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

Applying Data Warehousing to Community Health Assessment

WITS’99 Keynote Address

Alan R. Hevner

University of South Florida

[email protected]

December 11, 1999 2WITS'99 Keynote Address

Preface - WITS RetrospectiveAs 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

Research Motivation - Community Health Measurement and Assessment

The CATCH MethodologyA Data Warehousing SolutionData Dissemination ModesCommunity Health Decision MakingA CATCH Demonstration


AcknowledgementsCo-Principal Investigators

James Studnicki - College of Public Health, USFDon Berndt - College of Business Admin., USF

Research StaffCenter for Health Outcomes Research StaffDoctoral and Masters Students

FundingU.S. Dept. of Commerce TIIAP GrantBear Stearns Research LaboratoryFlorida Communities


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 PlanningInstitute of Medicine (IOM) 1988 Report on the

Future of Public HealthRecommends 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 HealthCalls 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 MakingMulti-Sector Community Health Stakeholders

Health OrganizationsPublic Sector AgenciesMedical Care ProvidersBusinessesReligious CommunityEducational InstitutionsGovernment Agencies

Decisions must be based on Unbiased, Timely Information


CATCH Methodology

Comprehensive Assessment for Tracking Community Health (CATCH)

Project initiated in 199114 Florida County ApplicationsMarion County, Indiana (Indianapolis)Potential Regional, National, and

International Applications

December 11, 1999 WITS'99 Keynote Address

Community Health Indicators

Indicator 1Indicator 2...Indicator i..

State Averages

Peer Community Averages

Additional Health Standard Comparisons

Indicator 1Indicator 2...Indicator i.

Indicator 1Indicator 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

CATCHMethodology


Data Collection and AnalysisTen Indicator Groups

DemographicsSocioeconomicMaternal and Child HealthSocial and Mental HealthPhysical Environmental HealthHealth Status: Morbidity/MortalitySentinel Events Infectious DiseasesHealth Resource AvailabilityBehavioral Risk Factors


Priority Filters

Number AffectedEconomic ImpactAvailability of Efficacious InterventionMagnitude of DifferenceTrend Analysis

PeerCRITERIA 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 Peer Comparison

Comparison MatrixComparison Matrix

FAVORABLE UNFAVORABLE

INDICATOR CO PEER ST Socioeconomic

Maternal &Child health

InfectiousDisease

Health Status

Sentinel Events

ResourceAvailability

Physical/Environmental

Social & Mental

Behavioral Risk

CATEGORY% Labor forceunemployed

5.2% 5.8% 6.6%

% Labor force unemployed

Tuberculosiscases

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

Drowningfatalities

11.3 10.8 12.3

Drowning fatalities 2.4 2.0 2.7

Late stagecervical cancer

Cervical cancerlate stage

51.3 41.7 45.6

STATE

PEER

FAV

UNFAV

Challenges:

Further Screening

Infant mortality:non-white

Domestic

viol. cases 1041.0 1041.8 864.1Current 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 andIntervention Magnitude

SCREENSPRIORITIZATION

Social and Mental HealthINDICATORS COMPARED TO STATE & PEER VALUES

Social and Mental HealthINDICATORS COMPARED TO STATE & PEER VALUES

STATE

FAVORABLE UNFAVORABLEChild 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-whiteUNFAVORABLE 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 SheetIndicator Fact Sheet

16

48

80

90 91 92 93 94

0

20

40

60

80

County Peer Florida

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 27.6Florida: 29.6 41.5 41.7 77.2 61.5

Source: PHIDS


CATCH Data Warehouse

Manual CATCH LimitationsLabor-Intensive and Slow

Four months per report

Longitudinal Trend Analyses are Cost ProhibitiveExtension of County Reports to State, National,

and International ReportsKnowledge Discovery Potential not Realized

CATCH Data Warehouse Solution


Data Warehouse Challenges - Construction

Data CollectionData SourcesData QualityExtraction, Transformation, and Transportation

Data Warehouse DesignStar Schemas

Data StagingSizing and CleansingQuality Assurance


Hospital Discharge Star SchemaLOAD EVENT# ID* USERNAME* STATUS* STARTo ENDo PROCESSo VERSIONo TYPEo ROWS_PROCESSEDo ROWS_REJECTEDo DESCRIPTIONo NOTE

INDICATOR# ID* NAME* DESCRIPTIONo ABBREVIATIONo NOTEo TYPEo FREQUENCYo GEO_GRAINo TIME_GRAINo ELECTRONICo MULTIPLIERo ECO_IMPACTo EFFICACYo LAST_LOADo NEXT_LOAD

