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Welcome to the CDC Enterprise Architecture Community of Practice

Welcome to the CDC Enterprise Architecture Community of Practice. November 20, 2009. Agenda. Introductory Remarks NCCDPHP: Approaching a common chronic disease surveillance data model and early ROI (Jason Bonander, Akaki Lekiachvili) Continue Discussion & Networking. Introductory Remarks.

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Welcome to the CDC Enterprise Architecture Community of Practice

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  1. Welcome to the CDC Enterprise Architecture Community of Practice November 20, 2009

  2. Agenda • Introductory Remarks • NCCDPHP: Approaching a common chronic disease surveillance data model and early ROI (Jason Bonander, Akaki Lekiachvili) • Continue Discussion & Networking

  3. Introductory Remarks

  4. Enterprise-level DB Implementation for Data Dissemination(Experience & lessons Learnt) November 20, 2009

  5. Background • 2007, January through May: • NCCDPHP surveillance system study from the informatics perspective

  6. Background • July 2007: • Focus to the data dissemination • Type of summary data • Types of measures for statistical significance • Stratifications and classifications • Reuse across the programs • Surveillance Data Model (SDM) is conceived • Fits with existing NCCD surveillance systems • Leverages best practices from the project’s research • Is scalable to support multiple surveillance data sets • Is appropriately normalized • Streamlines data exchange across surveillance systems

  7. Data Model Survey S. Instance S/Q. Instance Classification Q. Response Data Point DP Strat Stratification Strat. Type Q. Instance Question Q. Metadata

  8. Artifacts • Charter • Functional requirements • Detailed requirements • Database specifications • Architecture document • ERD • Data Dictionary - For DBAs (and developers, if needed) • Web Service Specifications document • Sample Data Template • Data Template specifications • SDM terminology definitions • “Layman’s guide to SDM”

  9. Disseminated data characteristics • Response of interest = positive response = numerator, etc. • For example: number of people with heart disease in Alabama – the actual number is the response of interest: lets say it is 230,000 people. • Another example: Cost of healthcare in New York: lets say it is $200M. • Denominator = population of interest • For example: total number of subjects within the entity of interest – for the question above it would be population of Alabama: lets say it is 5,000,000 • Another example: Healthcare costs in NY might have total NY expenditures as a denominator if they want to calculated the healthcare expenditures as a percent of total in the next step. Frequently, these types of indicators will not have any denominator. • Calculated value = measure/indicator/question value, etc. • Usually prevalence (point in time) or rate (within a timeframe) • Prevalence is usually proportion of object of interest at a given time (usually measured at a mid-year point) and expressed as a percentage. For example: prevalence of people with heart disease (people walking around with heart disease at a given time divided by the total population). • Rate is usually proportion of events during a time period. For example: rate of heart attacks (number of people who experienced heart attack during a given year divided by the total population. • Calculated value can also be percent, mean, average, etc. • It may have supporting parameters that convey statistical significance/validity of the calculated value. For example: CI, CL, P value, response rate, Cell size, etc. • From the previous examples: the calculated value will be prevalence of heart disease in Alabama that is 4,200/5,000,000 = 4.6%. At the same time, NY health expenditures may or may not have calculated value. • The calculated value, and less often, the response of interest is considered a “main value” for an indicator/choice.

  10. Cost Analysis

  11. SDM Implementation Status • In production: • OSH Global Youth Tobacco Survey http://apps.nccd.cdc.gov/osh_gtss/default/default.aspx • DACH State of Mental Health and Aging (MAHA) http://apps.nccd.cdc.gov/maha/MahaHome.aspx • DDT Vision Health Initiative (VHI) • In Development: • DHDSP Data Trends and Maps • DCPC US Cancer Statistics (USCS) • In Analysis/Design: • DASH YRBSS • DDT STRS • DDT CKD (Kidney disease)

  12. Future • Reusable Business Logic Layer for applications using SDM • Reusable Application Controls

  13. Lessons Learnt • Acceptance • Buy in • Engagement

  14. Questions?

  15. Networking

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