Abstract
Objective
This study aimed to estimate the incidence of giant cell arteritis (GCA) in Spain and to analyse its clinical manifestations, and distribution by age group, sex, geographical area and season.
Methods
We included all patients diagnosed with GCA between 1 June 2013 and 29 March 2019 at 26 hospitals of the National Health System. They had to be aged ≥50 years and have at least one positive results in an objective diagnostic test (biopsy or imaging techniques), meet 3/5 of the 1990 American College of Rheumatology classification criteria or have a clinical diagnosis based on the expert opinion of the physician in charge. We calculated incidence rate using Poisson regression and assessed the influence of age, sex, geographical area and season.
Results
We identified 1675 cases of GCA with a mean age at diagnosis of 76.9±8.3 years. The annual incidence was estimated at 7.42 (95% CI 6.57 to 8.27) cases of GCA per 100 000 people ≥50 years with a peak for patients aged 80–84 years (23.06 (95% CI 20.89 to 25.4)). The incidence was greater in women (10.06 (95% CI 8.7 to 11.5)) than in men (4.83 (95% CI 3.8 to 5.9)). No significant differences were found between geographical distribution and incidence throughout the year (p=0.125). The phenotypes at diagnosis were cranial in 1091 patients, extracranial in 337 patients and mixed in 170 patients.
Conclusions
This is the first study to estimate the incidence of GCA in Spain at a national level. We found a predominance among women and during the ninth decade of life with no clear variability according to geographical area or seasons of the year.
Keywords: epidemiology, incidence, giant cell arteritis, vasculitis
WHAT IS ALREADY KNOWN ON THIS TOPIC.
A meta-analysis carried out in 2021 estimated the incidence of giant cell arteritis (GCA) in Europe to be 7.26 (6.05–8.47), but most epidemiological studies collected data from specific regions of a country and very few studies included national registries.
In Spain, there are three epidemiological studies focusing on three specific health areas (Galicia, Catalonia and Andalusia) but no one national incidence study has been done to date.
WHAT THIS STUDY ADDS
ARTEritis by Sociedad Española de Reumatología is the first study to estimate the incidence of GCA in Spain at a national level.
Interestingly, no clearly identifiable geographical or seasonal variability in disease incidence was found.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
This study shows the incidence of GCA in Spain, estimated at 7.42 (95% CI 6.57 to 8.27) cases per 100 000 people over 50 years of age, which may have a high impact on public health systems attending old people.
Better knowledge of the epidemiological data of our population, such as the peak incidence, may help us to develop new integrated screening algorithms in the future although more studies are needed to do so.
Introduction
Giant cell arteritis (GCA) is a systemic vasculitis that affects medium-size and large-size arteries. It is the most common primary vasculitis in North America and Europe, especially in developed countries of North latitude, mainly affecting women with a mean age of 70–80 years at diagnosis.1 2 The clinical spectrum of GCA is more heterogeneous than previously thought. The disease can present with cranial involvement, characterised by involvement of branches of the carotid arteries, mainly presenting with headache and jaw claudication, leading to blindness in up to 15%–20% of patients; or extracranial involvement, characterised by involvement of the aorta and/or its main branches, predominantly with polymyalgia rheumatica and claudication of the limbs, although both phenotypes can occasionally overlap.3 4
Temporal artery biopsy, the classic diagnostic technique of choice, has been replaced in recent years by imaging techniques such as ultrasound. Furthermore, ultrasound and 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) scan or, alternatively, MRI or CT, have led to an improvement in the detection of extracranial vascular abnormalities in GCA.5–8
Some of these aspects have been incorporated into the new GCA classification criteria published in 2022 by the American College of Rheumatology (ACR)/European Alliance of Associations for Rheumatology, which could improve the diagnostic accuracy applied in clinical practice compared with those of 1990.9–12 Most of the previous epidemiological studies do not include these new imaging diagnostic techniques, therefore, GCA may have been underdiagnosed in the past.
The incidence and clinical manifestations of GCA vary considerably across different geographical regions and ethnic groups worldwide.2 13 In Europe, most epidemiological studies are performed in Northern and central Europe. However, little is known about its epidemiology in the South, including Spain. Furthermore, reported incidence rates vary widely between regions, and no study has collected sufficient data to estimate the national incidence of this disease.14–16 This observation is in line with most epidemiology studies performed in other parts of Europe, where authors also collect data from specific regions of a country,17–20 with very few establishing a national registry of GCA.21 22 Additionally, knowledge of the incidence of the disease is essential to determine its full impact on society and healthcare systems.
Taking all these considerations into account, we present, to the best of our knowledge, one of the largest geographical distributed national epidemiological study of GCA. The primary aims of this study were to estimate the incidence of GCA in Spain and to analyse its distribution by age group, sex, geographical area and season. The secondary objectives were to describe the clinical characteristics of GCA including new imaging diagnostic techniques and associated comorbidities at diagnosis.
Patients and methods
The ARTESER (ARTEritis by Sociedad Española de Reumatología) study, which was sponsored by the Spanish Society of Rheumatology, is a longitudinal observational study based on a review of the clinical registers of patients diagnosed with GCA between 1 June 2013 and 29 March 2019 (date of approval by the Medical Research Ethics Committee of Cantabria) at 26 hospitals of the Spanish National Health System. The hospitals were distributed across 11 autonomous communities providing various levels of healthcare (online supplemental file 1). The centres were chosen based on their ability to recruit patients and carry out research, as well as on their geographical location. It should be noted that the largest number of centres are concentrated in the communities with the highest percentage of population.
rmdopen-2023-003824supp001.pdf (129KB, pdf)
Population and sample
Since our goal was to analyse the epidemiology of GCA and to determine the incidence data, we included all patients diagnosed with GCA between 1 June 2013 and 29 March 2019 who fulfilled the eligibility criteria, namely, age ≥50 years and at least one of the following criteria:
-
Positive results in an objective diagnostic test such as temporal artery biopsy and/or imaging techniques (accompanied by clinical findings that justify a sufficient pretest probability in the opinion of the clinician), including:
Temporal artery ultrasound.
18F-FDG PET/CT scan.
CT angiography/MRI angiography.
Large-vessel ultrasound.
Subclavian ultrasound.
Axillary ultrasound.
