Surveillance Data Spatial - EpiSouth · 2018. 4. 11. · ArcGis® and Arcview) Compatible with Arcview 3.x, and ArcGIS 10.05.2004 Intro analysis of surveillance data, EPISOUTH, Madrid,

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10.05.2004 EPIET Workshop Bordeaux, May 2004Intro analysis of surveillance data, EPISOUTH, Madrid, September 2007

Spatial Analysis of Surveillance Data

Fernando Simón, Francisco Luquero, Victor Flores, Denis Coulombier


Early Neonatal Mortality


Cartogram: Malaria. N. cases


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Purpose of spatial analysisDescribe spatial distribution of data– Counts, rates, RR …– Mapping

Identify spatial association of cases– Identify clusters, OB …– Analysis of point processes

Estimating – Counts, rates, RR …– Geostatistics


Descriptive Analysis

Dot-density maps for count of casesAdministrative area maps for rates– Choice of administrative areas– Rates to account for population– Standardised rates to account for

population structure“Isorate” maps for sentinel surveillanceGIS when case coordinates available


Dot Density Map


Notification of Tuberculosis in France, 19964-Week Period Ending 31/12/1996

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Spatial Distribution of Polio CasesAlbania, April-September 1996

AprilMayJuneJulyAugustSeptember


Descriptive Analysis: Place and Rates

Count of cases does not represent riskAdministrative areas have different populationsPopulation may vary over time– Seasons – Population influx (refugees)

Rates allow to compare risksChoice of administrative areas (problem of small numbers of cases)Choice of ranges


Notification Rate of Tuberculosis in France, 1996

Cases/100,000


Distribution of cases of PERTUSSISLebanon, as of week 2003-15

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Cases/100,000/Y

0.178 – 0.5540.555 – 0.8720.873 – 1.7411.742 – 3.554No report

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Choosing map data If we map number of cases instead of rates:

Is the disease risk in A really similar to B?

Misleading because underlying population may be greater in A

A B


Use of Standardised Rates

Age structure

Disease Place

Population structurevaries across places

independently of disease

Disease occurrence varies across ages

independently of place

ConfoundingAge, independently related to disease and to location


Use of Standardised Rates

Direct standardisation

Indirect standardisation

Value of rate affected by the reference population

For comparison only


Distribution of Death by Falls by Province, Canada, 1998

Age Standardized Rate per 100,000Crude deaths rate per 100,000

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General mortality, 1995-1997

Not smoothed

Smoothed


BoxMap

Equal interval Equal n. of records

Natural breaking

Chloropleths: colors or patterns


Choropleth Maps

Natural break

The story we tell depends upon how we choose to create the legend

Equal ranges

Equal counts


Choice of Data Breakdown in Classes

Equal area

Equalinterval

Naturalbreaks

Quartiles

MeanSt Dev.

0

5

10

15

20

0.378 7.400 14.423 21.445 28.467

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Testing for Hypothesis place

Remove confounding (standardisation)Detection of clusters– Unexpected events: dot-maps

• Test for spatial correlation by nearest neighbour– Events with baseline historical data

• Test for spatial correlation by contiguity analysis

Risk factor identification– Overlaying exposure and outcome– Test for cross-correlation


Distribution of cases of Botulism France, Week 42-45, 2000


0.000000-0.0012500.001251-0.0020510.002052-0.0025920.002593-0.006114

Death per 100000Observed contiguity in high risk counties: 24Expected contiguity in high risk counties: 16.3Contiguity standard deviation: 3.46z statistic: 2.07, p=0.038

Testing for ContiguitiesGrimson Method

Sudden Infant Death Syndrome by County, North Carolina, 1974-1978


Procesos Puntuales

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Why identify geographic patterns?

Geographic patterns range from completely clustered to completely dispersed. A pattern between these extremes is said to be random.

Knowing there’s pattern in your data is useful if you need to gain a better understanding of a geographic phenomenon


Spatial Processes questionsIs there any systematic pattern or are my data distributed atrandom.

