Abstract
Background and Objectives
With the obesity epidemic within the United States, the prevalence of idiopathic intracranial hypertension (IIH) is predicted to rise. IIH prevalence and racial disparities have rarely been reported in the United States. The purpose of this study was to evaluate the prevalence of IIH in a large national database while stratifying by sex, age, race, and ethnicity.
Methods
This was a cross-sectional epidemiologic evaluation conducted in the TriNetX US Collaborative network using data from 2015 to 2022. Patients with an International Classification of Diseases code of IIH and papilledema or unspecified papilledema were included in the study. Any secondary cause of intracranial hypertension including cerebral neoplasms and hydrocephalus were excluded from the study. IIH trends were later compared with TriNetX cohort obesity trends. Prevalence and prevalence odds ratios (ORs) were calculated in Microsoft Excel and R Studio.
Results
Among 85 million patients in this database, a 1.35 times increase in the prevalence of IIH occurred between 2015 and 2022 from 7.3 (95% CI 6.9–7.7) individuals per 100,000 to 9.9 (95% CI 9.5–10.3) individuals per 100,000 in 2022. In 2022, Black female individuals had the highest prevalence of IIH with 22.7 individuals per 100,000 compared with the 13.7 White female individuals per 100,000. Patients aged 11–17 years showed the largest growth of IIH prevalence with female individuals increasing by 10 individuals per 100,000 by 2022. Overall, Black and Hispanic patients had the largest prevalence OR of IIH at 1.66 (95% CI 1.49–1.85) and 1.33 (95% CI 1.14–1.56), respectively, compared with White female patients.
Discussion
IIH is a rapidly increasing health care concern for the US population, particularly among adolescent patients. Black and Hispanic female individuals are most predominately affected by this incapacitating disorder.
Introduction
Idiopathic intracranial hypertension (IIH) is a rare vision-threatening1-3 disease mainly affecting overweight or obese women of reproductive age. In addition to being vision threatening, most patients with IIH experience debilitating headaches that significantly affect their quality of life3-6 and exhibit higher rates of anxiety, depression, and suicidality compared with age-matched and sex-matched controls.7 The most recent cost estimation in the United States, performed in 2007, calculated IIH had a direct cost of $444 million.8
Worldwide literature and a recent meta-analysis have estimated the incidence of IIH between 1989 and 2004 to be 0.03–2.6 persons per 100,0009 with this rate increasing to 5–49 persons per 100,000 reproductive aged women.10 Unfortunately, IIH has only been predicted to grow with the rising obesity epidemic.9,11-13 A previous study conducted in the United Kingdom found the prevalence of IIH grew from 26 per 100,000 female individuals in 2005 to 79 per 100,000 female individuals in 2017.14 While one of the most robust incidence and prevalence reports for IIH to date, it provides little insight into the prevalence of IIH within the United States. The IIH literature has called for a more robust epidemiology study to improve our understanding of IIH.9
IIH prevalence data are rarely reported worldwide and are especially limited within the United States. When it is reported, it typically only discusses the incidence in 1 state within the United States.9 As of 2014, it was also determined that there are no racial or ethnicity differences in the prevalence of IIH in this country.15 Of importance, the neuro-ophthalmology field has called for larger and higher-quality epidemiology studies to better understand IIH and inform health care and policy research.9 Therefore, the purpose of this study was to evaluate the prevalence of IIH from 2015 to 2022 in the United States while stratifying by sex, age, race, and ethnicity using a large national database.
Methods
To evaluate the prevalence, this study used the TriNetX US Collaborative Network, a platform containing aggregated, standardized, and deidentified electronic health record data that uses International Classification of Diseases, Ninth and Tenth Revision (ICD-9/10) codes. TriNetX includes more than 50 health care systems in the United States and holds data for more than 85 million patients. By obtaining data directly from the electronic health records, this database includes health care systems that support care for both insured and uninsured patients. Because all data in TriNetX are deidentified and used to evaluate population-level trends, these data have been deemed exempt from ethical approval by the Western Institutional Review Board through a qualified expert as defined in Section §164.514(b)(1) of the Health Insurance Portability and Accountability Act Privacy Rule. TriNetX has the ability to stratify searches by year, making obtaining prevalence data from 2015 to 2022 possible.
