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Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment: A Demonstration Proje

Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment: A Demonstration Project Brent McDaniel Epid 624, Winter 1999 UM-SPH. Periodontal Disease (Old Model) Gingivitis progressed to periodontitis with subsequent loss of bone and teeth

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Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment: A Demonstration Proje

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

  2. 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 > 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

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