ABSTRACT
Multiple sclerosis (MS) is an autoimmune demyelinating disease that often initially presents with optic neuritis (ON). Little is known about the demographic factors and familial histories that may be associated with the development of MS after a diagnosis of ON. We utilised a nationwide database to characterise specific potential drivers of MS following ON as well as analyse barriers to healthcare access and utilisation. The All of Us database was queried for all patients who were diagnosed with ON and for all patients diagnosed with MS after an initial diagnosis of ON. Demographic factors, family histories, and survey data were analysed. Multivariable logistic regression was performed to analyse the potential association between these variables of interest with the development of MS following a diagnosis of ON. Out of 369,297 self-enrolled patients, 1,152 were identified to have a diagnosis of ON, while 152 of these patients were diagnosed with MS after ON. ON patients with a family history of obesity were more likely to develop MS (obesity odd ratio: 2.46; p < .01). Over 60% of racial minority ON patients reported concern about affording healthcare compared with 45% of White ON patients (p < .01). We have identified a possible risk factor of developing MS after an initial diagnosis of ON as well as alarming discrepancies in healthcare access and utilisation for minority patients. These findings bring attention to clinical and socioeconomic risk factors for patients that could enable earlier diagnosis and treatment of MS to improve outcomes, particularly in racial minorities.
KEYWORDS: Optic neuritis, multiple sclerosis, All of Us, barriers to care, risk factors
Introduction
Optic neuritis (ON) is a relatively common cause of acute optic neuropathy in young patients. ON often presents as the first sign of multiple sclerosis (MS).1 One study estimated that 50% of ON patients are diagnosed with MS within 15 years.2 The same study also showed that magnetic resonance imaging (MRI) could be used to estimate the likelihood that a patient with ON will be later diagnosed with MS. Earlier and more accurate prediction of those who are likely to develop MS could enable more effective and targeted use of diagnostic tools and early treatments.
Little work has been done on whether or how demographic factors and family histories are associated with MS diagnosis following an ON diagnosis. While MS is more common in women, especially those with other autoimmune diseases or who have a family history of MS,3 it is unknown whether these associations remain when looking at the ON patient population. In this study, we have characterised the demographic and family-related drivers of MS development specifically within patients who have been diagnosed with ON. We have also characterised possible barriers to healthcare access and utilisation that are prevalent among the ON population, which may also lead to missed opportunities for earlier detection and treatment of MS, particularly in racial minorities. In contrast to previous works which have been limited by small sample sizes,4–6 we used a large nationwide database (the All of Us database produced by the National Institutes of Health) for our analyses. Based upon our review of the English language ophthalmological literature, we believe that our study is unique.
Methods
Data were collected from the All of Us research programme led by the National Institutes of Health. The All of Us Registered Tier Dataset v6 was used, which includes data on 369,297 self-enrolled patients in the United States (US) of America (USA). The dataset is meant to reflect the diversity of the US and include subjects from subpopulations that have been historically underrepresented in healthcare research. Clinical patient information as well as survey answers regarding topics like healthcare access and utilisation, lifestyle, and family history are included in the dataset.
The database was queried for all patients diagnosed with ON (SNOMED code 66760008). A subset of patients diagnosed with ON before or on the same day as an MS (SNOMED code 24700007) diagnosis was created as a comparison group. Patient demographics and survey data were analysed to characterise patient age, sex, race/ethnicity, family medical history, alcohol use, smoking tobacco use, and smokeless tobacco use. Survey data on patients’ healthcare access and utilisation were also collected, analysed, and grouped by race.
In accordance with the All of Us data sharing policy, all groups with fewer than 20 patients were combined with other groups or excluded from the analysis. All racial groups other than White were grouped together for the healthcare access and utilisation analysis to allow for results that meet the 20 patient minimum requirement from the data sharing policy. Multivariate logistic regression was used to characterise the association of the studied variables with development of MS after an initial diagnosis of ON. Only patients that responded to the family health history survey questions were included in the regression. Chi-squared tests for significance were used to compare healthcare utilisation and access survey responses by race. p-values < .05 were considered statistically significant. All analyses were conducted in Python 3.8.12 (Python Software Foundation, USA) using pandas 1.3.5, Numpy 1.21.5, and Statsmodels 0.13.1 packages.7
Results
Descriptive statistics of patient demographics
We included 1,152 ON patients in this study. Descriptive statistics of these patients can be found in Table 1. Of these, 15.8% of individuals (n = 182) were diagnosed with MS after their ON diagnosis. The mean age of the ON patients were 56.9 years old (standard deviation = 15.3 years). Sixty-nine per cent of the patients (n = 764) were female, while 31.0% (n = 343) were male. The races identified by the ON patients were 54.1% White, 22.9% Black, 17.9% Hispanic, 2.9% multiracial, and 2.2% Asian. Eighty-eight per cent (n = 1008) of the ON patients reported drinking alcohol, 38% (n = 439) reported smoking tobacco, and 6% (n = 70) reported using smokeless tobacco.