Because only population-level data were available, patients were included in this study only if they had an ICD-9/10 code (because these are automatically grouped in TriNetX) for IIH (G93.2) in addition to a code of papilledema (H47.1) or unspecified papilledema (H47.10). In addition, robust exclusion criteria were implemented to remove any causes of intracranial hypertension that were secondary to known etiology such as cerebral venous thrombosis, hydrocephalus, and neoplasms. This was determined using the updated UK consensus guidelines published by Mollan in 2018.2 All ICD codes for the exclusion criteria are listed in Table 1. The prevalence of IIH was determined through querying these inclusion and exclusion criteria and obtaining counts by age groupings, sex, race, and ethnicity combinations. Compared with the US Census, TriNetX has comparable percentages of White and Black patients at 54% and 13%, respectively. It has a slightly lower Hispanic prevalence of 8.8%.16 Other minority populations such as American Indian, Alaskan Native, and Asian were not reported because these cohorts are small within the US Census and in TriNetX, which also rounds small data counts causing inaccurate evaluations. The cohorts began at 11–17 years of age due to the low numbers of IIH in younger pediatric patients causing inaccurate data counts because TriNetX rounds any patient cohorts of 10 or less individuals. Therefore, stratifying by sex and race/ethnicity was not a viable option for patients younger than 11 years. Prevalence was calculated per 100,000 persons. Each year was determined by calendar year and began on January 1 and ended on December 31. Prevalence was calculated by dividing the number of patients with IIH within a group by the total amount of patients in TriNetX with that group (e.g., White patients with IIH divided by all White patients in TriNetX). Prevalence odds ratios (ORs) were calculated with 95% CIs. All statistics were completed through Microsoft Excel and R Studio (2021.09.0). Raw data are available in the supplemental material of this article (eTable 1, links.lww.com/WNL/D314). The authors take full responsibility for the data, analysis, interpretations, and conclusions made in this article.
Table 1.
Inclusion and Exclusion Criteria
| Inclusion criteria (patients are 18 y or older) | ICD-10 codes (ICD-9 codes in parentheses) |
| Idiopathic intracranial hypertension | G93.2 (348.2) |
| AND papilledema or unspecified papilledema | H47.1 (377.0), H47.10 (377.00) |
| Exclusion criteria (any of the following)2 | |
| Cerebral venous thrombosis | I67.7 (437.4) |
| Hydrocephalus | G91 (G91.0–G91.9) |
| Meningitis/encephalitis | G00 (320, 320.0, 320.1, 320.2, 320.3, 320.81, 320.89, 320.8, 320.82), G01 (320.7), G02 (115.01, 115.11, 321, 321.1–321.4, 321.8), G03 (322), G04 (320, 323.5), G05 (323.0, 323.4) |
| Brain tumor, abscess, etc. | C71 (191), C72 (192), C7A (209), C7B (209, 209.7), C79.3 (198.3, 198.4), C79.4 (198.4), D32 (225.2, 225.4), D33 (225), D3A (209), G93.3 (780.79), G93.4 (348.31, 348.39), G93.40 (348.30), G93.5 (348.4), G93.6 (348.5), G96.19 (349.2, 349.3), G06 (324.0, 324.1, 325.9), G07, G08 (325) |
| Antibiotics: tetracyclines, sulfonamides, and fluoroquinolone antibiotics | AM250, 10753 |
| Lupus | D68.62 (289.81), L93 (695.4) |
| Acute kidney failure and chronic kidney disease | N17-N19 (584, 585, 586) |
| Chiari malformation | Q07.02 (741.00), Q07.00, Q00-Q07 (741, 741.0, 741.9), Q93.5 |
| Addison disease | E27.49 (255.42, 255.5), E27.2 (255.41), E27.1 (255.41), E27.40 (255.41) |
| Cushing syndrome | E24 (255.0) |
| Hypoparathyroidism | E20 (275.4) |
| Hyperthyroidism | E05 (242, 242.8) |
| Down syndrome | Q90 (758.0) |
| Craniosynostosis | Q75.0 (756.0) |
| Turner syndrome | Q96 (758.6) |
Results
IIH Trends
The prevalence of IIH increased with 1,144 patients having IIH in 2015 compared with 2,077 patients in 2022 in the TriNetX network. This revealed a 1.35 times larger proportion of patients with IIH since 2015. The rate of IIH diagnoses increased from 7.3 individuals (95% CI 6.9–7.7) per 100,000 individuals in 2015 to 9.9 (95% CI 9.5–10.3) individuals per 100,000 in 2022. Black female individuals were predominately affected by an IIH diagnoses, with the rate increasing from 17.5 individuals per 100,000 in 2015 to 22.7 individuals per 100,000 in 2022. In addition, Hispanic female individuals had a 1.86 increase in IIH since 2015. This was the largest increase of any racial or ethnic group. Even after stratifying by race and ethnicity, the prevalence of this condition among male individuals overall seemed fairly consistent throughout the study period (Figure 1).
