic, simple and

mucopurulent chronic bronchitis

and unspecified chronic bronchitis

 J40–J42 12,790 2.8 (2.8) 43.6 0.4 3.5 11.3
107 Emphysema J43 5557 1.2 (1.2) 51.1 0.2 1.7 3.7
108 Chronic obstructive lung disease (COPD)c J44, J96, J13–J18 216,184 47.5 (47.6) 45.0 17.6 60.6 131.4
109 Asthma, status asthmaticusc J45–J46 361,129 79.3 (79.4) 42.1 64.2 86.5 118.4
110 Bronchiectasis J47 4362 1.0 (1.0) 35.0 0.3 1.4 2.2
111 Other diseases of the respiratory system J60–J84; J95, J97–J99 21,993 4.8 (4.9) 52.5 1.5 6.2 14.6
K—Diseases of the digestive system K25–K27; K40, K43, K50–52; K58–K59; K71–K77; K86–K87 329,337 72.3 (72.6) 44.7 41.3 86.3 157.4
112 Ulcersc K25–K27 157,379 34.5 (34.8) 43.2 15.3 42.4 91.9
113 Inguinal hernia K40 25,032 5.5 (5.5) 88.7 2.1 7.7 11.5
114 Ventral hernia K43 7941 1.7 (1.7) 44.7 0.7 2.5 3.3
115 Crohn’s diease K50 18,913 4.2 (4.2) 41.4 4.4 4.1 3.4
116 Ulcerative colitis K51 29,538 6.5 (6.5) 45.2 5.4 7.2 8.2
117 Other non-infective gastroenteritis and colitis K52 20,844 4.6 (4.6) 35.5 2.6 5.0 12.8
118 Irritable bowel syndrome (IBS) K58 37,593 8.3 (8.3) 29.9 7.5 9.0 8.4
119 Other functional intestinal disorders K59 51,933 11.4 (11.5) 37.0 6.5 11.8 34.1
120 Diseases of liver, biliary tract and pancreas K71–K77; K86–K87 26,956 5.9 (6.0) 48.6 2.4 8.8 8.5
L—Diseases of the skin and subcutaneous tissue L40 65,469 14.4 (14.5) 47.8 7.9 19.3 21.7
121 Psoriasisc L40 65,469 14.4 (14.5) 47.8 7.9 19.3 21.7
M—Diseases of the musculoskeletal system and connective tissue M01–M25; M30–M36; M40–M54; M60.1–M99 1,032,808 226.7 (227.1) 42.2 113.2 291.2 470.5
122 Infectious arthropathies M01–M03 9402 2.1 (2.1) 49.7 1.8 2.3 2.1
122A Inflammatory polyarthropathies and ankylosing spondylitisc M05–M14, M45 165,944 36.4 (36.5) 51.7 12.7 50.4 84.9
123 Rheumatoid arthritisc M05, M06, M07.1, M07.2, M07.3, M08, M09 77,345 17.0 (17.0) 34.2 8.1 23.1 30.5
124

