Dataset Information

Population intervention models to estimate ambient NO2 health effects in children with asthma.

ABSTRACT: Health effects of ambient air pollution are most frequently expressed in individual studies as responses to a standardized unit of air pollution changes (e.g., an interquartile interval), which is thought to enable comparison of findings across studies. However, this approach does not necessarily convey health effects in terms of a real-world air pollution scenario. In the present study, we use population intervention modeling to estimate the effect of an air pollution intervention that makes explicit reference to the observed exposure data and is identifiable in those data. We calculate the association between ambient summertime nitrogen dioxide (NO2) and forced expiratory flow between 25% and 75% of forced vital capacity (FEF25-75) in a cohort of children with asthma in Fresno, California. We scale the effect size to reflect NO2 abatement on a majority of summer days. The effect estimates were small, imprecise, and consistently indicated improved pulmonary function with decreased NO2. The effects ranged from -0.8% of mean FEF25-75 (95% confidence interval (CI): -3.4, 1.7) to -3.3% (95% CI: -7.5, 0.9). We conclude by discussing the nature and feasibility of the exposure change analyzed here given the observed air pollution profile, and we propose additional applications of population intervention models in environmental epidemiology.

SUBMITTER: Snowden JM

PROVIDER: S-EPMC4565777 | biostudies-literature | 2015 Nov-Dec

REPOSITORIES: biostudies-literature

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Population intervention models to estimate ambient NO2 health effects in children with asthma.

Snowden Jonathan M JM Mortimer Kathleen M KM Kang Dufour Mi-Suk MS Tager Ira B IB

Journal of exposure science & environmental epidemiology 20140903 6

Health effects of ambient air pollution are most frequently expressed in individual studies as responses to a standardized unit of air pollution changes (e.g., an interquartile interval), which is thought to enable comparison of findings across studies. However, this approach does not necessarily convey health effects in terms of a real-world air pollution scenario. In the present study, we use population intervention modeling to estimate the effect of an air pollution intervention that makes ex ...[more]

PMID: 25182844

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Project description:To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO2 and PM2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Project description:BackgroundSimulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply.MethodsWe developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios.ResultsThe algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population.ConclusionPolicy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

Dataset Information

Population intervention models to estimate ambient NO2 health effects in children with asthma.

Publications

Population intervention models to estimate ambient NO2 health effects in children with asthma.

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