Clinical opinion of the investigator (expert criteria).
Meeting three of the five criteria of the 1990 ACR classification.10
In order to locate all cases with a diagnosis of GCA during the study period, the research team at each centre reviewed the local administrative and/or clinical databases, as well as the databases of the departments involved in the diagnosis and/or treatment of patients with GCA.
To estimate the incidence of GCA, individuals at risk were selected from the reference population of the health areas corresponding to each of the hospitals that participated in the study, from which subjects of the susceptible age range (≥50 years) were selected, taking as a reference the mean of the distributions by sex and age of the corresponding province for the study period. These data were obtained from the Spanish National Institute of Statistics.
Clinical variables
The patient data analysed at diagnosis were as follows:
Sociodemographic variables
Date of birth, sex, race and ethnicity.
Comorbid conditions
The main comorbidities included were as follows: arterial hypertension, diabetes mellitus (types 1 and 2), dyslipidaemia (hypercholesterolaemia and/or hypertriglyceridaemia), osteoporosis, smoking (current, former, >1 year without smoking, never smoked), obesity (body mass index >30kg/m2), alcohol consumption, cardiovascular disease, aspirin consumption, chronic kidney failure and history of cancer.
Clinical data at diagnosis
Date of onset of symptoms, date of diagnosis of GCA, presence of headache, temporal artery tenderness or decreased pulsation, hypersensitive scalp, facial pain, jaw claudication, ‘amaurosis fugax’, permanent blindness, diplopia, confirmed optic neuritis, vertigo, hearing loss, transitory ischaemic accident, stroke, claudication of the upper limbs, claudication of the lower limbs, polymyalgia rheumatica, peripheral synovitis, asthenia, anorexia, weight loss, fever/low-grade fever.
Analytical data
Erythrocyte sedimentation rate (ESR (increased/not increased) using the age-adjusted and sex-adjusted formula (increased if greater than age at diagnosis/2 for men and greater than age at diagnosis +10 years/2 for women)),23 C reactive protein, haemoglobin, platelets and liver enzymes (alkaline phosphatase, alanine aminotransferase and aspartate aminotransferase).
Statistical analysis
Incidence was expressed as the number of new cases per 100 000 person-years with its 95% CI, assuming a Poisson distribution. Quantitative variables are expressed as mean±SD in the case of an approximately normal distribution and as the median (IQR 25th–75th percentile) in the case of a non-normal distribution. Categorical variables are expressed as absolute frequency and percentage. The statistical analysis was performed by using STATA statistical package, V.13.1 (StataCorp) and SPSS version 21.0 (IBM Released 2012. IBM SPSS Statistics for Windows, V.21.0, Armonk, New York).
Results
Incidence of GCA distributed by age and sex
During the study period (1 June 2013–29 March 2019), a total of 1675 patients were diagnosed with GCA (1178 women (70.3%) and 497 men (29.7%)). The mean age at diagnosis was 76.9±8.3 years, with no significant differences between sexes (p=0.979). Most of the patients were white (99.2%).
The annual incidence was estimated at 7.42 (95% CI 6.57 to 8.27) cases of GCA per 100 000 people ≥50 years. This was greater in women (10.06 (95% CI 8.7 to 11.5)) than in men (4.83 (95% CI 3.8 to 5.9)). Disagreement between centres that were geographically close led us to perform a sensitivity analysis. Five centres with incidence values below 5.5 and significantly lower than those of the neighbouring centres were excluded from the study. Therefore, the final incidence was 7.89 (95% CI 7.48 to 8.31) cases per 100 000 persons-year. Table 1 shows the yearly incidence of GCA in Spain between 2013 and 2019.
Table 1.
Annual incidence of GCA in the ARTESER registry
| Annual incidence adjusted by sex (×100 000 people ≥50 years) | ||||||
| Year of diagnosis | Men | Women | Total | |||
| N | Incidence (95% CI) | N | Incidence (95% CI) | N | Incidence (95% CI) | |
| 2013* | 58 | 5.56 (4.22–7.19) | 105 | 8.43 (6.89 to 10.20) | 163 | 7.12 (6.07 to 8.30)* |
| 2014 | 54 | 3.09 (2.88–3.29) | 189 | 9.31 (8.99 to 9.63) | 243 | 6.20 (6.01 to 6.39) |
| 2015 | 77 | 4.29 (4.08–4.51) | 162 | 7.97 (7.67 to 8.27) | 239 | 6.04 (5.86 to 6.23) |
| 2016 | 103 | 5.78 (5.50–6.05) | 218 | 10.71 (10.33 to 11.08) | 321 | 8.14 (7.91 to 8.37) |
| 2017 | 82 | 4.52 (4.30–4.74) | 221 | 10.81 (10.44 to 11.18) | 303 | 7.63 (7.41 to 7.85) |
| 2018 | 96 | 5.30 (5.03–5.56) | 208 | 10.07 (9.74 to 10.39) | 304 | 7.59 (7.38 to 7.80) |
| 2019* | 27 | 6.04 (3.98–8.78) | 75 | 14.04 (11.04 to 17.60) | 102 | 10.99 (8.96 to 13.34)* |
The annual calculation was estimated from the number of months included in each of those years, so in 2019 the annual incidence was calculated from that obtained in the first 3 months of the year (winter months and early spring), so the annual incidence for that particular year could be slightly overestimated, and with a greater Cl.
*In 2013, only cases were collected since 1 June to 31 December, and in 2019 only cases since 1 January to 29 March were included.
ARTESER, ARTEritis by Sociedad Española de Reumatología; GCA, giant cell arteritis.
The highest incidence by age group was recorded in patients aged 80–84 years (23.06 (95% CI 20.89 to 25.4)). Table 2 shows the distribution of the incidence rate by age group and sex.
Table 2.
