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At the Crossroads of Oral Health Inequities and Precision Public Health
UCSF Center to Address Disparities in Oral Health
Director: Stuart A. Gansky, DrPH Professor and Lee Hysan Chair of Oral Epidemiology
Member, Institute for Computational Health Sciences AIDPH, San Antonio, Jan 2018
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UCSF Center to Address Disparities in Oral Health
US DHHS NIH/NIDCR U54DE014251, R03DE018116, R21DE018650, R21DE019210, U54DE019285, P30DE020752, U01DE025514, UH2/UH3DE025507
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Presenter Disclosures
(1)The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months:
Stuart A. Gansky, MS, DrPH
My brother is a 3M employee, but a different division than the one that provided in-kind product.
(2) My presentation will include discussion of “off-label” use of the following:
The US FDA only has approved fluoride varnish as a device to be used for tooth sensitivity in a cavity lining preparation; caries prevention is an off-label use.
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2011
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OME
http://www.ucsf.edu/welcome-to-ome
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2011
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-omes
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Precision Medicine Initiative “All of Us”
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Precision Medicine Initiative All of Us
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Precision Medicine Initiative All of Us
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Precision Medicine Initiative All of Us
Possible Threats to Improving Public Health? Ethical, Legal, & Social Issues (Sankar & Parker Genet Med 2017)
• Cohort diversity & health disparities • Participant engagement • Privacy & security
Genomic Medicine Increase Disparities? (Moonesinghe et al. Ethn Dis 2009)
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Health Technologies Increased Health Status Disparities
Medical Health Diagnosis • Colonoscopy • Pap Smear Prevention • Hypertension Medication
(Carey) Treatment • Coronary Artery Bypass Graft
Dental Health Diagnosis • Oral Cancer Stage Prevention • Dental Sealant
Treatment • Tooth Implant
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Faustian Bargain? (Pauwels & Dratma Scientific American 2015)
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Turn Possible Threats to Opportunities,
Reduce Health Disparities
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h
Texas
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Texas, Arizona, New Mexico
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Arizona, California
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California
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California, Arizona
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Omics Methods May Increase Disparities
• 2-Tiered Healthcare + Omics 2-Tiered Personalized Healthcare (Alyass et al. BMC Med Genom 2015)
• Need Precision Public Health Done Right o Bias-Variance tradeoff curse of dimensionality
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Bias-Variance Tradeoff
Alyass et al. BMC Med Genom 2015
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Omics Methods May Increase Disparities
• 2-Tiered Healthcare + Omics 2-Tiered Personalized Healthcare (Alyass et al. BMC Med Genom 2015)
• Need Precision Public Health Done Right o Bias-Variance tradeoff curse of dimensionality o Variability from Noise & True Heterogeneity o Validate, Validate, Validate – Internal & External
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Why Validate?
Simple Example: Overfitting in only 2 dimensions
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Data
0
5
10
15
0 5 10 15 20 25
Predictor
Res
pons
e
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Linear Fit to Data
y = 0.3449x + 1.2802R2 = 0.9081
0
5
10
15
0 5 10 15 20 25
Predictor
Res
pons
e
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High Degree Polynomial Fit to Data
y = -0.0012x6 + 0.1196x5 - 4.8889x4 + 105.05x3 - 1250.4x2 + 7811.5x - 19989R2 = 1
0
5
10
15
0 5 10 15 20 25
Predictor
Res
pons
e
Bias – Variance Tradeoff: High Bias, Low Variance
• next data point high bias
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What is Knowledge Discovery and Data Mining (KDD)?
