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.

Early childhood caries, family functioning, and common mental disorders in a region of Belo Horizonte, Brazil / Cárie da Primeira Infância, funcionamento familiar e transtornos mentais comuns: realidade em uma região de Belo Horizonte, MG

Aim: This cross-sectional study aimed to analyze the need for dental treatment in preschool children concerning early childhood caries (ECC), evaluated by the School Health Program (SHP); family functioning; and common mental disorders (CMDs) within families in Belo Horizonte, Minas Gerais, Brazil. Methods:A structured questionnaire was applied to the main caretaker of the 61 children by telephone contact. The questions addressed socioeconomic and demographic data; the Questionnaire of General Family Functioning (GFF) sought to investigate family functioning; and the Self-Reporting Questionnaire(SRQ-20) served to screen for CMDs among the main caretakers. Secondary data were collected from the SHP to survey the need for oral health treatments. The dependent variable was the need fortooth restoration or extraction. The data were analyzed descriptively and by Pearson's chi-square and Fisher's exact tests (p < 0.05), using SPSS v.22.0. Results: The median age of the children was 5 years old, and 32.8% required treatment for dental caries. There was no association between the need for tooth restoration or extraction and socioeconomic factors, family relations, and the presence of CMDs in the main caretaker (p > 0.05). However, there was a greater frequency of need for treatment in families with low income (24.6%) and with less social support (family and friends < 1). Children whose family was included in the Family Grant Program (62.3%), the respondent was the mother (57.4%), had completed high school or higher (36.1%), no paid work (42.6%), with a traditional or nuclear family structure (27.8%), and living in a house with good sanitary conditions (63.9%) presented a lower frequency of need for treatment.The GFF and SRQ-20 values among preschool children with and without the need for dental treatment were similar. Conclusion:There was a trend of a higher need for ECC treatment in children from families with lower income and less familiar and social support.

Objetivo: Este estudo transversal teve como objetivo analisar a necessidade de tratamento odontológico em pré-escolares em relação a cárie da primeira infância (CPI), avaliados pelo Programa Saúde na Escola (PSE), o funcionamento familiar e transtornos mentais comuns em suas famílias, em Belo Horizonte, Minas Gerais. Brasil. Métodos: Um questionário estruturado foi aplicado ao principal cuidador das crianças avaliadas pelo PSE, por meio de contato telefônico (n = 61). As questões abordaram dados socioeconômico, demográficos, o Questionário de Funcionamento Geral Familiar (FGF), para investigação do funcionamento familiar e o Self-Reporting Questionnaire (SRQ-20) para triagem de transtornos mentais comuns no principal cuidador. Dados secundários foram coletados nos registros do banco de dados do PSE, para levantamento da necessidade de tratamento em saúde bucal. A variável dependente foi a necessidade de restauração ou extração dentária. Os dados foram analisados descritivamente e pelos Testes de Qui-quadrado de Pearson e Exato de Fisher (p < 0,05), usando o SPSS. v. 22.0.Resultados: A mediana de idade das crianças foi de 5 anos e 32,8% tinha necessidade de tratamento em relação à cárie dentária. Não houve associação entre a necessidade de restauração ou extração dentária e fatores socioeconômicos, relações familiares e a presença de transtornos mentais no cuidador principal (p > 0,05). Entretanto, houve maior frequência de necessidade de tratamento de CPI em famílias com baixa renda (24,6%) e com menor apoio social (família e amigos < 1). As crianças cujas famílias estavam incluídas no Programa Bolsa Família (62,3%), o entrevistado era a mãe (57,4%), tinha ensino médio completo ou superior (36,1%), não exercia atividade remunerada (42,6%), tinha estrutura familiar tradicional ou nuclear (27,8%) e morava em uma casa com boas condições sanitárias (63,9%) apresentaram menor frequência de necessidade de tratamento. Os valores do GFF e do SRQ-20 entre crianças pré-escolares com e sem necessidade de tratamento odontológico foram semelhantes. Conclusão: Observou-se maior frequência de necessidade de tratamento de CPI em crianças de famílias com menores renda e apoio social.

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.

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.