Applying Data Warehousing to Community Health Assessment WITS’99 Keynote Address

1. Applying Data Warehousing to Community Health AssessmentWITS’99 Keynote Address Alan R. Hevner University of South Florida ahevner@coba.usf.edu

2. Preface - WITS Retrospective • As we approach 2000, a quick look back: • WITS’91 - Boston (Ram and Wang) • WITS’92 - Dallas (Storey and Whinston) • WITS’93 - Orlando (Hevner and Kamel) • WITS’94 - Vancouver (De and Woo) • WITS’95 - Amsterdam (Jarke and Ram) • WITS’96 - Cleveland (Ernst and Sen) • WITS’97 - Atlanta (Segev and Vaishnavi) • WITS’98 - Helsinki (Bubenko and March) • WITS’99 - Charlotte (Narasimhan and Sarkar) WITS'99 Keynote Address

3. Outline • Research Motivation - Community Health Measurement and Assessment • The CATCH Methodology • A Data Warehousing Solution • Data Dissemination Modes • Community Health Decision Making • A CATCH Demonstration WITS'99 Keynote Address

4. Acknowledgements • Co-Principal Investigators • James Studnicki - College of Public Health, USF • Don Berndt - College of Business Admin., USF • Research Staff • Center for Health Outcomes Research Staff • Doctoral and Masters Students • Funding • U.S. Dept. of Commerce TIIAP Grant • Bear Stearns Research Laboratory • Florida Communities WITS'99 Keynote Address

5. Research Motivation • U.S. has the Highest Per Capita Health Expenditures in the World • Low Rank of U.S. as defined by Health Status Indicators • Transition from a Disease to Health focus and from a Treatment to a Prevention strategy • Health Priorities defined by Political Agendas and the Managerial Objectives of Health Organizations rather than Objective Evaluation • Pluralistic, Non-Integrated Health Care Systems • No Single Organization is Responsible for the Health of the Community • No Uniform Method to define the “Health of the Community” which is Universally Accepted and Consistently Applied WITS'99 Keynote Address

6. Community Health Planning • Institute of Medicine (IOM) 1988 Report on the Future of Public Health • Recommends a regular and systematic collection, assemblage, and analysis of information on the health status and needs of communities. • IOM 1997 Report on Using Performance Monitoring to Improve Community Health • Calls for a Community Health Profile which can be used to support priority setting, resource allocation decisions, and the evaluation of health program impacts. WITS'99 Keynote Address

7. Collaborative Health Decision Making • Multi-Sector Community Health Stakeholders • Health Organizations • Public Sector Agencies • Medical Care Providers • Businesses • Religious Community • Educational Institutions • Government Agencies • Decisions must be based on Unbiased, Timely Information WITS'99 Keynote Address

8. CATCH Methodology • Comprehensive Assessment for Tracking Community Health (CATCH) • Project initiated in 1991 • 14 Florida County Applications • Marion County, Indiana (Indianapolis) • Potential Regional, National, and International Applications WITS'99 Keynote Address

9. Indicator 1 Indicator 2 . . . Indicator i . CATCH Methodology State Favorable Unfavorable State Averages F I L T E R S Fav/Fav Indicators Fav/Unfav Indicators Fav. Peer Unfav. Indicator 1 Indicator 2 . . . Indicator i . . 1. Indicator i 2. Indicator j , . . Indicator 1 Indicator 2 . . . Indicator i . Unfav/Fav Indicators Health Challenges Peer Community Averages Community Health Indicators Prioritized List of Community Health Challenges CATCH N-Dimensional Comparison Matrix Additional Health Standard Comparisons

10. Data Collection and Analysis • Ten Indicator Groups • Demographics • Socioeconomic • Maternal and Child Health • Social and Mental Health • Physical Environmental Health • Health Status: Morbidity/Mortality • Sentinel Events • Infectious Diseases • Health Resource Availability • Behavioral Risk Factors WITS'99 Keynote Address

11. Priority Filters • Number Affected • Economic Impact • Availability of Efficacious Intervention • Magnitude of Difference • Trend Analysis WITS'99 Keynote Address

12. Peer Comparison Peer CRITERIA Hillsborough Group Duval Orange Polk % Population < Age 18 24.86% 25.41% 26.58% 24.84% 24.46% % Population > Age 64 12.71% 13.01% 11.27% 11.51% 18.37% % Non-white Population 15.32% 21.13% 27.20% 19.08% 14.76% % Families Below Poverty Level 9.5% 9.0% 9.8% 7.8% 9.4% Source: Florida County Comparisons 1995

