Seasonal variation in the incidence of central serous chorioretinopathy: A systematic review and meta‐analysis

Abstract

Central serous chorioretinopathy (CSC) is a chorioretinal disease characterised by serous subretinal fluid (SRF) in the macula, resulting in sudden central vision loss. It predominantly affects working‐age adults, particularly men aged 30 to 60 years. Its multifactorial pathophysiology is modulated by systemic factors, such as corticosteroid use, psychological stress, and hypertension. Previous studies suggest that broader environmental influences, including seasonal variation, may contribute to disease onset. This study aimed to systematically review and analyse the evidence on seasonal variation in CSC incidence. A systematic search was conducted across 10 databases (PubMed, Embase, Cochrane Central, Web of Science Core Collection, Current Contents Connect, Data Citation Index, Derwent Innovations Index, KCI‐Korean Journal Database, ProQuest™ Dissertations and Theses Citation Index, and SciELO Citation Index) on 22 June 2025. Eligible studies included observational designs reporting CSC incidence or frequency across seasons; case reports and reviews were excluded. Five studies, with 907 participants in total, met the inclusion criteria. Three studies with sufficient data were included in quantitative meta‐analysis, while the remaining two conference abstracts were included in the qualitative synthesis. Seasonal variation in CSC incidence was reported across studies. Meta‐analysis using summer as the reference season showed a significantly increased incidence of CSC in spring (OR 1.42; 95% CI: 1.18–1.73; p = 0.0003) and autumn (OR 1.23; 95% CI: 1.02–1.48; p = 0.03). No significant difference was found for winter (OR 0.97; 95% CI: 0.80–1.19; p = 0.80). The remaining 2 studies not included in the meta‐analysis also reported seasonal trends consistent with these findings. The findings indicate a significant seasonal variation in CSC incidence, with a consistently increased risk during spring and autumn compared with summer. Further research is needed to examine how seasonal environmental and physiological mechanisms may contribute to CSC development.

1. INTRODUCTION

Central serous chorioretinopathy (CSC) is a chorioretinal disease characterised by the accumulation of serous subretinal fluid (SRF) in the macula, the central part of the retina, resulting in central vision loss that is typically sudden (van Dijk & Boon, ²⁰²¹). This fluid buildup leads to the detachment of the neurosensory retina and predominantly affects working‐age adults, particularly men between 30 and 60 years, with an estimated annual incidence of 9.9 per 100 000 men and 1.7 per 100 000 women (Kitzmann et al., 2008; van Dijk & Boon, ²⁰²¹). It ranks as the fourth leading cause of vision‐threatening non‐surgical retinopathy, after age‐related macular degeneration, diabetic retinopathy, and branch retinal vein occlusion (Wang et al., 2008).

The pathophysiology of CSC is multifactorial. Alterations of the choroidal vasculature and subsequent dysfunction of the retinal pigment epithelium (RPE) lead to SRF leakage (Cheung et al., 2025). Moreover, scleral factors have recently been hypothesised to play a role (Keidel et al., 2023; Spaide & Gemmy Cheung et al., ²⁰²²). CSC is also modulated by systemic factors such as corticosteroid use, psychological stress, and hypertension. Increasingly, CSC is recognised as part of the pachychoroid disease spectrum, characterised by dilated vessels in the larger choroidal vessels and thinning of the choriocapillaris, resulting in a thickened choroid. This imbalance may disturb choroidal vascular homeostasis and secondarily impair the RPE and the outer retina (van Dijk & Boon, ²⁰²¹). Recent work has also introduced the concept of venous overload choroidopathy, a unifying theory that links CSC and other pachychoroid diseases to abnormal choroidal venous outflow (Spaide & Gemmy Cheung et al., 2022). In addition to this concept, Brinks et al. suggested that abnormal choroidal arteriovenous anastomoses could be a contributing factor underlying the observed venous overload, thereby potentially being the origin for CSC (Brinks et al., 2022). Although increased choroidal thickness (CT) has been widely reported in CSC, recent studies suggest that CT alone may be insufficient as a defining feature, as it can vary with age, axial length, blood pressure, and even diurnally (Cheung et al., 2025). These findings support the hypothesis that broader environmental influences may play a role in disease development.