Table 1.
Characteristics of optic neuritis patients.
| Characteristic | Count | %* |
|---|---|---|
| Age (years) | Mean = 56.9 (standard deviation = 15.3) | |
| Sex | ||
| Male | 343 | 31.0 |
| Female | 764 | 69.0 |
| Race (categories not mutually exclusive) | ||
| White | 592 | 54.1 |
| Black | 251 | 22.9 |
| Asian | 24 | 2.2 |
| Hispanic | 196 | 17.9 |
| More than one race | 32 | 2.9 |
| Family history* | ||
| Irritable bowel syndrome | 49 | 9.9 |
| Type 1 diabetes mellitus | 34 | 6.9 |
| Anaemia | 49 | 9.9 |
| Hypothyroidism | 70 | 14.2 |
| Hyperthyroidism | 30 | 6.1 |
| Rheumatoid arthritis | 63 | 12.8 |
| Neuropathy | 33 | 6.7 |
| Obesity | 85 | 17.2 |
| Smoking tobacco user | 439 | 38.1 |
| Alcohol user | 1008 | 87.5 |
| Smokeless tobacco user | 70 | 6.1 |
*Per cents were calculated out of the patients that responded to the family history survey (n = 494) and not all optic neuritis patients.
A survey on their family health histories was responded to by 494 ON patients. Seventeen percent of ON patients reported a family history of obesity (n = 85) and 14% reported one of hypothyroidism (n = 70); 13% reported one of rheumatoid arthritis (n = 63) while 10% reported one of irritable bowel syndrome (n = 49). A family history of MS occurred in too few individuals (n < 20) to be reported according to data privacy guidelines set by All of Us.
Barriers to healthcare access and utilisation
Rates of healthcare access and utilisation of ON patients are characterised in Table 2. This table also includes patient survey statistics on common barriers to care among this patient population. This survey was responded to by 544 out of the 1,152 ON patients. In the 12 months prior to the survey, 8.3% of respondents did not consult a primary care physician and 19.9% did not see an ophthalmologist or optometrist. Of those patients who consulted an ophthalmologist or optometrist, 44.7% made only one visit.
Table 2.
Survey results for healthcare access and utilisation of patients with optic neuritis.
| Condition | Count | %* |
|---|---|---|
| During the past 12 months, were you told by a health care provider or doctor’s office that they did not accept your health care coverage? | ||
| Yes | 69 | 12.8 |
| No | 470 | 87.2 |
| In regard to your health insurance or health care coverage, how does it compare to a year ago? | ||
| Better | 63 | 11.7 |
| Worse | 49 | 9.1 |
| About the same | 426 | 79.2 |
| Is there a place that you usually go to when you are sick or need advice about your health? | ||
| Yes, there is a place | 428 | 82.0 |
| No, there is no place | - | - |
| There is more than one place | 94 | 18.0 |
| What kind of place do you go to most often? | ||
| Doctor’s office | 479 | 95.4 |
| Urgent care | 23 | 4.6 |
| Emergency room | - | - |
| Some other place | - | - |
| Don’t go to one place most often | - | - |
| About how long has it been since you last saw or talked to a doctor or other health care provider about your own health? | ||
| Never | - | - |
| 6 months or less | 494 | 93.4 |
| More than 6 months, but not more than 1 year | 35 | 6.6 |
| More than 1 year, but not more than 2 years | - | - |
| More than 2 years, but not more than 5 years | - | - |
| More than 5 years | - | - |
| During the past 12 months, have you seen or talked to a general doctor who treats a variety of illnesses (a physician in general practice, primary care, family medicine, or internal medicine)? | ||
