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National Institutefor Public Healthand the Environment

Volksgezondheids toegevoegde waarde van GIS/ruimtelijke analyse bij enkele infectieziekten.

Wilfrid van Pelt, Agnetha Hofhuis, Ingrid Friesema, Jan van de Kassteele en vele vele anderen

1. Een simpele vorm van clustering in ruimte en tijd geimplementeerd in 1999 met een internet interface, van 1000 salmonella types voor wekelijkse signalering en retrospectieve inspectie.

2. Eenvoudige ecologische analyse van de ziekte van Lyme bij NL huisartsen (1995, 2001 en 2006).

3. Eerste test van een Bayesiaanse analyse van regionale dichtheid van runderen, kippen en varkens en het voorkomen van patienten met STEC (1999-2006).


Verloop van de vogelpest epidemie, 2003pluimveehouderijen, besmet, geruimd

Met dank aan Michiel van Boven


Rubella notifications by 4 digit postal code The Netherlands, 1-9-2004 – 13-9-2005


MMR-1 coverage by municipality The Netherlands, 2004


Votes for SGP party by municipality National Elections, The Netherlands, 2003

Percentage of those allowed to vote

Met dank aan Susan Hahne


Outbreak detectie door het vinden van clusters in regio & tijd bij

Salmonella


Algorithm for detection of outbreaks Prospectively expected frequencies and tolerances

• Optimising sensitivity and specificityNot miss outbreaks but also not too much false alarms

• time-geography and time-age clusters and demographic aberrations


Intranet Catalogue / Atlas since Mai 1998

• human/ animal/ food/ environment

• All sero and phagetypes

• Resistance

• Actual trends, Early Warning, GIS-clusters

Each week backcalculated starting in 1984

• >10.000 Tables and Figures


Explosions of S. Typhimurium ft 20

october 1997Groningen

April 1998Roermond October 1999

Twente

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ObservedExpectedTolerance

Region clusters

Age deviating

Time clusters

Early-Warning application


1st step signal verification:

region

Silver wedding:

• Case-control study rPHA

• Coburgerham, salting process insufficient


1st stepsignalverification:

region-crossing

Place, Age, Gender


1st step signalverification:

precedent

No cause found:

• RIVM trawling questionnair, too late

• Animal Health Service, no clou in region


Random median distance (residence) between patients with salmonella. Empirical results after 1.000 bootstrapsamples. 274 potential outbreaks, significantly clustering in time,

between 1995-2000 over 255 salmonella types

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Number in cluster

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(km

)

P2.5

P50

P97.5

Age+

Age-

How to detect clusters in space


• Methode is simpel, werkt goed en test duizenden potentiële clusters in enkele minuten

Ja, maar, regio’s verschillen toch in bevolkingsdichtheid?

• Klopt!! Maar dat heeft v.n.l. invloed op de grootte en het aantal clusters in een regio.


Say CHEESE


Explosion of Salmonella Typhimurium DT7 cases

In January 2006 up to April 2007 an explosion of S.T. DT7 infections occurred, resulting in an extra 297 lab-confirmed cases of salmonellosis.

(tip of the Iceberg).

Labconfirmed: 297 cases

Hospitalized: 60-75 cases

Death (<2jr): 5 - 6

Symptomatic InfectedGeneral population

General Practices

Laboratories

Hospital

†

Doctor visits: +/- 650

General Population:+/- 4455 GE-cases

COI: € 0.6 milj

DALY: 54

Schattingen!!


1 jan 2006

half mei 2006

april 2007

Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561


Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561

Januari tot half

mei 2006

half mei tot

eind 2006


Handmatig aangeven van geografie outbreak

Kaas affaire Twente Salmonella Typhimurium

Ft561/DT7januari-december 2006


Explosion of Salmonella Typhimurium DT104 casesIn the after summer of 2005 an explosion of DT104

infections occurred, resulting in an extra 261 lab-confirmed cases of salmonellosis

(tip of the Iceberg).

Labconfirmed: 261 cases

Hospitalized: 75-78 cases

Death (<2jr): 5 - 6

Symptomatic InfectedGeneral population

General Practices

Laboratories

Hospital

†

Doctor visits: +/- 650

General Population:+/- 4000 GE-cases

COI: € 0.5 milj

DALY: 47

Schattingen!!


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January February March April Mai June July August September October November December

DT

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Imported strainEndemic strainToleranceExpected

Outbreak strain different of endemic

Importance molecular typing (MLVA), identical DK strain


Case-control study

Case Control study

• Cases (109 eligible)

• Controls (411 eligible)

Result- Filet-Americain OR 4.2 (1.5–12.0)

- Mobile-Caterer OR 4.9 (1.1–22.1)


Eind december

2007

Begin April

2008

Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a


Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a

Eind december 2007

tot begin april 2008


Lyme disease in the NetherlandsAgnetha Hofhuis & Wilfrid van Pelt and many others


Lyme disease in Europe is caused by the Borrelia burgdorferi sensu lato group;

B. burgdorferi sensu stricto, B. afzelii, B. garinii

Transmission of Lyme disease

Transmission by the sheep tick (Ixodes ricinus).


