1. Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment: A Demonstration Project Brent McDaniel Epid 624, Winter 1999 UM-SPH

2. Disease Periodontal Disease (Old Model) Gingivitis progressed to periodontitis with subsequent loss of bone and teeth Everyone susceptible Susceptibility increased with age Main cause of tooth loss after age 35 Thought to be an inevitable consequence of aging

3. Changing Paradigm Although gingivitis often precedes periodontitis, few sites advance to periodontitis Only a small proportion of the population (5- 20%) will present with severe periodontitis Periodontal disease is not a ‘natural’ consequence of aging Periodontal disease is not the major cause of tooth loss (except the oldest age groups in some populations)

4. How do we measure periodontal disease? Pocket depth Loss of periodontal attachment “Ramjford teeth” (index teeth) used to facilitate and expedite data collection in large surveys May result in systematic under-reporting when dealing with LPA and pocket depth

5. Loss of Periodontal Attachment (LPA)

6. Data Source - Tecumseh, Michigan (1959) Included only people age 19 or greater (N=309) Excluded individuals with missing teeth (99) N=48 for spatial analysis Recoded LPA > 1 as 1, 88 (unerupted) as 0, 66 (not able to measure) as 1

7. Methods Scanned a graphical representation of the dentition and input into ArcView Digitized the areas of measurement (4 sites for each tooth) Used ArcView x,y coordinates to determine relative distance between sites; Dr. Long created connection (link) files for both arches Ran Stat! using Moran’s I (100 runs); 96 total data sets (48 upper and 48 lower)

8. Moran’s I Test for spatial autocorrelation in disease rates (global). Positive spatial autocorrelation means that nearby areas have similar rates, indicating spatial clusters Values > 0 indicates positive spatial autocorrelation < 0 indicates negative spatial autocorrelation Null hypothesis assumes disease rates are spatially independent; alternative hypothesis is that disease rates are not spatially independent When neighboring areas are similar, Moran’s I will be large and positive

9. Results: ArcView Aggregated LPA > 1mm N=48

10. Results: Stat! Simulation significance 0.05 Not assessable Mandibular 12 33 3 Maxillary 9 35 4 Moran’s I Range (Mand)-.032569 -.233815 p-value (Mand).851485 -.019802 Range (Max) -.020167 -.248362 p-value (Max).871287 -.019802

11. Discussion Using Moran’s I, loss of periodontal attachment did not show significant intraoral spatial autocorrelation in this population P(simes-mand)=.54728 P(simes-max)=.59406 With respect to simple visual examination of the aggregated data in ArcView, LPA did appear to occur more often (cluster) in the inter-proximal areas (but no ‘WOW’ effect)

12. Limitations Old data (1959) Problem of dealing with missing teeth - simply excluded to simplify analysis Use of LPA of 1mm - not a measure of periodontal disease Selection bias due to exclusions Only intra-arch spatial relations examined; need to account for inter-arch relations

13. Demonstration Project? Because of limitations imposed by data, this analysis examined only spatial relations of evidence of periodontal destruction and not active periodontal disease in a very select population Did demonstrate how spatial analytical tools can be applied to systems(?) other than those we normally associate them with (mouth vs. lat-lon, census tracts, topography etc.)

14. Summary -What is Needed Greater proportion of the population retaining their natural dentition (changing paradigm) Full mouth recording Longitudinal collection of data With appropriate, more recent data, spatial analytical techniques should be useful in evaluating space-time relations with respect to the current model of periodontal health/disease

15. Thanks to Mark, Geoff, Andy and Leah for much patience, hard work and an excellent class