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This PowerPoint 2007 template produces a 91cmx122cm presentation poster. You can use it to create your research poster and save valuable time placing titles, subtitles, text, and graphics. We provide a series of online tutorials that will guide you through the poster design process and answer your poster production questions. To view our template tutorials, go online to PosterPresentations.com and click on HELP DESK. When you are ready to print your poster, go online to PosterPresentations.com Need assistance? Call us at 1.510.649.3001

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How to change the template color theme You can easily change the color theme of your poster by going to the DESIGN menu, click on COLORS, and choose the color theme of your choice. You can also create your own color theme. You can also manually change the color of your background by going to VIEW > SLIDE MASTER. After you finish working on the master be sure to go to VIEW > NORMAL to continue working on your poster.

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Text size

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How to remove the info bars

If you are working in PowerPoint for Windows and have finished your poster, save as PDF and the bars will not be included. You can also delete them by going to VIEW > MASTER. On the Mac adjust the Page-Setup to match the Page-Setup in PowerPoint before you create a PDF. You can also delete them from the Slide Master.

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RESEARCH POSTER PRESENTATION DESIGN © 2015

www.PosterPresentations.com

How    define  a  good  choice  of  controls?    MieNnen   (1985)   stated  that   a   control   should   be  someone   who   –if   he   had  developed   the   disease-­‐  would  have  been  recruited  among   the   cases   of   the  study.  [1]  

BACKGROUND  

METHODS  

RESULTS  

REFERENCES  

For every set of “virtuals controls”, we compared the distance to the closest vineyards between cases and controls. Here are the results for one set of “virtuals controls” sampled in rural locations inside circles including 90% of rural cases around each clinical center.

•[1] Miettinen, O. S. (1985). « The "case-control" study: valid selection of subjects. » J Chronic Dis 38(7): 543-548. • A.J. Valleron / C.R. Acad. Sci. Paris, Sciences de la vie / Life Sciences 323 (2000) 617–628 • [2] http://www.insee.fr/fr/methodes/default.asp?page=zonages/unites_urbaines.htm • [3] Gambino, J. G. (2005). « pps: Functions for PPS sampling. » http://CRAN.R-project.org/package=pps. • [4] http://professionnels.ign.fr/bdparcellaire

New opportunities for environmental epidemiology arise with the increasing availability of public databases informing on local environmental exposures. These public databases can be mapped, using geographic information systems (GIS) to the addresses of geolocalized patients of interest. This can be used to compare environmental exposures by mapping the addresses of the cases and of the controls to the environmental geographic databases of interest. A more straightforward approach is to choose randomly a set of places on the map, then compute the exposure at these places, and compare the exposure of the cases of interest to this reference. In other words, the idea is to define “virtual controls” instead of “physical controls” to evaluate the reference exposure. This is appealing because: 1/ it avoids to get (costly) “physical controls” whose choice bears some arbitrariness 2/ the choice of virtual controls (defined by a given algorithm) is more flexible Common sense tells however that these virtual controls cannot be just randomly sampled on the map, as they would in this cases most likely fall in uninhabited places, while cases would be in majority in high population density spots. Instead of selecting arbitrarily a unique set of virtual controls, we sampled different algorithms for an optimal choice of “virtual controls”.

Adrien  de  La  Vaissière,  David  Baker,    Pierre  Bougnères  et  Alain-­‐Jacques  Valleron  

Inserm  U1169  –  Hôpital  Bicêtre    

“Virtual  controls”  and  GeolocalizaNon  to  search  for  Environmental  correlates  of  Hypospadias  

OBJECTIVES  

CONCLUSION  

Incidence  of  hypospadias  varies  considerably  across  countries,  ranging  from  4  to  43  cases  per   10,000   births   .   Environmental   factors   might   explain   these   differences.   The   classical  approach   is   to   use   case-­‐control   studies   and   ques/onnaires   to   iden/fy   these   factors.  However,   this   approach   suffers   from   the   unavoidable   arbitrariness   of   the   defini/on   of  controls  and  from  recall  bias.    

To describe our methodology as a proof-of-concept of a “virtual controls”approach to search for environmental markers (factors) of a disease, applied here to hypospadias.

I- The starting point is to define the population in which the virtual controls will be sampled. This population must be identical to the population from which cases are extracted. The basic idea is that, if the controls would have got the disease, they would have had the same probability of being selected than cases. When the cases are recruited by clinical centers, the base population is harder to define because it depends on location and characteristics of these clinical centers. Our cohort included 8766 cases of hypospadias coming from 16 specialized surgical centers. We defined several different populations from which we draw the « virtual controls ». For exemple - in squares around each case - in circles containing k% of the cases around each clinical center. We also considered in another sampling only the population living in rural locations. (thanks to INSEE definition [2])

Figure 1: the past exposures to the studied environmental factor observed in a group of patients with the disease of interest (“cases”) is compared with the past exposures of comparable subjects without the disease (“controls”).

II- The French Statistical body (INSEE) provides annual estimates of the population numbers by age-class in each square of a 200m x 200m grid covering France. The map can be viewed as a set of 2 278 213 pixels, each of them corresponding to an elementary square of the grid. Assume that the environmental exposures are known for 1000 subjects, and that 1000 virtual controls are needed, the problem is to appropriately choose at random 1000 pixels across the grid. The random selection process chosen guarantees that the geographical density of the virtual controls be equal to the density of the population of reference. To achieve this goal, the probability for selecting each “pixel” was taken proportional to the population density in the “square” corresponding to this pixel. This was achieved by using the R package PPS developed by Jack G.[3] This algorithm provides virtual control subjects that mirror closely the general population.

III- We can now use the Corine Land Cover (CLC) database, which is an inventory of land cover in 44 classes. France is divided in 100m*100m squares, and in each of them, we have one of the 44 values (for example: Vineyards, Mixed forest, Discontinuous urban fabric …). In our study, we are going to focus on vineyards.

For each adress (case or “virtual controls”), we can draw circles and rings of different radius (250m, 500m, 1km, 2km, 5km…) around each point and check if there is any vineyard inside or not.

<250m   250/500m    

500/1000m    

1000/2000m    

2000/5000m    

>5000m  (non  exposed)  

Hypospadias   126   169   201   252   397   1638  

«  virtual  controls  »  

59   90   127   187   336   1698  

Odds-­‐ra/o   2,21   1,95   1,64   1,40   1,22  

P-­‐value    P  <  0,0001    P  <  0,0001    P  <  0,0001    0,0011    0,0132  Min(95%CI)   1,61   1,49   1,30   1,14   1,04  

Max(95%CI)   3,04   2,54   2,07   1,71   1,44  

Table 1 - odds-ratios comparing expositions to vineyards in the different rings for « virtuals controls » drawn in rural locations inside circles including 90% of rural cases around each clinical center.

 Fig.  2  –  Example  of  a  CLC  map  around  the  residence  of  a  case

The results are consistent with an association between hypospadias and vineyards. Nevertheless, we obtain similar results with other types of CLC, like forest. Further investigation is needed. We are also going to crosscheck those results with other databases, in particular the “parcel”[4] database. Computational optimization is needed to reduce computation times.

346--P1Pierre Bougneres DOI: 10.3252/pso.eu.55ESPE.2016

Gonads & DSD