Incidence of GCA by age group and sex (results per 100 000 persons-year (95%CI))
| Age group (years) | Men | Women | Total |
| 50–54 | 0.279 (0.10–0.61) | 0.325 (0.13–0.67) | 0.302 (0.16–0.52) |
| 55–59 | 0.594 (0.30–1.06) | 1.42 (0.94–2.05) | 1.02 (0.73–1.39) |
| 60–64 | 1.6 (1.03–2.36) | 2.79 (2.07–3.69) | 2.23 (1.75–2.8) |
| 65–69 | 3.58 (2.65–4.73) | 7.45 (6.20–8.89) | 5.71 (4.89–6.63) |
| 70–74 | 6.68 (5.24–8.4) | 13.95 (12.09–16) | 10.87 (9.63–12.23) |
| 75–79 | 15.52 (12.88–18.54) | 22.33 (19.80–25.08) | 19.96 (18.06–22.01) |
| 80–84 | 21.06 (17.47–25.16) | 22.91 (20.35–25.7) | 23.06 (20.89–25.4) |
| 85–89 | 26.23 (20.59–32.93) | 15.34 (12.96–18.04) | 19.24 (16.78–21.96) |
| ≥90 | 17.91 (10.43–28.67) | 7.6 (5.62–10.04) | 10.58 (8.18–13.46) |
GCA, giant cell arteritis.
Incidence of GCA distributed by season of the year and geographical area
The seasonality-based assessment of the distribution revealed that winter and spring are the seasons with the highest incidence for symptoms initiation. The results are summarised in online supplemental file 1.
We analysed the association between geographical distribution and annual incidence taking the areas of Madrid (centre) and Barcelona (Mediterranean area) as the most widely represented in the study (six hospitals each city). The age-adjusted incidence (adjusted directly for the standard European population in 2013) was 8.08 (6.20–10.07) and 7.22 (4.03–8.57) cases per 100 000 persons ≥50 years per year, respectively. No significant differences were found between the different regions studied throughout the country (p=0.125).
Demographic characteristics and fulfilment of selection criteria
The mean age at onset of symptoms was 76.7±8.2 years, with a mean time to diagnosis of 2.9±5.7 months. Table 3 shows demographic data and fulfilment of the eligibility criteria set out in the Methods section. Most patients fulfilled the 1990 ACR classification criteria (83.6%), and 75.1% had at least one positive diagnostic test result, with temporal artery biopsy being the most common (46.3%), followed by ultrasound (28.8%). As for the remaining diagnostic imaging tests, 377 (22.5%) patients underwent 18F-FDG PET/CT scan. In 245 (65%) of these patients, the images were compatible with GCA at diagnosis. The frequency of this technique increased from 14.8% of patients in 2014 (positive in 58.3%) to 30.3% in 2018 (positive in 66.3%). Only 3.7% of patients diagnosed with GCA in our study did not fulfil the above-mentioned criteria and were included only based on the clinical opinion of the investigator. online supplemental file 1 shows the results of the tests performed to the patients.
Table 3.
Demographic characteristics and fulfilment of selection criteria
| Men | Women | Total | |
| Sex, n (%) | 497 (29.7) | 1178 (70.3) | 1675 |
| Age at diagnosis, years, mean (SD) | 76.9 (8.3) | 76.9 (8.0) | 76.9 (8.1) |
| Age at onset of symptoms, years, mean (SD) | 76.7 (8.3) | 76.7 (8.1) | 76.7 (8.2) |
| Fulfilling 1990 ACR classification criteria, n (%) | 400 (80.5) | 1000 (84.9) | 1400 (83.6) |
| Positive objective diagnostic test, n (%) | 384 (77.3) | 874 (74.2) | 1258 (75.1) |
|
240 (48.3) | 536 (45.5) | 776 (46.3) |
|
169 (34.0) | 313 (26.6) | 482 (28.8) |
|
138 (27.8) | 290 (24.6) | 428 (25.6) |
| Positron emission tomography, n (%) | 69 (13.9) | 176 (14.9) | 245 (14.6) |
| CT angiography/angio-MRI, n (%) | 18 (3.6) | 46 (3.9) | 64 (3.8) |
| 1990 ACR classification criteria+positive objective diagnostic test result, n (%) | 311 (62.6) | 734 (62.3) | 1045 (62.4) |
| Investigator’s clinical opinion, n (%) | 24 (4.8) | 38 (3.2) | 62 (3.7) |
ACR, American College of Rheumatology.
Clinical manifestations and GCA phenotypes at diagnosis
The phenotypes at diagnosis, according to vessel size, were consistent with the cranial phenotype in 1091 patients and with the extracranial phenotype in 337 patients. Both cranial and extracranial phenotypes were recorded in 170 patients. There was a discrete increase in the frequency of extracranial involvement over time as shown in figure 1.
Figure 1.
Distribution of GCA phenotypes over the years. GCA, giant cell arteritis.
The most frequent clinical manifestations were headache (n=1.337; 79.9%), temporal artery tenderness or decreased pulsation (n=824; 49.2%) and polymyalgia rheumatica (n=699; 41.8%). Observing their distribution according to sex, we found that headache, polymyalgia rheumatica and asthenia were more frequent in women with statistically significant differences between sexes (p<0.05). Dysphagia was significantly higher in men (5.0% vs 2.6%; p=0.013).
Regarding comorbidities, arterial hypertension stands out as the most common comorbidity in this population. Data on clinical manifestations and comorbid conditions are shown in tables 4 and 5.
Table 4.