One line of evidence supporting the role of environmental influences comes from studies examining the temporal clustering of CSC cases. More than four decades ago, Cassel et al. observed an excess of CSC presentations in March–April among Canadian patients. Dookeran and Greve similarly identified noticeable seasonal trends in CSC presentations in Northern Alberta (Cassel et al., 1984; Dookeran & Greve, ²⁰⁰⁴), with a higher incidence in spring and an association with daylight hour changes. In Japan, a study by Kida et al. found the highest CSC incidence in spring, while Liu et al. in China also reported a monthly peak in April and a lower incidence in February, with an overall higher incidence in spring than in winter (Kida et al., 2018; Liu et al., ²⁰²¹). These studies raise the possibility that factors such as ambient temperature, photoperiod, or seasonal behaviours may influence disease onset. This phenomenon is often referred to as the seasonality of a disease, which is defined as a cyclic change in either disease occurrence or severity over a year (Christiansen et al., 2012) and may provide important etiological clues to CSC (Dookeran & Greve, 2004). Nonetheless, the reproducibility of these findings across studies has been limited, and the current evidence remains fragmented.

Understanding such seasonal patterns is particularly important given the potential burden of CSC on individuals and health systems. Although acute CSC is often self‐limiting, its chronic form may cause irreversible visual impairment, significantly affecting the quality of life and productivity during an individual's most economically active years (Breukink et al., 2017; van Dijk & Boon, ²⁰²¹). Moreover, healthcare services only contribute ~20% to overall health outcomes. The remaining majority is influenced by socio‐economic, environmental, and behavioural determinants, including stress, occupational conditions, and exposure to artificial or natural light patterns (Alami et al., 2023). Therefore, CSC presents not only clinical challenges but also broader socio‐economic and healthcare burdens.

Exploring the seasonality offers a natural experiment to examine how environmental exposures intersect with individual susceptibility across time and place. It may support the development of health policies, especially for middle‐aged male workers exposed to chronic stress or disrupted circadian rhythms, and to inform community‐level preventive measures.

This systematic review and meta‐analysis aimed to summarise and understand the available evidence on the seasonal variation in CSC incidence.

2. MATERIALS AND METHODS

2.1. Study design

This study was a systematic review and a meta‐analysis. The protocol was registered a priori in the PROSPERO database (protocol no. CRD420251080426). We followed the recommendations of the Cochrane Handbook for Systematic Reviews and reported the study according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) (Higgins et al., 2024; Moher et al., ²⁰⁰⁹).

2.2. Eligibility criteria

We considered studies for this review according to the following criteria:

2.2.1. Population

Humans diagnosed with CSC, regardless of age, gender, or geographic location. Had no further demographic restrictions applied.

2.2.2. Exposure

Season (defined as time of year) of diagnosis or symptom onset using any form of temporal categorisation (e.g., by month, quarter, or climate‐related grouping). Meteorological seasons were pre‐specified as March–May (spring), June–August (summer), September–November (autumn), and December–February (winter). For Southern Hemisphere studies, seasons were mapped inversely.

2.2.3. Outcomes

Incidence or frequency of CSC cases assessed across different time periods.

2.2.4. Study design

We included observational studies that investigated seasonal or temporal patterns in the CSC incidence. Eligible study designs included cross‐sectional, cohort, and case–control studies. We excluded single case reports and case series. Studies without original data (e.g., editorials, commentaries, reviews) were not considered eligible. We did not apply eligibility restrictions based on geographic region or journal of publication. For practicality, only studies published in English were considered eligible. In addition, conference abstracts with original data were considered eligible for qualitative synthesis only, given their limited details on methodology and quantitative information.

