Limits...
Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling.

Thelen B, French NH, Koziol BW, Billmire M, Owen RC, Johnson J, Ginsberg M, Loboda T, Wu S - Environ Health (2013)

Bottom Line: The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case).An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region.The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA. bthelen@mtu.edu.

ABSTRACT

Background: A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time.

Methods: Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model.

Results: The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke.

Conclusions: The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

Show MeSH

Related in: MedlinePlus

Contour representation of the humidity/temperature bivariate function for odds effect. (a) Bivariate rugplot for the weather function odds effect for the San Diego County region model. (b) Bivariate rugplot for the weather function odds effect for the SR model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3842653&req=5

Figure 9: Contour representation of the humidity/temperature bivariate function for odds effect. (a) Bivariate rugplot for the weather function odds effect for the San Diego County region model. (b) Bivariate rugplot for the weather function odds effect for the SR model.

Mentions: We expected interactions between relative humidity and temperature as well as threshold effects at the most extreme observed weather values. We fit a bivariate function using a tensor product spline model based on univariate natural cubic splines, which is recommended for fitting bivariate splines of two variables with differing measurement units [36]. Contour representations of the bivariate functions for the county region-level and SR-level models are shown in FiguresĀ 9a and b. The contour plots reveal an inverse relationship between minimum temperature and mean relative humidity in terms of odds effect; an increase in one variable associated with a decrease in the other variable causes the odds to rise. The highest odds are observed at moderate (minimum) temperatures and higher humidity, possibly evening or nightime. When both parameters approach their maximum together, odds decrease. One explanation is that combined high humidity and high temperature results in reduced activity level and, hence, less exposure during particularly humid, hot periods. The function estimates between the models, by qualitative observation, appear stable because they generate similar patterns of interaction between relative humidity and temperature. The contour plots indicate a nonlinear model is appropriate for including temperature (as maximum daily temperature) and relative humidity (as mean daily relative humidity) in the predictive models.


Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling.

Thelen B, French NH, Koziol BW, Billmire M, Owen RC, Johnson J, Ginsberg M, Loboda T, Wu S - Environ Health (2013)

Contour representation of the humidity/temperature bivariate function for odds effect. (a) Bivariate rugplot for the weather function odds effect for the San Diego County region model. (b) Bivariate rugplot for the weather function odds effect for the SR model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3842653&req=5

Figure 9: Contour representation of the humidity/temperature bivariate function for odds effect. (a) Bivariate rugplot for the weather function odds effect for the San Diego County region model. (b) Bivariate rugplot for the weather function odds effect for the SR model.
Mentions: We expected interactions between relative humidity and temperature as well as threshold effects at the most extreme observed weather values. We fit a bivariate function using a tensor product spline model based on univariate natural cubic splines, which is recommended for fitting bivariate splines of two variables with differing measurement units [36]. Contour representations of the bivariate functions for the county region-level and SR-level models are shown in FiguresĀ 9a and b. The contour plots reveal an inverse relationship between minimum temperature and mean relative humidity in terms of odds effect; an increase in one variable associated with a decrease in the other variable causes the odds to rise. The highest odds are observed at moderate (minimum) temperatures and higher humidity, possibly evening or nightime. When both parameters approach their maximum together, odds decrease. One explanation is that combined high humidity and high temperature results in reduced activity level and, hence, less exposure during particularly humid, hot periods. The function estimates between the models, by qualitative observation, appear stable because they generate similar patterns of interaction between relative humidity and temperature. The contour plots indicate a nonlinear model is appropriate for including temperature (as maximum daily temperature) and relative humidity (as mean daily relative humidity) in the predictive models.

Bottom Line: The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case).An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region.The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Michigan Tech Research Institute, Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA. bthelen@mtu.edu.

ABSTRACT

Background: A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time.

Methods: Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model.

Results: The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke.

Conclusions: The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

Show MeSH
Related in: MedlinePlus