| Yes | 487 | 91.7 |
| No | 44 | 8.3 |
| What is the total number of general doctor visits you made in the last 12 months? | ||
| 1 | 65 | 13.8 |
| 2–3 | 169 | 36.0 |
| 4–5 | 89 | 18.9 |
| 6–7 | 42 | 8.9 |
| 8–9 | 22 | 4.7 |
| 10–12 | 32 | 6.8 |
| 13–15 | - | - |
| 16 or more | 51 | 10.9 |
| During the past 12 months, have you seen or talked to an optometrist, ophthalmologist, or eye doctor (someone who prescribes eyeglasses)? | ||
| Yes | 363 | 80.1 |
| No | 90 | 19.9 |
| What is the total number of optometrist, ophthalmologist, or eye doctor visits you made in the last 12 months? | ||
| 1 | 160 | 44.7 |
| 2–3 | 118 | 33.0 |
| 4–5 | 39 | 10.9 |
| 6 or more | 41 | 11.5 |
| During the past 12 months, have you seen or talked to a doctor who specialises in a particular medical disease or problem? | ||
| Yes | 367 | 83.8 |
| No | 71 | 16.2 |
| What is the total number of visits you made to a doctor who specialises in a particular medical disease or problem in the last 12 months? | ||
| 1 | 50 | 13.9 |
| 2–3 | 145 | 40.3 |
| 4–5 | 67 | 18.6 |
| 6–7 | 27 | 7.5 |
| 8–15 | 41 | 11.4 |
| 16 or more | 30 | 8.3 |
| In the past 12 months, have you delayed getting care for any of the following reasons? | ||
| Didn’t have transportation | ||
| Yes | 59 | 11.1 |
| No | 472 | 88.9 |
| You live in a rural area where distance to the health care provider is too far | ||
| Yes | 22 | 4.2 |
| No | 498 | 95.8 |
| You were nervous about seeing a health care provider | ||
| Yes | 57 | 11.3 |
| No | 449 | 88.7 |
| Couldn’t get time off work | ||
| Yes | 43 | 8.7 |
| No | 451 | 91.3 |
| Couldn’t get child care | ||
| Yes | 30 | 6.4 |
| No | 442 | 93.6 |
| Couldn’t afford the copay | ||
| Yes | 45 | 9.6 |
| No | 426 | 90.4 |
| Your deductible was too high/or could not afford the deductible | ||
| Yes | 53 | 11.4 |
| No | 413 | 88.6 |
| You had to pay out of pocket for some or all of the procedure | ||
| Yes | 70 | 15.0 |
| No | 397 | 85.0 |
| During the past 12 months, was there any time when you needed any of the following, but didn’t get it because you couldn’t afford it? | ||
| Prescription medication | ||
| Yes | 83 | 15.5 |
| No | 451 | 84.5 |
| Eyeglasses | ||
| Yes | 74 | 15.4 |
| No | 405 | 84.6 |
| To see a regular doctor or general health provider | ||
| Yes | 21 | 4.3 |
| No | 463 | 95.7 |
| To see a specialist | ||
| Yes | 53 | 11.2 |
| No | 422 | 88.8 |
| Follow-up care | ||
| Yes | 35 | 7.5 |
| No | 432 | 92.5 |
| If you get sick or have an accident, how worried are you that you will be able to pay your medical bills? | ||
| Very worried | 68 | 12.9 |
| Somewhat worried | 185 | 35.2 |
| Not at all worried | 273 | 51.9 |
| During the past 12 months, were any of the following true for you? | ||
| You skipped medication to save money | ||
| Yes | 54 | 10.1 |
| No | 481 | 89.9 |
| You took less medicine to save money | ||
| Yes | 55 | 10.4 |
| No | 476 | 89.6 |
| You delayed filling a prescription to save money | ||
| Yes | 82 | 15.6 |
| No | 443 | 84.4 |
| You asked your doctor for a lower cost medication to save money | ||
| Yes | 102 | 20.2 |
| No | 403 | 79.8 |
| You used alternative therapies to save money | ||
| Yes | 43 | 8.7 |
| No | 449 | 91.3 |
*Per cents are out of total responders to the healthcare access and utilisation survey (n = 544).