Figure 1. Prevalence of IIH From 2015 to 2022 Stratified by Race/Ethnicity and Sex.
IIH = idiopathic intracranial hypertension.
Although the pediatric cases in patients younger than 11 years were too small to stratify by race and ethnicity, data were extracted for this age range in 2022. An estimated 2.25 children per 100,000 aged 0–10 years were diagnosed with IIH (60/2,666,873), of which 45% were female individuals.
When stratified by ages 11–17 years, the race with the largest increase in prevalence was White female individuals who experienced a 7-fold increase in IIH diagnoses, with 19.9 individuals per 100,000 affected in 2022. Black female individuals also had a large rise in prevalence from 7.5 individuals to 18.2 individuals per 100,000 (Figure 2A).
Figure 2. Prevalence of IIH Among All Age Groups Stratified by Year, Race/Ethnicity, and Sex.
(A–G) Prevalence trends of IIH in different age groups. (A) 11- to 17-year-olds, (B) 18- to 25-year-olds, (C) 26- to 30-year-olds, (D) 31- to 35-year-olds, (E) 36- to 40-year-olds, (F) 41- to 50-year-olds, and (G) 51 years and older. IIH = idiopathic intracranial hypertension.
For ages 18–25 years, all female individuals experienced a large increase in IIH prevalence over the study period. The prevalence increased 2.5-fold from 11.9 individuals to 30.3 individuals per 100,000 in Black female individuals and increased 4.5-fold from 7.5 individuals to 33.7 individuals per 100,000 in Hispanic female individuals aged 18–25 years (Figure 2B). In all age groups beyond 25 years of age, Black female individuals demonstrated the highest prevalence of IIH by 2022. IIH diagnoses increased in prevalence during the study period in women 26–30 years of age, particularly among Black female individuals who experienced a 1.9-fold increase in IIH (Figure 2C).
Female individuals aged 31–35 years had the highest prevalence of IIH diagnoses. Black female individuals in this age range were particularly affected, with 62.3 Black female individuals per 100,000 receiving an IIH diagnosis. Overall, the total female population had 38.0 female individuals per 100,000 receiving an IIH diagnosis in 2022 (Figure 2D).
Individuals aged 36–40 years exhibited a modest increase in IIH from 2015 to 2022, suggesting the prevalence was stabilizing. This trend was also observed for individuals aged 41–50 and 50 years and older. IIH had its smallest prevalence among individuals aged 51 years and older with a projected estimate of 2 individuals affected per 100,000 (Figure 2, E–G).
Prevalence ORs were calculated and used to compare which populations were most affected by IIH in 2022. Compared with White male individuals, all female groups had an increased prevalence OR of receiving an IIH diagnosis with Black female individuals having the largest prevalence OR of 1.66 (95% CI 1.49–1.85). Hispanic female individuals had the second largest prevalence OR of 1.33 (95% CI 1.14–1.56). Both Black and Hispanic male individuals had comparable prevalence ORs with those of White male individuals (Figure 3, A and B).
Figure 3. Forest Plot of Prevalence Odds Ratio of IIH in 2022.