Inflammatory polyarthropathies

– except rheumatoid arthritisc

 M074–M079, M10–M14, M45 115,945 25.5 (25.5) 58.3 7.9 35.4 63.3
125 Polyarthrosis [arthrosis] M15 16,935 3.7 (3.7) 20.8 0.2 5.4 12.6
126 Coxarthrosis [arthrosis of hip] M16 104,115 22.9 (22.7) 43.3 2.0 26.8 108.1
127 Gonarthrosis [arthrosis of knee] M17 178,811 39.3 (39.4) 44.2 5.0 58.6 113.5
128 Arthrosis of first carpometacarpal joint and other arthrosis M18–M19 91,101 20.0 (20.1) 41.7 4.2 31.0 43.4
129 Acquired deformities of fingers and toes M20 55,730 12.2 (12.3) 21.3 5.5 17.9 17.1
130 Other acquired deformities of limbs M21 20,584 4.5 (4.5) 34.9 2.7 5.8 6.9
131 Disorders of patella (knee cap) M22 38,999 8.6 (8.6) 38.0 13.7 5.0 0.6
132 Internal derangement of knee M230, M231, M233, M235, M236, M238 9192 2.0 (2.0) 55.6 2.8 1.5 0.4
133 Derangement of meniscus due to old tear or injury M232 36,374 8.0 (8.0) 53.6 6.9 10.2 2.5
134 Internal derangement of knee, unspecified M239 28,206 6.2 (6.2) 50.0 6.9 6.3 2.3
135 Other specific joint derangements M24, except M240–M241 5923 1.3 (1.3) 57.2 1.9 0.9 0.5
136 Other joint disorders, not elsewhere classified M25 12,043 2.6 (2.7) 34.7 2.3 3.1 1.9
137 Systemic connective tissue disorders M30–M36, except M32,M34 42,631 9.4 (9.4) 24.7 4.2 10.0 32.3
138 Systemic lupus erythematosus M32 3376 0.7 (0.7) 12.9 0.5 1.0 0.7
139 Dermatopolymyositis M33 1137 0.2 (0.2) 39.8 0.1 0.4 0.4
140 Systemic sclerosis M34 1675 0.4 (0.4) 21.0 0.1 0.6 0.5
141 Kyphosis, lordosis M40 4160 0.9 (0.9) 47.7 0.7 1.1 0.7
142 Scoliosis M41 17,686 3.9 (3.9) 31.9 5.0 2.7 4.3
143 Spinal osteochondrosis M42 8034 1.8 (1.8) 63.4 1.4 2.2 1.0
144 Other deforming dorsopathies M43 23,756 5.2 (5.3) 42.4 2.2 7.3 9.9
145 Other inflammatory spondylopathies M46 7086 1.6 (1.6) 45.5 1.1 1.9 2.0
146 Spondylosis M47 61,999 13.6 (13.6) 45.9 2.4 21.2 31.4
147 Other spondylopathies and spondylopathies in diseases classified elsewhere M48, M49 50,805 11.2 (11.2) 44.8 1.1 14.7 43.7
148 Cervical disc disorders M50 11,476 2.5 (2.5) 46.4 1.5 3.8 1.2
149 Other intervertebral disc disorders M51 40,161 8.8 (8.9) 49.4 6.4 11.4 7.6
150 Other dorsopathies, not elsewhere classified M53 7246 1.6 (1.6) 40.9 1.4 1.9 1.2
151 Dorsalgia M54 40,780 9.0 (9.0) 42.9 7.3 10.0 11.9
152 Soft tissue disorders M60–M63, except M60.0 13,422 2.9 (3.0) 28.6 3.2 2.8 2.4
153 Synovitis and tenosynovitis M65 19,104 4.2 (4.2) 37.5 3.1 5.4 3.8
154 Disorders of synovium and tendon M66–68 19,669 4.3 (4.3) 41.7 4.7 4.3 2.1
155 Soft tissue disorders related to use, overuse and pressure M70 11,090 2.4 (2.4) 42.4 1.6 3.0 3.4
156 Fibroblastic disorders M72 43,600 9.6 (9.6) 63.7 2.3 14.2 22.6
157 Shoulder lesions M75 58,112 12.8 (12.7) 50.3 7.2 19.0 8.6
158 Enthesopathies of lower limb, excluding foot M76 11,223 2.5 (2.5) 49.4 2.7 2.6 0.9
159 Other enthesopathies M77 10,500 2.3 (2.3) 40.4 1.9 2.9 1.0
160 Rheumatism, unspecified M790 6852 1.5 (1.5) 13.7 0.7 2.3 1.2
161 Myalgia M791 10,168 2.2 (2.2) 36.8 1.4 2.9 3.0
162 Other soft tissue disorders, not elsewhere classified M792– M794; M798–M799 7939 1.7 (1.7) 36.9 1.5 2.0 1.7
163 Other soft tissue disorders, not elsewhere classified: pain in limb M796 22,201 4.9 (4.9) 41.5 3.9 5.5 6.7
164 Fibromyalgia M797 3399 0.7 (0.7) 4.5 0.6 1.0 0.3
165 Osteoporosisc M80–M81 158,813 34.9 (34.8) 15.3 0.7 43.5 163.6
166 Osteoporosis in diseases classified elsewhere M82 1007 0.2 (0.2) 35.2 0.1 0.3 0.7
167 Adult osteomalacia and other disorders of bone density and structure M83, M85, except M833 43,271 9.5 (9.5) 19.2 1.9 14.4 22.7
168 Disorders of continuity of bone M84 1865 0.4 (0.4) 51.6 0.3 0.5 0.5
169 Other osteopathies M86–M90 24,251 5.3 (5.3) 38.4 2.2 7.0 12.4
170 Other disorders of the musculoskeletal system and connective tissue M95–M99 30,038 6.6 (6.6) 52.1 5.4 7.4 8.2
N—Diseases of the genitourinary system N18 20,162 4.4 (4.5) 59.4 1.0 4.8 20.0
171 Chronic renal failure (CRF)c N18 20,162 4.4 (4.5) 59.4 1.0 4.8 20.0
Q—Congenital malformations, deformations and chromosomal abnormalities Q00–Q56; Q60–Q99 124,898 27.4 (27.5) 41.7 33.9 23.2 16.2
172 Congenital malformations: of the nervous, circulatory and respiratory systems, cleft palate and cleft lip, urinary tract, bones and muscles, other and chromosomal abnormalities not elsewhere classified Q00–Q07; Q20–Q37; Q60–Q99 85,534 18.8 (18.9) 37.8 23.7 15.6 9.8
173 Congenital malformations of eye, ear, face and neck Q10–Q18 19,689 4.3 (4.3) 38.8 6.2 3.0 1.8
174 Other congenital malformations of the digestive system Q38–Q45 6481 1.4 (1.4) 41.8 1.0 1.6 2.9
175 Congenital malformations of the sexual organs Q50–Q56 16,192 3.6 (3.6) 64.3 4.1 3.3 1.9
F—Mental and behavioural disorders F00–99 683,194 150.0 (150.7) 41.0 135.2 150.2 223.7
176 Dementiac F00, G30, F01, F02.0, F03.9, G31.8B, G31.8E, G31.9, G31.0B 36,803 8.1 (8.1) 36.3 0.0 3.7 71.7
177 Organic, including symptomatic, mental disorders F04–F09 26,430 5.8 (5.9) 48.8 2.5 5.8 22.6
178 Mental and behavioural disorders due to use of alcohol F10 59,143 13.0 (13.2) 67.0 9.4 17.5 7.9
179 Mental and behavioural disorders due to psychoactive substance use F11–F19 53,669 11.8 (11.9) 54.2 13.3 11.2 7.2
180 Schizophreniac F20 29,422 6.5 (6.5) 58.2 7.1 6.6 2.2
181 Schizotypal and delusional disorders F21–F29 39,694 8.7 (8.8) 50.3 9.2 8.7 6.2
182 Bipolar affective disorderc F30–F31 22,669 5.0 (5.0) 40.6 3.6 6.2 5.7
183 Depressionc F32, F33, F34.1, F06.32 454,933 99.9 (100.2) 35.5 79.1 107.5 165.8
184 Mood (affective) disorders F340, F348–F349, F38–F39 6887 1.5 (1.5) 35.9 1.3 1.6 1.7
185 Phobic anxiety disorders F40 14,324 3.1 (3.2) 33.4 5.0 1.9 0.3
186 Other anxiety disorders F41 38,079 8.4 (8.4) 32.6 10.0 7.3 5.3
187 Obsessive compulsive disorder (OCD)c F42 10,062 2.2 (2.2) 36.7 3.8 1.0 0.5
188 Post-traumatic stress disorder F431 16,055 3.5 (3.6) 48.6 3.9 3.8 0.4
189 Reactions to severe stress and adjustment disorders F432–F439 61,701 13.5 (13.7) 39.1 18.6 10.4 4.4
190 Dissociative (conversion) disorders, somatoform disorders and other neurotic disorders F44, F45, F48 21,420 4.7 (4.7) 31.7 4.6 5.2 3.1
191 Eating disorders F50 7751 1.7 (1.7) 5.2 3.5 0.3 0.1
192 Behavioural syndromes associated with physiological disturbances and physical factors F51–F59 6163 1.4 (1.3) 46.3 1.9 1.0 0.3
193 Emotionally unstable personality disorder F603 21,848 4.8 (4.9) 21.2 7.7 2.8 0.2
194 Specific personality disorders F602, F604–F609 50,415 11.1 (11.2) 39.3 14.4 9.4 2.7
195 Disorders of adult personality and behaviour F61–F69 17,533 3.8 (3.9) 44.3 5.0 3.3 0.7
196 Mental retardation F70–F79 13,822 3.0 (3.1) 53.9 3.8 2.6 1.2
197 Disorders of psychological development F80–F89 9911 2.2 (2.2) 65.4 4.1 0.6 0.3
198 Hyperkinetic disorders (ADHD)c F90 42,908 9.4 (9.5) 60.6 17.1 3.5 1.0
199 Behavioural and emotional disorders with onset usually occurring in childhood and adolescence F91–F99 39,602 8.7 (8.8) 48.3 11.8 6.6 3.7
Having one or more chronic conditions 2,989,441 656.2 (657.2) 45.5 480.5 771.5 953.9
Open in a new tab