YEAR# YEARo HALF DECADEo DECADEo NOTE

RACE# ID* CATEGORYo ABBREVIATION

GENDER# ID* DESCRIPTION* ABBREVIATION

CT DISCHARGE* VALUE

COUNTY# ID* NAMEo MIL BASEo MIL BASE CNTo COASTALo REGIONo HEALTH DISTRICT* VITAL STATS ID

AGE# ID* AGE* UNITo CATEGORYo Y5 BANDo Y10 BANDo CUST BAND 1

load dimension

discharge fact

indicator dimension

discharge fact

age dimension

discharge fact

gender dimension

discharge fact

county dimension

discharge fact

year dimension

discharge fact

race dimension

discharge fact


ICD-9 Code Dimension Hierarchy

ICD9 PROC SECTION# ID* DESCRIPTION

ICD9 DX SECTION# ID* DESCRIPTION

ICD9 PROC CHAPTER# ID* DESCRIPTION

ICD9 DX CHAPTER# ID* DESCRIPTION

CCHPR PROCEDURE# ID* DESCRIPTION

CCHPR DX# ID* DESCRIPTION

ICD9 CODE# CODE* DISEASEo CATEGORY

included in

includes

included in

includes included in

includes

included in

includes

included in

includes

included in

includes


Data Warehouse Challenges - Operations

User InterfacesPerformanceSecurityBackup and RecoveryKnowledge Discovery

Data Mining


Data Dissemination Modes

Effective Presentation of CATCH Information to Community Decision Makers

Data Dissemination ModesPre-defined Reports Data BrowsingAd-hoc QueriesInternet Access

Hypertext Information ScreensDynamic Access to Data Warehouse


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 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

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

Produce a Table of Florida Counties and Infant Mortality Data

Sort and Graph the InformationCluster the Counties into Four GroupingsSelect Factors for Analysis and CorrelationPerform Further In-Depth Analyses

Data Mining Neural NetworksMultivariate Statistics


ConclusionsThe Application of Data Warehousing Technology to

Community Health Care can make a Social Contribution

Technical Research ChallengesCollaborative Group Decision Making: What factors are

associated with effective community use of CATCH data?

LeadershipInfrastructureDecision-Making ProcessPublic/Private Sector Cooperation


Appendix:CATCH Data Indicators


Data IndicatorsDEMOGRAPHIC CHARACTERISTICS

% Total population by gender% Total population by age% Total population by race% Population rural% Labor force by genderMedian AgeNet migrationLive births per 1,000 populationDeaths per 1,000 population


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 IndicatorsMATERNAL 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

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

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 IndicatorsSOCIAL 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 IndicatorsHEALTH STATUS INDICATORS

Morbidity CasesMelanoma Prostate cancerBreast cancer Cervical cancerColorectal cancer Lung & bronchus cancer Smoking related cancers

Age Adjusted Mortality Rates (Crude)Chronic liver disease & cirrhosis (crude) Melanoma Pneumonia/Influenza (crude) Breast cancerDiabetes Mellitus (crude) Cervical cancerCardiovascular disease Colorectal cancerHeart disease (crude) Lung/smoking rel. cancer Stroke (crude) Preventable cancerC.O.L.D. Prostate cancerYPLL All cancers (crude)


Data IndicatorsSENTINEL EVENTS

Vaccine Preventable DiseasesMeasles RubellaMumps Pertussis

Late Stage CancersBreast cancer cases - % late stageCervical cancer cases - % late stage

Avoidable HospitalizationsAsthma Immunizable conditions Cellulitis Malignant hypertension Congestive heart failure Perforated/bleeding ulcerDiabetes PneumoniaGangrene PyelonephritisHypokalemia Ruptured appendix


Data IndicatorsHEALTH RESOURCE AVAILABILITY

Licensed Beds Hospitals Nursing homes

Licensed ProfessionalsDoctors Dentists

RNs LPNs

Pharmacists Dieticians

Nurse Midwives Psychologists

Opticians/optometrists

Ratio of Medicaid Eligibles to Participating Physicians


Data IndicatorsBEHAVIORAL RISK FACTORS

MammogramsPap smearsBlood pressure screeningCholesterol screeningSmokingObesitySeat Belt Use & Child Seat UseBicycle Helmet UseCheck-up in last yearHealth Care Foregone due to cost

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

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