Dataset Information

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

ABSTRACT: Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe-Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.

SUBMITTER: Trivelli L

PROVIDER: S-EPMC7828801 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

Trivelli Leonardo L Borrelli Paola P Cadum Ennio E Pisoni Enrico E Villani Simona S

International journal of environmental research and public health 20210114 2

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-li ...[more]

PMID: 33466700

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Project description:The study aimed to investigate (i) the occurrence of third-generation cephalosporins and/or carbapenems non-sensitive Enterobacterales in Pavia surface and groundwaters, (ii) their resistance determinants, and (iii) the clonal features of the most relevant strains. During May 13 and 14, 2019, n = 18 water samples from n = 12 sampling sites in the urban/peri-urban area of Pavia (Po Plain, Northern Italy) have been evaluated. At first, hydrochemical analysis and bacterial plate counts were carried out on all the water samples. One milliliter of each water sample was then screened on both MacConkey agar (MC) added with cefotaxime (1 mg/L; 2 mg/L) and MC plus meropenem (0.25 mg/L; 4 mg/L). Species identification and antimicrobial susceptibilities were assessed by MicroScan autoSCAN-4. Double Disk Synergy (DD) test, CT103XL microarray, acc(6')-Ib-cr, qnrS, blaCTX-M-/MOX-/VEB-/OXA-type genes targeted PCR and sequencing, Pulsed-Field Gel Electrophoresis (PFGE), MultiLocus Sequence Typing (MLST), and Whole-Genome Sequencing on selected strains were performed. A total of n = 30 isolates grown on β-lactams enriched MC: Escherichia coli (n = 21; 70%), Klebsiella spp. (n = 5; 16.6%), Citrobacter freundii (n = 2; 6.7%), and Kluyvera intermedia (n = 2; 6.7%). All E. coli and K. pneumoniae were ESβL-producers by DD. The 66.6, 38.0, and 19.0% of E. coli were ciprofloxacin/levofloxacin, trimethoprim-sulfamethoxazole, and gentamicin resistant (EUCAST 2019 breakpoints), respectively. A blaCTX-M-type determinant was identified in E. coli (n = 20/21; 95.2%) and K. pneumoniae (n = 2/3; 66.7%). The remaining E. coli was blaVEB-1 and blaMOX-2 genes positive. The aac(6')-Ib-cr determinant was found in n = 7 E. coli and n = 1 K. pneumoniae, while qnrS was found in n = 1 E. coli and n = 2 K. pneumoniae. PFGE showed clonal heterogeneity among ESβL-E. coli. Two out of four E. coli detected as blaOXA-244-positive, belonged to the pandemic ST131. One XDR K. pneumoniae from a stream sample, detected as blaKPC-2 positive, resulted of ST258. The epidemiological impact of blaOXA-244 ST131 E. coli and blaKPC-2 ST258 K. pneumoniae presence in surface waters of an urban area in Northern Italy must not be underestimated.

Project description:Health effects of fine particulate matter (PM2.5) vary by chemical composition, and composition can help to identify key PM2.5 sources across urban areas. Further, this intra-urban spatial variation in concentrations and composition may vary with meteorological conditions (e.g., mixing height). Accordingly, we hypothesized that spatial sampling during atmospheric inversions would help to better identify localized source effects, and reveal more distinct spatial patterns in key constituents. We designed a 2-year monitoring campaign to capture fine-scale intra-urban variability in PM2.5 composition across Pittsburgh, PA, and compared both spatial patterns and source effects during "frequent inversion" hours vs 24-h weeklong averages. Using spatially distributed programmable monitors, and a geographic information systems (GIS)-based design, we collected PM2.5 samples across 37 sampling locations per year to capture variation in local pollution sources (e.g., proximity to industry, traffic density) and terrain (e.g., elevation). We used inductively coupled plasma mass spectrometry (ICP-MS) to determine elemental composition, and unconstrained factor analysis to identify source suites by sampling scheme and season. We examined spatial patterning in source factors using land use regression (LUR), wherein GIS-based source indicators served to corroborate factor interpretations. Under both summer sampling regimes, and for winter inversion-focused sampling, we identified six source factors, characterized by tracers associated with brake and tire wear, steel-making, soil and road dust, coal, diesel exhaust, and vehicular emissions. For winter 24-h samples, four factors suggested traffic/fuel oil, traffic emissions, coal/industry, and steel-making sources. In LURs, as hypothesized, GIS-based source terms better explained spatial variability in inversion-focused samples, including a greater contribution from roadway, steel, and coal-related sources. Factor analysis produced source-related constituent suites under both sampling designs, though factors were more distinct under inversion-focused sampling.

Dataset Information

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

Publications

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley.

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