Standardised rates in brackets

See table with 10-year age intervals in Supplementary Material 2 in the electronic supplementary material

Conditions marked ‘A’, overlap with other conditions and are thus not counted twice [44]

ICD-10 International Statistical Classification of Diseases, 10th Revision

c Complex defined conditions; see reference for further details [44]

** 2-year prevalence

In general, prevalence naturally increases with increasing age across most conditions (see also Supplementary Material 2 and 6 with 10-year age intervals). Moreover, patients are relatively older within cancers and endocrine, nutritional and metabolic diseases and diseases of the circulatory system, the eye and adnexa, the ear and mastoid process, and the musculoskeletal system and connective tissue compared to within other conditions. A relatively younger patient population is seen within diseases of the respiratory system (especially allergies and asthma) and mental- and behavioural disorders. Mental disorders actually show a decrease in prevalence in later life. The conditions with the youngest population are, for example, seen within human immunodeficiency virus (HIV) and hepatitis.

Gender differences are seen across conditions, where women are overrepresented within most conditions—except, for example, heart failure and ischaemic heart diseases, stroke, some cancers, diabetes, disturbances in lipoprotein circulation and other lipids, inguinal hernia, hearing loss such as tinnitus, chronic renal failure, sleep disorders, schizophrenia, attention deficit/hyperactivity disorder (ADHD), mental and behavioural disorders due to the use of alcohol, and others. Coherently, women are treated more often than men with medication, except for ischaemic heart medications.

Some further characteristics and differences between conditions are seen and described in Supplementary Material 1–6. This includes geographic regional tables, further age characteristics with mean age and SD, more age intervals, further comments on common selected conditions and regional differences, etc.

Discussion

Based on the present findings, almost two-thirds of the entire Danish population aged 16 years or above have one or more chronic condition. Seen in the context of previous studies, this is around 10–20 percentage points higher than several other Danish studies [6, 7] as well as international studies [27, 28]. The overall estimates are also twice the official estimates of the Danish National Board of Health [8]. However, both national and international comparisons are difficult due to differences in methodology and data possibilities, so estimates should be compared with caution. For example, some American studies report that about 50% of the US population has a chronic condition [52, 53], but reliable overall estimates are difficult to obtain as to why improvement has been recommended [17]. Furthermore, as the US often has higher disease prevalence than the EU—often differing by 20–100% across different conditions [54] —the present study suggests that the US estimates might be even higher.

Strengths and Limitations

The strength of this study is the detailed, full nationwide register-based collection and categorisation of all data on actual chronic conditions and treatments for Danes in public and private healthcare. Several limitations exist, however, one being the obvious fact that being treated for a chronic condition is not necessarily the same as being truly ill. There may be cases of defensive medicine or patients being treated on suspicion of a chronic disease, or even wrong (over-) diagnosis.

As found in previous studies, one of the main limitations of register studies in general is the opposite, i.e. not being able to identify either chronic patients who have not been treated or diagnosed at any time or patients with self-treated conditions, who consequently are not reported in hospital or other registers (i.e. data source limitations, etc.) [44, 55]. This may lead to under-reporting of some less severe chronic conditions, such as asthma, allergies, COPD and type 2 diabetes, which are mostly treated in primary care where there is no reporting of diagnoses. The same might apply for other conditions such as glaucoma, cataracts, age-related hearing loss, other eye and ear conditions, and less severe mental conditions that are untreated, such as mild forms of depression or anxiety [44]. Thus, differences in the present study are compared with the national self-reported prevalence rates for 2013 for 18 broadly defined conditions where possible, since they cover a wide range of conditions of importance for assessment [6]. In comparison, differences and limitations of register-based definitions might be found within four of the 18 conditions: osteoarthritis, migraine/headache, tinnitus and cataracts. These conditions are discussed in more detail in Supplementary Material 3. All in all, these register-based less severe conditions may not be sufficiently estimated at this time, which is why register-based prevalence will be underestimated and should be used with caution.

While some estimated conditions show limitations compared to self-reported conditions, most other conditions, such as hypertension, rheumatoid arthritis, osteoporosis, diabetes, COPD, bronchitis and other lung diseases, cancers, heart condition and stroke, all have higher or slightly higher register-based than self-reported prevalence [6], as well as estimates in line with other studies [7, 10, 33, 55, 56]. The same applies to mental conditions overall, which typically have a lower survey response [57], which could explain the lower self-reported prevalence. Added to the fact that these estimates are free of self-reported bias, this clearly strengthens the reliability of most register-based conditions reported, not to mention the enhanced precision of doctor-reported diagnoses.

However, similar to non-registration issues in community-based surveys, misclassification issues also exist in register studies as sources of bias. Reasons for this include different coding practices between hospitals [30], different access to specialists, clinical disagreements, different clinical and administrative practices and interpretation of the ICD-10 criteria [58]. However, we do not have evidence of systemic misclassification. On the contrary, studies have validated reported diagnoses from registers for several psychiatric and somatic conditions, with good overall results [55, 59–64]. Nevertheless, the estimates of some complex-aetiology, ill-defined and debated rheumatoid conditions such as fibromyalgia and chronic fatigue syndrome, even though common, should be used with caution since they are clearly under-reported compared to other studies [58, 65, 66]. Further discussion and references regarding the validity of diagnosis codes in registers can be found elsewhere [44].

