Article Text

Original research
Colombian ocular inflammatory diseases epidemiology study (COIDES): prevalence, incidence and sociodemographic characterisation of Scleritis in Colombia, 2015–2020
  1. Carlos Cifuentes-González1,
  2. William Rojas-Carabali1,
  3. Germán Mejia-Salgado1,2,
  4. Juan Sebastián Pineda-Sierra1,2,
  5. Paula Tatiana Muñoz-Vargas1,
  6. Laura Boada-Robayo1,
  7. Danna Lesley Cruz3,
  8. Alejandra de-la-Torre1,2
  1. 1Neuroscience (NEUROS) Research Group, Neurovitae Research Center, Institute of Translational Medicine (IMT), Universidad Del Rosario Escuela de Medicina y Ciencias de la Salud, Bogotá, Capital District, Colombia
  2. 2Ophthalmology Interest Group, Neuroscience (NEUROS) Research Group, Neurovitae Research Center, Institute of Translational Medicine (IMT), Universidad Del Rosario Escuela de Medicina y Ciencias de la Salud, Bogotá, Capital District, Colombia
  3. 3Grupo de Investigación Clínica, Universidad del Rosario, Bogota, Colombia
  1. Correspondence to Professor Alejandra de-la-Torre; alejadelatorre{at}


Objective To describe the epidemiological and demographic characteristics of scleritis in Colombia.

Methods and analysis Population-based study using the national database from the Colombian Ministry of Health, using the International Classification of Diseases-10 code for Scleritis (H150) to estimate the prevalence and incidence from 2015 to 2019. Additionally, we evaluated the impact of the COVID-19 pandemic lockdown on the epidemiology of the disease during 2020, using the Gaussian Random Markov Field model (conditional autoregressive; CAR model). Finally, a standardised morbidity rate map was made to assess the geographic distribution of scleritis in the country.

Results The 5-year average prevalence and incidence of scleritis in Colombia were 0.6 (95% CI 0.59 to 0.6) and 0.65 (95% CI 0.64 to 0.64) cases per 100 000 inhabitants, respectively. We found 1429 registers of scleritis throughout the country between 2015 and 2019. Women represented 64.3%. The age groups with most cases were between 40 and 69 years in both sexes. However, women between 30 and 39 years and men between 20 and 29 years presented the highest number of new cases. In 2020, the pandemic reduced approximately 0.23 points the incidence of scleritis. Bogotá, Valle del Cauca and Antioquia had most of the cases, the latter two with an increased risk over time.

Conclusion Colombia has a lower incidence of scleritis than the reported in other latitudes, with a pattern of presentation at younger ages. Furthermore, the lockdown derived from the CODIV-19 pandemic affected the follow-up and diagnosis of patients with scleritis. This is the first epidemiological description of scleritis in a developing country and South America.

  • Epidemiology
  • Sclera and Episclera
  • Inflammation
  • Public health

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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What is already known on this subject

  • Scleritis epidemiology has been described in developed countries. Its prevalence has been calculated between 1.7 and 93.62 cases per 100 000 inhabitants, and its incidence between 1.0 and 5.54 cases per 100 000 inhabitants per year.

What this study adds

  • This study describes the epidemiological characteristics of scleritis in a developing country in South America for the first time, finding that the prevalence and incidence of scleritis in Colombia are 0.6 (95% CI 0.59 to 0.6) and 0.65 (95% CI 0.64 to 0.64) cases per 100 000 inhabitants, respectively. With a high number of new cases in the young population. Also, the pandemic’s negative impact on the diagnosis and follow-up of patients with scleritis was evidenced.

How this study might affect research, practice or policy

  • This article will promote the generation of new research questions in a population that did not have previous studies on this topic. Also, the data highlight that younger people present more new cases and may need more attention from ophthalmologists. This might be due to a lower incidence of scleritis in the elderly population with autoimmune diseases owing to the treatment with immunomodulatory medications.


Scleritis is the eyeball wall (sclera) inflammation that may extend to the adjacent tissues, causing ocular complications.1 According to the aetiology, scleritis can be classified into infectious scleritis, secondary to viral, bacterial, parasitic and fungal infections,2 and non-infectious scleritis, associated with autoimmune and systemic diseases such as rheumatoid arthritis (RA), granulomatosis with polyangiitis and other systemic vasculitides. Other causes are masquerade syndromes secondary to intraocular tumours, postsurgery and post-trauma.3 It can be classified anatomically depending on the inflammation site or by the degree of inflammation using conventions similar to those used in uveitis.4 5

