Editorial

Bayesian analysis: what does it add to studies of the natural history of MS?

We aim to validate an algorithm based on routinely-collected healthcare data to detect incidence of multiple sclerosis (MS) in the Campania Region (South Italy) and to explore its spatial and temporal variations.

We included individuals resident in the Campania Region who had at least one MS record in administrative datasets (drug prescriptions, hospital discharge, outpatients), from 2015 to 2020. We merged administrative to the clinical datasets to ascertain the actual date of diagnosis, and validated the minimum interval from our study baseline (Jan 1, 2015) to first MS records in administrative datasets to detect incident cases.

We used Bayesian approach to explore geographical distribution, also including deprivation index as a covariate in the estimation model. We used the capture-recapture method to estimate the proportion of undetected cases.

The best performance was achieved by the 12-month interval algorithm, detecting 2,150 incident MS cases, with 74.4% sensitivity (95%CI = 64.1%, 85.9%) and 95.3% specificity (95%CI = 90.7%, 99.8%). The cumulative incidence was 36.68 (95%CI = 35.15, 38.26) per 100,000 from 2016 to 2020. The mean annual incidence was 7.34 (95%CI = 7.03, 7.65) per 100,000 people-year. The geographical distribution of MS relative risk shows a decreasing east-west incidence gradient. The number of expected MS cases was 11% higher than the detected cases.

We validated a case-finding algorithm based on administrative data to estimate MS incidence, and its spatial/temporal variations. This algorithm provides up-to-date estimates of MS incidence, and will be used in future studies to evaluate changes in MS incidence in relation to different risk factors.

Prognostic factors to determine the patient's likelihood of developing MS are important for several reasons. Prognostic factors are important to the patient who wants to be informed about his/her prospects, to the clinician who needs to individuate the patients who deserve immune treatments, and to the researcher who needs to improve the design and the analysis of the therapeutic trials. In addition, with the development of new immune therapies, whose early use is strongly encouraged, it is crucial to dispose of reliable clinical predictors to identify the patients who are candidates for early or aggressive therapies.

Several studies have indicated that a poor prognosis is related to male gender; a late age at onset; motor, cerebellar, and sphincter involvement at onset; a progressive course at onset; a short inter‐attack interval; a high number of early attacks; and a relevant early residual disability. Paraclinical support for MS prognosis is given by imaging techniques, cerebrospinal fluid analysis, and evoked potential examinations. The most sensitive paraclinical test to predict conversion from suspected demyelinating disease to definite MS is MRI.

The geographical analysis of a disease risk is particularly difficult when the disease is non-frequent and the area units are small. The practical use of the Bayesian modelling, instead of the classical frequentist one, is applied to study the geographical variation of multiple sclerosis (MS) across the province of Pavia, Northern Italy.

464 MS-affected individuals resident in the province of Pavia were identified on December 31st 2000. The overall prevalence was 94 per 100,000 inhabitants. This estimate indicates an increasing MS prevalence in the province, in accordance with the vast majority of the Italian areas where prevalence studies have been repeated.

We mapped the geographical variation of MS prevalence across the 190 communes of the province both with a classical approach and a Bayesian approach. The frequentist approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable.

Our study underlines the usefulness of Bayesian methods to obtain reliable maps of disease prevalence and to identify possible clusters of disease where to carry out further epidemiological investigations.