Lyme disease

Early local infection:

erythema migrans (EM) 75 - 90% of B. burgdorferi infections

Early disseminated infection:

manifestations in nervous system, skin, joints and heart

Chronic Lyme borreliosis…


retrospective studies among general practitioners (GP’s)

? Incidence of tick bites and erythema migrans

? Geographical distribution in the Netherlands

? Ecological risk factors for tick bites and erythema migrans

retrospective analysis of hospital admissions for Lyme disease

? Occurrence of hospital admissions for Lyme disease

? Seasonal and annual trends in hospital admissions for Lyme disease

collecting ticks in 4 different biotopes

? Density of ticks & infection rate of ticks with Borrelia

? Seasonal & annual trends

Studies on Lyme disease in the Netherlands


All (± 8.000) general practitioners (GP’s) in 1995, 2002 & 2006

received pre-coded questionnaire about previous year

1. How many patients with tick bites have you seen?

2. How many erythema migrans case-patients have you seen?

3. How many people are included in your practice population?

Retrospective GP-study postal questionnaire


74,3

103,8

38,6

371,9

446,3

191,3

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Response, coverage: 88% in 1994

68% in 2001

71% in 2005

Tick bite consultations:

1994 30.000

2005 73.000

EM consultations:

1994 6.000

2005 17.000

Retrospective GP-study results

Incidence of EM & tick bites per 100.000 inhabitants


Information on risk factors per municipality

Roe deerRabbitsHorsesSheep & goatsCattleWoodsDegree of urbanizationTourist nights per yearPrecipitationParks & public gardensSandy soilUncultivated wet soilUncultivated dry soilDunes

Retrospective GP-study ecological risk factors

Risk analysis for GP-studies of 1994, 2001 & 2005 together:

Poisson regression: (offset: city population) city repeated measure year “confounder” no random (un)structered effects, sofar no Bayesian smoothing, sofar


Retrospective GP-study ecological risk factors

Risk factors for tick bites

area with sandy soil

mean precipitation

density of roe deer

rural areas ( houses/ km2)

tourist areas

cattle / km2

rabbits / km2

Risk factors for erythema migrans

area covered with woods

area with sandy soil

mean precipitation

roe deer / km2

rural area ( houses/ km2)

tourist areas


Risk of infection after a tick bite?

Infection rate of ticks: Borrelia,

Ehrlichia, Babesia, Rickettsia.

Serology and clinical aspects after a

tick bite or erythema migrans.

Case-control study: risk factors for tick

bites and erythema migrans.

In 2007 and 2008 200 GPs selected in

hotspot areas for tick bites

and erythema migrans consultations.

National Tick Bites study GP-based prospective study


Aims

Accurate incidence figures across Europe for Lyme disease- regional comparison and analysis of regional risk factors

Feasibility of setting up a network of GPs across Europe- answer simple health care questions with a high response- with respect to a known denominator population.

Internet GIS-tool for questioning of physicians- immediately mapping and feeding back the results to GP

EU GIS internet GP-study (MedVetNet)


Geographical distribution of STEC in the Netherlands

Wilfrid van Pelt ,Loes Bertens, Ingrid Friesema, Jan van de Kassteele (spatial statistician)


Bekende genoemde risicofactoren: Consumptie rauwe melk/kaas (16%) Contact landbouwhuisdier (21%) Persoon-persoon overdracht (18%) Consumptie kant en klare groente (28%)

Belangrijkste reservoir: Runderen en Kalveren

Acute gastroenteritis met Complicaties HUS (15%) en Ziekenhuisopname (41%), m.n. 0-4 jarigen

Wat algemeenheden STEC-O157 infecties

Doel was inventariserend:Spatial relation Cattle density and STEC incidence


STEC casesIncidence / 1.000.000

[0,1.62](1.62,4.7](4.7,7.34](7.34,11.1](11.1,16.8](16.8,22.4](22.4,31.6]

STEC-O157 in NL 1999-2006 (2.4 / 106 inhabitants; 40-50 / yr.; N=400)

→Bayesian smoothing

for low population areas


Seasonality STEC in Veals (1st), 1-2 wks later Humans and Dairy cattle

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1999 2000 2001 2002 2003 2004 2005 2006

week of onset of disease

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Dairy cattle (faeces)

Veal calves (faeces)


STEC-O157 cases incidence and Cattle densities,1999-2006


[0,1.62](1.62,4.7](4.7,7.34](7.34,11.1](11.1,16.8](16.8,22.4](22.4,31.6]


[0,1.62](1.62,4.7](4.7,7.34](7.34,11.1](11.1,16.8](16.8,22.4](22.4,31.6]


[0,1.62](1.62,4.7](4.7,7.34](7.34,11.1](11.1,16.8](16.8,22.4](22.4,31.6]