Feeling either ‘very worried’ or ‘somewhat worried’ about affording healthcare was reported by 48.1% of respondents. In addition: 20.2% asked their physician for lower-cost medications; 15.6% delayed filling prescriptions to save money; 10.1% reported skipping medications to save money; 12.8% reported having their insurance denied at a healthcare provider’s office; 11.4% and 9.6% reported delaying care due to inability to pay a deductible or copay, respectively; 11.1% reported delaying care due to difficulty in obtaining transportation to a healthcare provider’s location; and 8.7% delayed care due to their inability to miss work.
Rates of healthcare access and utilisation of ON patients by race are reported in Table 3. Responses to the survey were received from 361 White ON patients and 151 minority (Black, Asian, Hispanic, or more than one race) ON patients. Only survey questions with at least 20 patients in all response categories were included in the table per the All of Us data sharing policy. Seeing a specialist in the past year was reported by 87.2% of White respondents compared with 77.8% of minority respondents (p = .02). Delays in getting care were reported by 17.1% of minority respondents compared with 8.2% of White respondents (p < .01). Delaying care because of inability to pay a deductible was reported by 9.9% of White respondents versus 16.0% of minority respondents (p = .07). Not seeing a specialist because they could not afford it was reported by 18.2% of minority respondents as opposed to 8.1% of White respondents (p < .01). Feeling either ‘very worried’ or ‘somewhat worried’ about affording healthcare was reported by 60.7% of minority respondents versus 45% of White respondents (p < .01).
Table 3.
Select survey results for healthcare access and utilisation of White and minority (Black, Asian, Hispanic, or more than one race) patients with optic neuritis. *percents are out of total responders to the healthcare access and utilisation survey in each demographic group.
| White (n = 361) |
Minority (n = 151) |
||||
| Condition |
Count |
%* |
Count |
%* |
p-value |
| During the past 12 months, were you told by a health care provider or doctor’s office that they did not accept your health care coverage? | |||||
| Yes | 43 | 12.0 | 23 | 15.5 | .28 |
| No | 316 | 88.0 | 125 | 84.5 | |
| During the past 12 months, have you seen or talked to an optometrist, ophthalmologist, or eye doctor (someone who prescribes eyeglasses)? | |||||
| Yes | 240 | 80.0 | 101 | 78.9 | .80 |
| No | 60 | 20.0 | 27 | 21.1 | |
| During the past 12 months, have you seen or talked to a doctor who specialises in a particular medical disease or problem? | |||||
| Yes | 252 | 87.2 | 98 | 77.8 | .02 |
| No | 37 | 12.8 | 28 | 22.2 | |
| In the past 12 months, have you delayed getting care for any of the following reasons? | |||||
| Didn’t have transportation | |||||
| Yes | 29 | 8.2 | 25 | 17.1 | <.01 |
| No | 324 | 91.8 | 121 | 82.9 | |
| Your deductible was too high/or could not afford the deductible | |||||
| Yes | 31 | 9.9 | 21 | 16.0 | .07 |
| No | 281 | 90.1 | 110 | 84.0 | |
| You had to pay out of pocket for some or all of the procedure | |||||
| Yes | 45 | 14.4 | 24 | 18.2 | .32 |
| No | 267 | 85.6 | 108 | 81.8 | |
| During the past 12 months, was there any time when you needed any of the following, but didn’t get it because you couldn’t afford it? | |||||
| Prescription medication | |||||
| Yes | 52 | 14.5 | 30 | 20.5 | .10 |
| No | 306 | 85.5 | 116 | 79.5 | |
| To see a specialist | |||||
| Yes | 26 | 8.1 | 24 | 18.2 | <.01 |
| No | 294 | 91.9 | 108 | 81.8 | |
| If you get sick or have an accident, how worried are you that you will be able to pay your medical bills? | |||||
| Very worried | 30 | 8.6 | 35 | 24.1 | <.01 |
| Somewhat worried | 127 | 36.4 | 53 | 36.6 | |
| Not at all worried | 192 | 55.0 | 57 | 39.3 | |
| During the past 12 months, were any of the following true for you? | |||||
| You delayed filling a prescription to save money | |||||
| Yes | 54 | 15.4 | 26 | 18.2 | .44 |
| No | 297 | 84.6 | 117 | 81.8 | |
| You asked your doctor for a lower cost medication to save money | |||||
| Yes | 70 | 21.1 | 30 | 21.0 | .98 |
| No | 262 | 78.9 | 113 | 79.0 | |
*Per cents are out of total responders to the healthcare access and utilisation survey in each demographic group.