2.3. Information sources, search strategy, and study selection

A comprehensive literature search was conducted by one author trained at the PhD level in evidence synthesis (Y.S.) across the following databases: PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science Core Collection, Current Contents Connect, Data Citation Index, Derwent Innovations Index, KCI‐Korean Journal Database, ProQuest™ Dissertations and Theses Citation Index, and SciELO Citation Index. No restrictions were placed on publication date. The search was performed and completed on 22 June 2025. Records from the literature search were imported to EndNote X9.3.1 for Mac (Clarivate Analytics, Philadelphia, PA, USA) for screening. One author (Y.S.) screened all titles and abstracts to remove obviously irrelevant studies and duplicates, and the rest were retrieved in full text for evaluation of potential eligibility. All reference lists were evaluated for potentially further eligible studies. Eligibility evaluation was made by two authors (F.H. and E.Ø.) independently according to the predefined eligibility criteria. Discrepancies were resolved through discussion. If consensus could not be reached, a third reviewer (Y.S.) acted as arbiter. Only articles published in English were considered eligible.

2.4. Data extraction, outcome measures, and risk of bias within studies

Data study characteristics, population details, methods for CSC diagnosis, exposure categorisation, and outcome measures were extracted using predesigned extraction forms. The outcome of interest was the incidence of CSC at different seasonal time points. Risk of bias within studies was evaluated using the relevant items from the Agency for Healthcare Research and Quality (AHRQ) checklist for Cross‐Sectional Studies (Zeng et al., 2015). Data extraction and risk of bias within study evaluation were made by two authors (F.H. and E.Ø.) independently. Discrepancies were resolved through discussion. If consensus could not be reached, a third reviewer (Y.S.) acted as arbiter.

2.5. Data synthesis, meta‐analysis, risk of bias across studies, and forecasting analyses

Studies were reviewed qualitatively in the manuscript text and in tables. All meta‐analyses were conducted using MetaXL v.5.3 (EpiGear International, Sunrise Beach, QLD, Australia) for Microsoft Excel v.2205 (Microsoft, Redmond, WA, USA). We summarised monthly incidence rates according to the four seasons (winter, spring, summer, autumn) based on extracted data from individual studies, calculated patients at risk within the same period assuming the incidence of CSC as published by Kido et al., and calculated odds ratio (OR) of incident CSC during different seasons with the summer season as the reference group (Kido et al., 2021). The summer season was chosen based on the hypothesis of its association with the lowest physiological stress and cortisol levels, as these are known in the pathophysiology of CSC. Therefore, it was presumed that CSC incidence is relatively lower in the summer compared with other seasons. The random effects model was used to account for potential heterogeneity across studies. Because of the low number of studies eligible for the meta‐analysis, we deemed it not possible to perform meaningful heterogeneity analyses, funnel plot analyses, or sensitivity analyses. All summary estimates are presented using 95% confidence intervals (95% CI).

3. RESULTS

3.1. Study selection

Our literature search identified 35 records, of which 9 were duplicates, while 20 did not address the outcome of interest. The remaining 6 records were evaluated in full text for potential eligibility. The reference list review did not identify any further eligible studies. Five studies met the eligibility criteria of the qualitative review, and three of these were eligible for the quantitative review (Figure 1).

FIGURE 1.

Study selection process as illustrated using the PRISMA flow diagram.

3.2. Study characteristics

This review summarised data from five eligible studies involving a total of 248 988 participants (Cassel et al., 1984; Dookeran & Greve, ²⁰⁰⁴; Kida et al., ²⁰¹⁸; Kido et al., ²⁰²¹; Liu et al., ²⁰²¹). The studies were conducted in the United States (n = 1), Canada (n = 1), Japan (n = 2), and China (n = 1). Four studies used a retrospective observational study design (Cassel et al., 1984; Dookeran & Greve, ²⁰⁰⁴; Kida et al., ²⁰¹⁸; Liu et al., ²⁰²¹), while 1 was a population‐based cohort study utilising a national health insurance database (Kido et al., 2021).

