volksgezondheids toegevoegde waarde van gis/ruimtelijke analyse bij enkele infectieziekten
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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. - PowerPoint PPT PresentationTRANSCRIPT
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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).
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Verloop van de vogelpest epidemie, 2003pluimveehouderijen, besmet, geruimd
Met dank aan Michiel van Boven
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Rubella notifications by 4 digit postal code The Netherlands, 1-9-2004 – 13-9-2005
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MMR-1 coverage by municipality The Netherlands, 2004
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Votes for SGP party by municipality National Elections, The Netherlands, 2003
Percentage of those allowed to vote
Met dank aan Susan Hahne
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Outbreak detectie door het vinden van clusters in regio & tijd bij
Salmonella
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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
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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
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Explosions of S. Typhimurium ft 20
october 1997Groningen
April 1998Roermond October 1999
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Region clusters
Age deviating
Time clusters
Early-Warning application
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1st step signal verification:
region
Silver wedding:• Case-control study rPHA• Coburgerham, salting
process insufficient
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1st stepsignalverification:
region-crossing
Place, Age, Gender
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1st step signalverification:
precedentNo cause found:• RIVM trawling
questionnair, too late• Animal Health
Service, no clou in region
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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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P2.5P50P97.5Age+Age-
How to detect clusters in space
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• 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.
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Say CHEESE
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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: +/- 650General Population:+/- 4455 GE-cases
COI: € 0.6 milj
DALY: 54
Schattingen!!
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1 jan 2006
half mei 2006
april 2007
Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561
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Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561
Januari tot half mei 2006
half mei tot eind 2006
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Handmatig aangeven van geografie outbreak
Kaas affaire Twente Salmonella Typhimurium
Ft561/DT7januari-december 2006
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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: +/- 650General Population:+/- 4000 GE-cases
COI: € 0.5 milj
DALY: 47
Schattingen!!
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Importance molecular typing (MLVA), identical DK strain
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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)
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Eind december 2007
Begin April 2008
Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a
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Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a
Eind december 2007 tot begin april 2008
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Lyme disease in the NetherlandsAgnetha Hofhuis & Wilfrid van Pelt and many others
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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).
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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…
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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
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All (± 8.000) general practitioners (GP’s) in 1995, 2002 & 2006received 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
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74,3
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88% in 1994 68% in 2001 71% in 2005
Tick bite consultations: 1994 30.0002005 73.000
EM consultations:1994 6.0002005 17.000
Retrospective GP-study results
Incidence of EM & tick bites per 100.000 inhabitants
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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
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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
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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 bitesand erythema migrans consultations.
National Tick Bites study GP-based prospective study
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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)
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Geographical distribution of STEC in the NetherlandsWilfrid van Pelt ,Loes Bertens, Ingrid Friesema, Jan van de Kassteele (spatial statistician)
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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
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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
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Seasonality STEC in Veals (1st), 1-2 wks later Humans and Dairy cattle
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week of onset of disease
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STEC-O157 cases incidence and Cattle densities,1999-2006
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 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 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]
Cattle densityAnimals / km2
[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]
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]
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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]
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]
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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
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# 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)
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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
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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]
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 “smoothed” version (SMR)
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STEC-O157 model:fixed part (explained by cattle/pigs/poultry)
Cattle densityAnimals / km2
[0,35.7](35.7,75.4](75.4,121](121,172](172,230](230,325](325,547]
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]
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]
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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]
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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