Evidence map›Paper›PMID 32414017›Full record

ArticleInternational journal of environmental research and public health2020

Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015-2017 Prevalence.

Marcello Moccia, Vincenzo Brescia Morra, Roberta Lanzillo, Ilaria Loperto, Roberta Giordana, Maria Grazia Fumo, Martina Petruzzo, Nicola Capasso, Maria Triassi, Maria Pia Sormani and 1 more

Abstract readValidation Study
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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  5. Impact of COVID-19 and system recovery in delivering healthcare to people with multiple sclerosis: a population-based Study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Marcello MocciaMultiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy.ORCID 0000-0003-2613-3090
Vincenzo Brescia MorraMultiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy.
Roberta LanzilloMultiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy.
Ilaria LopertoDepartment of Public Health, Federico II University, 80131 Naples, Italy.ORCID 0000-0003-4943-6780
Roberta GiordanaCampania Region Healthcare System Commissioner Office, 80131 Naples, Italy.
Maria Grazia FumoRegional Healthcare Society (So.Re.Sa), 80131 Naples, Italy.
Martina PetruzzoMultiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy.
Nicola CapassoMultiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy.
Maria TriassiDepartment of Public Health, Federico II University, 80131 Naples, Italy.
Maria Pia SormaniBiostatistics Unit, Department of Health Sciences, University of Genoa, 16121 Genoa, Italy.
Raffaele PalladinoDepartment of Public Health, Federico II University, 80131 Naples, Italy.ORCID 0000-0002-3437-812X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aim to validate a case-finding algorithm to detect individuals with multiple sclerosis (MS) using routinely collected healthcare data, and to assess the prevalence of MS in the Campania Region (South Italy). To identify individuals with MS living in the Campania Region, we employed an algorithm using different routinely collected healthcare administrative databases (hospital discharges, drug prescriptions, outpatient consultations with payment exemptions), from 1 January 2015 to 31 December 2017. The algorithm was validated towards the clinical registry from the largest regional MS centre (n = 1460). We used the direct method to standardise the prevalence rate and the capture-recapture method to estimate the proportion of undetected cases. The case-finding algorithm including individuals with at least one MS record during the study period captured 5362 MS patients (females = 64.4%; age = 44.6 ± 12.9 years), with 99.0% sensitivity (95% CI = 98.3%, 99.4%). Standardised prevalence rate per 100,000 people was 89.8 (95% CI = 87.4, 92.2) (111.8 for females [95% CI = 108.1, 115.6] and 66.2 for males [95% CI = 63.2, 69.2]). The number of expected MS cases was 2.7% higher than cases we detected. We developed a case-finding algorithm for MS using routinely collected healthcare data from the Campania Region, which was validated towards a clinical dataset, with high sensitivity and low proportion of undetected cases. Our prevalence estimates are in line with those reported by international studies conducted using similar methods. In the future, this cohort could be used for studies with high granularity of clinical, environmental, healthcare resource utilisation, and pharmacoeconomic variables.

Indexed as

AlgorithmsMultiple SclerosisFemaleHumansItalyMalePrevalenceRetrospective StudiesItalymultiple sclerosisprevalenceroutinely collected healthcare data

Identifiers

PMID32414017
PMCPMC7277756

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.