Although all studies focused on CSC patients, their sample sizes and patient characteristics varied. Four studies included between 150 and 362 subjects (Cassel et al., 1984; Dookeran & Greve, ²⁰⁰⁴; Kida et al., ²⁰¹⁸; Liu et al., ²⁰²¹), whereas Kido et al. included a substantially larger cohort of 247 930 participants from a national claim‐based registry (Kido et al., 2021). While this registry‐based study has a different methodology from the retrospective clinical cohorts, it was included because of its population‐based design, comprehensive nationwide coverage, and relevance to incidence estimates. The mean age of the patients ranged from 41 to 54.7, although Cassel et al. did not provide the mean age: they only included patients aged ≤40 years (Cassel et al., 1984). Across all studies, male predominance was consistent, ranging from 74% to 86%.

Only two studies clearly stated inclusion and exclusion criteria (Cassel et al., 1984; Liu et al., ²⁰²¹), both of which excluded patients with other retinal or systemic abnormalities. The remaining studies did not specify any eligibility criteria.

The diagnosis of CSC was primarily based on clinical examination and imaging in three studies. Cassel et al. used clinical examination supported by fluorescein angiography (FA). Kida et al. and Liu et al. used multimodal imaging, such as FA, optical coherence tomography (OCT), and indocyanine green angiography (ICGA) (Kida et al., 2018). One study solely relied on the diagnosis code provided from a national health insurance database, although the specific coding system (e.g., ICD‐9/ICD‐10) was not reported, as the study was available only in conference abstract form (Kido et al., 2021). The study by Dookeran and Greve did not describe its diagnostic procedure.

Only Cassel et al. provided a clear definition of CSC, described as an isolated serous detachment of the macula, confirmed by FA with pinpoint leakage, and without subretinal haemorrhage or other retinal/systemic diseases (Cassel et al., 1984). Further study characteristics and diagnostic details from each study are summarised in Tables 1 and 2.

TABLE 1.

Study characteristics.

References	Design	Country	Study population	Eligibility criteria
Cassel et al. (1984)	Retrospective clinic‐based cohort study	USA	Patients with CSC referred to Retina Service of Wills Eye Hospital between 1969 and 1979. Men constituted 86% of the study population	Age ≤ 40 First‐time diagnosis of CSC (not a follow‐up), confirmed clinically and via fluorescein angiography Patients with other retinal or systemic pathologies were excluded
Dookeran and Greve (2004)	Retrospective clinic‐based cohort study	Canada	Patients with CSC seen in Northern Alberta from January 1999 to April 2003. Average age was 43 years and 84% were men	Not stated
Kida et al. (2018)	Retrospective clinic‐based cohort study	Japan	Patients with CSC seen in Osaka Medical College from January 2011 to December 2016. Average age was 41 years and 74% were men	Not stated
Kido et al. (2021)	Retrospective population‐based cohort study	Japan	Patients with a diagnosis code of CSC in the National Database of Health Insurance Claims and Specific Health Checkups of Japan from 2011 to 2018. Mean age was 50.5 ± 12.5 years in males and 54.7 ± 13.5 years in females. Men constituted 76% of the population	Not stated
Liu et al. (2021)	Retrospective clinic‐based cohort study	China	Patients with CSC seen in the Shanghai Tenth People's Hospital from 2012 to 2019. Mean age was 48.7 ± 10.2 years and 80% were men	Inclusion criteria: CSC without other fundus abnormalities Exclusion criteria: patients with subretinal blood, optic nerve head pathology, other retinal/choroidal diseases, corticosteroid use, or pregnancy

Abbreviations: CSC, central serous chorioretinopathy; USA, United States of America.

TABLE 2.

Diagnosis and definition of central serous chorioretinopathy.

