Surveillance and forecasting of respiratory health ... · Forecasting Limitations Contributions...
Transcript of Surveillance and forecasting of respiratory health ... · Forecasting Limitations Contributions...
Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Surveillance and forecasting of respiratory health
outcomes associated with forest fire smoke exposure
Syndromic Surveillance WorkshopResearch Protocol Presentation
March 17, 2014
Kathryn Morrison, PhD StudentMcGill Surveillance Laboratory
Supervisor: Dr. David BuckeridgeMcGill Clinical and Health Informatics Research Group
Co-supervisor: Dr. Sarah HendersonEnvironmental Epidemiologist, BC Centre for Disease Control
Committee member: Dr. Gavin ShaddickDepartment of Mathematical Sciences, University of Bath
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
I have no conflicts of interest.
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Forest fire smoke as a public health exposure
Natural seasonal hazard inwestern Canada and US,Australia
Smoke severely degrades airquality, >20 times typical urbanair safety standards
Smoke toxicology showsinhaling PM is dangerous,acute and chronic respiratoryand cardiovascular effects
If 100,000 people in BC were exposed to typical smoke levels,anticipated increases per day: 20 additional asthma physician visits,3 respiratory hospitalizations, 2 deaths
Forest fire seasons are getting longer and more severe
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Epidemiological studies on forest fire smoke
Air pollution literature showing negative health impacts of PMexposure
Epidemiological studies show consistent, significant health effect fromacute smoke exposure
Time series analyses,case-crossover studies,one cohort study
Exposure measuredecologically
Health outcomesgenerally measured byhealthcare utilization
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Public health surveillance of forest fire smoke
Need to take thisevidence, movetowards real-timesurveillance
Officials currentlyrely on simple orad-hoc methods
Interventions range from warning public to limit activitythrough to evacuating entire communities
Data are available, but suitable methods are needed
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Public health surveillance of forest fire smoke
Need to take thisevidence, movetowards real-timesurveillance
Officials currentlyrely on simple orad-hoc methods
Interventions range from warning public to limit activitythrough to evacuating entire communities
Data are available, but suitable methods are needed
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Public health surveillance of forest fire smoke
Unique surveillance application area
Need to linkenvironmentalexposure to healthoutcomes data
No gold standardhealth effect(lab-confirmed)
Each outcome reveals part of overall picture
Desire to link data sources
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Research goal
My research goal is to develop evidence to guideappropriate use of hierarchical multivariate methods in
surveillance of forest fire smoke health effects, that couldbe used in public health practice and ultimately inform
intervention decisions.
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Research objectives
1 Review the literature to critically assess the multivariate time seriesand space-time series methods for applied public health forecasting ofthe short-term impacts from acute environmental exposures.
2 Evaluate the difference in model performance for univariate andbivariate time series forecasting models: (i) using real surveillancedata, and (ii) via a simulation study.
3 Evaluate the difference in model performance for univariate andbivariate space-time series forecasting models (i) using realsurveillance data, and (ii) performing a sensitivity analysis to explorethe impact of spatial neighbourhood definition on forecast accuracy.
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objective 1: Methodological scoping review
There are no review papers on multivariate methodsrelevant to public health surveillance in environmentalexposures
Search criteria to retrieve studies about
. . . acute environmental exposures
. . . using multivariate methods
. . . time series or space-time series data
Synthesize: strengths and limitations of multivariatemethods different relevant scenarios, approaches toestimation, approaches to implementation
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objectives 2-3: Study setting
British Columbia Local Health Areas & Fire Locations - 2010
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Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objectives 2-3: Study setting
British Columbia Local Health Areas & Fire Locations - 2010
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Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
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Contributions
Objectives 2-3: Study setting
British Columbia Local Health Areas & Fire Locations - 2010
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Analysis
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Forecasting
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Contributions
Objectives 2-3: Health data
→ Daily counts per local health area, 2003-present
Salbutamol dispensations
• treats acutesymptoms of asthma
• prescriptions logged todatabase in real-timevia Ministry of HealthPharmaNet program
Physician visits
• billings data fromMedical Services Plan
• classified by ICD-9code (respiratory)
Salbutamol dispensations May-Oct 2010 / regional population
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
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Contributions
Objectives 2-3: Exposure data
No gold standard forexposure measurement
Air quality monitorsmeasure PM; temporallyresolved, spatially sparse
Remotely sensed images:spatially/temporallyresolved, crude measure
Combine best data into validated predictive model developed by theBCCDC
Predictive model uses PM measurements, remote sensing, andclimate data; validated with PM measurements
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objectives 2-3: Exposure data
No gold standard forexposure measurement
Air quality monitorsmeasure PM; temporallyresolved, spatially sparse
Remotely sensed images:spatially/temporallyresolved, crude measure
Combine best data into validated predictive model developed by theBCCDC
Predictive model uses PM measurements, remote sensing, andclimate data; validated with PM measurements
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objectives 2-3: Exposure data
No gold standard forexposure measurement
Air quality monitorsmeasure PM; temporallyresolved, spatially sparse
Remotely sensed images:spatially/temporallyresolved, crude measure
Combine best data into validated predictive model developed by theBCCDC