Possibilities: clusteringregularity

Scale of the clustrereing

The pattern is due to:

• natural variation in the population• obvious a priori heterogeneity• associated with proximity to other features of interestAre events that aggregate in space also clustered in time?


Is this clustered?

YES!


Is this clustered?

NO:

Its regularly dispersed

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Is this clustered?

Maybe: (Complete Spatially Random)


Point processes

The process is stationary if the jointdistribution of N(A) is invariant totranslation by an arbitrary amount x

The process is isotropic if the jointdistribution of N(A) is invariant torotation through an arbitrary angle


Point processesTheorem 2: For a homogeneous planar Poisson process


Point processes

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Point processes


Point processes


RANDOM UNIFORM CLUSTERED


Global and Local Tests

Cluster detection methods

Global (first-order) tests detect the presence or absence of clustering over the whole study regionwithout specifying the spatial location.

Local (second-order) tests additionally specify the location and if extended to consider temporal patterns, can specify spatio-temporal clusters.

A special case of local tests is the focussed test which is used to detect raised incidence of disease around some pre-specified source, such as an incinerator.

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Choosing a method

There are two critical aspects, statistical power, and confounding.

Methods that can control for known confounding effects should be used in the first instance.

Statistical power is the ability to detect a real effect.

Readers will become acquainted in the literature with the ability of methods to identify true clusters (true positives) but also the frequency with which the methods report clusters falsely (false positives).

Comparative evaluations of statistical power, often by running competing cluster methods against a set of simulated data with known properties, can provide guidance in the choice and application of particular methods. Confounding is the erroneous attribution of a disease cluster to a factor which is both related to an exposure and a disease outcome.

Change in background population density

demographic factors such as age, gender or ethnicity.


Maps for Sentinel SystemsIncidence of diarrhea in France, 1995

Cases / 100,000 population

Source: Réseau National Télématique des Maladies Transmissibles


Distribution of Syndromic Influenza,France, Week 1-19, 2003


Interpretation of Significant Tests

The role of artefacts– Errors…

The role of confounding– Rates (time)– Standardised rates (place)

The role of chance– Statistical testing (place dependency)

True disease pattern

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George W. Comstock

The art of epidemiological thinkingis to draw conclusions

from imperfect data


Epi Map MappingShapefiles + Data file (.mdb)

Slides based in a previous Paolo D’Ancona presentation for EPIET


Epi MapGIS component of Epi-InfoProgrammed with MapObjects language (Shapefiles), Developed by ESRI (Makers of ArcGis® and Arcview)Compatible with Arcview 3.x, and ArcGIS


ShapefilesGIS data set– Data represented by coordinates– Point, line, area (polygon)– Attributes stored in separate dbf file

• Names and other information

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Structure of a simple dataset

Main file: xy.shpIndex file: xy.shxdBASE file or Access: xy.dbf or xyMDB

The project file– In arcview: xy.apr– In epimap: xy.map


ESRI shapefileEPI MAP

Europe.shp Polygons



Europe.shp Polygons

Europe.dbf Attributes (names)

Greece

France



Europe.shp Polygons

Europe.dbf Attributes

Europe.shx File Structure

Greece

France

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ESRI shapefile & Access data tableEPI MAP

Europe.shp Polygons



Greece

France



Europe.shp Polygons



Attributes to match

Greece

France



Europe.shp Polygons



Attributes to matchCount variables

Greece

France



Europe.shp Polygons



Attributes to matchCount variablesOther Information (Size, Pop...)

Greece

France

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Data file (.mdb)

Contains– Geographical variable

• To link data to specific features in shapefile• Unique relationship

– At least one numeric variable• Disease count, rate, etc.

Individual data– Must be processed to produce a summary file– Only one record per geographic entity


= Epi-Info Map file

Dot density Chloropleth

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Surveillance Data Spatial - EpiSouth · 2018. 4. 11. · ArcGis® and Arcview) Compatible with Arcview 3.x, and ArcGIS 10.05.2004 Intro analysis of surveillance data, EPISOUTH, Madrid,