NMMAPSNational Morbidity, Mortality, and Air Pollution Study
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Previous analyses of NMMAPS data have used a hierarchical Bayes approach (e.g., Bell et al.
The use of Bayesian methods in multicity analyses was pioneered by the first investigators of NMMAPS (Dominici et al.
The NMMAPS analyses estimated a national mean increase of 0.27% in mortality for a 10-[micro]g/[m.sup.3] increase in P[M.sub.10] on the previous day using GAMs, and a 0.22% increase using a GLM.
In a reanalysis of data from the NMMAPS (Katsouyanni and Samet 2009), the [O.sub.3] mortality estimates were highly sensitive to the degree and type of smoothing used for seasonality control, and inclusion of [PM.sub.10] markedly reduced the [O.sub.3] mortality estimates.
The only previous multi-city analyses of these three pollutants were conducted during reanalyses of NMMAPS (Dominici et al.
urban communities based on data from the National Morbidity, Mortality, and Air Pollutions Study (NMMAPS).
(2009) reassessed the relationship between ozone and short-term mortality for the NMMAPS urban communities, including an investigation of alternative ozone exposure metrics--namely, daily maximum 8-hr or 1-hr averages, as alternatives to the 24-hr average ozone levels used by Bell et al.
(2009) of the association between ozone and short-term mortality in the NMMAPS cities, and the availability of the detailed estimates of infiltration rate distributions in representative U.S.
cities that were included in the NMMAPS study and were selected by Persily er al.
Based on the resulting linear relationship, we estimated [[lambda].sub.infilt] for each of the 18 targeted NMMAPS cities.
Key parameters and calculated results for 18 NMMAPS cities with published infiltration rate distributions (Persily et al.
Using Equation 4, we estimated average national ozone exposure coefficients for summer and winter, based on data for the 18 representative NMMAPS cities weighted for population, and found that the national average exposure coefficient was larger in the summer (2.36) than in the winter (2.01).