• “Semi-automatic discovery of patterns, associations, anomalies, and statistically significant structures in data” – MIT Tech Review (2001)
• Interface of – Artificial Intelligence – Machine Learning – Computer Science – Engineering – Statistics
• Association for Computing Machinery Special Interest Group on Knowledge Discovery in Data and Data Mining (ACM SIGKDD sponsors KDD Cup)
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Data Mining as Alchemy
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Supervised Learning • Regression • k nearest neighbor • Trees (CART, MART,
boosting, bagging) • Random Forests • Multivariate Adaptive Regression
Splines (MARS) • Neural Networks (NN) • Support Vector Machines • Machine Learning / Deep Learning
Unsupervised Learning • Hierarchical clustering • k-means
No outcome / dependent (output)
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KDD Steps
Collect & Store
Sample Merge Warehouse
Pre- Process
Clean Impute Transform Standardize Register
Analyze
Supervised Unsupervised Visualize
Validate
Internal Split Sample Cross-validate Jackknife* Bootstrap External
*Leave 1 Out CV
Act
Intervene Set Policy
Gansky 2003
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Data Quality
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Common Mistakes with Artificial NN (Scwartzer et al, StatMed, 2000)
• Too many parameters for sample size • No validation • No model complexity penalty
(eg Akaike Information Criterion (AIC)) • Incorrect misclassification estimation • Implausible function • Incorrectly described network complexity • Inadequate statistical competitors • Insufficient comparison to competing models
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precision public health with textmining
• Adverse childhood experiences / homelessness & health (Bejan et al. JAMIA 2017)
• Care coordination helps overcome psychosocial distress (Oreskovic et al. JMIR Med Inform 2017)
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© Stuart A. Gansky, 2001-2006
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Model Face Validity
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Prediction
data
model
and
only as good as the
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Social Determinants of Health (SDOH) Individual, Family, & Community Levels • Poverty • Education • Health Literacy • Home conditions
o Mold respiratory conditions Asthma (Beck et al. Health Affairs 2017)
o Secondhand tobacco smoke resp cond o Lead in water neuro development, caries
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CAT Results in California Oral Health Needs Assessment of Children 1993-94
• 48% actually have caries • CAT estimates 5% at low risk
95% at moderate/high risk • Sn: 98% Sp: 8% (high % False Positives)
N=2648
48% Caries
98% Hi/Mod Risk (TP)
2% Low Risk (FN)
52% No caries
93% Hi/Mod Risk (FP)
7% Low Risk (TN)
Over-classification of risk (95% at mod/high)
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Conceptual Framework of Children’s Oral Health Child, family, & community influences on child oral health outcomes
Microflora
Substrate (diet)
Host & Teeth
Oral Health
Child Level Influences
Development
Use of Dental Care
Dental Insurance Biologic and
Genetic Endowments
Physical Attributes
Health Behaviors and Practices
Family Level Influences Socioeconomic Status
Social Support
Health Status of Parents
Family Composition
Family Function
Health Behaviors, Practices, and Coping Skills of Family
Social Environment
Community Level Influences
Dental Care System Characteristics
Health Care System Characteristics
Physical Environment
Culture Social Capital Physical Safety
Community Oral Health Environment
Time
Fisher-Owens et al. Pediatrics 2007 NIH/NIDCR R03DE016571
Fisher-Owens, Gansky et al. 2007
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Integrative Social Molecular Pathologic Epidemiology (ISMPE)
Nishi et al. Expert Rev Mol Diagn 2016
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Metagenomics (Metagene) Ling Zhan, PI
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Metagenomics (Metagene) Ling Zhan, PI
Mediation Model Jing Cheng, PI
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CaMBRA Intervention
(CHX rinse, F rinse)
caries risk (high/low)
Dental Outcome: DMFS Increment
Featherstone et al. Caries Res 2012
Mechanism of action?