In summary, the prevalence of some less severe conditions may be underestimated in register studies [55]. On the other hand, community-based self-reported studies may underestimate, especially, the more severe conditions due to, among other things, non-responders as well as people living in institutions or jails, homeless people, and those currently in inpatient treatment. Existing studies have already shown varying or poor overlap between self-reports and hospital reports [67], some concluding that the use of self-reports is less reliable in cases of, for example, stroke and ischaemic heart disease [68, 69], while severe conditions [67] such as cancers cannot reliably be self-diagnosed. Thus, a strength of the current study is that moderate or severe conditions are estimated more accurately and are naturally free of the bias of self-reported conditions. From this perspective, community-based survey studies contrast with and complement register studies [55].

Another strength of the study is that the definitions used were evaluated by epidemiologists and clinicians to strengthen reliability and provide the best possible representation [44]. In addition, the long register time periods are noticeable strengths compared to other register studies [7, 24, 26, 29–31]. Lastly, the use of a complete population of 4,555,439 people, not to mention the large number of ICD-10 doctor-reported conditions included, is another strength not or rarely seen in other studies.

Implications

Existing international register studies usually help determine current disease burden with implications for, for example, the funding of new treatments or others—but usually not multiple conditions [70–72].

The prevalence estimates of the current study provide unbiased, independent important basic information regarding the burden of disease for use in healthcare planning, as well as economic, aetiological and other research for a broad range of conditions. As it is based on uniform methodology within a single study, it also makes reliable comparisons, contrary to most existing studies. In many types of health economic studies, such as analyses of cost of illness and the budgetary consequences of the introduction of new health technologies, economic calculations may not provide an accurate, reliable picture if based on invalid information. This comprehensive catalogue of prevalence information of nationwide chronic conditions enables comprehensive comparisons of chronic diseases for policymakers, patient associations and researchers. Thus, it may serve as an off-the-shelf tool for decision makers and researchers for health economic modelling.

Future Studies

The World Health Organization (WHO) have recommended further improvement in data surveillance of chronic conditions worldwide [17]; in addition, other studies have criticised, as well as recommended, methodological improvements [73–76]. Future studies should compare these prevalence rates based on actual patient pathways and registrations of treatment with other methods for estimating disease prevalence. Future studies could also focus on the development over time of disease burden, including the newest register data and analysis of possible trends in diagnosis or possible over-diagnosis. Moreover, while there are several studies exploring the coherence between self-reported conditions and hospital records, we found no studies assessing whether less severe self-reported conditions are more accurately estimated than register-reported conditions. Further studies should be carried out to assess whether chosen less severe self-reported conditions are estimated accurately, and if so, how register and self-reported study designs could best complement each other and for which conditions.

Conclusions

The current study provides a catalogue of prevalence for 199 different doctor-reported chronic conditions and groups of conditions by gender, based on a complete nationwide population sample.

To the best of the authors’ knowledge, this study provides the most comprehensive descriptive register study of the prevalence of treatment of chronic conditions. Hence, the overall prevalence rate found is higher than that found in several previous studies, indicating that almost two-thirds of the entire Danish population aged 16 years or above either have a hospital diagnosis and/or are in medical treatment for one or more chronic condition.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 2267 kb) (2.2MB, pdf)

Acknowledgements

Special thanks to data management specialists Ole Schou Rasmussen and Thomas Mulvad Larsen from the North Denmark Region, and Niels Bohrs Vej, 9220 Aalborg OE, Denmark, for very useful and helpful suggestions and assistance in data management and SAS programming of the definitions, which has been much appreciated.

Author Contributions

All authors contributed to the study design. MFH did all data collection and programming and drafted the manuscript. All authors discussed and interpreted empirical findings. Critical manuscript revision and final approval of the manuscript was done by all authors.

Data Availability Statement

Due to the European General Data Protection Regulation (GDPR) and Danish data protection laws, it is not possible to provide the individually based, personal data of the current study directly. However, researchers can apply to Statistics Denmark for access to the data described in the “Methods” section. Thus, the same data of the current study can be provided via Statistics Denmark if the applicant fulfils legal and other requirements. See https://www.dst.dk/da/TilSalg/Forskningsservice. Software codes can be provided by the author when relevant, and data can be legally accessed, etc.

Compliance with Ethical Standards

Funding

The project was supported financially by the North Denmark Region, the Tax Foundation (public) and Aalborg University.

Conflict of interest

Michael Falk Hvidberg: None declared. Soeren Paaske Johnsen: None declared. Michael Davidsen: None declared. Lars Ehlers: None declared.

