Keynote

Applying Data Warehousing to Community Health AssessmentWITS’99 Keynote Address: 

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

Preface - WITS Retrospective: 

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)

Outline: 

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

Acknowledgements: 

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

Research Motivation: 

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

Community Health Planning: 

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.

Collaborative Health Decision Making: 

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

CATCH Methodology: 

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

Slide9: 

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

Data Collection and Analysis: 

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

Priority Filters: 

Priority Filters Number Affected Economic Impact Availability of Efficacious Intervention Magnitude of Difference Trend Analysis

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 Peer Comparison

Comparison Matrix: 

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

Priority Filters: 

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

Social and Mental HealthINDICATORS COMPARED TO STATE & PEER VALUES: 

Social and Mental Health INDICATORS 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

Indicator Fact Sheet: 

Indicator Fact Sheet 1994 AIDS CASES, Incidence rate per 100,000 population FIVE YEAR TREND ANALYSIS INDICATOR: AIDS CASES 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

CATCH Data Warehouse: 

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

Data Warehouse Challenges - Construction: 

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

Hospital Discharge Star Schema: 

Hospital Discharge Star Schema

ICD-9 Code Dimension Hierarchy: 

ICD-9 Code Dimension Hierarchy

Data Warehouse Challenges - Operations: 

Data Warehouse Challenges - Operations User Interfaces Performance Security Backup and Recovery Knowledge Discovery Data Mining

Data Dissemination Modes: 

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

Community Group Decision Making: 

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.

Group Decision Making Issues: 

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

CATCH Data Warehouse Demonstration: 

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?”

Data Browsing Strategy: 

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

Conclusions: 

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

Appendix:CATCH Data Indicators: 

Appendix: CATCH Data Indicators

Data Indicators: 

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

Data Indicators: 

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

Data Indicators: 

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

Data Indicators: 

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

Data Indicators: 

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

Data Indicators: 

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

Data Indicators: 

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)

Data Indicators: 

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

Data Indicators: 

Data Indicators HEALTH RESOURCE AVAILABILITY Licensed Beds Hospitals Nursing homes Licensed Professionals Doctors Dentists RNs LPNs Pharmacists Dieticians Nurse Midwives Psychologists Opticians/optometrists Ratio of Medicaid Eligibles to Participating Physicians

Data Indicators: 

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