References	Diagnosis of CSC	Definition of CSC
Cassel et al. (1984)	Based on clinical examination which also included FA	Isolated serous detachment of the macula without subretinal haemorrhage or other retinal/systemic diseases; supported by pinpoint leakage on fluorescein angiography
Dookeran and Greve (2004)	Not stated	Not stated
Kida et al. (2018)	Clinical examination which included measurement of the BCVA, retinal fundoscopy, colour fundus photography, OCT, FA, and ICGA	Not stated
Kido et al. (2021)	Based on a diagnosis code of CSC in the National Database of Health Insurance Claims and Specific Health Checkups of Japan	Not stated
Liu et al. (2021)	Clinical examination which included measurement of the BCVA, intraocular pressure, slit‐lamp examination, fundus photography, enhanced depth OCT, FA, and ICGA	Not stated

Abbreviations: BCVA, best‐corrected visual acuity; CSC, central serous chorioretinopathy; FA, fluorescein angiography; ICGA, indocyanine green angiography; OCT, optical coherence tomography.

3.3. Results of individual studies

Five studies analysed the seasonal distribution of CSC presentations throughout the year with different time durations. One study (Cassel et al.) reported the distribution of CSC for each month, while the remaining four studies reported in seasons (Dookeran & Greve, 2004; Kida et al., ²⁰¹⁸; Kido et al., ²⁰²¹; Liu et al., ²⁰²¹).

Cassel et al. analysed 345 CSC cases in patients aged ≤40 years, with a male‐to‐female ratio of 6:1, and reported a significant variation in monthly distribution (p < 0.01), with peaks in March and April. However, a seasonal trend analysis using Edwards' method was not statistically significant (p > 0.1). To exclude the possibility that this distribution solely represented the general patient flow to the Retina Service, the authors also analysed the monthly number of all patients seen at the service.

Dookeran and Greve analysed 150 CSC cases between 1999 and 2003, with 84% of patients being men, with an average age of 43, observing a statistically significant association between CSC incidence and the month of presentation among male patients (p = 0.02). Additionally, daylight hours were significantly associated with time to resolution of CSC in male patients (p = 0.047). A higher number of CSC cases was observed in spring, which the authors hypothesised to be associated with reduced daylight exposure during the preceding winter months (Dookeran & Greve, 2004). Kida et al. included a total of 201 CSC patients between January 2011 and December 2016, consisting of 149 men (74.1%) and 52 women (25.9%), with a mean age of 41.0 years. Unilateral CSC was observed in 174 patients (86.8%) (Kida et al., 2018). A statistically significant seasonal variation in CSC incidence was observed (p < 0.05, Fisher's exact test and anova), primarily due to greater case numbers in the spring (n = 62) and autumn (n = 59) than in the summer (n = 37). There was no significant difference between spring and autumn. Monthly peaks were observed in March (n = 24), April (n = 22), and November (n = 23).

Kido et al. (2021) included a total number of 247 930 CSC cases from 2011 to 2018 in a nationwide Japanese claims database. Of these, 75.9% were male. The total crude incidence rate was 34.04 per 100 000 person‐years (95% CI: 33.90–34.17), with a higher rate in men (54.16 per 100 000 person‐years; 95% CI: 53.91–54.40) than in women (15.67; 95% CI: 15.54–15.79). The average age at onset was lower in men than in women (50.5 ± 12.5 years vs. 54.7 ± 13.5 years). The number of CSC cases varied significantly based on geographical, climatic, and seasonal variations. In particular, incidence was lower in rural areas compared with urban areas and in colder, snowy climates compared with regions with a higher temperature. Regarding seasonality, incidence peaked in spring and autumn, while significantly fewer cases were observed in summer and winter.