Several population-based and hospital-based studies describe the epidemiology of scleritis in developed countries. In population-based studies, its incidence is 3.4 to 4.1 cases per 100 000 inhabitants per year in the USA and between 2.8 and 4.2 cases per 100 000 inhabitants in 1997 and 2018, respectively in the United Kingdom (UK).6–8 In the same way, the prevalence of scleritis in population-based studies varies between 5.2 cases per 100 000 inhabitants in the USA and 93.6 cases per 100 000 inhabitants in the UK. Similarly, in hospital-based studies, the prevalence has been calculated at 5.1 cases per 100 000 consultations in Australia.6 7 9

There is a scarcity of epidemiological data on scleritis worldwide, and just a few investigations described the frequency of scleritis in rheumatological settings, such as the studies of Bettero et al,10 in which 2% of patients with RA had scleritis, and of Uribe-Reina et al,11 which described a scleritis prevalence of 1.25% in a rheumatology centre in Bogotá, Colombia. Nevertheless, no studies are found in South America focusing on general scleritis epidemiology.12 13 Therefore, this study aims to describe Colombia’s epidemiological and demographic characteristics of scleritis in 2015–2020.

Materials and methods


We conducted a population-based study on patients with scleritis diagnosed in Colombia with no age limit between 2015 and 2020. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.14


The information and data in this study were obtained from the national database created by the Colombian Ministry of Health, known as the System of Information of Social Protection (SISPRO), which stores, processes and systematises Colombian citizens' health records.15 The data are collected by medical staff during each medical contact (inpatient or outpatient) from private and public health providers and insurers using the International Classification of Diseases (ICD-10). In addition, the demographic and clinical data are grouped in the individual Registry of Health Services Provision.16

It should be noted that the Colombian Health System has one of the most prominent coverages in Latin America, encompassing 50 million inhabitants that represent the 97.78% of the population in 2020, according to the last measurement of the National Administrative Department of Statistics.17

Patient and public involvement

Patients or the public were not involved in the design, or conduct, or reporting or dissemination plans of our research.

Data collection

Data were extracted from the SISPRO dynamic tables from 2015 to 2020 using the ICD-10 code for scleritis (H-150), similar to the study of Xu et al.18 To delimit the results in our searches, we performed the first filter by year (2015 to 2020) and the type of diagnosis (‘confirmed new’ or ‘confirmed repeated’) to determine the prevalence. Then, we used the same extraction method to identify new cases with scleritis, using only the ‘confirmed new’ filter to estimate the incidence.

Afterward, additional filters, such as residence location, age and sex, were added to describe the disease’s sociodemographic status in the country. Finally, a database in Microsoft Excel (Microsoft, Redmond, Washington) was elaborated and validated to record the information.

Statistical analysis

The crude incidence rate and prevalence of scleritis in the Colombian population were estimated for 6 years (2015–2020) using a standardised crude rate per 100 000 inhabitants. The patients were divided and stratified by sex and age in quinquennium for the incidence and prevalence analysis. Since the data are secondary and may not represent the parameter of the total number of cases in the population because of under-registration, a 95% CI for the rates was calculated assuming a Poisson error. These analyses were performed with R V.4.0.4.

In this work, we use a statistical model for spatial data to predict the real prevalence/incidence of the disease in the year 2020. This was done since the results were significantly affected by the pandemic. The statistics for spatial data has been divided into two large classes by Cressie et al, the geostatistical models and the spatial network models, also called ‘area models’.19 We worked with spatial models for area data, specifically with the covariance structure defined by the spatial nature of the data.19 20 We used the conditional autoregressive (CAR) model, which is based on the Gaussian Markov Fields, where the random vector distribution (finite-dimensional) is a normal distribution satisfying the conditional independence assumptions. Random fields are multivariate distributions generally used to describe the spatial association between variables X. A Markov random field extends the Markov chain concept to a spatial context and assumes that as a joint distribution.

We used the CAR model to describe the overall behaviour of random effects θ = (θ1,…, θn), where θi is the spatial effect associated with area i on a map. Such effects are latent factors representing the spatial dependence beyond the small geographical boundary area. A key point regarding the CAR model construction is the specification of an appropriate neighbourhood structure. We take as neighbours any pair of areas sharing geographic boundaries. The adjacency information is included in the model through an n×n neighbourhood matrix A with binary entries aik=1 if areal units k and i share a common border (denoted k ∼ i), and aik=0 otherwise. This approach is popular because it can be easily calculated using Bayesian computation; we used the CARBayesST Package.21

The CAR model is specified considering the set of n univariate full conditional distributions given by

Embedded Image

where θ−i = (θ1,…, θi −1, θi+1,…, θn), Embedded Image is the number of neighbours of region i, M=diag{m1,…, mn} and ρ is a spatial autocorrelation parameter. Consequently, the joint distribution of θ is:

Embedded Image

where M −ρ A is a positive-definite matrix if 1/λ1 < ρ < 1/λρ, where λ1 and λρ denote the smallest and the largest eigenvalue of the matrix Embedded Image. The Markov property defined in terms of this neighbourhood structure induces a sparse precision matrix facilitating the Bayesian computational approaches.

