Affinito, Giuseppina (2023) Use of Routinely Collected Healthcare Data to support Decision Making in Public Health: The Case Study of Multiple Sclerosis. [Tesi di dottorato]

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Abstract

Routinely collected healthcare data plays a pivotal role in modern healthcare systems and public health research. These datasets encompass a wide array of information gathered as part of routine healthcare operations. Data sets of routinely collected healthcare are invaluable in offering a comprehensive description of disease epidemiology, comorbidities, and treatment pathways, particularly when linked to clinical registries. Additionally, big data can be interconnected longitudinally and across diverse data sources to create comprehensive individual records and multi-tiered data structures. This approach allows for a multifaceted understanding and management of diseases in various aspects, including identifying incident cases, treatment and medication management, resource utilization, and cost analysis. Multiple sclerosis (MS) is a complex neurological condition characterized by inflammation, demyelination, and degeneration of the central nervous system. This multifaceted disease presents one of the most formidable challenges in modern medicine, primarily due to its high social impact and associated costs. Routinely collected data is an indispensable resource for addressing the challenges posed by MS. It empowers healthcare providers, researchers, and patients to understand better, manage, and potentially find a cure for this complex neurological condition. By utilizing data effectively, we can improve patient outcomes, enhance quality of life, and optimize healthcare resource utilization, ultimately reducing the social and economic burden of MS. In the first part of the study, we validated 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 individual’s 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. In the second part of the study, we provide real-world evidence on the use of DMTs for treating multiple sclerosis (MS), with specific regard to prescription pattern, adherence, persistence, healthcare resource utilization and related costs, also in relation to other disease-modifying treatments (DMTs). We collected hospital discharge records, drug prescriptions, and related costs, and calculated persistence (time from first prescription to discontinuation or switch to other DMT), adherence (proportion of days covered (PDC)), annualized hospitalization rate (AHR) for MS-related hospital admissions, and DMT costs. Ocrelizumab stands out as one of the most commonly prescribed disease-modifying therapies (DMTs), accounting for 26% of prescriptions to treatment-naïve patients. This suggests its pivotal role in addressing unmet clinical needs, particularly as the first approved treatment for primary progressive multiple sclerosis (MS). Notably, Ocrelizumab demonstrates the highest treatment persistence, underscoring its favourable benefit-risk profile. Moreover, the costs associated with Ocrelizumab are lower compared to similarly effective DMTs, all while not resulting in increased healthcare resource utilization. Moreover, we evaluated the impact on healthcare resources and costs of adopting Extended-Interval Dosing (EID) for Natalizumab. Findings indicate that Natalizumab EID is associated with reduced direct treatment costs, apparently without additional healthcare burden. Finally, we evaluated the impact on healthcare delivery to people with MS and the recovery of the system have never been explored. In this population-based study in the Campania Region (Italy), we included MS people across pre-COVID-19, lockdown, pre-vaccination, and vaccination periods. Differences in continuous outcomes between periods were explored using linear mixed models (annualized hospitalization rate (AHR) and adherence measured as medication possession ratio (MPR)). Differences in new disease-modifying treatment (DMT), prescription rates (first DMT prescription, any DMT switch, switch from platform to highly effective DMT, and combination of first DMT prescription and any DMT switch) were assessed employing an interrupted time series design. In this population-based study in the Campania Region (Italy), we included MS people across pre-COVID-19, lockdown, pre-vaccination, and vaccination periods. Differences in continuous outcomes between periods were explored using linear mixed models (annualized hospitalization rate (AHR) and adherence measured as medication possession ratio (MPR)). Differences in new disease-modifying treatment (DMT), prescription rates (first DMT prescription, any DMT switch, switch from platform to highly effective DMT, and combination of first DMT prescription and any DMT switch) were assessed employing an interrupted time series design. In conclusion DMT usage returning to pre-pandemic levels reflects good health system recovery. However, adherence has remained lower than in the past, as from suboptimal care. Assessing long-term COVID-19 impact on MS healthcare needed.

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