Liu et al. included 410 eyes from 362 CSC patients, consisting of 290 men and 72 women. Unilateral CSC was observed in 314 patients and bilateral CSC in 48 patients. The average age was 48.7 ± 10.2 years, with bilateral cases showing a higher average age (53.4 ± 9.1 years). The study reported the thickest choroid in spring (426.63 ± 19.86 μm) and the thinnest in summer (377.27 ± 7.52 μm), although seasonal differences in choroidal thickness were not statistically significant (p = 0.176). Additionally, a positive correlation was observed between the monthly number of CSC cases and the thickness of the subfoveal choroid (R² = 0.651, p = 0.022). Regarding CSC incidence, the seasonal distribution revealed that spring had the most CSC cases (n = 104), followed by autumn (n = 97), summer (n = 86), and winter (n = 75). However, these differences did not reach statistical significance (overall p = 0.65). In contrast, the monthly distribution revealed a significant variation (p < 0.001), with April having the most cases (n = 38) and February the fewest (n = 16).

3.4. Risk of bias within studies

The Agency for Healthcare Research and Quality checklist (Zeng et al., 2015) was utilised to assess the risk of bias of the included studies. In general, we found that all studies were transparent in reporting the source of data, the time period of investigation, and consecutive recruitment of participants. However, the eligibility criteria of the study participants were only clearly defined by Cassel et al. and Liu et al., placing them at a lower risk of selection bias compared with the remaining studies. It is also important to note that Dookeran and Greve and Kido et al. were available only as conference abstracts with limited methodological details, leading us to conclude that these studies had an unclear overall risk of bias. These studies were only included in the qualitative synthesis. A summary of risk of bias assessments for individual studies is presented in Table 3.

TABLE 3.

Risk of bias within individual studies.

References	Defines source	Eligibility criteria	Time period	Consecutive recruitment	Explains exclusions
Cassel et al. (1984)	Yes	Yes	Yes	Yes	Not relevant a
Dookeran and Greve (2004)	Yes	No	Yes	Yes	Not relevant a
Kida et al. (2018)	Yes	No	Yes	Yes	Not relevant a
Kido et al. (2021)	Yes	No	Yes	Yes	Not relevant a
Liu et al. (2021)	Yes	Yes	Yes	Yes	Not relevant a

Note: Studies are assessed Yes/No/Unclear on the following items from the Agency for Healthcare Research and Quality checklist: Defines source: Defines the source of information. Eligibility criteria: Lists inclusion and exclusion criteria or refers to previous publications. Time period: Indicates period used for identifying participants. Consecutive recruitment: Indicates whether subjects were consecutively recruited for the study. Explains exclusions: Explains any patient exclusions from analysis.

^a Studies without any exclusions in their study sample are neither evaluated with Yes nor No; instead we have outlined that no exclusions were made in these studies.

3.5. Seasonal variation in the incidence of CSC: Meta‐analysis

Although Dookeran and Greve and Kido et al. investigated seasonal variation in the incidence of CSC and reported a statistically significant seasonal difference, these two conference abstracts only reported p‐values without further statistical details. Thus, these two studies could not provide data for the meta‐analysis. Our meta‐analysis was therefore based on data from three studies comprising a total of 907 participants (Cassel et al., 1984; Kida et al., ²⁰¹⁸; Liu et al., ²⁰²¹). The meta‐analysis was based on the summer season as the reference period for evaluating winter, spring, and autumn. Winter was found not to be a statistically significant period of increased risk of CSC, with an OR of 0.97 (95% CI: 0.80–1.19; p = 0.8). However, both spring and autumn were found to be statistically significant periods of increased risk of CSC with an OR of 1.42 (95% CI: 1.18–1.73; p = 0.0003) and 1.23 (95% CI: 1.02–1.48; p = 0.03), respectively. Due to the limited number of included studies in this meta‐analysis (n = 3), heterogeneity statistics, funnel plot analyses, or sensitivity analyses were not possible to be performed as their interpretability and statistical power are considered insufficient when fewer than five studies are available. The calculations are presented in Table 4.

TABLE 4.

Meta‐analysis of the seasonal variation in the incidence of central serous chorioretinopathy.