(A) Prevalence OR of females, stratified by race/ethnicity, with White female individuals serving as the reference group. (B) Prevalence OR of male individuals, stratified by race/ethnicity, with White male individuals serving as the reference group. IIH = idiopathic intracranial hypertension; OR = odds ratio.
TriNetX Population and Obesity Trends
Within the TriNetX network, the population was stratified by sex and race and evaluated for mean age and mean body mass index (BMI) within the entire network and in 2022 (Table 2). White female individuals made up most of the population at 8,801,142 in 2022. Overall, all female groups had a greater mean BMI compared with that of their male counterparts, and Black female individuals had the greatest BMI at 29 kg/m2 in 2022.
Table 2.
Characterization of the TriNetX Analytic Network Cohort, Mean Age, and BMI
| Race/ethnicity and sex | Number in TriNetX | Mean age in TriNetX (SD) | Mean BMI in TriNetX (SD) | Number in 2022 | 2022 mean age in TriNetX (SD) | 2022 mean BMI in TriNetX (SD) |
| White male | 22,707,228 | 47 (25) | 26.3 (7.05) | 6,862,238 | 45 (25) | 26.5 (7.28) |
| White female | 25,801,266 | 48 (25) | 26.7 (7.69) | 8,801,142 | 47 (24) | 26.9 (7.83) |
| Black male | 5,611,499 | 40 (23) | 25.9 (7.37) | 1,595,572 | 38 (24) | 26 (7.73) |
| Black female | 6,662,401 | 42 (23) | 28.5 (8.62) | 2,239,015 | 41 (23) | 29 (8.8) |
| Hispanic male | 3,681,516 | 32 (22) | 25.6 (7.16) | 1,021,465 | 29 (22) | 25.7 (7.51) |
| Hispanic female | 4,222,642 | 34 (21) | 26.9 (7.78) | 1,305,549 | 32 (22) | 27.1 (7.92) |
Abbreviation: BMI = body mass index.
Obesity prevalence was determined for the TriNetX population who had a BMI value available. Obesity was defined as having a BMI of greater than 30 kg/m2, the same value defined in the study conducted by Miah et al.13 In 2015, 4,396,387 patients had a BMI value available compared with 7,255,513 patients in 2022. Overall, obesity prevalence was found to increase by approximately 1.5% from 2015 to 2022 in the total population. When stratified by race and sex, Black female individuals were found to have a higher prevalence of obesity in all years. The percent of Black female individuals with obesity increased from 37.42% to 38.8% in 2022. Overall, the total female population had a higher prevalence of obesity at 27.14% compared with the total male population at 23.06%. Compared with the female population, Black female individuals had a 9.93% higher prevalence of obesity (Figure 4A).
Figure 4. Mapping Obesity Trends of Patients Who Have a BMI Value Available (BMI >30 kg/m2).
(A) Obesity trends within TriNetX among patients aged 18 years and older. (B) Obesity trends within TriNetX among the entire population (not stratified by age). (C) Obesity trends within TriNetX among patients aged 0–17 years. BMI = body mass index.
When stratified by age, patients younger than 18 years showed a large increase in obesity rates with Black female, Hispanic female, and Hispanic male groups having the highest prevalence of obesity. Within the entire pediatric group, obesity increased from 0.73% (4,170/570,763) in 2015 to 5.60% (87,205/1,556,881) in 2022 (Figure 4C). The TriNetX population aged 18 years or older had comparable obesity rates with that of the total population, with Black female individuals having the highest prevalence of obesity at 43.55% (210,423/483,223) in 2022. Hispanic female individuals had the second highest prevalence of 38.24% (99,313/259,690) in 2022 (Figure 4, A–C).