References

  • 1.Makady A, ten Ham R, de Boer A, Hillege H, Klungel O, Goettsch W. Policies for use of real-world data in health technology assessment (HTA): a comparative study of six HTA agencies. Value Health [Internet]. Elsevier Inc. 2017;20:520–532. doi: 10.1016/j.jval.2016.12.003. [DOI] [PubMed] [Google Scholar]
  • 2.Garrison LP, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health [Internet]. International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2007;10:326–335. doi: 10.1111/j.1524-4733.2007.00186.x. [DOI] [PubMed] [Google Scholar]
  • 3.Mauskopf J, Earnshaw SR, Brogan A, Wolowacz SBT. Budget-impact analysis of health care interventions. Manchester: Adis; 2017. [Google Scholar]
  • 4.Angelis A, Tordrup D, Kanavos P. Socio-economic burden of rare diseases: a systematic review of cost of illness evidence. Health Policy (New York) [Internet]; Elsevier Ireland Ltd. 2015;119:964–979. doi: 10.1016/j.healthpol.2014.12.016. [DOI] [PubMed] [Google Scholar]
  • 5.Faleiros DR, Álvares J, Almeida AM, de Araújo VE, Andrade EIG, Godman BB, et al. Budget impact analysis of medicines: updated systematic review and implications. Expert Rev Pharmacoecon Outcomes Res [Internet] 2016;16:257–266. doi: 10.1586/14737167.2016.1159958. [DOI] [PubMed] [Google Scholar]
  • 6.Christensen AI, Davidsen M, Ekholm O, Pedersen PV, Juel K. Danskernes sundhed—den nationale sundhedsprofil 2013 [Danes health—the national health profile 2013] [Internet]. Sundhedsstyrelsen [National Board Heal. Copenhagen K, Denmark; 2014. http://www.si-folkesundhed.dk/Udgivelser/Bøgerograpporter/2014/Danskernessundhed-dennationalesundhedsprofil2013.aspx.
  • 7.Robinson KM, Juel Lau C, Jeppesen M, Vind AB, Glümer C. Kroniske sygdomme—forekomst af kroniske sygdomme og forbrug af sundhedsydelser i Region Hovedstaden v2.0 [Chronic diseases—prevalence of chronic diseases and use of health services in the Capital Region v2.0] [Internet]. Forskningscenter Forebygg. og Sundh. [Research Cent. Prev. Heal. Glostrup, Denmark; 2012. https://www.regionh.dk/fcfs/publikationer/Documents/Kroniskesygdomme-forekomstafkroniskesygdommeogforbrugafsundhedsydelseriRegionHovedstaden.pdf.
  • 8.Sundhedsstyrelsen [Danish National Board of Health]. Kronisk sygdom [Chronic Illness] [Internet]; 2017. https://www.sst.dk/da/sygdom-og-behandling/kronisk-sygdom#. Cited 25 May 2017.
  • 9.Rosendahl Jensen HA, Davidsen M, Ekholm O, Christensen AI. Danskernes Sundhed—Den Nationale Sundhedsprofil 2017 [Danes health—the national health profile 2017] [Internet]. Sundhedsstyrelsen [National Board Heal. Copenhagen K; 2018. http://www.si-folkesundhed.dk/Udgivelser/Bøgerograpporter/2018/DanskernesSundhed.DenNationaleSundhedsprofil2017.aspx.
  • 10.Lau C, Lykke M, Andreasen A, Bekker-Jeppesen M, Buhelt L, Robinson K, et al. Sundhedsprofil 2013—Kronisk Sygdom [Health Profile 2013—Chronic Disease] [Internet]. Glostrup Hospital, Nordre Ringvej 57, Afsnit 84-85, 2600 Glostrup, Copenhagen Denmark e-mail: fcfs@regionh.dk, Denmark; 2015. https://www.regionh.dk/fcfs/publikationer/Documents/Sundhedsprofil2013-Kronisksygdom.pdf. Cited 2 July 2015.
  • 11.Naghavi M, Wang H, Lozano R, Davis A, Liang X, Zhou M, et al. Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet [Internet]. Elsevier Ltd. 2015;385:117–171. doi: 10.1016/S0140-6736(14)61682-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Murray CJL, Ezzati M, Flaxman A. Supplementary appendix—comprehensive systematic analysis of global epidemiology: definitions, methods, simplification of DALYs, and comparative results from the Global Burden of Disease Study 2010. Lancet [Internet]. 2012;380:2063–66. http://linkinghub.elsevier.com/retrieve/pii/S0140673615606924.
  • 13.Murray Christopher JL, Ezzati Majid, Flaxman Abraham D, Lim Stephen, Lozano Rafael, Michaud Catherine, Naghavi Mohsen, Salomon Joshua A, Shibuya Kenji, Vos Theo, Wikler Daniel, Lopez Alan D. GBD 2010: design, definitions, and metrics. The Lancet. 2012;380(9859):2063–2066. doi: 10.1016/S0140-6736(12)61899-6. [DOI] [PubMed] [Google Scholar]
  • 14.Vos T, Barber RM, Bell B, Bertozzi-Villa A, Biryukov S, Bolliger I, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet [Internet]. 2015;386:743–800. http://linkinghub.elsevier.com/retrieve/pii/S0140673615606924. [DOI] [PMC free article] [PubMed]
  • 15.World Health Organization (WHO). World Health Statistics 2015 [Internet]. World H. 2015. p. 164. http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1. Cited 22 Apr 2016.
  • 16.World Health Organization (WHO). Global status report on noncommunicable diseases 2014 [Internet]. World Health; 2014. http://www.who.int/nmh/publications/ncd-status-report-2014/en/. Cited 18 Nov 2015.
  • 17.World Health Organization (WHO). Global status report on noncommunicable diseases 2010 [Internet]. World Health; 2010. http://www.who.int/nmh/publications/ncd_report2010/en/. Cited 18 Nov 2015.
  • 18.Murray CJL, Barber RM, Foreman KJ, Ozgoren AA, Abd-Allah F, Abera SF, et al. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition. Lancet [Internet]. 2015;386:2145–91. http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)61340-X/abstract. [DOI] [PMC free article] [PubMed]
  • 19.Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet [Internet]. 2012;380:2197–223. http://linkinghub.elsevier.com/retrieve/pii/S0140673612616894. [DOI] [PubMed]
  • 20.Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from deceases, injuries and risk factors in 1990 and projected to 2010 [Internet]. Harvard Univ. Press. World Health Organization; 1996. http://apps.who.int/iris/bitstream/10665/41864/1/0965546608_eng.pdf.
