BIG DATA and Oral Health Research
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1 BIG DATA and Oral Health Research Bruce A. Dye, DDS, MPH 19 April 2016
2 Informatics BIG DATA Oral Health Research The Landscape Ahead Brief Introduction to Health Informatics Brief Description of what BIG DATA is Highlight some BIG DATA projects using publicly available data
3 Informatics BIG DATA Oral Health Research
4 Informatics BIG DATA Oral Health Research Health Informatics and Evolution of BIG DATA Informatics Science is one of the fastest growing research fields It is a multidisciplinary approach to information and knowledge management in biomedical research, clinical care, and public health Origins in computer science / information technology Evolving with more of a focus on Data Science
5 Informatics BIG DATA Oral Health Research Health Informatics and Evolution of BIG DATA Informatics is Transforming the Way We Work Data mining, Natural language / text processing, Cognitive science, Clinical research, Genomics / proteomics, and Database management and data analytics for large amounts of data generated in health care delivery or produced through public health activities.
6 Informatics BIG DATA Oral Health Research Health Informatics and Evolution of BIG DATA Recognized Core Areas of Informatics (AMIA) Translational Bioinformatics Clinical Research Informatics Clinical Informatics Consumer Health Informatics Public Health Informatics
7 Informatics BIG DATA Oral Health Research Health Informatics and Evolution of BIG DATA Why is BIG DATA becoming a big deal in Informatics? Facilitating biomedical science Promoting the quality and safety of patient care Improving population health
8 Informatics BIG DATA Oral Health Research Health Informatics and Evolution of BIG DATA BIG DATA can be characterized by: The amount of collected data (Volume) The source and type of data (Variety) The speed of generated data (Velocity) The quality of incoming data (Veracity)
9 Informatics BIG DATA Oral Health Research What creates BIG DATA? Data Accumulation Matters (Velocity) Data accumulates expansively No economic trade off between retaining vs deleting data Data Quality Matters (Veracity) More data doesn t always give us more value Information from data becomes more valuable when the data is more reliable Data driven decision making requires accurate and reliable data
10 Informatics BIG DATA Oral Health Research What creates BIG DATA? Data Size Matters (Volume) At its most elementary level BIG DATA is about bringing datasets together There is no critical mass of data alone needed to make it Big. Data Complexity Matters (Variety) You need a mix of varying types and sources of data to make it complex and large. Data linkages are important
11 Informatics BIG DATA Oral Health Research What creates BIG DATA? Big Data begins to form when a group of data sets are brought together become so large and complex that it begins to challenge contemporary data processing and analytical approaches.
12 Informatics BIG DATA Oral Health Research Technology is also propelling this phenomena of Big Data and it s use in research. Technology is allowing us to collect, process, and share data easier and faster then ever before.
13 Informatics BIG DATA Oral Health Research Technology Paper Record Electronic Record Technology and creation of Electronic Health Record (EHR). Big Data analytics are not about analyzing a provider s EHRs in isolation. For example, to identify how many widgets were rendered It is about analyzing provider health recorders to identify factors that are associated with those widgets rendered.
14 Informatics BIG DATA Oral Health Research The contribution of technology to the evolution of BIG DATA described well by Kenneth Cukier (TED talk). We use to look at small data to try to understand the world. The idea is that more data doesn t let us see more of the same but it allows us to see better, to see different, to see something new. Inherent to this is that BIG DATA methods permits us to observe varying amounts of information in a way that we can extract value faster and more meaningful.
15 Informatics BIG DATA Oral Health Research Observation is fundamental to Epidemiology. Epidemiology is the study of the distribution and determinants of diseases and conditions in man. Epidemiology is essentially all about collecting data and analyzing it to reveal patterns, trends, and associations. BIG DATA is about observing better identifying patterns in ways that make us better understand the world we live in.
16 What is Big Data? BIG DATA is: Very large complex datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human health, behavior, and interactions.