Discussion
Our study highlights the growing concern of IIH in the United States, with a 1.35-fold increase in prevalence between 2015 and 2022 equating to 11.3 female individuals per 100,000 in 2015 to 15.4 female individuals per 100,000 in 2022. Specifically, Black female individuals had the highest prevalence of IIH compared with that in any other race or ethnicity group. These findings support previous literature showcasing a higher incidence of IIH in Black female individuals compared with White female individuals.17 In addition, this research found an increased prevalence OR of IIH in Black and Hispanic or Latino female individuals compared with White female individuals, which was also supported by prior literature.18
Our study also highlighted the increasing prevalence of IIH in patients between the ages of 11 and 35 years. White female individuals had the highest prevalence among pediatric patients aged 11–17 years, but this did not correspond to the pediatric obesity trend found. This may be due to racial disparities within diagnosis and treatment of disease in the United States.19 Otherwise, Black female groups had the largest prevalence of IIH by 2022 in each age group. Patients aged 11–17 years also seemed to have the largest prevalence growth of IIH with a 7-fold increase in White female individuals and 2.4-fold increase in Black female individuals. This increasing IIH trend is likely due to the rising rates of obesity. Especially among pediatric patients, obesity roughly doubled during the coronavirus disease 2019 (COVID-19) pandemic.20 This correlates with our data showing the largest increase of IIH from 11- to 17-year-olds occurring after 2020 when the COVID-19 pandemic began. Our data also showcased a large increase in obesity prevalence in pediatric patients aged 17 years and younger between 2018 and 2021. Since 2015, the obesity prevalence grew 4.87% in pediatric patients.
IIH has a known association with obesity and was predicted to rise with obesity levels.9,12,13 Our study found that obesity increased in adults aged 18 years and older by 2.67% from 2015 to 2022. With this increase in obesity, it is unsurprising that IIH prevalence has also increased. In addition, because Black female groups had the largest obesity percentage with a 10.42% increase over the total female group, this may be why Black female individuals are disproportionately affected by IIH within the United States. Hispanic female groups also exhibited a 5.12% higher obesity percentage compared with the total female group.
Disproportionate effect of obesity on minority patients is a multifaceted problem within the United States predominately affecting low-income populations.21,22 Potentially caused by health behaviors, biological factors, and or social environmental factors, tackling obesity disparities is a complex issue that will require multidisciplinary interventions.21 Although obesity was found to disproportionately affect the Black population in our analysis, there are multiple socioeconomic factors affecting BMI including education, access to food, and neighborhood resources.23 To fully address the disparities found in IIH, it will likely be necessary to target the disparities prevalent in obesity itself. Although most patients with IIH are overweight or obese, some patients have a normal BMI and still develop IIH. Determining the etiology of IIH will allow researchers and physicians alike to better understand the role of obesity within this disease.
Similar to the United States, the obesity trend in Wales seemed to increase with approximately 28% of the population being obese in 2015. Our study found 25.4% of the population was obese in 2015; however, our BMI criteria were slightly higher at greater than 30 kg/m2. Although Wales found a larger prevalence of IIH at 75 persons per 100,000, our study had many additional secondary exclusions of IIH, which may account for this difference.13
The increasing prevalence of IIH is a high health care concern. Limited treatments and therapies exist for IIH11,24 especially in terms of how to manage headaches in patients.2 Likely, this contributed to the 442% increased in-hospital admission between 2022 and 2014 with 38% of patients requiring a repeat admission.11 In addition to these barriers to care, patients have significant mental health comorbidities with higher rates of depression, anxiety, and suicidality.7
There are limitations to this study design. Because TriNetX uses ICD codes, we were not able to specify the exact modified Dandy criteria in our study such as a lumbar puncture opening pressure.2 Because of this limitation, our study aimed to have robust exclusion criteria to eliminate nonidiopathic causes of intracranial hypertension. This also means we excluded the rare presentation of IIH without papilledema. TriNetX also has a rounding feature when there are very few patients in a specific search to protect patient identities, making obtaining pediatric data for patients younger than 11 years not possible for this study. In addition, this rounding features sometimes caused a minute discrepancy in our raw data sheet (eTable 1, links.lww.com/WNL/D314). In addition, it is important to note that TriNetX obtains information directly from electronic health record systems of organizations. Because there are more than 50, it is not reported how each organization and center collects data such as race and ethnicity; therefore, it is not known whether this information is self-reported or physician reported.