  • 21.Schiller JS, Lucas JW, Ward BW, Peregoy JA. Summary health statistics for U.S. adults: National Health Interview Survey, 2011. Vital Heal Stat [Internet]. 2012;10:1–207. http://www.ncbi.nlm.nih.gov/pubmed/22834228. [PubMed]
  • 22.Garrett Nancy, Martini E. Mary. The Boomers Are Coming: A Total Cost of Care Model of The Impact of Population Aging on The Cost of Chronic Conditions in The United States. Disease Management. 2007;10(2):51–60. doi: 10.1089/dis.2006.630. [DOI] [PubMed] [Google Scholar]
  • 23.Sundhedsstyrelsen [Danish National Board of Health]. Kronisk sygdom: patient, sundhedsvæsen og samfund [Chronic conditions: patient, health care and society] [Internet]. Natl. Boards Heal. Copenhagen: Sundhedsstyrelsen; 2005. http://www.sst.dk/publ/Publ2005/PLAN/Kronikere/Kronisk_sygdom_patient_sundhedsvaesen_samfund.pdf. Cited 1 May 2015.
  • 24.Esteban-Vasallo M., Dominguez-Berjon M., Astray-Mochales J, Genova-Maleras R, Perez-Sania A, Sanchez-Perruca L, Aguilera-Guzman M, Gonzalez-Sanz F. Epidemiological usefulness of population-based electronic clinical records in primary care: estimation of the prevalence of chronic diseases. Family Practice. 2009;26(6):445–454. doi: 10.1093/fampra/cmp062. [DOI] [PubMed] [Google Scholar]
  • 25.Epping-Jordan J, Pruitt S, Bengoa R, Wagner E. Improving the quality of health care for chronic conditions. Qual Saf Health Care [Internet]. 2004;13:299–305. http://qualitysafety.bmj.com/lookup/doi/10.1136/qshc.2004.010744. Cited 22 Mar 2015. [DOI] [PMC free article] [PubMed]
  • 26.Wiréhn Ann-Britt E., Karlsson H. Mikael, Carstensen John M. Estimating disease prevalence using a population-based administrative healthcare database. Scandinavian Journal of Public Health. 2007;35(4):424–431. doi: 10.1080/14034940701195230. [DOI] [PubMed] [Google Scholar]
  • 27.Schneider KM, O’Donnell BE, Dean D. Prevalence of multiple chronic conditions in the United States’ Medicare population. Health Qual Life Outcomes [Internet]. 2009;7:82. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2748070&tool=pmcentrez&rendertype=abstract. Cited 20 Apr 2014. [DOI] [PMC free article] [PubMed]
  • 28.Vogeli C, Shields AE, Lee T a, Gibson TB, Marder WD, Weiss KB, et al. Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J Gen Intern Med [Internet]. 2007;22:391–5. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2150598&tool=pmcentrez&rendertype=abstract. Cited 26 Mar 2014. [DOI] [PMC free article] [PubMed]
  • 29.Klinisk Epidemiologisk Afdeling [Department of Clinical Epidemiology]. Sygehuskontakter og lægemiddelforbrug for udvalgte kroniske sygdomme i Region Nordjylland [Hospital contacts and drug consumption for selected chronic diseases in North Jutland] [Internet]. Dep Clin Epidemiol. Denmark; 2007. http://www.kea.au.dk/file/pdf/36.pdf. Cited 1 May 2014.
  • 30.Sundhedsstyrelsen [Danish National Board of Health]. Beskrivelse af Sundhedsstyrelsens monitorering af kronisk sygdom [Description of National Board of Health’s monitoring of chronic disease] [Internet]. Natl Board Health; 2012. http://www.ssi.dk/~/media/Indhold/DK-dansk/Sundhedsdataogit/NSF/Dataformidling/Sundhedsdata/Kroniker/MetodebeskrivelseafSundhedsstyrelsensmonitoreringafkronisksygdom.ashx. Cited 15 June 2015.
  • 31.Statens Serum Institute. Monitorering af kronisk sygdom hos Statens Serum Institut [Monitoring of chronic disease at Statens Serum Institute] [Internet]. Statens Serum Inst.; 2012. http://www.ssi.dk/Sundhedsdataogit/Sundhedsvaesenetital/Monitoreringer/Kronisksygdom/Monitorering-Kronisksygdom2011.aspx. Cited 10 Nov 2014.
  • 32.Hommel K., Rasmussen S., Madsen M., Kamper A.-L. The Danish Registry on Regular Dialysis and Transplantation:completeness and validity of incident patient registration. Nephrology Dialysis Transplantation. 2009;25(3):947–951. doi: 10.1093/ndt/gfp571. [DOI] [PubMed] [Google Scholar]
  • 33.Carstensen Bendix, Kristensen Jette Kolding, Marcussen Morten Munk, Borch-Johnsen Knut. The National Diabetes Register. Scandinavian Journal of Public Health. 2011;39(7_suppl):58–61. doi: 10.1177/1403494811404278. [DOI] [PubMed] [Google Scholar]
  • 34.Info@norpen.org. Nordic PharmacoEpidemiological Network (NorPEN) [Internet]. http://www.norpen.org/pages/publications.html. Cited 16 Apr 2019.
  • 35.Wettermark B, Persson ME, Wilking N, Kalin M, Korkmaz S, Hjemdahl P, et al. Forecasting drug utilization and expenditure in a metropolitan health region. BMC Health Serv Res. 2010;10. [DOI] [PMC free article] [PubMed]
  • 36.Eriksson I, Wettermark B, Persson M, Edström M, Godman B, Lindhé A, et al. The early awareness and alert system in Sweden: history and current status. Front Pharmacol. 2017;8:1–8. doi: 10.3389/fphar.2017.00674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Olsen Jørn, Basso Olga, Sørensen Henrik Toft. What is a population-based registry? Scandinavian Journal of Public Health. 1999;27(1):78–78. doi: 10.1177/14034948990270010601. [DOI] [PubMed] [Google Scholar]
  • 38.Kamper-Jørgensen F. New editor and new publisher for the Scandinavian Journal of Public Health. Scand J Public Health. 2008;36:1–2. doi: 10.1177/1403494807087289. [DOI] [Google Scholar]
  • 39.Lynge Elsebeth, Sandegaard Jakob Lynge, Rebolj Matejka. The Danish National Patient Register. Scandinavian Journal of Public Health. 2011;39(7_suppl):30–33. doi: 10.1177/1403494811401482. [DOI] [PubMed] [Google Scholar]
  • 40.Mors Ole, Perto Gurli P., Mortensen Preben Bo. The Danish Psychiatric Central Research Register. Scandinavian Journal of Public Health. 2011;39(7_suppl):54–57. doi: 10.1177/1403494810395825. [DOI] [PubMed] [Google Scholar]
