ORCID Profile
0000-0002-0824-4478
Current Organisation
University College Dublin
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Publisher: Elsevier BV
Date: 12-2012
Publisher: MDPI AG
Date: 18-12-2020
Abstract: Background: Air pollution is an important risk factor for the disease burden however there is limited evidence in Indonesia on the effect of air pollution on health, due to lack of exposure and health outcome data. The objective of this study is to evaluate the potential use of the IFLS data for response part of urban-scale air pollution exposure–health response studies. Methods: Relevant variables were extracted based on IFLS5 documentation review. Analysis of the spatial distribution of respondent, data completeness, prevalence of relevant health outcomes, and consistency or agreement evaluation between similar variables were performed. Power for ideal s le size was estimated. Results: There were 58,304 respondents across 23 provinces, with the highest density in Jakarta (750/district). Among chronic conditions, hypertension had the highest prevalence (15–25%) with data completeness of 79–83%. Consistency among self-reported health outcome variables was 90–99%, while that with objective measurements was 42–70%. The estimated statistical power for studying air pollution effect on hypertension (prevalence = 17%) in Jakarta was approximately 0.6 (α = 0.1). Conclusions: IFLS5 data has potential use for epidemiological study of air pollution and health outcomes such as hypertension, to be coupled with high quality urban-scale air pollution exposure estimates, particularly in Jakarta.
Publisher: Environmental Health Perspectives
Date: 11-2013
DOI: 10.1289/EHP.1306770
Publisher: Environmental Health Perspectives
Date: 08-2014
DOI: 10.1289/EHP.1307271
Publisher: Elsevier BV
Date: 11-2014
DOI: 10.1016/J.IJHEH.2014.05.004
Abstract: Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.
Publisher: Elsevier BV
Date: 03-2014
DOI: 10.1016/J.JACI.2013.07.048
Abstract: Evidence on the long-term effects of air pollution exposure on childhood allergy is limited. We investigated the association between air pollution exposure and allergic sensitization to common allergens in children followed prospectively during the first 10 years of life. Five European birth cohorts participating in the European Study of Cohorts for Air Pollution Effects project were included: BAMSE (Sweden), LISAplus and GINIplus (Germany), MAAS (Great Britain), and PIAMA (The Netherlands). Land-use regression models were applied to assess the in idual residential outdoor levels of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5), the mass concentration of particles between 2.5 and 10 μm in size, and levels of particulate matter with an aerodynamic diameter of less than 10 μm (PM10), as well as measurement of the blackness of PM2.5 filters and nitrogen dioxide and nitrogen oxide levels. Blood s les drawn at 4 to 6 years of age, 8 to 10 years of age, or both from more than 6500 children were analyzed for allergen-specific serum IgE against common allergens. Associations were assessed by using multiple logistic regression and subsequent meta-analysis. The prevalence of sensitization to any common allergen within the 5 cohorts ranged between 24.1% and 40.4% at the age of 4 to 6 years and between 34.8% and 47.9% at the age of 8 to 10 years. Overall, air pollution exposure was not associated with sensitization to any common allergen, with odds ratios ranging from 0.94 (95% CI, 0.63-1.40) for a 1 × 10(-5) ∙ m(-1) increase in measurement of the blackness of PM2.5 filters to 1.26 (95% CI, 0.90-1.77) for a 5 μg/m(3) increase in PM2.5 exposure at birth address. Further analyses did not provide consistent evidence for a modification of the air pollution effects by sex, family history of atopy, or moving status. No clear associations between air pollution exposure and development of allergic sensitization in children up to 10 years of age were revealed.
Publisher: European Respiratory Society (ERS)
Date: 16-10-2014
DOI: 10.1183/09031936.00083614
Abstract: The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter μm (PM 10 ), PM 2.5 , coarse particulate matter and PM 2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, ided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4–5 and 8–10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from in idual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure ( e.g. adjusted OR (95%CI) for asthma at age 8–10 years and exposure at the birth address (n=10377): 1.10 (0.81–1.49) per 10 μg·m -3 nitrogen dioxide 0.88 (0.63–1.24) per 10 μg·m -3 PM 10 1.23 (0.78–1.95) per 5 μg·m -3 PM 2.5 ). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts.
Publisher: Elsevier BV
Date: 11-2015
DOI: 10.1016/J.ENVINT.2015.04.015
Abstract: An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75) Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75) and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM.
Publisher: American Chemical Society (ACS)
Date: 10-2012
DOI: 10.1021/ES301948K
Abstract: Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating in idual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 09-2014
Publisher: American Chemical Society (ACS)
Date: 16-04-2013
DOI: 10.1021/ES305129T
Abstract: Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.
Publisher: Environmental Health Perspectives
Date: 2014
DOI: 10.1289/EHP.1306755
Publisher: Elsevier BV
Date: 12-2012
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Anna Mölter.