A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database.

BACKGROUND: Validated methods to identify neuro-ophthalmologists in administrative data do not exist. The development of such method will facilitate research on the quality of neuro-ophthalmic care and health care utilization for patients with neuro-ophthalmic conditions in the United States. METHODS: Using nationally representative, 20% sample from Medicare carrier files from 2018, we identified all neurologists and ophthalmologists billing at least 1 office-based evaluation and management (E/M) outpatient visit claim in 2018. To isolate neuro-ophthalmologists, the National Provider Identifier numbers of neuro-ophthalmologists in the North American Neuro-Ophthalmology Society (NANOS) directory were collected and linked to Medicare files. The proportion of E/M visits with International Classification of Diseases-10 diagnosis codes that best distinguished neuro-ophthalmic care ("neuro-ophthalmology-specific codes" or NSC) was calculated for each physician. Multiple logistic regression models assessed predictors of neuro-ophthalmology specialty designation after accounting for proportion of ophthalmology, neurology, and NSC claims and primary specialty designation. Sensitivity, specificity, and positive predictive value (PPV) for varying proportions of E/M visits with NSC were calculated. RESULTS: We identified 32,293 neurologists and ophthalmologists who billed at least 1 outpatient E/M visit claim in 2018 in Medicare. Of the 472 NANOS members with a valid individual National Provider Identifier, 399 (84.5%) had a Medicare outpatient E/M visit in 2018. The model containing only the proportion of E/M visits with NSC best predicted neuro-ophthalmology specialty designation (odds ratio 1.05 [95% confidence interval 1.04, 1.05]; P < 0.001; area under the receiver operating characteristic [AUROC] = 0.91). Model predictiveness for neuro-ophthalmology designation was maximized when 6% of all billed claims were for NSC (AUROC = 0.89; sensitivity: 84.0%; specificity: 93.9%), but PPV was low (14.9%). The threshold was unchanged when limited only to neurologists billing ≥1% ophthalmology claims or ophthalmologists billing ≥1% neurology claims, but PPV increased (33.3%). CONCLUSIONS: Our study provides a validated method to identify neuro-ophthalmologists who can be further adapted for use in other administrative databases to facilitate future research of neuro-ophthalmic care delivery in the United States.

Sociodemographic and Geographic Variation in Access to Neuro-Ophthalmologists in the United States.

BACKGROUND: Neuro-ophthalmologists have expertise in rare and complex disorders, but the ability of patients to access neuro-ophthalmic care has not been examined at a nationwide level. METHODS: Using the 2020 directory of all 502 members of the North American Neuro-Ophthalmology Society as a reference, we found the practice locations of 461 confirmed practicing members and converted each street address to latitude and longitude coordinates. We calculated the travel distance and time from each census tract to the nearest practice location and calculated population-weighted averages by state, region, and other prespecified factors. Choropleth maps were used to visualize the distribution of travel distances and times across the United States. RESULTS: California had the most practicing neuro-ophthalmologists out of any state (50), whereas 4 states (DE, MT, SD, and WY) had none. Washington, DC and MA had the most neuro-ophthalmologists per capita. The average travel distance and time to the nearest neuro-ophthalmologists were found to be 40.90 miles and 46.50 minutes, respectively, although a large portion of western plains and mountain regions had travel times of over 120 minutes. Patients in rural areas had longer travel times than those in urban areas, and Native American patients had the longest travel times of any racial or ethnic group. CONCLUSION: The travel time to see a neuro-ophthalmologist varies widely by state, region, and rurality, with Native American patients and rural patients being disproportionately affected. By identifying the areas with the greatest travel burdens, future policies can work to alleviate these potential barriers to care.