  • 41.Sahl Andersen John, De Fine Olivarius Niels, Krasnik Allan. The Danish National Health Service Register. Scandinavian Journal of Public Health. 2011;39(7_suppl):34–37. doi: 10.1177/1403494810394718. [DOI] [PubMed] [Google Scholar]
  • 42.Wallach Kildemoes Helle, Toft Sørensen Henrik, Hallas Jesper. The Danish National Prescription Registry. Scandinavian Journal of Public Health. 2011;39(7_suppl):38–41. doi: 10.1177/1403494810394717. [DOI] [PubMed] [Google Scholar]
  • 43.Pedersen CB, Gøtzsche H, Møller JO, Mortensen PB. The Danish Civil Registration System. A cohort of eight million persons. Dan Med Bull [Internet]. 2006;53:441–9. http://www.ncbi.nlm.nih.gov/pubmed/17150149. [PubMed]
  • 44.Hvidberg Michael F., Johnsen Søren P., Glümer Charlotte, Petersen Karin D., Olesen Anne V., Ehlers Lars. Catalog of 199 register-based definitions of chronic conditions. Scandinavian Journal of Public Health. 2016;44(5):462–479. doi: 10.1177/1403494816641553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Hvidberg Michael F., Johnsen Søren P., Glümer Charlotte, Petersen Karin D., Olesen Anne V., Ehlers Lars. Catalog of 199 register-based definitions of chronic conditions. Scandinavian Journal of Public Health. 2016;44(5):462–479. doi: 10.1177/1403494816641553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Hvidberg M. A framework for identifying disease burden and estimating health-related quality of life and prevalence rates for 199 medically defined conditions. Aalborg: Aalborg University Press; 2016. [Google Scholar]
  • 47.Paez Kathryn Anne, Zhao Lan, Hwang Wenke. Rising Out-Of-Pocket Spending For Chronic Conditions: A Ten-Year Trend. Health Affairs. 2009;28(1):15–25. doi: 10.1377/hlthaff.28.1.15. [DOI] [PubMed] [Google Scholar]
  • 48.Sullivan PW, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Making [Internet]. 2006;26:410–20. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2634296&tool=pmcentrez&rendertype=abstract. Cited 23 Apr 2012. [DOI] [PMC free article] [PubMed]
  • 49.Sullivan Patrick W., Slejko Julia F., Sculpher Mark J., Ghushchyan Vahram. Catalogue of EQ-5D Scores for the United Kingdom. Medical Decision Making. 2011;31(6):800–804. doi: 10.1177/0272989X11401031. [DOI] [PubMed] [Google Scholar]
  • 50.Naing NN. Easy way to learn standardization: direct and indirect methods. Malays J Med Sci [Internet]. 2000;7:10–5. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3406211&tool=pmcentrez&rendertype=abstract. [PMC free article] [PubMed]
  • 51.Jensen Vibeke M., Rasmussen Astrid W. Danish education registers. Scandinavian Journal of Public Health. 2011;39(7_suppl):91–94. doi: 10.1177/1403494810394715. [DOI] [PubMed] [Google Scholar]
  • 52.Ward BW, Schiller JS, Goodman RA. Multiple chronic conditions among US adults: a 2012 update. Prev Chronic Dis [Internet]. 2014;11:130389. http://www.cdc.gov/pcd/issues/2014/13_0389.htm. [DOI] [PMC free article] [PubMed]
  • 53.Centers for Disease Control (CDC). Chronic diseases: the leading causes of death and disability in the United States [Internet]. Chronic Dis. Overv. 2015. http://www.cdc.gov/chronicdisease/overview/index.htm.
  • 54.Thorpe KE, Howard DH, Galactionova K. Differences in disease prevalence as a source of the U.S.-European health care spending gap. Health Aff [Internet]. 2007;26. http://www.euro.who.int/_data/assets/pdf_file/0008/96632/E93736.pdf. [DOI] [PubMed]
  • 55.Pedersen Carsten Bøcker, Mors Ole, Bertelsen Aksel, Waltoft Berit Lindum, Agerbo Esben, McGrath John J., Mortensen Preben Bo, Eaton William W. A Comprehensive Nationwide Study of the Incidence Rate and Lifetime Risk for Treated Mental Disorders. JAMA Psychiatry. 2014;71(5):573. doi: 10.1001/jamapsychiatry.2014.16. [DOI] [PubMed] [Google Scholar]
  • 56.Christensen Jakob, Vestergaard Mogens, Pedersen Marianne G., Pedersen Carsten B., Olsen Jørn, Sidenius Per. Incidence and prevalence of epilepsy in Denmark. Epilepsy Research. 2007;76(1):60–65. doi: 10.1016/j.eplepsyres.2007.06.012. [DOI] [PubMed] [Google Scholar]
  • 57.Alonso J., Angermeyer M.C., Lepine J.P. The European Study of the Epidemiology of Mental Disorders (ESEMeD) project: an epidemiological basis for informing mental health policies in Europe. Acta Psychiatrica Scandinavica. 2004;109(s420):5–7. doi: 10.1111/j.1600-0047.2004.00325.x. [DOI] [PubMed] [Google Scholar]
  • 58.Falk Hvidberg M, Brinth LS, Olesen A V., Petersen KD, Ehlers L. The health-related quality of life for patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Furlan R, editor. PLoS One [Internet]. 2015;10. http://dx.plos.org/10.1371/journal.pone.0132421. [DOI] [PMC free article] [PubMed]
  • 59.Kessing LV. Validity of diagnoses and other clinical register data in patients with affective disorder. European Psychiatry. 1998;13(8):392–398. doi: 10.1016/S0924-9338(99)80685-3. [DOI] [PubMed] [Google Scholar]
  • 60.Pedersen Merete, Klarlund Mette, Jacobsen Søren, Svendsen Anders J., Frisch Morten. Validity of rheumatoid arthritis diagnoses in the Danish National Patient Registry. European Journal of Epidemiology. 2004;19(12):1097–1103. doi: 10.1007/s10654-004-1025-0. [DOI] [PubMed] [Google Scholar]
  • 61.Phung Thien Kieu Thi, Andersen Birgitte Bo, Høgh Peter, Kessing Lars Vedel, Mortensen Preben Bo, Waldemar Gunhild. Validity of Dementia Diagnoses in the Danish Hospital Registers. Dementia and Geriatric Cognitive Disorders. 2007;24(3):220–228. doi: 10.1159/000107084. [DOI] [PubMed] [Google Scholar]