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CaMBRA Effects
Intervention
Anti-bacterial & Fluoride treatments
Outcome
New DMFS at 2 years
Mediator
Overall risk at 12 m: Salivary log10 MS Salivary log10 LB
Salivary fluoride (F)
Direct Effect: -0.58 (-1.57, 0.42), p=0.25
Mediation Effect: -0.38 (-0.82, -0.03), p=0.03
About 36% of the intervention effect on 24-month DMFS increment was through its mediation effect on the 12-month overall risk (p=0.03). Total effect: -0.96 (-2.01, 0.08), p=0.07
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Cheng et al. JDR 2015
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LRP KL2 Benjamin Chaffee
UCSF RAP R03 Ann Lazar
Precision Public Health Analyses
R01 subcontract R03-like grant Jing Cheng
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Chaffee, Gansky et al. JDR 2014
N=243
Children w caries at 36mo
Children w/o caries at 36mo
Does mother’s salivary mutans strep cause caries in preschoolers?
Mothers w/ high MS over 33 mo period had kids with signif-icantly greater chance of caries at 36mo
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Lazar, Gansky et al. JDOCE 2014
Baseline
Does Fluoride Varnish better prevent caries in some preschoolers?
Kids with more baseline MS had signif-icantly greater pre-ventive benefit from FV
Methods to determine Heterogeneity of Treatment Effect: Johnson- Neyman and STEPP
Overall OR=0.33
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Social Determinants of Health
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2004-5 cohnac • Stratified, complex survey sample
Strata: 21 Regions x Density Clusters: 186 Schools Grades: Kindergarten and 3rd
• Probability proportional to size (PPS) sampling
• ASTDD design
Association of State and Territorial Dental Directors. Basic Screening Surveys: An Approach to Monitoring Community Oral Health. www.astdd.org
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oral health measures
• Untreated caries (dt / DT > 0) • Caries experience (dft / DFT > 0) • Rampant caries (dft / DFT > 7) • Treatment urgency
(none, early, urgent)
Trained calibrated examiners (dentists, hygienists, nurses)
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factors potentially related to caries
Demographics Race/ethnicity, Gender, Age, Grade
Socioeconomic status Free/reduced cost lunch (FRL) program Individual Percent of children at School CA School Academic Perfomance Index
Acculturation Non-English language at home
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-5
0
5
10
15
20
25
30
Perc
ent
Hispanic Asian African-American Other Race/Ethnicity FRL Participant %FRL≥75 %FRL: 50-75 %FRL: 25-50
Race/Ethnicityref: non-Hispanic
White
%FRL(school level)
ref: <25
FRL Participant
(child level)ref:
Not a FRL Participant
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California Oral Health Needs Assessment of Children 2004-5 Absolute difference (95% CI) in rampant caries prevalence by race/ethnicity and socioeconomic position
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2004-5 California Oral Health Needs Assessment of Children (Smile Survey)
Socioeconomic Status & Language
Individual child level: free/reduced price lunch participation NS language other than English spoken at home p<0.05
N=10,450 Third Graders in 182 schools
R03 DE 018116 Dental Health Fdn, HRSA, CDA Fdn, ASTDD, CA First 5, CA Endowment
Mejia et al. CDOE 2010
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Approach: Population vs High Risk
Targeted Risk (Lalonde, 1974) Prevent Disease in Higher Risk Individuals or Whole Population Approach (Rose, 1992) Increase Overall Public Health but also Disparities Third Way: Targeted Vulnerable Popns (Frolich&Potvin, 2008) Reduce Disparities in Health Proportionate Universalism
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Proportionate Universalism
Ontario Agency for Health Protection and Promotion (Public Health Ontario), Lu, Tyler. 2015
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Precision Proportionate Universalism Public Health Needs to Answer:
• Who are the vulnerable groups? • What is the public health prevention/intervention? • Where are the populations? • How do we deliver precise public health?
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MeForYou.Org campaign
We do it for Georgia http://www.ucsf.edu/news/2013/05/105616/meforyou-campaign-rallies-public-join-push-precision-medicine
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https://www.polyvore.com/meet_robert_johnson_at_crossroads/set?id=4219175
At the Crossroads: what direction will we choose?
what bargains will we strike?