Multivariate logistic regression results
Table 4 contains the results from the multivariate logistic regression model, which characterises drivers of eventual MS diagnosis among the ON population. Of the 1,152 ON patients in the database, 494 of them responded to the family health history survey and were thus included in the regression analysis. ON patients classified as Black, Asian, or multiracial had lower odds of eventual MS diagnosis compared with White patients, but these findings were not statistically significant. ON patients with a family history of obesity were significantly more likely to be diagnosed with MS compared with those without a family history of obesity (obesity odds ratio [OR]: 2.46; p < .01). ON patients with a family history of irritable bowel syndrome (IBS) were significantly less likely to develop MS (OR: 0.30; p = .02). All other surveyed familial medical conditions, such as rheumatoid arthritis, type 1 diabetes mellitus, anaemia, and neuropathy, had no significant association with eventual MS diagnosis. Use of alcohol, cigarettes, or smokeless tobacco were not statistically significantly associated with MS diagnosis. Gender was also not associated with the risk of MS diagnosis following ON diagnosis.
Table 4.
Multivariate logistic regression of factors associated with a diagnosis of multiple sclerosis after an initial diagnosis of optic neuritis.
| Covariate | Odds Ratio | 95% CI | p-value |
|---|---|---|---|
| Age | 0.96 | 0.94, 0.97 | <.01 |
| Sex | |||
| Male | 1.00 | - | |
| Female | 0.99 | 0.52, 1.89 | .98 |
| Race (categories not mutually exclusive) | |||
| White | 1.00 | - | |
| Black | 0.87 | 0.42, 1.84 | .72 |
| Asian | 0.71 | 0.14, 3.60 | .68 |
| More than one race | 0.12 | 0.01, 1.00 | .05 |
| Family history | |||
| Irritable bowel syndrome | 0.30 | 0.11, 0.82 | .02 |
| Type 1 diabetes mellitus | 0.86 | 0.33, 2.25 | .76 |
| Anaemia | 1.40 | 0.65, 3.03 | .39 |
| Hypothyroidism | 1.08 | 0.55, 2.14 | .82 |
| Hyperthyroidism | 1.74 | 0.65, 4.65 | .27 |
| Rheumatoid arthritis | 1.37 | 0.67, 2.80 | .40 |
| Neuropathy | 1.83 | 0.73, 4.60 | .20 |
| Obesity | 2.46 | 1.37, 4.39 | <.01 |
| Alcohol drinker | 1.02 | 0.31, 3.33 | .97 |
| Cigarette smoker | 1.18 | 0.70, 1.98 | .54 |
| Smokeless tobacco user | 0.88 | 0.32, 2.42 | .80 |
CI = confidence intervals
Discussion
Our large database study has characterised ON patients who go on to be diagnosed with MS and identifies a possible risk factor of this diagnosis. We have also identified a small, but significant, portion of ON patients who reported low utilisation of healthcare and difficulties in accessing providers and treatment, which may lead to missed MS diagnoses and delayed care. Additionally, we have identified disparities in healthcare access and utilisation between Whites and minorities. The findings presented may allow clinicians to identify patients at greater risk of being diagnosed with MS and monitor them accordingly. The findings also highlight the relative inability of minorities to access and utilise healthcare at the same rate as Whites, raising concerns for under-diagnosis of MS in traditionally under-represented and disadvantaged racial groups.
ON is widely known as an initial sign of MS.8 Abnormal brain MRI findings in ON patients are a strong predictor of future development of MS.2 However, in the absence of abnormal imaging findings at the time of ON diagnosis, other factors, such as those found in this study, can play a role assessing the risk of diagnosis of MS. More accurate prediction of ON patients who are likely to go on to be diagnosed with MS can enable more efficient use of diagnostic tools and early treatment. Conversely, accurate and timely prediction of those who are not likely to be diagnosed with MS following an ON diagnosis may lead to reduction of unnecessary screenings and significant cost-savings.9,10 We have identified one predictor, family history of obesity, of MS diagnosis following an ON diagnosis that is supported by prior research.3,11 We found that the association between obesity and the diagnosis of MS is consistent when looking at an isolated population of ON patients. We did not find that family history of autoimmune diseases like diabetes mellitus, thyroid disorder, and rheumatoid arthritis were significantly correlated with a greater risk of being diagnosed with MS. Some previous work has suggested there may be a correlation between familial autoimmune diseases and MS, but the evidence is mixed.12,13 Our study also shows that White ON patients had a higher likelihood of being diagnosed with MS, a finding which was not statistically significant, but is consistent with the observed prevalence of MS stratified by race.3 We found a family history of IBS reduces the odds of ON patients being diagnosed with MS. This finding is in opposition to well established connections of MS to autoimmune and inflammatory conditions; the reliability of this finding is limited, relating to the variability in the prevalence of IBS diagnoses as a result of the functional nature of IBS, variation in diagnostic criteria, and differences in collection methods of diagnostic patient survey question data.14 Alcohol and chewing tobacco use have previously been cited as protective factors against MS development, while smoking tobacco has been shown to be a risk factor.15 Our findings for those substance uses were statistically inconclusive.