Predictive model uses PM measurements, remote sensing, andclimate data; validated with PM measurements
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Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objectives 2-3: Measuring forecast accuracy
Many ways to compare surveillance models: model fit,validation, mean square error
MAPE: compare forecasted values to observed values
Mean Absolute Percentage Error (MAPE) = 1N
∑ni=1
yt−ytyt
Standardized (unitless) relative measures are better forcomparison between models
Can be affected by size of denominators, must be non-zero
Subset most recent 5% of data for time series validation;can also exclude one region for spatial validation
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Objectives 2-3: Statistical analysis approach
Generalized linear mixed (hierarchical) models
Bayesian parameter estimation: intuitive framework formodeling correlation via levels of hierarchy
Conceptualize exposure as a latent process → healthoutcomes arise. . . via covariates (measured). . . via random effects (unmeasured)
Assuming health outcomes are independent, conditionedon latent process
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Review Paper
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Objective 2a: Forecasting in time
Univariate time series model
Separate model foreach healthoutcome d
Continuouscovariates may benon-linear
Dummy variables forday-of-week effects
Assess residuals
Ytd ∼ Pois(µtd)
log(µtd) = Xtβd + νtd + btd
νtd ∼ norm(0, σ2ν)
btd = ρdbt−1,d + wtd
wtd ∼ N(0, σ2wd
)
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Review Paper
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Data
Measures
Analysis
Temporal
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Forecasting
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Contributions
Objective 2a: Forecasting in time
Bivariate time series model
Simultaneouslymodel both healthoutcomes
Estimatingcovariance matrix
May provide moreinformation thanseparate models
Ytd ∼ Pois(µtd)
log(µtd) = Xtβd + νtd + btd
νtd ∼ norm(0, σ2ν)
btd = ρdbt−1,d + wtd
wtd ∼ MVN([0, 0]T ,Σwd)
Σwd∼Wishart(A, a)
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Review Paper
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Data
Measures
Analysis
Temporal
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Forecasting
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Contributions
Objective 2b: Simulation study
Assess impact:
bivariatecorrelation
effect size
randomvariability
data volume
θ = AR(1) process
X1 = N(θ, σ2x)
RR: varied between 1.0 and 2.0
µ1 = exp(β0 + β1X1 + AR(1))
Y1 = poisson(µ1)
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Background
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Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objective 3a: Forecasting in space and time
Univariate spatiotemporal model
Entire study area modelled as a collection of contiguousaggregate regions
Explicitly account for spatial autocorrelation viaconditional autoregressive (CAR) prior on correlatedrandom effect λ
λi |λj 6=i ∼ N(∑
j 6=i wijλj∑j 6=i wij
,σ2λ∑
j 6=i wij)
Non-stationary model relies on definition of neighbourhood
Spatiotemporal models will assume space-time separability
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Background
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Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Objective 3a: Forecasting in space and time
Bivariate spatiotemporal model
One unifyingmodel for theentire studyarea, bothhealth datastreams
Potentialbenefits ofboth bivariateand spatialcorrelation
Yitd ∼ Pois(µitd)
log(µitd) = Xitβd + νitd + btd + λid
νitd ∼ norm(0, σ2ν)
btd = ρdbt−1,d + wtd
wtd ∼ MVN([0, 0]T ,Σwd)
Σwd∼Wishart(A, a)
λid ∼ CAR(σ2λd
)
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Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
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Contributions
Objective 3b: Sensitivity analyses
Spatial neighbourhood definition based on convention
Research has shown that level of smoothing and model fitcan be dependent on choice of neighbourhood structure
Compare different approaches to neighbourhood definition,assess impact on model fit, forecast accuracy
Examples: 1st vs 2nd order adjacency, absolute distancefrom centroid, different definitions of centroid
Ideally, definition should be based on empirical evidence ortheoretical understanding of process being modelled
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Analysis
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Using proposed models for forecasting
For the time series models:Yd ,t+1 = exp(Xtβd + νd + ρdbt−1 + wtd)
For the spatiotemporal models:Yid ,t+1 = exp(Xitβd + νid + ρdbt−1 + wtd + λid)
Forecasts of new values will be based on the current values(assuming 1st order autoregressive model)
In the bivariate models, correlation between the twooutcome variables included in wtd
In the spatiotemporal model, forecasts will also be afunction of neighbouring regions
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Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Using proposed models for forecasting
Once models aredeveloped and evaluatedusing historic data, theycould be put “online” foruse during fire season
Can provide dailymonitoring and short-termforecasts (e.g., 24 hours,48 hours)
Information can inform public health decision-making byproviding information on the estimated public healthimpact of the smoke
Potential to use surveillance data and proposed models forretrospective intervention evaluations
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Review Paper
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Data
Measures
Analysis
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Spatiotemporal
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Limitations
Contributions
Limitations of study
Limitations of syndromicsurveillance data without goldstandard
Challenges of exposuremeasurement
Misclassification and measurement error
Spatial and temporal separability
Only bivariate, could be extended
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Analysis
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Spatiotemporal
Forecasting
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Contributions
Filling evidence gap in publichealth smoke surveillance byproposing and evaluating models
Could be used for real-timemonitoring and forecasting in BC
Results relevant to other forest fire prone regions, directionon how to proceed methodologically
Novel application of multivariate time series andspatiotemporal methods
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Background
Research Goal
Objectives
Review Paper
Study Area
Data
Measures
Analysis
Temporal
Spatiotemporal
Forecasting
Limitations
Contributions
Thank-you
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