References	OR	95% CI	Weight (%)
Winter
Cassel et al. (1984)	1.01	0.73–1.40	38.1
Kida et al. (2018)	1.14	0.73–1.77	20.4
Liu et al. (2021)	0.87	0.64–1.19	41.5
Pooled estimate	0.97	0.80–1.19	100.0
Spring
Cassel et al. (1984)	1.55	1.15–2.08	38.4
Kida et al. (2018)	1.68	1.12–2.52	21.1
Liu et al. (2021)	1.21	0.91–1.61	40.5
Pooled estimate	1.42	1.18–1.73	100.0
Autumn
Cassel et al. (1984)	1.16	0.85–1.59	36.5
Kida et al. (2018)	1.59	1.06–2.41	21.1
Liu et al. (2021)	1.13	0.84–1.51	42.4
Pooled estimate	1.23	1.02–1.48	100.0

Note: Incidence during the summer season is used as the reference. Summer season is used as the reference.

Abbreviations: 95% CI, 95% confidence interval; OR, odds ratio.

4. DISCUSSION

In this systematic review and meta‐analysis, our aim was to investigate seasonal or temporal patterns in the CSC incidence by combining evidence from existing literature. We included five studies involving 248 988 individuals and conducted a meta‐analysis using the seasonal data from three studies. Our results showed a statistically significant seasonal variation in CSC incidence, peaking in spring and autumn compared with summer, while in winter this did not differ. Effect sizes were heterogeneous, and prediction intervals crossed the null, highlighting the need for careful interpretation. These findings support our hypothesis of seasonal influence on CSC incidence.

Seasonal variation may reflect environmental or behavioural factors, such as stress levels, sleep patterns, exposure to sunlight, or hormonal fluctuations. Despite population differences, seasonality has also been documented in psychiatric outcomes. Suicidality among adolescents, for instance, peaks in spring and autumn (Kim et al., 2023), suggesting that psychosocial stressors change with the seasons and could contribute indirectly to CSC variation. Even in the absence of CSC or any ophthalmological diseases, a recent systematic review revealed an association between choroid alterations and psychiatric disorders, including anxiety, insomnia, and depression (Pandolfo et al., 2021). Another potential contributor is allergy‐related seasonality. Long‐term studies from the Netherlands have shown that seasonal allergic rhinitis peaks in spring and extends into summer (Schreurs et al., 2022). Corticosteroid use, including nasal sprays, is commonly used to treat these conditions, known as the most important CSC risk factor. However, the pathophysiology of CSC remains complex and multifactorial, and it is not completely elucidated yet.

Several of the included studies provide insights regarding potential mechanisms that may cause CSC. For instance, Kida et al. (2018) observed the highest incidence of CSC in spring and hypothesised that the lower CSC incidence in summer may be associated with increased water evaporation from the body, which may influence choroidal homeostasis. Moreover, their study also discussed seasonal variation in choroidal vascular permeability, which is often thought to be essential in CSC pathogenesis, as a possible biological mechanism (Kida et al., 2018). Mediators released during allergic inflammation, such as histamine, prostaglandins, and catecholamines, may also alter choroidal vascular permeability and thereby contribute to CSC onset. Increased plasma levels of epinephrine and norepinephrine have been observed in patients with acute CSC, and these were correlated with macular thickness and visual acuity (Sun et al., 2003). These findings are consistent with the observations by Jampol et al. (2002), who emphasised that both corticosteroids and catecholamines may have additive effects on the choroidal circulation and RPE. This is because corticosteroids can enhance vasoconstrictive responses mediated by catecholamines, leading to the sensitisation of choroidal vessels or the RPE to the effects of endogenous catecholamines, thereby promoting vascular hyperpermeability and contributing to CSC development (Jampol et al., 2002).