17 Examples of BIG DATA Projects Using linked data for BIG DATA research projects
18 Examples of BIG DATA Projects e
19 Examples of BIG DATA Projects Review Study Design Heirarchy & Levels of Evidence
20 Examples of BIG DATA Projects What is the potential of BIG DATA? Observational studies on steroids Where the sum has more added value than the individual data files
21 Examples of BIG DATA Projects Boosting NHANES from a good x sectional study to a good cohort study accounting for temporal influence
22 Examples of BIG DATA Projects Example 1: NHANES and Death Records
23 Examples of BIG DATA Projects Obesity really associated with increase risk of death? NHANES contributes baseline information Age in Body Measures: Obese 30 BMI Lab Profiles: Metabolic Health NDI contributes mortality information: Underlying and multiple causes of death through 2006
24 Examples of BIG DATA Projects Compared to healthy lean group Risk of all cause mortality was twice as high in Metabolic unhealthy obese group No risk difference in metabolic healthy obese group Strengths Nationally representative sample of the US population Robust end point (all cause mortality) Relatively long follow up period (~15 years) Measured rather than self reported weight and height Data collection is standardized and use appropriate quality controls. Durward CM1, Hartman TJ, Nickols Richardson SM. All cause mortality risk of metabolically healthy obese individuals in NHANES III. J Obes. 2012; doi: /2012/
25 Examples of BIG DATA Projects Example 2: NHANES and Medicare Records
26 Examples of BIG DATA Projects Bone mineral density really associated with increase risk of fracture? NHANES contributes baseline information Bone Mineral Density in Femur (DEXA) in Age 65 and older Medicare contributes event information: Incident fracture from Medicare Claims Files through 2007
27 Examples of BIG DATA Projects Compared to healthy lean group For each SD decrease in BMD: risk of fractured doubled Age, sex, R/E was still associated with increase risk of fracture even after controlling for femur BMD Strengths Nationally representative sample of the US population Clinically diagnosed end point (ICD 9) At least a decade long follow up period Data collection was standardized and used appropriate quality controls. Looker AC. Femur neck bone mineral density and fracture risk by age, sex, and race or Hispanic origin in older US adults from NHANES III. Arch Osteoporos doi: /s
28 Examples of BIG DATA Projects Example 3: NHANES, USRDS and CMS Data
29 Examples of BIG DATA Projects Greater risk of ESRD after live kidney donation? US Renal Data System Registry of all kidney donations (~96,000 from ) Identify the SES, BMI, Smoking and Blood Pressure in Cases CMS information identifies ESRD CMS Medical Evidence Form 2728 Certification of ESRD NHANES information creates healthy non donor group: Data used to create population control Replacement Matching use SES, BMI, Smoking and Blood Pressure information to build a control group of ~96,000 people
30 Examples of BIG DATA Projects Estimated Risk of ESRD at 15 Years of DOnation 30.8 / 10,000 for donors 3.9 / 10,000 for healthy non donors Strengths Inclusion of all kidney donors in 20 year period Use of data from people not selected based on existing health condition to create healthy non donor pool Availability of clinically diagnosed end points A ~15 year follow up period Data collection was standardized and used appropriate quality controls. Muzaale AD, Massie AB, Wang MC, Montgomery RA, McBride MA, Wainright JL, Segev DL. Risk of endstage renal disease following live kidney donation. JAMA Feb 12;311(6):
31 Examples of BIG DATA Projects Example 4: NHANES and Medicaid Claims Data
32 Examples of BIG DATA Projects What is the econometric cost of obesity to Medicaid? NHANES contributes baseline information Age 20+ in Body Measures: Obese 30 BMI Medicaid Analytic extract (MAX) data contributes: Amount of FFS costs paid through Medicaid same year
33 Examples of BIG DATA Projects Average FFS costs paid by Medicaid in same year as obesity was determined during $95.02 for non obese Medicaid receiptents $ for obese Medicaid receiptants Strengths Nationally representative sample of the US population Measured rather than self reported weight and height Actual costs paid based on claims data Data collection was standardized and used appropriate quality controls. Medicaid Policy Brief by MATHEMATICA January Allison Dodd and Phillip Gleason authors
34 Informatics BIG DATA Oral Health Research NHANES and BIG DATA Information from data becomes more valuable when the data is more reliable With NHANES we have Veracity It s about observing better identifying patterns in ways that make us better understand the world we live in With Linkage we obtain Volume, Variety and Velocity Facilitating Data driven decision making
35 Informatics BIG DATA Oral Health Research Thanks!
36 Informatics BIG DATA Oral Health Research
37 Informatics BIG DATA Oral Health Research
38 Informatics BIG DATA Oral Health Research
39 Informatics BIG DATA Oral Health Research
40 NIDCR Training Opportunities Big Data Research Residency in Dental Public Health and Oral Health Informatics (Emerging) Partnership with National Library of Medicine (NLM) For dentists with an interest in informatics science An MPH or equivalent public health related degree required 2 Year Full time Residency / Fellowship Mentors and Faculty come from NLM and NIDCR
41 NIDCR Training Opportunities Big Data Research Residency in Dental Public Health and Oral Health Informatics (Emerging) Informatics Research Area Strength: Public Health Focus on Big Data projects Other Potential Research Areas: Bioinformatics and Clinical Research Certificate in DPH NIDCR Residency in DPH is CODA accredited Meets education qualifications for graduates to challenge DPH certifying exam Certificate in Health Informatics Goal is to begin application process Summer 2016
42 NIDCR Training Opportunities Big Data Research Questions about Residency in Dental Public Health and Oral Health Informatics: Bruce Dye NIDCR Residency Program in Dental Public Health
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