Approximately half of all ON patients reported some concern regarding being able to afford healthcare. When race was used to further break down this result, we found that 45% of White respondents reported concern about affording healthcare while over 60% of minority respondents reported the same concern (p < .01). Many patients also reported delaying care due to difficulties with denied health insurance, costly medications, and unaffordable deductibles and copays. Further race-based analysis found minorities were also more likely to report delays in receiving care due to a lack of transport (p < .01). Minorities also experienced more delays in care due to inability to afford the deductible or copay, but those findings were not statistically significant. A significant portion of ON patients also reported low levels of healthcare utilisation; approximately half of all patients reported visiting an optometrist or ophthalmologist once or less in the year prior to the survey. While minorities were not less likely than Whites to have seen an optometrist or ophthalmologist in the past year (p = .80), they were more likely to have not seen a specialist because they could not afford it (p < .01).
Significant barriers to care and low levels of healthcare utilisation among the ON population can lead to delayed MS diagnoses and lack of appropriate speciality care.16 Additionally, once diagnosed, MS can be very costly, as the condition requires frequent treatment and care, which itself requires adequate access to healthcare resources.17 There are also more treatment options available in the earlier stages of MS.18 ON patients who are unable to afford or otherwise access care are not only at greater risk of developing MS but are also more likely to suffer more severe MS complications.19 Early effective treatment has been shown to reduce long-term morbidity due to MS.20 When these factors are examined under the lens of race-based disparities in access and affordability found in this study, it is evident that there are likely patients who are not receiving adequate care or have not yet received an MS diagnosis. These patients who experience delays in diagnosis are also at risk for worse outcomes due to delays in initiating treatment.
We recognise that there are limitations of this study. First, despite the use of a large nationwide database, the sample sizes may still be too small to fully identify important predictors of MS development. Second, there is response bias as patients were not required to complete all surveys. Recall bias causing under/over reporting of familial health history must also be considered for this study. Third, due to our relatively small sample size, our regression model was also unable to account for other possible risk factors for MS diagnosis, as this could have led to potential overfitting and inaccurate model estimators.
Despite this limitation, our sample size is larger than those of many other studies that have focused on a similar topic.4–6 Additionally, the All of Us dataset is composed of self-registered US individuals. It is possible that the All of Us patient population is not a representative sample of the US population; some groups may be over-represented or under-represented, which may limit the study’s generalisability. Finally, patients within the dataset were not required to answer every question, so there are some missing data.
In conclusion, we have identified a possible risk factor of MS diagnosis after an initial diagnosis of ON as well as gaps in healthcare access and utilisation for some ON patients, particularly those from minority racial groups. These findings can enable earlier diagnosis for ON patients likely to develop MS and bring attention to the significant access and affordability concerns experienced by a disproportionate amount of minority ON patients. Delays in diagnosis and the subsequent delays in treatment increase the risk of poorer outcomes for these minority patients that are already struggling to receive necessary care. The inequities identified in this study may also inform diagnosis and care of other conditions linked to ON not investigated in our study such as neuromyelitis optica spectrum disorders and myelin oligodendrocyte glycoprotein antibody disorders. Future work can focus on better understanding and addressing these racial disparities within the ON patient population as well as identifying other risk or protective factors for eventual MS diagnosis.
Funding Statement
The authors reported there is no funding associated with the work featured in this article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
The data used for this study are available at https://www.researchallofus.org/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used for this study are available at https://www.researchallofus.org/.