Validation of an Algorithm to Detect Multiple Sclerosis Cases in Administrative Health Databases in Piedmont (Italy): An Application to the Estimate of Prevalence by Age and Urbanization Level.

INTRODUCTION: Italy is considered a high-risk country for multiple sclerosis (MS). Exploiting electronic health archives (EHAs) is highly useful to continuously monitoring the prevalence of the disease, as well as the care delivered to patients and its outcomes. The aim of this study was to validate an EHA-based algorithm to identify MS patients, suitable for epidemiological purposes, and to estimate MS prevalence in Piedmont (North Italy). METHODS: MS cases were identified, in the period between January 1, 2012 and December 31, 2017, linking data from 4 different sources: hospital discharges, drug prescriptions, exemptions from co-payment to health care, and long-term care facilities. Sensitivity of the algorithm was tested through record linkage with a cohort of 656 neurologist-confirmed MS cases; specificity was tested with a cohort of 2,966,293 residents presumably not affected by MS. Undercount was estimated by a capture-recapture method. We calculated crude, and age- and gender-specific prevalence. We also calculated age-adjusted prevalence by level of urbanization of the municipality of residence. RESULTS: On December 31, 2017, the algorithm identified 8,850 MS cases. Sensitivity was 95.9%, specificity was 99.97%, and the estimated completeness of ascertainment was 91.9%. The overall prevalence, adjusted for undercount, was 152 per 100,000 among men and 286 among women; it increased with increasing age and reached its peak value in the 45- to 54-year class, followed by a progressive reduction. The age-adjusted prevalence of residents in cities was 15% higher than in those living in the countryside. DISCUSSION/CONCLUSION: We validated an algorithm based on EHAs to identify cases of MS for epidemiological use. The prevalence of MS, adjusted for undercount, was among the highest in Italy. We also found that the prevalence was higher in highly urbanized areas.

Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy.

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016-2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research.

Identification of dementia and MCI cases in health information systems: An Italian validation study.

Introduction: The identification of dementia cases through routinely collected health data represents an easily accessible and inexpensive method to estimate the prevalence of dementia. In Italy, a project aimed at the validation of an algorithm was conducted. Methods: The project included cases (patients with dementia or mild cognitive impairment [MCI]) recruited in centers for cognitive disorders and dementias and controls recruited in outpatient units of geriatrics and neurology. The algorithm based on pharmaceutical prescriptions, hospital discharge records, residential long-term care records, and information on exemption from health-care co-payment, was applied to the validation population. Results: The main analysis was conducted on 1110 cases and 1114 controls. The sensitivity, specificity, and positive and negative predictive values in discerning cases of dementia were 74.5%, 96.0%, 94.9%, and 79.1%, respectively, whereas in detecting cases of MCI these values were 29.7%, 97.5%, 92.2%, and 58.1%, respectively. The variables associated with misclassification of cases were also identified. Discussion: This study provided a validated algorithm, based on administrative data, which can be used to identify cases with dementia and, with lower sensitivity, also early onset dementia but not cases with MCI.