Cattle densityAnimals / km2

[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]


[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]

[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]


Average (1997-2006) densities of farm animals / km2

Poultry densityAnimals / km2

[0,974](974-2700](2700-5440](5440-9400](9400,15500](15500-23500](23500-28800]

Poultry densityAnimals / km2

[0,974](974-2700](2700-5440](5440-9400](9400,15500](15500-23500](23500-28800]

Pigs densityAnimals/ km2

[0-204](204-561](561-1040](1040-1770](1770-2490](2490-3290](3290-4760]

Pigs densityAnimals/ km2

[0-204](204-561](561-1040](1040-1770](1770-2490](2490-3290](3290-4760]


[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]

[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]


Neighbour matrix of 496 communitiesAchtkarspelen 59 15 56 65 79 90 737Ameland 60het Bildt 63 70 81 83 1722Bolsward 64 683 710Dantumadeel 65 58 59 79 737 1722Franekeradeel 70 63 72 83 140 710Harlingen 72 70 710Heerenveen 74 51 55 85 86 98Kollumerland c,a, 79 56 58 59 65 1663Leeuwarden 80 55 81 83 140 737Leeuwarderadeel 81 63 80 83 737 1722Lemsterland 82 51 98 171 181 653Menaldumadeel 83 63 70 80 81 140Ooststellingwerf 85 74 86 98 1699 1701 1731Opsterland 86 22 25 55 74 85 90 1699Schiermonnikoog 88Smallingerland 90 15 25 55 59 86 737Sneek 91 51 55 683Terschelling 93Vlieland 96Weststellingwerf 98 51 74 82 85 181 1701

Rotterdam 599 489 492 493 501 502 503 519 542 556 565 567 568 597 600 606 612 613 614 622 643 1666 1926


# y[i] = observed counts # lambda[i] = RR.total[i]*E[i] = intensity # E[i] = expected number based on population properties # beta[1] = baseline log(RR) # beta[.] = association log(RR) for covariates # b.struc[i] = area specific spatially structured random effect for residual or unexplained log(RR) # b.unstr[i] = area specific unstructured (exchangeable) random effect for residual or unplained log(RR) # b.struc[i]+b.unstr[i] = effect of latent (unobserved) risk factors (convolution prior) # RR.total[i] = RR.fixed[i]*RR.resid[i] = total relative risk # RR.fixed[i] = RR of all known risk factors together # RR.resid[i] = RR of latent (unobserved) risk factors

# # model for (i in 1:n) { y[i] ~ dpois(lambda[i]) log(lambda[i]) <- log(E[i]) + fixed[i] + resid[i] fixed[i] <- beta[1] + beta[2]*cattle[i] + beta[3]*pigs[i] + beta[4]*poultry[i] + resid[i] <- b.struc[i] + b.unstr[i]

WinBUGS model called from R using R2WinBUGS library

(i is community.age.sex unit)

(E[i] is community.age.sex population offset)

(classical poisson model)

(age and sex fixed components)

(standard random effect)(spatial dependence)


Model results

RR P2.5 P97.5(Intercept) 0,6 0,4 0,8cattle: 1000/km2 6,4 0,8 45,2pigs: 1000/km2 1,0 0,7 1,4poultry: 1000/km2 1,0 0,9 1,0Age 0-4 4,5 3,4 5,9Age 5-9 1,6 1,2 2,1Age 10-49 0,5 0,4 0,7Age 50+ 1,0Males 1,0Females 1,3 1,0 1,6


RR total

[0.202,0.734](0.734,0.969](0.969,1.24](1.24,1.6](1.6,2.11](2.11,3](3,4.25]


[0,1.62](1.62,4.7](4.7,7.34](7.34,11.1](11.1,16.8](16.8,22.4](22.4,31.6]

STEC-O157 in NL 1999-2006 (2.4 / 106 inhabitants; 40-50 / yr.; N=400)

Bayesian “smoothed” version (SMR)


STEC-O157 model:fixed part (explained by cattle/pigs/poultry)


[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]


[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]

[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]

RR fixed

[0.606,0.767](0.767,0.832](0.832,0.905](0.905,0.987](0.987,1.08](1.08,1.24](1.24,2.09]


STEC-O157 model:Structured and unstructured random effects (residuals)

RR unstructured

[0.418,0.975](0.975,1.19](1.19,1.41](1.41,1.68](1.68,2.05](2.05,2.55](2.55,3.62]

RR structured

[0.356,0.614](0.614,0.878](0.878,1.02](1.02,1.14](1.14,1.26](1.26,1.48](1.48,10.3]


First WINBUGS-modelling attempt (1 week work) succeeded, but needs refinement

WinBUGS model called from R using R2WinBUGS library seems elegant

Seasonality, year are not included in the model as yet

Stratified analysis for age and season probably necessary to clearly show the effect of cattle density

Clearly other unknown regional effects are involved

Spatial relation Cattle density and STEC incidenceConclusions

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