Our study also depended on ICD coding; this is likely an underrepresentation of the actual number of cases of IIH present because this dataset is only representative of those who were able to obtain medical care from a health care system represented on TriNetX. Underreporting of IIH is further supported by the current obesity trends within the United States likely being higher than what was identified within the TriNetX population because only patients with a reported BMI were used in this analysis. In 2016, the prevalence of obesity for persons aged 20 years or older was found to be 39.8% compared with our reported result in 2016 of 29.66%. This may be due to the different age inclusion and BMI criteria where our study required a BMI >30 kg/m2, whereas they used a measure of greater than or equal to 30 kg/m2.25 In addition, it should be noted that our study used BMI as a marker of obesity within the United States; however, BMI does not take into account muscle mass and fitness levels of individuals.26
IIH had a 1.35-fold increase in prevalence between 2015 and 2022 confirming a growth in prevalence from 11.3 female individuals per 100,000 in 2015 to 15.4 female individuals per 100,000 in 2022. In addition, pediatric cases had the largest increase in growth of IIH from 2.5 persons per 100,000 in 2015 to 12.5 per 100,000 in 2022. Black and Hispanic female groups had the largest prevalence OR of having IIH, which may be due to the higher obesity prevalence among both populations compared with the total population. In conclusion, IIH is a rapidly growing health care concern for Americans in which Black and Hispanic female groups are most predominately affected by this blinding disorder.
Acknowledgment
The authors thank Dr. David Kaelber and MetroHealth for TriNetX network access. This work was presented at the North American Neuro-Ophthalmology meeting in Orlando, FL, March 2023.
Glossary
- BMI
body mass index
- COVID-19
coronavirus disease 2019
- ICD-9/10
International Classification of Diseases, Ninth and Tenth Revision
- IIH
idiopathic intracranial hypertension
- OR
odds ratio
Appendix. Authors
| Name | Location | Contribution |
| Jacqueline K. Shaia, MS | Case Western Reserve University School of Medicine; Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data; study concept or design; and analysis or interpretation of data |
| Neha Sharma, BA, MPH | Case Western Reserve University School of Medicine; Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data |
| Madhukar Kumar, BSE | Case Western Reserve University School of Medicine; Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH | Major role in the acquisition of data |
| Jeffrey Chu, BS | Case Western Reserve University School of Medicine; Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH | Drafting/revision of the article for content, including medical writing for content; major role in the acquisition of data |
| Christopher Maatouk, MD | Case Western Reserve University School of Medicine; Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH | Drafting/revision of the article for content, including medical writing for content |
| Katherine Talcott, MD | Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland; Cole Eye Institute, Cleveland Clinic, OH | Drafting/revision of the article for content, including medical writing for content; study concept or design; and analysis or interpretation of data |
| Rishi Singh, MD | Center for Ophthalmic Bioinformatics Research at the Cole Eye Institute, Cleveland, OH; Cleveland Clinic Martin Hospitals, Stuart, FL | Drafting/revision of the article for content, including medical writing for content; study concept or design; and analysis or interpretation of data |
| Devon A. Cohen, MD | Cole Eye Institute, Cleveland Clinic, OH | Drafting/revision of the article for content, including medical writing for content; study concept or design; and analysis or interpretation of data |
Study Funding
This project was supported by the Clinical and Translational Science Collaborative (CTSC) of Cleveland, which is funded by the NIH, National Center for Advancing Translational Science (NCATS), Clinical and Translational Science Award (CTSA) grant, UL1TR002548. P30EY025585(BA-A), Research to Prevent Blindness (RPB) Challenge Grant, and Cleveland Eye Bank Foundation Grant National Eye Institute: T32 EY024236 (J.K.S.).
Disclosure
J.K. Shaia: funding through the National Eye Institute: T32 EY024236. N. Sharma reports no disclosures relevant to this manuscript. M. Kumar reports no disclosures relevant to this manuscript. J. Chu reports no disclosures relevant to this manuscript. C. Maatouk reports no disclosures relevant to this manuscript. K.E. Talcott reports personal fees from Genentech/Roche, Apellis, and Eyepoint and research fees from Zeiss and Regenxbio. R.P. Singh reports personal fees from Genentech/Roche, Alcon, Novartis, Regeneron, Asclepix, Gyroscope, Bausch and Lomb, and Apellis. D.A. Cohen has no disclosures relevant to this manuscript. Go to Neurology.org/N for full disclosures.
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