Clinical manifestations and laboratory abnormalities at diagnosis in patients with giant cell arteritis
| Clinical manifestations | Total | Men | Women | P (men vs women) |
| Cranial | ||||
| Recent-onset headache, n (%) | 1337 (79.9) | 382 (76.9) | 955 (81.1) | 0.028* |
| Temporal artery tenderness or decreased pulsation, n (%) | 824 (49.2) | 231 (46.5) | 593 (50.4) | 0.079 |
| Visual symptoms, n (%) | 605 (36.1) | 194 (39.0) | 411 (34.9) | 0.101 |
| Jaw claudication, n (%) | 597 (35.7) | 172 (34.6) | 425 (36.1) | 0.621 |
| Hypersensitive scalp, n (%) | 451 (26.9) | 127 (25.6) | 324 (27.5) | 0.290 |
| Facial pain, n (%) | 213 (12.7) | 55 (11.1) | 158 (13.4) | 0.169 |
| Vertigo, n (%) | 127 (7.6) | 38 (7.6) | 89 (7.6) | 0.994 |
| Ischaemic and/or haemorrhagic stroke, n (%) | 63 (3.8) | 25 (5.0) | 38 (3.2) | 0.08 |
| Dysphagia, n (%) | 56 (3.3) | 25 (5.0) | 31 (2.6) | 0.013* |
| Hearing loss, n (%) | 45 (2.7) | 16 (3.2) | 29 (2.5) | 0.404 |
| Transitory ischaemic accident, n (%) | 32 (1.9) | 13 (2.6) | 19 (1.6) | 0.176 |
| Extracranial | ||||
| Polymyalgia rheumatica, n (%) | 699 (41.8) | 178 (35.8) | 521 (44.3) | 0.003* |
| Claudication—lower limbs, n (%) | 157 (9.4) | 53 (10.7) | 104 (8.8) | 0.269 |
| Claudication—upper limbs, n (%) | 152 (9.1) | 38 (7.6) | 114 (9.7) | 0.173 |
| Peripheral synovitis, n (%) | 86 (5.2) | 27 (5.5) | 59 (5.1) | 0.641 |
| General | ||||
| Asthenia, n (%) | 873 (52.2) | 239 (48.1) | 634 (53.9) | 0.035* |
| Anorexia, n (%) | 608 (36.3) | 180 (36.2) | 428 (36.4) | 0.824 |
| Weight loss, n (%) | 541 (32.3) | 174 (35.0) | 367 (31.2) | 0.106 |
| Fever/low-grade fever, n (%) | 367 (21.9) | 113 (22.7) | 254 (21.6) | 0.728 |
| Laboratory findings at diagnosis | ||||
| High ESR, n (%) | 1409 (84.12) | 404 (81.3) | 1005 (85.3) | 0.039* |
| ESR, mm/hour, mean (SD) | 75.9 (33.6) | 72.3 (34.7) | 77.4 (33.0) | 0.005* |
| C reactive protein, mg/L, median (IQR) | 62.0 (22.0–116.1) | 64.3 (25.7–114.5) | 61.5 (20.9–116.9) | 0.151 |
| Haemoglobin, g/dL, mean (SD) | 11.9 (1.6) | 12.3 (1.8) | 11.6 (1.5) | 0.892 |
| Platelets, ×109/L, mean (SD) | 326.6 (180.0) | 302.3 (144.3) | 337.0 (192.5) | <0.001* |
| Alkaline phosphatase, IU/L, mean (SD) | 111.5 (95.9) | 109.8 (92.6) | 112.3 (97.3) | 0.693 |
| Alanine aminotransferase, IU/L, mean (SD) | 22.4 (21.0) | 24.6 (18.9) | 21.4 (21.8) | 0.013* |
| Aspartate aminotransferase, IU/L, mean (SD) | 21.9 (17.0) | 23.0 (15.7) | 21.5 (17.5) | 0.160 |
Differences according to sex.
*Significant differences in bold=p<0.05.
ESR, erythrocyte sedimentation rate.
Table 5.
Comorbidities at diagnosis
| Comorbidity | Men | Women | Total | P value |
| Arterial hypertension, n (%) | 330 (66.8) | 749 (63.7) | 1079 (64.6) | 0.187 |
| Dyslipidaemia, n (%) | 238 (48.3) | 563 (47.9) | 801 (48.0) | 0.772 |
| Smoking status | <0.001* | |||
| Former smokers, n (%) | 63 (13.4) | 69 (6.3) | 132 (8.4) | |
| Current, n (%) | 216 (46.0) | 84 (7.7) | 300 (19.1) | |
| Never, n (%) | 191 (40.6) | 944 (86.1) | 1135 (72.4) | |
| Cardiovascular disease, n (%) | 163 (33.1) | 204 (17.4) | 367 (22.0) | <0.001* |
| Diabetes mellitus, n (%) | 134 (27.2) | 217 (18.6) | 351 (21.1) | <0.001* |
| Osteoporosis, n (%) | 22 (4.5) | 260 (22.3) | 282 (17.0) | <0.001* |
| History of cancer, n (%) | 95 (19.3) | 109 (9.3) | 204 (12.3) | <0.001* |
| Chronic kidney failure, n (%) | 55 (11.2) | 112 (9.6) | 167 (10.0) | 0.325 |
| Obesity, n (%) | 34 (7.0) | 115 (9.8) | 149 (9.0) | 0.162 |
| Alcohol consumption, n (%) | 93 (18.9) | 26 (2.2) | 119 (7.1) | <0.001* |
*Significant differences in bold=p<0.05.
Discussion
This is the first study to report the epidemiological characteristics of patients diagnosed with GCA throughout Spain during the period 1 June 2013 to 29 March 2019, because until now, the ones we had were focused exclusively on very specific areas of our country. We have observed an incidence rate of 7.42 cases per 100 000 persons-year. This finding was consistent with data recorded in Europe in a 2021 meta-analysis, which estimated the incidence of GCA to be 7.26 (6.05–8.47) cases per 100 000 persons aged >50 years. The limitations of this meta-analysis for estimating incidence included the lack of standardised criteria for the definition of GCA and variability in inclusion criteria from the 1990 ACR classification to biopsy-proven cases. Furthermore, studies not published in English were excluded.24
The incidence we report is similar to that reported in Mediterranean countries such as Italy and France, where the incidence of GCA has been reported to be 8.3 (95% CI 7.1 to 9.4) and 9.6 (95% CI 9.5 to 9.8) cases, respectively.17 20 However, these findings differ considerably from those reported in the North of Europe. In Norway, for example, the annual incidence reported was 16.8 (14.6–19.2) cases per 100 000 persons aged over 50 years, and in the South of Sweden, the incidence reported was 13.3 (12.6–14.0) cases.17 18 This greater incidence in Scandinavian countries is probably due to a genetic susceptibility that favours the onset of the disease.13 In Norway, the peak incidence by age group has been recorded in people aged 70–79 years (36.5 (95% CI 29.5 to 45.1) cases), with a mean age at diagnosis of 73.2±8.6 years, whereas in Spain, GCA is diagnosed at a mean of 3.7 years later; thus, the peak incidence was recorded in persons aged 80–84 years.18 In terms of gender distribution, the highest incidence was found in men aged 85–89 years, and in women aged 80–84 years. Figure 2 shows the updated worldwide incidence of GCA in different countries. The data for the elaboration of the figure are given in online supplemental file 1.
Figure 2.
Updated global incidence of giant cell arteritis.