Here, we use the CAR model for the spatial effects defined in a Poisson response regression model, the observed (Embedded Image and the expected counts Embedded Image . The expected value Embedded Image is calculated using the age–sex population distribution and assuming that the age-specific risk is constant on the entire map. The data are analysed assuming that:

Embedded Image

the λi follows the log-linear regression structure, such that:

Embedded Image

where θi is the spatial random effects at area i.

Additionally, considering that Colombia is a country that is administratively, economically, culturally and politically divided into 32 departments, standardised morbidity rate (SMR) maps were created to analyse the disease in all departments. We used a CAR model to visualise the geographic domain as an undirected graph with a vertex in each department and an edge between two vertices if the corresponding departments share a geographic edge. This model creates well-defined neighbours for each department, which are used to define the joint or conditional distribution. The distribution will be the multivariate normal distribution. Analysis of the CAR model is concentrated on the covariance matrix Σ, which is defined by the graph of the geographic domain and the parameter ρ. Cases without a report of the residence location (94 patients) were excluded from this analysis. More detailed information about the statistical analysis is available in online supplemental material 1.

Bias control

In this study, selection bias may occur due to several filters applied to diagnostic data in SISPRO and by underestimation and misclassification bias; to prevent this, we only included patients with a new diagnosis and confirmed repeated diagnosis of the H150 ICD-10 code. Additionally, since we do not have access to clinical charts, we cannot assert there are no repeated patients because it is secondary information. After applying filters, the total accuracy of data cannot be guaranteed because the database does not allow us to rectify the ID numbers. However, the Colombian Ministry of Health and healthcare provider institutions constantly update and correct data with errors to control this bias. Previous studies based on SISPRO have determined that the concordance rate of the database using ICD-10 with medical records is up to 83.4%.22 23 Therefore, these data are the most accurate approximation available.24


The SISPRO database from 2015 to 2020 recorded 23 372 396 to 37 083 655 consultations. In the 2015–2020 period, there were 1827 cases of scleritis. However, due to the COVID-19 pandemic, under-reporting generated a bias in the data from 2020. Therefore, we initially worked with the data until 2019, when 1429 cases were reported, with women representing 64.3%.

Frequency of cases

Regarding age and sex, men in the quinquennial group of 50–54 years (8.8%) had the highest number of cases, followed by the group of 60–64 years (8.6%) and 40–44 years old (7.8%). Moreover, in women, the quinquennial group with the highest number of cases was 55–59 years old (10.8%), followed by 45–49 years old (10.4%) and 50–54 years old (9.9%). More information is available in table 1.

Table 1

Distribution of the incidence and prevalence of scleritis in Colombia 2015–2019, by sex and age


The prevalence during the 5 years was presented in a range between 0.36 (95% CI 0.53 to 0.54) and 0.78 (95% CI 0.77 to 0.78) cases per 100 000 inhabitants, with a mean prevalence in 5 years of 0.6 (95% CI 0.59 to 0.6). More detailed information is shown in table 2. Regarding the analysis of 2020 presented in figure 1A, the prevalence of scleritis calculated based on SISPRO is 0.5 (95% CI 0.5 to 0.5) cases per 100 000 inhabitants, but we found that the estimate for this year is 0.74 (95% CI 1.17 to 0.66) cases per 100 000 inhabitants. Regarding prevalence by age, we found that in men, the group with the highest prevalence was between 60 and 64 years old, followed by the group between 65 and 69 years old and the group between 75 and 79 years old. As for women, we observed that the group with the highest prevalence was between 75 and 79 years old, followed by those between 60 nd 64 years old and those between 55 and 59 years old (more detailed information is in table 1).

Table 2

Prevalence and incidence per year in Colombia from 2015 to 2019

Figure 1

Effect of the pandemic on the epidemiology of scleritis in Colombia The blue lines show the calculated prevalence and incidence in SISPRO (2015–2020), in 2020, the red lines represent the estimated prevalence and incidence based on SISPRO data using the CAR analysis.15 Each point represents the prevalence or incidence calculated in each year of study with its 95% CI. (A) Line graph represents the prevalence in the 6 years studied. In 2020, the difference between the calculated (blue line) and the estimated (red line) prevalence had a difference of 0.24 cases per 100 000. (B) Line graph represents the incidence in the 6 years studied, showing a between the calculated (blue line) and the estimated (red line) prevalence had a difference of 0.26 cases per 100 000. CAR, conditional autoregressive; SISPRO, System of Information of Social Protection.


