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

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Objectives

Review Paper

Study Area

Data

Measures

Analysis

Temporal

Spatiotemporal

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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

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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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Objectives

Review Paper

Study Area

Data

Measures

Analysis

Temporal

Spatiotemporal

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Limitations

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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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Review Paper

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Data

Measures

Analysis

Temporal

Spatiotemporal

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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

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Objectives

Review Paper

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Data

Measures

Analysis

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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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Review Paper

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Data

Measures

Analysis

Temporal

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Objectives 2-3: Study setting

British Columbia Local Health Areas & Fire Locations - 2010

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Review Paper

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Analysis

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Objectives 2-3: Study setting

British Columbia Local Health Areas & Fire Locations - 2010

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Objectives 2-3: Study setting

British Columbia Local Health Areas & Fire Locations - 2010

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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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Review Paper

Study Area

Data

Measures

Analysis

Temporal

Spatiotemporal

Forecasting

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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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Background

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

Study Area

Data

Measures

Analysis

Temporal

Spatiotemporal

Forecasting

Limitations

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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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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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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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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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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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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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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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Review Paper

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Data

Measures

Analysis

Temporal

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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

Temporal

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Forecasting

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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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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

Relevant for infectious disease surveillance24 / 25

Background

Research Goal

Objectives

Review Paper

Study Area

Data

Measures

Analysis

Temporal

Spatiotemporal

Forecasting

Limitations

Contributions

Thank-you

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