A previous retrospective study conducted in Spain recording data from Northwestern Spain, at Lugo Regional Hospital, analysed the incidence between 1981 and 2005. The authors applied strict diagnostic criteria, requiring a positive temporal artery biopsy for the diagnosis of GCA. They included patients with typical symptoms of GCA and those with symptoms exclusively of polymyalgia rheumatic who were older than 50 years and had constitutional syndrome and/or elevated ESR (>80 mm/hour) at the time of diagnosis. Comparing the incidence recorded in our series with that of the Lugo’s study, namely, 10.13 (95% CI 8.93 to 11.46) annual cases per 100 000 persons aged over 50 years, it can be observed that our values are lower.14 This reduction in annual incidence can also be observed in other areas, such as Scandinavia, where incidence has fallen from 42.3 cases per 100 000 persons over 50 years in 1981 to 13.4 cases in 2017, suggesting that the overall reduction in annual incidence in the abovementioned meta-analysis is 0.41 per 100 000 persons aged over 50 years.24
As for geographical distribution, rates vary widely in Spain between regions such as Lugo, in the North of Spain, with an annual incidence of 10.13, and others in the South and Mediterranean area such as Malaga and Sabadell (Catalonia), where the incidence is 2.2 and 4.1, respectively. However, this variation may be due to differences in the methodology applied.14–16
Analysis of our data reveals no statistically significant differences between the incidence of the disease in a central region such as Madrid and a Northeastern Mediterranean region such as Barcelona, although it is true that there is little difference in latitude between these both regions. However, in the above-mentioned meta-analysis, a statistically significant positive linear correlation was observed between latitude and incidence.24 If the incidence recorded in Spain is compared with that recorded in countries at a similar latitude, such as Italy, it can be seen that the incidence rates are comparable (7.4 and 8.3 cases per 100 000 persons older than 50 years, respectively).17 However, further studies are necessary if more precise information is to be obtained; hence, the conception of the ARTESER registry in Spain.
In terms of seasonal distribution, the incidence of symptoms was found to be higher in winter, although the disease was diagnosed more frequently during spring due to a mean delay of diagnosis around 2.9 months from the onset of symptoms. However, no major differences between seasons were found to establish a stable relationship. Published data vary widely, with some authors reporting a higher incidence during spring and summer,25 and others finding no differences between seasons.14 26 27 Therefore, it is not possible to identify a strong association between the both variables.
The distribution by phenotype in our registry is somewhat similar to that reported in a study published in 2022 on the epidemiology and clinical characteristics of GCA in Canterbury, New Zealand, where 93% of the patients had a cranial phenotype and 7% an extracranial phenotype. Moreover, the most common clinical manifestations at diagnosis in our study (eg, headache (79.9%), temporal artery tenderness (49.2%) and symptoms of polymyalgia (41.8%)) were similar to those reported elsewhere.3 28
The increased use of new diagnostic methods such as18F-FDG PET/CT scan in hospitals during the study period was reflected in a higher incidence of extracranial phenotype throughout the study, with extracranial abnormalities being diagnosed in up to 66.3% of patients who underwent this examination. This value is comparable to that reported in another study performed in Spain, where, using computed axial tomography, the authors found abnormalities in 67.5% of the patients selected.29 This was somewhat lower than the 83% reported in a Belgian study, in which the authors applied very lax criteria for defining the result as positive.30
Comparing comorbidities at diagnosis in our series with the findings of the National Health Survey for 2017 in the general population aged >55 years in Spain,31 we find higher values in our study for arterial hypertension (66.8% vs 43.3% in men and 63.7% vs 42% in women), osteoporosis (4.5% vs 1.9% in men and 22.3% vs 15.2% in women), diabetes mellitus (27.2% vs 20.6% in men and 18.6% vs 15.9% in women) and active smoking (46% vs 19.6% in men and 7.7% vs 2.4% in women), but lower values for obesity (7% vs 22.4% in men and 9.8% vs 21.3% in women) and ex-smokers (13.4% vs 52.8% in men and 6.3% vs 17.4% in women).
Spain has a universal healthcare system in which public healthcare is free and covers almost 100% of the population. In addition, almost all patients with autoimmune diseases are referred to hospitals for specialist monitoring of their disease. Therefore, when choosing as denominator the reference population over 50 years of age in the health area and as nominator the population diagnosed with GCA in the same area (both outpatients and inpatients settings), we consider that there is scarce possibility of bias in estimating the national incidence of GCA.
However, we consider that our study has some limitations. First, its retrospective design could have led to information bias. We attempted to resolve this issue by designing a questionnaire for data collection based on variables that were specific and common in this disease to ensure that as little information as possible was lost. External monitoring was also performed to ensure adequate control of the quality of the data collected. Second, given the discordance in incidence rates between the five centres previously mentioned, we performed a sensitivity analysis to estimate the incidence without including these centres. No major changes were observed (7.9 in the sensitivity analysis vs 7.4 when all the centres were taken into consideration). Another possible limitation can be found in the categorisation of phenotypes. The implementation of imaging techniques that facilitate extracranial variant diagnosis in recent years may have underestimated the incidence of this phenotype in the years when these techniques were not so extended. Finally, since not all hospitals had a rapid fast-track for diagnosis of GCA, it could lead to underestimate the true incidence of the disease in our country because of some of the patients had been previously treated by emergency or primary care services, without record in rheumatology units.32
In conclusion, ARTESER is the first study to estimate the incidence of GCA in Spain, which stands at 7.42 cases per 100 000 persons older than 50 years. This finding is as expected in this part of Europe, being the disease more frequent in women and during the ninth decade of life. Although there is no clearly identifiable variability according to geographical area or season of the year, we found the disease to be more common during the winter months.
Acknowledgments
We would like to thank Daniel Seoane and Fernando Alonso for their support in the preparation of this article. We would also like to thank the Spanish Society of Rheumatology for promoting and covering this study.