13. Comparison Matrix CATEGORY INDICATOR CO PEER ST % Labor force unemployed 5.2% 5.8% Socioeconomic Maternal & Child health Infectious Disease Health Status Sentinel Events Resource Availability Physical/ Environmental Social & Mental Behavioral Risk 6.6% STATE Infant mortality: non-white 12.6 14.4 11.9 FAVORABLE UNFAVORABLE Tuberculosis cases 0.31 0.25 0.57 % Labor force unemployed Infant mortality: non-white FAV Colorectal cancer 11.3 10.8 12.3 Cervical cancer late stage PEER 51.3 41.7 45.6 Challenges: Drowning fatalities UNFAV Late stage cervical cancer Licensed hosp. beds 5.9 4.7 4.5 Drowning fatalities 2.4 2.0 2.7 Domestic viol.cases 1041.0 1041.8 864.1 Further Screening Current smokers 24.8 26.9 23.1

14. Priority Filters PRIORITIZATION SAMPLE HIGH PRIORITY AREAS SCREENS Avoidable Hosp.: Asthma Low birthweight Gonorrhea cases Stroke Cervical cancer: %late stage Pneumonia/ Influenza Availability Economic Number of Magnitude Trend of Impact People of Direction Efficacious Affected Difference and Intervention Magnitude

15. Social and Mental HealthINDICATORS COMPARED TO STATE & PEER VALUES STATE FAVORABLE UNFAVORABLE Child maltreatment Burglary offenses Elderly abuse Forcible sex assaults FAVORABLE Homicide AA mortality Crude homicide rate: total Crude homicide rate:non-white Illegal drug sales Domestic violence cases P Crude suicide rate: white Simple assaults E Aggravated assaults E Illegal drug possession R Crude homicide rate: white Suicide AA mortality Crude suicide rate: total, non-white UNFAVORABLE Intentional injury AA mortality Alcohol related motor vehicle accidents Alcohol related motor vehicle mortality Psychiatric admissions % w/ good mental health AA = Age Adjusted

16. Indicator Fact Sheet INDICATOR: AIDS CASES 1994 AIDS CASES, Incidence rate per 100,000 population FIVE YEAR TREND ANALYSIS KEY: Thick line = County value, Thin line = Florida value 1990 1991 1992 1993 1994 ________________________________________________________________ County: 19.5 24.6 26.2 55.3 27.6 Florida: 29.6 41.5 41.7 77.2 61.5 Source: PHIDS

17. CATCH Data Warehouse • Manual CATCH Limitations • Labor-Intensive and Slow • Four months per report • Longitudinal Trend Analyses are Cost Prohibitive • Extension of County Reports to State, National, and International Reports • Knowledge Discovery Potential not Realized • CATCH Data Warehouse Solution WITS'99 Keynote Address

18. Data Warehouse Challenges - Construction • Data Collection • Data Sources • Data Quality • Extraction, Transformation, and Transportation • Data Warehouse Design • Star Schemas • Data Staging • Sizing and Cleansing • Quality Assurance WITS'99 Keynote Address

19. Hospital Discharge Star Schema WITS'99 Keynote Address

20. ICD-9 Code Dimension Hierarchy WITS'99 Keynote Address

21. Data Warehouse Challenges - Operations • User Interfaces • Performance • Security • Backup and Recovery • Knowledge Discovery • Data Mining WITS'99 Keynote Address

22. Data Dissemination Modes • Effective Presentation of CATCH Information to Community Decision Makers • Data Dissemination Modes • Pre-defined Reports • Data Browsing • Ad-hoc Queries • Internet Access • Hypertext Information Screens • Dynamic Access to Data Warehouse WITS'99 Keynote Address

23. Community Group Decision Making • Research Field: IT Support for Group Decision Making • Research Question: How will communities make most effective use of the CATCH data for health care decision making? • Research Testbed: During 2000 we will provide CATCH reports to all 67 Florida counties. WITS'99 Keynote Address

24. Group Decision Making Issues • Motivation of community to use data • Presence of a champion for specific actions • Size and make-up of the decision making group • Speed of the decision making process • Stakeholders around the table and their influence • Resource constraints • Political nature of the process • Differential accesses to data among communities • Ease of access and usefulness of the data • Requests for customized analyses • Information exchange patterns and practices WITS'99 Keynote Address

25. CATCH Data Warehouse Demonstration • Policy Question on Racial Disparity in Infant Mortality in Florida: “What is the pattern of variation in infant mortality between whites and non-whites throughout Florida? What factors best explain this variation?” WITS'99 Keynote Address

26. Data Browsing Strategy • Produce a Table of Florida Counties and Infant Mortality Data • Sort and Graph the Information • Cluster the Counties into Four Groupings • Select Factors for Analysis and Correlation • Perform Further In-Depth Analyses • Data Mining Neural Networks • Multivariate Statistics WITS'99 Keynote Address

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39. Conclusions • The Application of Data Warehousing Technology to Community Health Care can make a Social Contribution • Technical Research Challenges • Collaborative Group Decision Making: What factors are associated with effective community use of CATCH data? • Leadership • Infrastructure • Decision-Making Process • Public/Private Sector Cooperation WITS'99 Keynote Address

