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

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.

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

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  1. 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 • 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. • Eenvoudige ecologische analyse van de ziekte van Lyme bij NL huisartsen (1995, 2001 en 2006). • Eerste test van een Bayesiaanse analyse van regionale dichtheid van runderen, kippen en varkens en het voorkomen van patienten met STEC (1999-2006).

  2. Verloop van de vogelpest epidemie, 2003pluimveehouderijen, besmet, geruimd Met dank aan Michiel van Boven

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

  4. MMR-1 coverage by municipality The Netherlands, 2004

  5. Votes for SGP party by municipality National Elections, The Netherlands, 2003 Percentage of those allowed to vote Met dank aan Susan Hahne

  6. Outbreak detectie door het vinden van clusters in regio & tijd bijSalmonella

  7. Algorithm for detection of outbreaksProspectively expected frequencies and tolerances • Optimising sensitivity and specificity Not miss outbreaks but also not too much false alarms • time-geography and time-age clusters and demographic aberrations

  8. 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

  9. Explosions of S. Typhimurium ft 20 Early-Warning application

  10. 1st step signal verification:region Silver wedding: • Case-control study rPHA • Coburgerham, salting process insufficient

  11. 1st stepsignalverification:region-crossingPlace, Age, Gender

  12. 1st step signalverification:precedent No cause found: • RIVM trawling questionnair, too late • Animal Health Service, no clou in region

  13. How to detect clusters in space

  14. 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.

  15. Say CHEESE

  16. † Death (<2jr): 5 - 6 Hospitalized: 60-75 cases Hospital Labconfirmed: 297cases Laboratories Doctor visits: +/- 650 General Practices General Population: +/- 4455 GE-cases Symptomatic Infected General population 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). COI: € 0.6 milj DALY: 54 Schattingen!!

  17. Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561 april 2007 1 jan 2006 half mei 2006

  18. Automatische geografische outbreak detectieKaas affaire Twente Salmonella Typhimurium Ft561 Januari tot half mei 2006 half mei tot eind 2006

  19. Handmatig aangeven van geografie outbreakKaas affaire Twente Salmonella Typhimurium Ft561/DT7januari-december 2006

  20. † Death (<2jr): 5 - 6 Hospitalized: 75-78 cases Hospital Labconfirmed: 261cases Laboratories Doctor visits: +/- 650 General Practices General Population: +/- 4000 GE-cases Symptomatic Infected General population Explosion of Salmonella Typhimurium DT104 cases In 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). COI: € 0.5 milj DALY: 47 Schattingen!!

  21. Importance molecular typing (MLVA), identical DK strain Outbreak strain different of endemic

  22. 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)

  23. Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a Eind december 2007 Begin April 2008

  24. Automatische geografische outbreak detectieOnopgeloste zuivel affaire Salm. Typhimurium Ft651/DT14a Eind december 2007 tot begin april 2008

  25. Lyme disease in the Netherlands Agnetha Hofhuis & Wilfrid van Pelt and many others

  26. Transmission of Lyme disease Lyme disease in Europe is caused by the Borrelia burgdorferi sensu lato group; B. burgdorferi sensu stricto, B. afzelii, B. garinii Transmission by the sheep tick (Ixodes ricinus).

  27. 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…

  28. Studies on Lyme disease in the Netherlands  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

  29. Retrospective GP-study postal questionnaire All (± 8.000) general practitioners (GP’s) in 1995, 2002 & 2006 received pre-coded questionnaire about previous year How many patients with tick bites have you seen? How many erythema migrans case-patients have you seen? How many people are included in your practice population?

  30. Retrospective GP-study results • Response, coverage: 88% in 1994 68% in 2001 71% in 2005 • Tick bite consultations: 1994  30.0002005  73.000 • EM consultations:1994  6.0002005  17.000 Incidence of EM & tick bites per 100.000 inhabitants

  31. Retrospective GP-study ecological risk factors Information on risk factors per municipality Roe deer Rabbits Horses Sheep & goats Cattle Woods Degree of urbanization Tourist nights per year Precipitation Parks & public gardens Sandy soil Uncultivated wet soil Uncultivated dry soil Dunes • 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

  32. Retrospective GP-study ecological risk factors • 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 factors for tick bites • area with sandy soil • mean precipitation • density of roe deer • rural areas ( houses/km2) • tourist areas • cattle / km2 • rabbits / km2

  33. National Tick Bites study GP-based prospective study • 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.

  34. EU GIS internet GP-study (MedVetNet) 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

  35. Geographical distribution of STEC in the Netherlands Wilfrid van Pelt ,Loes Bertens, Ingrid Friesema, Jan van de Kassteele (spatial statistician)

  36. Wat algemeenheden STEC-O157 infecties 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 Doel was inventariserend:Spatial relation Cattle density and STEC incidence

  37. [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 cases Incidence / 1.000.000 STEC-O157 in NL 1999-2006 (2.4 / 106 inhabitants; 40-50 / yr.; N=400) →Bayesian smoothing for low population areas

  38. Seasonality STEC in Veals (1st), 1-2 wks later Humans and Dairy cattle 40% 9 Dairy cattle (faeces) 35% Veal calves (faeces) 8 30% 7 6 25% 5 Positive farms Human STEC O157 cases 20% 4 15% 3 10% 2 5% 1 0 0% 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 8 16 24 32 40 48 1999 2000 2001 2002 2003 2004 2005 2006 week of onset of disease

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

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

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

  42. WinBUGS model called from R using R2WinBUGS library # 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] (i is community.age.sex unit) (E[i] is community.age.sex population offset) (age and sex fixed components) (classical poisson model) (spatial dependence) (standard random effect)

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

  44. [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 cases Incidence / 1.000.000 STEC-O157 in NL 1999-2006 (2.4 / 106 inhabitants; 40-50 / yr.; N=400) Bayesian “smoothed” version (SMR)

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

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

  47. Spatial relation Cattle density and STEC incidenceConclusions • 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

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