Footnotes
Collaborators: ARTESER Project Collaborative Group: Elvira Díez Álvarez, Trinidad Pérez Sandoval, Ismael González Fernández (Complejo Asistencial Univ. de León, León); Javier Mendizábal-Mateos, María Concepción Fito Manteca, Natividad del Val del Amo, Loreto Horcada Rubio, Inmaculada Paniagua Zudaire, Laura Garrido Courel, Ricardo Gutiérrez Polo, Juliana Restrepo Vélez, Eduardo Loza Cortina (Complejo Hospitalario de Navarra, Pamplona); Elisa Fernández Fernández, Patricia Carreira, Tomás Almorza (Hospital 12 de Octubre); Leticia Léon Mateos, Luis Rodríguez Rodríguez, Judit Font Urgelles, Pia Mercedes Lois Bermejo (Hospital Clínico San Carlos); Selene Labrada Arrabal (Hospital de Mar); Anne Riveros Frutos, Susana Holgado Pérez, Jordi Camins, (Hospital Germans Trías i Pujol); Clara Molina Almela, Cristina Campos Fernández, Amalia Rueda Cid, Javier Calvo Catalá (Hospital Gral. De Valencia); Rafael Benito Melero, Francisco Maceiras, Nair Pérez, Ceferino Barbazán, José María Pego, Irena Altabás, John Guzman (Comp. Hosp. Univ. de Vigo); Paula Valentina Estrada Alarcón (Hospital Moises Broggi); Héctor Corominas, Iván Castellví, Berta Magallares, Ana Milena Millán (Hospital Santa Creu i Sant Pau); María Alcalde Villar, Ana F. Cruz Valenciano, Félix Cabero del Pozo, Ana Belén Rodríguez Cambrón, Cristina Macia Villa, Eva Álvarez de Andrés (Hospital Severo Ochoa); Antonio Juan Mas, Inmaculada Ros Vilamajó, Mónica Ibáñez Barceló, Elide Toniolo, Ana Paula Cacheda, (Hospital Son Llatzer); María Sagrario Bustabad Reyes, María García González, Alicia García Dorta, Vanesa Hernández Hernández (Hospital Univ. Canarias); Margarida Vasques Rocha, Jaime Calvo Allen (Hospital Univ. De Araba); Elisa Fernández Fernández (Hospital Univ. La Paz); Miren Uriarte-Ecenarro, Cristina Valero Martínez, Esther F. Vicente Rabaneda (Hospital Univ. La Princesa); Carlos García Porrúa, Carlota Laura Iñiguez Ubiaga, Noelia Álvarez Rivas, Tomás Ramón Vázquez Rodríguez, José Alberto Miranda Filloy, Amalia Sánchez-Andrade Fernández (Hospital Univ. Lucus Augusti); Miguel Ángel González-Gay (Hospital Univ. Marqués de Valdecilla, Departamento de Medicina, Universidad de Cantabria); Carlos Galisteo Lencastre Da Veiga (Hospital Univ. Parc Tauli); María Jesús García Villanueva, Patricia Morán Álvarez, Marina Tortosa Cabañas, Marta Serrano Warleta, Aliuska Palomeque Vargas (Hospital Univ. Ramón y Cajal); Alberto Ruiz Román, Clara Aguilera Cros, Alejandro Muñoz Jimenez (Hospital Univ. Virgen del Rocio); José A. Román Ivorra, Carmen Riesco Bárcena, Anderson Huaylla (Hospital Univ. y Politécnico La Fe); Itziar Calvo Zorrilla (Hospital Universitario Basurto); Judit Lluch (Hospital Universitario de Bellvitge); Jesús A. Valero-Jaimes, Luis López Domínguez, Cesar Antonio Egues Dubuc (Hospital Universitario Donostia); Lucia Silva Fernández (Comp. Hospitalario Universitario de A Coruña).
Contributors: DF-L and RB participated in the design of the work, the acquisition and interpretation of data and were major contributors in writing the manuscript, acting as guarantors. JN and SC participated in the design of the work, the analysis and interpretation of data and contributed significantly to the drafting of the manuscript. All authors revised the manuscript critically, and read and approved the final version.
Funding: ROCHE FARMA, S.A. contributes to the financial support of this study.
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Competing interests: Disclosures that might be interpreted as constituting possible conflict(s) of interest for the study: Ed Research funding/consulting and conferences fees from: Abbvie, Novartis, Roche, Pfizer, Janssen, Lilly, MSD, BMS, UC Pharma, Grünenthal and Sanofi. JL had consultation fees/participation in company-sponsored speaker’s bureau from Roche, Galápagos, Novartis, UCB Pharma, MSD, Celgene, Astra Zeneca and Grünenthal and received support for attending meetings and/or travel from Janssen, Abbvie, Roche, Novartis, MSD, UCB Pharma, Celgene, Lilly, Pfizer, Galápagos. Patricia Moya Alvarado had consultation fees/participation in company-sponsored speaker’s bureau from Roche, Novartis, Abbvie, MSD, Lilly, Pfizer and Celgene and received support for attending meetings and/or travel from Novartis, Lilly and, Pfizer. SC has received research support from MSD and Pfizer and had consultation fees/participation in company-sponsored speaker’s bureau from Amgen, BMS, Eli-Lilly, MSD, Roche, Gedeon-Richter, Grünenthal Pharma and UCB. SC is also assistant professor of the cátedra EPID-Future, funded by UAM-Roche, Universidad Autónoma de Madrid (UAM), Spain. RB received grants/research support from AbbVie, MSD and Roche, and had consultation fees/participation in a company-sponsored speaker’s bureau from AbbVie, Pfizer, Roche, Lilly, UCB, Bristol-Myers, Janssen, and MSD. The following authors did not declare financial disclosure: DF-L, IH-R, JN, MD-Á, MS-D, JMB, CMM, JS, EG-A, VA, LA, NG-P, CL, VAN, JC, IC-S, FO-S and TCSM.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Contributor Information