40. Appendix:CATCH Data Indicators WITS'99 Keynote Address

41. Data Indicators • DEMOGRAPHIC CHARACTERISTICS • % Total population by gender • % Total population by age • % Total population by race • % Population rural • % Labor force by gender • Median Age • Net migration • Live births per 1,000 population • Deaths per 1,000 population WITS'99 Keynote Address

42. Data Indicators • SOCIOECONOMIC CHARACTERISTICS • Non-graduates of high school High school dropouts • Per capita income Labor force unemployed • Persons below poverty level WIC eligibles • Medicaid eligibles % Medicaid births • HMO enrollment % enrolled in a health plan • Families with children < age 18 below poverty level • Population receiving food stamps • Students eligible for free/reduced lunch program • %Low income persons with access to dental care WITS'99 Keynote Address

43. Data Indicators • MATERNAL AND CHILD HEALTH • Infant Mortality Child mortality • Neonatal mortality Post neonatal mortality • Low birthweight Very low birthweight • Perinatal condition mortality • Birth Defects Mortality • % Live births w/1st trimester prenatal care • % Live births w/3rd trimester prenatal care • % Live births w/ no prenatal care • Live births to mothers < age 15 • Live births to mothers age 15 - 17 • Live births to mothers age 18 - 19 • Repeat births to teens WITS'99 Keynote Address

44. Data Indicators • PHYSICAL ENVIRONMENTAL HEALTH • Salmonella cases Campylobacter cases • Shigella cases Rabies in animals • Lead poisoning Fluoridated water • Firearm fatalities Drowning fatalities • Poisoning fatalities Bicycle fatalities • Contaminated wells Septic tank repair permits • Enteric disease cases: total and in children < age 6 • Foodborne and waterborne outbreaks • Motor vehicle mortality - age adjusted • Unintentional injury mortality - age adjusted WITS'99 Keynote Address

45. Data Indicators • INFECTIOUS DISEASE • AIDS incidence, cumulative cases, & mortality • HIV seropositivity • Infectious Syphilis cases • Congenital Syphilis cases • Gonorrhea cases • Chlamydia cases • Hepatitis A and B cases • Meningitis cases • Tuberculosis cases • Tuberculosis mortality - age adjusted • % Vaccinated by kindergarten WITS'99 Keynote Address

46. Data Indicators • SOCIAL AND MENTAL HEALTH • Alcohol Related motor vehicle accidents & mortality • Assaults: Forcible sex, Burglary, Simple and Aggravated • Juvenile delinquency rates • Suicide - crude & age adjusted • Intentional injury - age adjusted • Homicide - crude & age adjusted • Child Abuse, Elderly Abuse - reported and confirmed cases • Domestic Violence - Reported cases • Mental health of adults: days/month w/o good mental health • Hospitalization rates for: • Baker Act, Psychoses, Depression, Alzheimer's Disease, Alcohol abuse & Drug abuse WITS'99 Keynote Address

47. Data Indicators • HEALTH STATUS INDICATORS • Morbidity Cases • Melanoma Prostate cancer • Breast cancer Cervical cancer • Colorectal cancer Lung & bronchus cancer • Smoking related cancers • Age Adjusted Mortality Rates (Crude) • Chronic liver disease & cirrhosis (crude) Melanoma • Pneumonia/Influenza (crude) Breast cancer • Diabetes Mellitus (crude) Cervical cancer • Cardiovascular disease Colorectal cancer • Heart disease (crude) Lung/smoking rel. cancer • Stroke (crude) Preventable cancer • C.O.L.D. Prostate cancer • YPLL All cancers (crude) WITS'99 Keynote Address

48. Data Indicators • SENTINEL EVENTS • Vaccine Preventable Diseases Measles Rubella Mumps Pertussis • Late Stage Cancers Breast cancer cases - % late stage Cervical cancer cases - % late stage • Avoidable Hospitalizations Asthma Immunizable conditions Cellulitis Malignant hypertension Congestive heart failure Perforated/bleeding ulcer Diabetes Pneumonia Gangrene Pyelonephritis Hypokalemia Ruptured appendix WITS'99 Keynote Address

49. Data Indicators • HEALTH RESOURCE AVAILABILITY • Licensed BedsHospitals Nursing homes • Licensed Professionals Doctors Dentists RNs LPNs Pharmacists Dieticians Nurse Midwives Psychologists Opticians/optometrists • Ratio of Medicaid Eligibles to Participating Physicians WITS'99 Keynote Address

50. Data Indicators • BEHAVIORAL RISK FACTORS • Mammograms • Pap smears • Blood pressure screening • Cholesterol screening • Smoking • Obesity • Seat Belt Use & Child Seat Use • Bicycle Helmet Use • Check-up in last year • Health Care Foregone due to cost WITS'99 Keynote Address