Liu et al. identified seasonal allergens as an additional environmental trigger and hypothesised that temporal patterns in CSC may be caused by climate‐driven changes in choroidal physiology. Similar findings were observed by Dookeran and Greve, who reported a spring peak and a significant association between longer daylight hours and faster resolution of CSC in men, suggesting a possible circadian or photobiological influence. In one of the earliest studies on this topic, Cassel et al. reported an increased number of cases in March and April, although their formal seasonal trend analysis did not reach statistical significance.

Further supporting a role for environmental conditions, Kido et al. found lower CSC incidence in colder, snowier regions and in both summer and winter. Interestingly, they also reported that incidence was lower in regions with the highest total daylight hours compared with those with moderate exposure, suggesting that the relationship between light and CSC may be non‐linear and influenced by other confounding factors. Early experimental findings showed that light adaptation raises dopamine and norepinephrine levels in the retina and choroid of animal models (Nichols et al., 1967). According to recent clinical evidence, individuals with acute CSC appear to have significantly higher plasma levels of dopamine, adrenaline, and norepinephrine than healthy controls (Gungor Kobat et al., 2021). These results suggest that sympathetic and dopaminergic pathways might also play a role in the development of CSC.

To the best of our knowledge, this is the first meta‐analysis to specifically investigate the seasonal variation of CSC incidence. However, two out of these five included studies are over 20 years old. Differences in diagnostic procedures, clinical classification of CSC, and data sources may contribute to heterogeneity, especially as these older studies have been included in the current paper. Additionally, retrospective designs of the included studies may introduce temporal misalignments between symptom onset and initial presentation, which may bias the seasonal attribution. Moreover, due to the limited number of studies, we were unable to assess heterogeneity, perform sensitivity analyses, or publication bias. Another important limitation is the geographical representation of the available evidence. Europe, South America, Africa, and Oceania were not represented in the majority of the included research, which came from North America and East Asia. This limits the generalizability of the findings and emphasises the necessity of more research from a wider range of geographical areas. Furthermore, not all countries may be able to use the concept of four distinct seasons. In tropical or subtropical regions with only two major seasons (e.g., wet and dry), seasonal variation in CSC may show different patterns, which have not yet been thoroughly explored.

Despite these limitations, the observed seasonal variation might be explained by a number of biologically plausible explanations. Seasonal shifts in photoperiod modify the circadian melatonin–cortisol axis and autonomic tone, affecting choroidal vascular regulation and permeability. Psychosocial stress, which frequently varies throughout the year, enhances glucocorticoid and mineralocorticoid signalling, well‐known CSC triggers, by activating the hypothalamus‐pituitary‐adrenal axis and catecholaminergic pathways. Furthermore, environmental factors, such as temperature, humidity, and seasonal allergens may further contribute to choroidal congestion and leak. These factors provide a coherent mechanical explanation for the modest spring and autumn peaks from our analysis.

Nonetheless, the identified significant seasonal variation may possess practical implications, especially ORs. The generated ORs may strengthen the quality of risk stratification, one of the key steps in the Population Health Management approach, enabling healthcare systems to design more tailored and evidence‐based interventions to achieve desired health objectives. In addition, they complement the existing body of quantifiable risk factors for CSC based on systematic reviews and meta‐analysis, such as corticosteroid use, male sex, or hypertension (Liu et al., 2016), highlighting the incorporation of environmental factors, alongside demographic and clinical characteristics, into preventive and clinical strategies, such as patient identification and education, and also future research targeting environmental factors with CSC pathogenesis.

In conclusion, this meta‐analysis demonstrates a small but statistically significant seasonal variation in CSC incidence, with increased risk observed in spring and autumn. Although the precise underlying mechanisms remain unclear, environmental and physiological factors, such as choroidal thickness, daylight exposure, and stress, may contribute to these patterns. Due to the variability of study designs, the retrospective nature of most included studies, and the limited geographical representation, future prospective studies across diverse regions with more consistent diagnostic criteria are necessary. Understanding the temporal dynamics of CSC may provide valuable insights into its pathogenesis and generate future preventative strategies, patient education programmes, and timely clinical interventions, particularly during high‐risk periods.