METHODS: The Multiple Sclerosis International Federation third edition of the Atlas of MS was a survey that assessed the current global state of diagnosis including adoption of MS diagnostic criteria; barriers to diagnosis with respect to the patient, health care provider, and health system; and existence of national guidelines or national standards for speed of MS diagnosis.
RESULTS: Coordinators from 107 countries (representing approximately 82% of the world population), participated. Eighty-three percent reported at least 1 "major barrier" to early MS diagnosis. The most frequently reported barriers included the following: "lack of awareness of MS symptoms among general public" (68%), "lack of awareness of MS symptoms among health care professionals" (59%), and "lack of availability of health care professionals with knowledge to diagnose MS" (44%). One-third reported lack of "specialist medical equipment or diagnostic tests." Thirty-four percent reported the use of only 2017 McDonald criteria (McD-C) for diagnosis, and 79% reported 2017 McD-C as the "most commonly used criteria." Sixty-six percent reported at least 1 barrier to the adoption of 2017 McD-C, including "neurologists lack awareness or training" by 45%. There was no significant association between national guidelines pertaining to MS diagnosis or practice standards addressing the speed of diagnosis and presence of barriers to early MS diagnosis and implementation of 2017 McD-C.
DISCUSSION: This study finds pervasive consistent global barriers to early diagnosis of MS. While these barriers reflected a lack of resources in many countries, data also suggest that interventions designed to develop and implement accessible education and training can provide cost-effective opportunities to improve access to early MS diagnosis.
OBJECTIVE: To evaluate the degree to which using data-driven methods to simultaneously select an optimal Patient Health Questionnaire-9 (PHQ-9) cutoff score and estimate accuracy yields (1) optimal cutoff scores that differ from the population-level optimal cutoff score and (2) biased accuracy estimates.
DESIGN, SETTING, AND PARTICIPANTS: This study used cross-sectional data from an existing individual participant data meta-analysis (IPDMA) database on PHQ-9 screening accuracy to represent a hypothetical population. Studies in the IPDMA database compared participant PHQ-9 scores with a major depression classification. From the IPDMA population, 1000 studies of 100, 200, 500, and 1000 participants each were resampled.
MAIN OUTCOMES AND MEASURES: For the full IPDMA population and each simulated study, an optimal cutoff score was selected by maximizing the Youden index. Accuracy estimates for optimal cutoff scores in simulated studies were compared with accuracy in the full population.
RESULTS: The IPDMA database included 100 primary studies with 44 503 participants (4541 [10%] cases of major depression). The population-level optimal cutoff score was 8 or higher. Optimal cutoff scores in simulated studies ranged from 2 or higher to 21 or higher in samples of 100 participants and 5 or higher to 11 or higher in samples of 1000 participants. The percentage of simulated studies that identified the true optimal cutoff score of 8 or higher was 17% for samples of 100 participants and 33% for samples of 1000 participants. Compared with estimates for a cutoff score of 8 or higher in the population, sensitivity was overestimated by 6.4 (95% CI, 5.7-7.1) percentage points in samples of 100 participants, 4.9 (95% CI, 4.3-5.5) percentage points in samples of 200 participants, 2.2 (95% CI, 1.8-2.6) percentage points in samples of 500 participants, and 1.8 (95% CI, 1.5-2.1) percentage points in samples of 1000 participants. Specificity was within 1 percentage point across sample sizes.
CONCLUSIONS AND RELEVANCE: This study of cross-sectional data found that optimal cutoff scores and accuracy estimates differed substantially from population values when data-driven methods were used to simultaneously identify an optimal cutoff score and estimate accuracy. Users of diagnostic accuracy evidence should evaluate studies of accuracy with caution and ensure that cutoff score recommendations are based on adequately powered research or well-conducted meta-analyses.