Collaborators: on behalf of the ARTESER Project Collaborative Group, Elvira Díez Álvarez, Trinidad Pérez Sandoval, Ismael González Fernández, Javier Mendizábal-Mateos, María Concepción Fito Manteca, Natividad del Val del Amo, Loreto Horcada Rubio, Inmaculada Paniagua Zudaire, Laura Garrido Courel, Ricardo Gutiérrez Polo, Juliana Restrepo Vélez, Eduardo Loza Cortina, Elisa Fernández Fernández, Patricia Carreira, Tomás Almorza, Leticia Léon Mateos, Luis Rodríguez Rodríguez, Judit Font Urgelles, Pia Mercedes Lois Bermejo, Selene Labrada Arrabal, Anne Riveros Frutos, Susana Holgado Pérez, Jordi Camins, Clara Molina Almela, Cristina Campos Fernández, Amalia Rueda Cid, Javier Calvo Catalá, Rafael Benito Melero, Francisco Maceiras, Nair Pérez, Ceferino Barbazán, José María Pego, Irena Altabás, John Guzman, Paula Valentina Estrada Alarcón, Héctor Corominas, Iván Castellví, Berta Magallares, Ana Milena Millán, María Alcalde Villar, AnaF Cruz Valenciano, Félix Cabero del Pozo, AnaBelén Rodríguez Cambrón, Cristina Macia Villa, Antonio Juan Mas, Inmaculada Ros Vilamajó, Mónica Ibáñez Barceló, Elide Toniolo, Ana Paula Cacheda, María Sagrario Bustabad Reyes, María García González, Alicia García Dorta, Vanesa Hernández Hernández, Margarida Vasques Rocha, Jaime Calvo Allen, Elisa Fernández Fernández, Miren Uriarte-Ecenarro, Cristina Valero Martínez, Esther F Vicente Rabaneda, Carlos García Porrúa, Carlota Laura Iñiguez Ubiaga, Noelia Álvarez Rivas, Tomás Ramón Vázquez Rodríguez, José Alberto Miranda Filloy, Amalia Sánchez-Andrade Fernández, Miguel Ángel González-Gay, Carlos Galisteo Lencastre Da Veiga, María Jesús García Villanueva, Patricia Morán Álvarez, Marina Tortosa Cabañas, Marta Serrano Warleta, Aliuska Palomeque Vargas, Alberto Ruiz Román, Clara Aguilera Cros, Alejandro Muñoz Jimenez, JoséA Román Ivorra, Carmen Riesco Bárcena, Anderson Huaylla, Itziar Calvo Zorrilla, Judit Lluch, Jesús A Valero-Jaimes, Luis López Domínguez, Cesar Antonio Egues Dubuc, and Lucia Silva Fernández
Data availability statement
Data are available on reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and was approved by Medical Research Ethics Committee of Cantabria (Santander) (No. 05/2019). Given the primary objective and the retrospective nature of the study, which obviated the need for a clinical interview with the patient, and the approval by the Medical Research Ethics Committee of Cantabria (Santander) (No. 05/2019), it was not necessary to obtain the participants’ informed consent. The data were processed confidentially in accordance with the general data protection regulation (GDPR), namely, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and the free movement of such data.
References
- 1. Sharma A, Mohammad AJ, Turesson C. Incidence and prevalence of giant cell arteritis and Polymyalgia Rheumatica: A systematic literature review. Semin Arthritis Rheum 2020;50:1040–8. 10.1016/j.semarthrit.2020.07.005 [DOI] [PubMed] [Google Scholar]
- 2. Watts RA, Hatemi G, Burns JC, et al. Global epidemiology of vasculitis. Nat Rev Rheumatol 2022;18:22–34. 10.1038/s41584-021-00718-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Dejaco C, Duftner C, Buttgereit F, et al. The spectrum of giant cell arteritis and Polymyalgia Rheumatica: revisiting the concept of the disease. Rheumatology (Oxford) 2017;56:506–15. 10.1093/rheumatology/kew273 [DOI] [PubMed] [Google Scholar]
- 4. González-Gay MA, García-Porrúa C, Llorca J, et al. Visual manifestations of giant cell arteritis: trends and clinical spectrum in 161 patients. Medicine 2000;79:283–92. 10.1097/00005792-200009000-00001 [DOI] [PubMed] [Google Scholar]
- 5. Dejaco C, Ramiro S, Bond M, et al. EULAR recommendations for the use of imaging in large vessel vasculitis in clinical practice: 2023 update. Ann Rheum Dis 2023:ard-2023-224543. 10.1136/ard-2023-224543 [DOI] [PubMed] [Google Scholar]
- 6. Grayson PC, Alehashemi S, Bagheri AA, et al. 18 F-Fluorodeoxyglucose-positron emission tomography as an imaging biomarker in a prospective, longitudinal cohort of patients with large vessel vasculitis. Arthritis Rheumatol 2018;70:439–49. 10.1002/art.40379 Available Mar:70; 3: 439–49 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Molina-Collada J, Castrejón I, Rivera J, et al. The role of ultrasound and FDG-PET/CT to detect Extracranial artery involvement in patients with suspected large vessel vasculitis. Mod Rheumatol 2023;33:549–56. 10.1093/mr/roac058 [DOI] [PubMed] [Google Scholar]
- 8. De Miguel E, Sanchez-Costa JT, Estrada P, et al. Influence of the EULAR recommendations for the use of imaging in large vessel vasculitis in the diagnosis of giant cell arteritis: results of the ARTESER register. RMD Open 2022;8:e002507. 10.1136/rmdopen-2022-002507 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Ponte C, Grayson PC, Robson JC, et al. American college of rheumatology/EULAR classification criteria for giant cell arteritis. Ann Rheum Dis 2022;81:1647–53. 10.1136/ard-2022-223480 Available Dec: 81;12: 1647–1653 doi:10.1136/ard-2022-223480 [DOI] [PubMed] [Google Scholar]
- 10. Hunder GG, Bloch DA, Michel BA, et al. The American college of rheumatology 1990 criteria for the classification of giant cell arteritis. Arthritis & Rheumatism 1990;33:1122–8. 10.1002/art.1780330810 Available: https://onlinelibrary.wiley.com/toc/15290131/33/8 [DOI] [PubMed] [Google Scholar]
- 11. Molina-Collada J, Castrejón I, Monjo I, et al. Performance of the 2022 ACR/EULAR giant cell arteritis classification criteria for diagnosis in patients with suspected giant cell arteritis in routine clinical care. RMD Open 2023;9:e002970. 10.1136/rmdopen-2022-002970 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. van Nieuwland M, van Bon L, Vermeer M, et al. External validation of the 2022 ACR/EULAR classification criteria in patients with suspected giant cell arteritis in a Dutch fast-track clinic. RMD Open 2023;9:e003080. 10.1136/rmdopen-2023-003080 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Nordborg E, Nordborg C. Giant cell arteritis: Epidemiological clues to its pathogenesis and an update on its treatment. Rheumatology (Oxford) 2003;42:413–21. 10.1093/rheumatology/keg116 [DOI] [PubMed] [Google Scholar]