  • 62.Bock C, Bukh J, Vinberg M, Gether U, Kessing L. Validity of the diagnosis of a single depressive episode in a case register. Clin Pract Epidemiol Ment Health [Internet]. 2009;5:4. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2660321&tool=pmcentrez&rendertype=abstract. [DOI] [PMC free article] [PubMed]
  • 63.Uggerby P, Østergaard SD, Røge R, Correll CU, Nielsen J. The validity of the schizophrenia diagnosis in the Danish Psychiatric Central Research Register is good. Dan Med J [Internet]. 2013;60:A4578. http://www.ncbi.nlm.nih.gov/pubmed/23461991. [PubMed]
  • 64.Thygesen SK, Christiansen CF, Christensen S, Lash TL, Sørensen HT. The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of Patients. BMC Med Res Methodol [Internet]. BioMed Central Ltd; 2011;11:83. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3125388&tool=pmcentrez&rendertype=abstract. Cited 27 Aug 2014. [DOI] [PMC free article] [PubMed]
  • 65.Deodhar Atul, Marcus Dawn A. Fibromyalgia. New York, NY: Springer New York; 2011. [Google Scholar]
  • 66.Branco Jaime C., Bannwarth Bernard, Failde Inmaculada, Abello Carbonell Jordi, Blotman Francis, Spaeth Michael, Saraiva Fernando, Nacci Francesca, Thomas Eric, Caubère Jean-Paul, Le Lay Katell, Taieb Charles, Matucci-Cerinic Marco. Prevalence of Fibromyalgia: A Survey in Five European Countries. Seminars in Arthritis and Rheumatism. 2010;39(6):448–453. doi: 10.1016/j.semarthrit.2008.12.003. [DOI] [PubMed] [Google Scholar]
  • 67.Robinson KM, Juel Lau C, Jeppesen M, Vind AB, Glümer C. Kroniske sygdomme—hvordan opgøres kroniske sygdomme [Chronic disease—how is chronic disease defined] [Internet]. Glostrup Hospital, Nordre Ringvej 57, Afsnit 84-85, 2600 Glostrup, Copenhagen Denmark e-mail: fcfs@regionh.dk, Denmark; 2011. https://www.regionh.dk/fcfs/publikationer/nyhedsbreve/setember-2014/PublishingImages/Sider/NyKroniske_sygdomme__metoderapport.pdf. Cited 5 May 2014.
  • 68.Carter Kristie, Barber P. Alan, Shaw Caroline. How Does Self-Reported History of Stroke Compare to Hospitalization Data in a Population-Based Survey in New Zealand? Stroke. 2010;41(11):2678–2680. doi: 10.1161/STROKEAHA.110.598268. [DOI] [PubMed] [Google Scholar]
  • 69.Bergmann M. M., Byers T., Freedman D. S., Mokdad A. Validity of Self-reported Diagnoses Leading to Hospitalization: A Comparison of Self-reports with Hospital Records in a Prospective Study of American Adults. American Journal of Epidemiology. 1998;147(10):969–977. doi: 10.1093/oxfordjournals.aje.a009387. [DOI] [PubMed] [Google Scholar]
  • 70.Barbosa Wallace Breno, Costa Juliana de Oliveira, de Lemos Lívia Lovato Pires, Gomes Rosângela Maria, de Oliveira Helian Nunes, Ruas Cristina Mariano, Acurcio Francisco de Assis, Barbui Corrado, Bennie Marion, Godman Brian, Guerra Augusto Afonso. Costs in the Treatment of Schizophrenia in Adults Receiving Atypical Antipsychotics: An 11-Year Cohort in Brazil. Applied Health Economics and Health Policy. 2018;16(5):697–709. doi: 10.1007/s40258-018-0408-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Maia Diniz I, Guerra AA, Jr, Pires Lovato, de Lemos L, Souza KM, Godman B, Bennie M, et al. The long-term costs for treating multiple sclerosis in a 16-year retrospective cohort study in Brazil. PLoS One. 2018;13:e0199446. doi: 10.1371/journal.pone.0199446. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Godói Isabella Piassi, Da Silva Leonardo Vinicius Dias, Sarker Abdur Razzaque, Megiddo Itamar, Morton Alec, Godman Brian, Alvarez-Madrazo Samantha, Bennie Marion, Guerra-Junior Augusto Afonso. Economic and epidemiological impact of dengue illness over 16 years from a public health system perspective in Brazil to inform future health policies including the adoption of a dengue vaccine. Expert Review of Vaccines. 2018;17(12):1123–1133. doi: 10.1080/14760584.2018.1546581. [DOI] [PubMed] [Google Scholar]
  • 73.Plaß D. The Burden of Disease framework—success story but necessities for adaptations: potentials and challenges when measuring the disease burden of infectious diseases and the disease burden at a sub-national level. Ph.D. dissertation [Internet]. Bielefeld: Universität Bielefeld; 2014. https://pub.uni-bielefeld.de/publication/2694785.
  • 74.Polinder S, Haagsma JA, Stein C, Havelaar AH. Systematic review of general burden of disease studies using disability-adjusted life years. Popul Health Metr [Internet]. 2012;10:21. http://www.pophealthmetrics.com/content/10/1/21. [DOI] [PMC free article] [PubMed]
  • 75.Nord Erik. Disability weights in the Global Burden of Disease 2010: Unclear meaning and overstatement of international agreement. Health Policy. 2013;111(1):99–104. doi: 10.1016/j.healthpol.2013.03.019. [DOI] [PubMed] [Google Scholar]
  • 76.Nord E. Uncertainties about disability weights for the Global Burden of Disease study. Lancet Glob Heal [Internet]. Nord. Open Access article distributed under the terms of CC BY-NC-ND; 2015;3:e661–2. http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(15)00189-8.pdf. [DOI] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 (PDF 2267 kb) (2.2MB, pdf)

Data Availability Statement

Due to the European General Data Protection Regulation (GDPR) and Danish data protection laws, it is not possible to provide the individually based, personal data of the current study directly. However, researchers can apply to Statistics Denmark for access to the data described in the “Methods” section. Thus, the same data of the current study can be provided via Statistics Denmark if the applicant fulfils legal and other requirements. See https://www.dst.dk/da/TilSalg/Forskningsservice. Software codes can be provided by the author when relevant, and data can be legally accessed, etc.

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