From 2015 to 2019, we found 1013 new cases. The highest number of cases in men was between the ages of 25 and 29 (8.5%), followed by the group of 50 and 54 years (8.2%) and the groups of 20 and 24 and 40 and 44 years of age (8.0%, each one). As for women, the group with the highest number of cases was between the ages of 30 and 34 years (10.4%), followed by 50 and 54 years (9.4%) and 45 and 49 years (9.1%); more information in table 1. On the other hand, the incidence was presented in a range between 0.48 (95% CI0.48 to 0.48) and 0.71 (95% CI 0.7 to 0.71) cases per 100 000 consultations annually, with an average incidence in 5 years of 0.65 (95% CI 0.64 to 0.65) cases per 100 000 consultations; detailed information in table 2. Regarding the analysis of the pandemic effect in 2020, the estimated incidence based on SISPRO database was 0.57 (95% CI 0.57 to 0.58) cases in 100 000 consultations; however, the estimated projection of the incidence was 0.8 (95% CI 0.7 to 1.39) cases per 100 000 consultations in 2020 (figure 1B).

Geographic analysis

As mentioned, Colombia is divided into 32 departments, distributed in five regions (Andean, Caribbean, Pacific, Oriniquía and Amazon) (figure 2). The geographic analysis shows that the areas with the highest number of cases were Bogotá (21.29%), Antioquia (16.75%) and Valle del Cauca (11.06%) (online supplemental table 2). Furthermore, SMR maps show a higher risk of the disease in the Andean region, which tends to increase over the analysis period (2015 to 2019), particularly in the departments of Antioquia and Valle del Cauca (figure 2).

Figure 2

The standardised morbidity rate in Colombia from 2015 to 2019. (A) Shows the distribution by departments of Colombia, where the Andean region is also observed in the blue border, the Caribbean region in red, the Pacific region in green, the Orinoquia region in orange and the Amazon region in brown. (B) Shows the standardised morbidity rate (SMR) maps show a constant evolution of risk from 2015 to 2019. Bogota, Valle del Cauca and Antioquia evidence an increasing trend of the risk over time.15


Since 2007, Smith et al25 stated that the epidemiology of scleritis requires population-based studies to determine the dimensions of this health problem that leads to a significant visual loss.13 26 Furthermore, a recent publication by Sainz De la Maza et al13 recognised the lack of information regarding scleritis’s actual incidence and prevalence in the world population. Although studies have been carried out in the UK, the USA and Australia to determine the prevalence and incidence of this disease,6 7 9 27 there are no epidemiological descriptions in developing countries.

According to the reported data, the prevalence of scleritis ranges from 1.7 to 93.62 cases per 100 000 inhabitants, and the incidence from 1.0 to 5.54 cases per 100 000 inhabitants per year.6–9 Our general results show lower incidence and prevalence than those reported in the literature. The heterogeneity in the different methodologies used to calculate these epidemiological parameters can explain this because they are hospital-based studies and studies performed in a single region that do not represent the accurate data for an entire country.7–9 Another possible explanation is that the studies that report higher incidences have significantly smaller samples and a smaller number of new cases compared with our study population.7 8 Also, it is important to consider the years in which the descriptions were carried out since they are from 1997 to 2018.6–9 Therefore, our data are difficultly comparable with most reported studies since we describe all the cases registered in Colombia from 2015 to 2020.

Comparing our study head-to-head, we evidenced that the most similar study was conducted by Braithwaite et al in the UK. In this study, the incidence in the last 21 years decreased by approximately 1.51 points since, in 1997, the incidence was 4.3 cases per 100 000 persons/year, and in 2018, it was 2.79 cases per 100 000 persons/year.6 In comparison to our data for 2018, we observed a lower number of cases; however, the difference is not as large as with studies carried out with other methodologies. Similarly, when we compare our data with the study conducted in 2015 by Thong et al, which found an overall incidence rate of 1.0 (95% CI 0.7 to 1.4) per 100 000 consultations in Australia, we evidence a lower incidence in our population (0.69 (95% IC 0.68 to 0.69) per 100 000 consultations).9 Nevertheless, we must consider the intrinsic differences in the study design since Thong et al conducted a hospital-based study.