- 14. Gonzalez-Gay MA, Miranda-Filloy JA, Lopez-Diaz MJ, et al. Giant cell arteritis in northwestern Spain: A 25-year epidemiologic study. Medicine (Baltimore) 2007;86:61–8. 10.1097/md.0b013e31803d1764 [DOI] [PubMed] [Google Scholar]
- 15. Bustamante Maldonado E, Marí Alfonso B, Monteagudo Jiménez M, et al. Análisis de una Serie de 55 Pacientes con arteritis de Células Gigantes Confirmada Por Biopsia. An Med Interna (Madrid) 2004;21:473–6. 10.4321/S0212-71992004001000002 [DOI] [PubMed] [Google Scholar]
- 16. Romero-Gómez C, Aguilar-García JA, García-de-Lucas MD, et al. Epidemiological study of primary systemic Vasculitides among adults in Southern Spain and review of the main Epidemiological studies. Clin Exp Rheumatol 2015;33:S–11. [PubMed] [Google Scholar]
- 17. Muratore F, Boiardi L, Mancuso P, et al. Incidence and prevalence of large vessel vasculitis (giant cell arteritis and Takayasu arteritis) in northern Italy: A population-based study. Semin Arthritis Rheum 2021;51:786–92. 10.1016/j.semarthrit.2021.06.001 [DOI] [PubMed] [Google Scholar]
- 18. Andersen JB, Myklebust G, Haugeberg G, et al. Incidence trends and mortality of giant cell arteritis in Southern Norway. Arthritis Care Res (Hoboken) 2021;73:409–14. 10.1002/acr.24133 Available: https://acrjournals.onlinelibrary.wiley.com/toc/21514658/73/3 [DOI] [PubMed] [Google Scholar]
- 19. Stamatis P, Turkiewicz A, Englund M, et al. Epidemiology of biopsy-confirmed giant cell arteritis in southern Sweden-an update on incidence and first prevalence estimate. Rheumatology (Oxford) 2021;61:146–53. 10.1093/rheumatology/keab269 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Guittet L, de Boysson H, Cerasuolo D, et al. Whole-country and regional incidences of giant cell arteritis in French Continental and overseas territories: A 7-year nationwide database analysis. ACR Open Rheumatol 2022;4:753–9. 10.1002/acr2.11472 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Elling P, Olsson AT, Elling H. Synchronous variations of the incidence of temporal arteritis and Polymyalgia Rheumatica in different regions of Denmark; association with epidemics of Mycoplasma pneumoniae infection. J Rheumatol 1996;23:112–9. [PubMed] [Google Scholar]
- 22. Petri H, Nevitt A, Sarsour K, et al. Incidence of giant cell arteritis and characteristics of patients: data-driven analysis of Comorbidities. Arthritis Care Res (Hoboken) 2015;67:390–5. 10.1002/acr.22429 Available: https://acrjournals.onlinelibrary.wiley.com/toc/21514658/67/3 [DOI] [PubMed] [Google Scholar]
- 23. Miller A, Green M, Robinson D. Simple rule for calculating normal Erythrocyte sedimentation rate. Br Med J (Clin Res Ed) 1983;286:266. 10.1136/bmj.286.6361.266 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Li KJ, Semenov D, Turk M, et al. A meta-analysis of the epidemiology of giant cell arteritis across time and space. Arthritis Res Ther 2021;23:82. 10.1186/s13075-021-02450-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Bas-Lando M, Breuer GS, Berkun Y, et al. The incidence of giant cell arteritis in Jerusalem over a 25-year period: annual and seasonal fluctuations. Clin Exp Rheumatol 2007;25:S15–7. [PubMed] [Google Scholar]
- 26. Mohammad AJ, Nilsson J-Å, Jacobsson LTH, et al. Incidence and mortality rates of biopsy-proven giant cell arteritis in southern Sweden. Ann Rheum Dis 2015;74:993–7. 10.1136/annrheumdis-2013-204652 [DOI] [PubMed] [Google Scholar]
- 27. Catanoso M, Macchioni P, Boiardi L, et al. Incidence, prevalence, and survival of biopsy-proven giant cell arteritis in northern Italy during a 26-year period. Arthritis Care Res (Hoboken) 2017;69:430–8. 10.1002/acr.22942 Available: https://acrjournals.onlinelibrary.wiley.com/toc/21514658/69/3 [DOI] [PubMed] [Google Scholar]
- 28. Lyne SA, Ruediger C, Lester S, et al. Giant cell arteritis: A population-based retrospective cohort study exploring incidence and clinical presentation in Canterbury, Aotearoa New Zealand. Front Med (Lausanne) 2022;9:1057917. 10.3389/fmed.2022.1057917 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Prieto-González S, Arguis P, García-Martínez A, et al. Large vessel involvement in biopsy-proven giant cell arteritis: prospective study in 40 newly diagnosed patients using CT angiography. Ann Rheum Dis 2012;71:1170–6. 10.1136/annrheumdis-2011-200865 [DOI] [PubMed] [Google Scholar]
- 30. Blockmans D, de Ceuninck L, Vanderschueren S, et al. Repetitive 18F-Fluorodeoxyglucose positron emission tomography in giant cell arteritis: a prospective study of 35 patients. Arthritis Rheum 2006;55:131–7. 10.1002/art.21699 [DOI] [PubMed] [Google Scholar]
- 31. National Health Survey . National Statistics Institute. 2017. Available: https://www.ine.es/dynt3/inebase/index.htm?type=pcaxis&path=/t15/p419/a2017/p03/&file=pcaxis
- 32. González García A, Del Arco C, Lucas Fernández D, et al. Executive summary on the optimization of the Multidisciplinary and integrated approach to Polymyalgia Rheumatica and giant cell arteritis in Madrid region. Rev Clin Esp (Barc) 2024;224:48–56. 10.1016/j.rceng.2023.12.009 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
rmdopen-2023-003824supp001.pdf (129KB, pdf)
Data Availability Statement
Data are available on reasonable request.