Health impact assessment of particulate pollution in Tallinn using fine spatial resolution and modeling techniques

<p>Abstract</p> <p>Background</p> <p>Health impact assessments (HIA) use information on exposure, baseline mortality/morbidity and exposure-response functions from epidemiological studies in order to quantify the health impacts of existing situations and/or alternative scenarios. The aim of this study was to improve HIA methods for air pollution studies in situations where exposures can be estimated using GIS with high spatial resolution and dispersion modeling approaches.</p> <p>Methods</p> <p>Tallinn was divided into 84 sections according to neighborhoods, with a total population of approx. 390 000 persons. Actual baseline rates for total mortality and hospitalization with cardiovascular and respiratory diagnosis were identified. The exposure to fine particles (PM_2.5) from local emissions was defined as the modeled annual levels. The model validation and morbidity assessment were based on 2006 PM₁₀or PM_2.5levels at 3 monitoring stations. The exposure-response coefficients used were for total mortality 6.2% (95% CI 1.6–11%) per 10 μg/m³increase of annual mean PM_2.5concentration and for the assessment of respiratory and cardiovascular hospitalizations 1.14% (95% CI 0.62–1.67%) and 0.73% (95% CI 0.47–0.93%) per 10 μg/m³increase of PM₁₀. The direct costs related to morbidity were calculated according to hospital treatment expenses in 2005 and the cost of premature deaths using the concept of Value of Life Year (VOLY).</p> <p>Results</p> <p>The annual population-weighted-modeled exposure to locally emitted PM_2.5in Tallinn was 11.6 μg/m³. Our analysis showed that it corresponds to 296 (95% CI 76528) premature deaths resulting in 3859 (95% CI 10236636) Years of Life Lost (YLL) per year. The average decrease in life-expectancy at birth per resident of Tallinn was estimated to be 0.64 (95% CI 0.17–1.10) years. While in the polluted city centre this may reach 1.17 years, in the least polluted neighborhoods it remains between 0.1 and 0.3 years. When dividing the YLL by the number of premature deaths, the decrease in life expectancy among the actual cases is around 13 years. As for the morbidity, the short-term effects of air pollution were estimated to result in an additional 71 (95% CI 43–104) respiratory and 204 (95% CI 131–260) cardiovascular hospitalizations per year. The biggest external costs are related to the long-term effects on mortality: this is on average €150 (95% CI 40–260) million annually. In comparison, the costs of short-term air-pollution driven hospitalizations are small €0.3 (95% CI 0.2–0.4) million.</p> <p>Conclusion</p> <p>Sectioning the city for analysis and using GIS systems can help to improve the accuracy of air pollution health impact estimations, especially in study areas with poor air pollution monitoring data but available dispersion models.</p

Surveillance of Gram-negative bacteria: impact of variation in current European laboratory reporting practice on apparent multidrug resistance prevalence in paediatric bloodstream isolates.

This study evaluates whether estimated multidrug resistance (MDR) levels are dependent on the design of the surveillance system when using routine microbiological data. We used antimicrobial resistance data from the Antibiotic Resistance and Prescribing in European Children (ARPEC) project. The MDR status of bloodstream isolates of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa was defined using European Centre for Disease Prevention and Control (ECDC)-endorsed standardised algorithms (non-susceptible to at least one agent in three or more antibiotic classes). Assessment of MDR status was based on specified combinations of antibiotic classes reportable as part of routine surveillance activities. The agreement between MDR status and resistance to specific pathogen-antibiotic class combinations (PACCs) was assessed. Based on all available antibiotic susceptibility testing, the proportion of MDR isolates was 31% for E. coli, 30% for K. pneumoniae and 28% for P. aeruginosa isolates. These proportions fell to 9, 14 and 25%, respectively, when based only on classes collected by current ECDC surveillance methods. Resistance percentages for specific PACCs were lower compared with MDR percentages, except for P. aeruginosa. Accordingly, MDR detection based on these had low sensitivity for E. coli (2-41%) and K. pneumoniae (21-85%). Estimates of MDR percentages for Gram-negative bacteria are strongly influenced by the antibiotic classes reported. When a complete set of results requested by the algorithm is not available, inclusion of classes frequently tested as part of routine clinical care greatly improves the detection of MDR. Resistance to individual PACCs should not be considered reflective of MDR percentages in Enterobacteriaceae

Different types of housing and respiratory health outcomes

Evidence has shown that housing conditions may substantially influence the health of residents. Different types of housing have different structures and construction materials, which may affect indoor environment and housing conditions. This study aimed to investigate whether people living in different types of housing have different respiratory health outcomes. The data from the 1999–2006 National Health and Nutrition Examination Survey were used for the analyses. The types of housing included houses, townhouses, apartments, and mobile homes. Respiratory symptoms included wheezing, coughing, sputum, and dyspnea; respiratory diseases included asthma, chronic bronchitis, emphysema, and chronic obstructive pulmonary disease (COPD). Multiple logistic regression was used to calculate odds ratio (OR) and 95% confidence interval (CI) after adjustment for potential confounding factors. A total of 11,785 participants aged 40 years and older were included in the analyses. Compared with those living in single family houses, participants living in mobile homes were more likely to have respiratory conditions, the OR (95% CI) was 1.38 (1.13–1.69) for wheezing, and 1.49 (1.25–1.78) for dyspnea; whereas participants living in apartments were less likely to have respiratory conditions, the OR (95% CI) was 0.58 (0.36–0.91) for chronic bronchitis, and 0.69 (0.49–0.97) for COPD. Compared with living in single family houses, living in mobile home was associated with worse, whereas living in apartments was associated with better, respiratory health outcomes. Further research is needed to better understand the underlying mechanisms and prevent adverse respiratory effects associated with living in mobile homes