Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Battaglia, Mario Alberto; Policardo, Laura; Meucci, G.; Ulivelli, Monica; Bartalini, Sabina; Pennacchio, Francesco; Francesconi, Paolo

doi:10.1159/000441567

Cited by 25 publications

(26 citation statements)

References 29 publications

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“…Considering that previously published MS case-finding algorithms to identify people with MS using administrative healthcare databases varied in relation to the number of MS records considered for each patient for case identification [26][27][28][29], we aimed to validate two versions of the algorithm (aim 1), which considered the presence of: 1) at least one MS record during the study period; and 2) at least two MS records during the study period. To validate the algorithm, we merged our dataset with a clinical registry and identified individuals who accessed the MS Clinical Care and Research Centre at the "Federico II" University of Naples using the information provided by the algorithm.…”

Section: Discussionmentioning

confidence: 99%

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

Moccia

Morra

Lanzillo

et al. 2020

IJERPH

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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.

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Section: Discussionmentioning

confidence: 99%

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

Moccia

Morra

Lanzillo

et al. 2020

IJERPH

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“…On the other hand, specificity could not be calculated correctly using the results above, as we were examinig those consecutive patients who have had received at least once the diagnosis of G35 in a given period and thus are at risk of having MS, hence the low number of true negative cases. Instead, we have applied the method used by Bezzini et al [22] with some necessary modifications. First, from the original patient-database including 4.29 million subjects, we created an other cohort of individuals who were presumably not affected by MS.…”

Section: Validation Of Sensitivity Of Case Definition Versus Clinicalmentioning

confidence: 99%

“…In their consecutive works on prevalence and incidence of MS in Tuscany, Bezzini et al [22,52,53] used and validated the administrative case definition of meeting at least one of the following 4 criteria: at least one hospital discharge record with MS diagnosis or one active payment exemption for MS or at least 2 prescriptions for MS-specific drugs or MS diagnosis in home and residential long-term care data. The sensitivity of this definition turned out to be 98%, while specificity was 99%.…”

Section: Nr Of Patients Refilling Any Dmd First Time and Fulfilling Omentioning

confidence: 99%

Incidence and prevalence of multiple sclerosis in Hungary based on record linkage of nationwide multiple healthcare administrative data

et al. 2020

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Objectives As there were only regional studies in Hungary about the prevalence of multiple sclerosis (MS), we aimed to estimate its epidemiological features using data of Hungary's singlepayer health insurance system. Methods Pseudonymized database of claims reported by hospitals and outpatient services between 2004-2016 was analyzed and linked with an independent database of outpatient pharmacy refills between 2010-2016. We established an administrative case definition of MS and validated it on medical records of 309 consecutive patients. A subject was defined as MSpatient if received MS diagnosis (International Classification of Diseases, 10th edition, code G35) on three or more occasions at least in 2 calendar years and at least once documented by a neurologist. Patients were counted as incident cases in the year of the first submitted claim for MS. We allowed a 6-year-long run-in period, so only data between 2010-2015 are discussed. Results Sensitivity of the administrative case definition turned out to be 99%, while specificity was >99%. Crude prevalence of MS has increased from 109.3/100,000 in 2010 to 130.8/ 100,000 in 2015 (p-value = 0.000003). Crude incidence declined from 7.1/100,000 (2010) to 5.4/100,000 (2015) (p-value = 0.018). Direct standardization − based on European standard population and results of nationwide Hungarian census of 2011 − revealed that age standardized prevalence was 105.2/100,000 (2010), which has grown to 127.2/100,000 (2015) (p-value = 0.000001). Age standardized incidence rate declined from 6.7/100,000 (2010) to

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“…The RxRiskV Index was improved for our study to include updated ATC codes for medications licensed in Italy currently and adding the pertaining ICD-9 CM code for each condition. These amendments were made according to previously published works [22][23][24][25][26][27][28][29]. Individuals were classified as having one of the conditions listed if they received at least ≥2 consecutive dispensations of a drug for treatment of a specific class of disease and/or one hospital discharge with the diagnoses coded with the specific ICD-9-CM (S2 Table).…”

Section: Identifying Clinical Predictors Of Sars-cov-2 Infectionmentioning

confidence: 99%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Orlando

Rea

Savaré

et al. 2020

Preprint

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BackgroundThe novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the population most at risk of infection. Hence, we aimed to identify clinical predictors of SARS-CoV-2 infection risk and to develop and validate a score predicting SARS-CoV-2 infection risk comparing it with unspecific surrogates.MethodsRetrospective case/control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each subject with Covid-19 confirmed diagnosis (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS), was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. SIS was validated by comparison with specific and unspecific predictors of SARS-CoV-2 infection.ResultsSubjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS=4), 1.74 times higher than those unaffected by any SIS contributing conditions (SIS=1).ConclusionThis study identified conditions and diseases making individuals more vulnerable to SARS-CoV-2 infection. Our results are a decision-maker support tool for identifying population most at risk allowing adoption of preventive measures to minimize a potential new relapse damage.

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Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Cited by 25 publications

References 29 publications

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

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

Incidence and prevalence of multiple sclerosis in Hungary based on record linkage of nationwide multiple healthcare administrative data

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

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