Braithwaite et al proposed that the incidence of scleritis has declined due to the availability of novel immunomodulatory and antimicrobial therapies, such as biological therapy used to treat many immune-mediated diseases, preventing the development of scleritis and, therefore, decreasing its incidence in the UK.6 We cannot fully confirm this proposal, but our data and those of Thong et al support that there are a smaller number of new cases of scleritis in the older population in Colombia, Australia and the UK.6 9 More population-based studies of scleritis are needed to confirm this hypothesis.

Based on the data in figure 1A,B, we can conclude that the COVID-19 pandemic significantly impacted patients’ access to the health system. This could cause irreversible damage since many patients with scleritis had no follow-up by the ophthalmologist. Additionally, the data show that although there may be an under-registration, many patients with the disease could remain undiagnosed, leading to possible complications affecting their visual health, psychological health, quality of life and psychosocial well-being.28 29 Unfortunately, no published articles report the incidence of scleritis during the global pandemic; therefore, we do not have any data to compare our findings in this regard.

Our study found a female predominance in scleritis; women represented 64.3% of the cases, with a female:male ratio of 1.72. Our findings are consistent with a retrospective case series reporting that 60%–74% of patients with scleritis are women.30 Similarly, in a previous study from the UK, among patients with incident scleritis, 1831 (62.2%) were women, and the Pacific Ocular Inflammation Study evidenced a similar pattern.6 8 As scleritis is an inflammatory condition, this could also be related to the common finding of female predominance in immune-mediated diseases.31

Regarding age, our results are similar to those described worldwide, where the most significant number of cases lie between the ages of 39 and 59 years.30 In our population, the group between 40 and 69 years of age represents 50.3% of all reported cases. Nonetheless, the study conducted in the UK reported a peak of scleritis onset in women between the ages of 50 and 59 years and formen between 70 and 79, contrary to us, where the peak of scleritis onset in women was between the ages of 30 and 39 years (19.5%) and 20 and 29 years for men (16.5%). The lower age of scleritis onset may be explained due to the changing pattern of incidence proposed by Braithwaite et al or due to a greater number of cases secondary to infectious aetiologies in young men as described in Southeast Asia.6 32 However, we cannot confirm these theories because our database does not allow us to see the aetiology of scleritis. More studies are needed in our population to confirm or refute these hypotheses.6 32

Finally, the SMR map (figure 2) demonstrates that the regions with the highest density of Afro-Colombian population, such as Valle del Cauca and Antioquia, have a higher morbidity risk.33 Zhang et al described that infectious scleritis has a higher prevalence among African Americans (7.5 per 100 000 inhabitants), whose has an increased risk of infectious scleritis (OR: 1.2, CI (1.08 to 1.72)).27 However, it is crucial to consider that the departments with the main cities (Antioquia, Bogotá and Valle del Cauca), part of the departments mentioned with the highest prevalence, have the majority of referral centres and specialists in ocular inflammation.34


Due to the large number of patients and health personnel involved in the raw data recovery contained in the national databases, the claims database and population-based studies, as ours, cannot be totally accurate, containing underestimation or overestimation, due to the coverage and content errors, respectively. However, these studies are the best approximations to analyse with an acceptable degree of certainty the dimension of a disease in an entire country.24 35 36 Also, SISPRO has achieved a good concordance between the reported data and the clinical records, reaching 83.4%.23 As the SISPRO does not allow us to see the patient’s identification number, we cannot ensure that there were no repeated patients and that duplicates are not found in cases.


The 5-year average incidence and prevalence of scleritis in Colombia are 0.6 (95% CI 0.59 to 0.6) and 0.65 (95% CI 0.64 to 0.64) cases per 100 000 inhabitants, respectively. Its distribution by age and sex is similar to that previously described; however, it stands out that a greater number of new cases among the group of 30–59 years in women and 20–29 years in men. More studies are needed in our population to confirm the effect of aetiology (infectious vs non-infectious) in the distribution pattern of scleritis. Additionally, we provide evidence of how the pandemic affected the follow-up and diagnosis of patients with scleritis. Further studies are needed on this disease in Latin America.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication


We thank Universidad del Rosario for financing the publication of this article.


Supplementary material

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  • Contributors CC-G, WR-C, GM-S, PTM-V, LB-R, JSP-S, DLC: conception and design of the study, data acquisition, analysis, and interpretation of data, drafting the article, revising it critically for important intellectual content, final approval of the version to be submitted. Ad-l-T: thematic authority, conception, and design of the study, data acquisition, analysis, and interpretation of data drafting the article, revising it critically for important intellectual content, final approval of the version to be submitted. Finaly, Ad-l-T is accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

  • Funding No funding was required to carry out this study.

  • Map disclaimer The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • 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.