Understanding Unduplicated Count and Data … HMIS Conference September 14th and 15th, 2004 Chicago, IL Sponsored by the U.S. Department of Housing and Urban Development 1 Understanding

National HMIS ConferenceSeptember 14th and 15th, 2004

Chicago, ILSponsored by the U.S. Department of Housing and Urban Development 1

Understanding Unduplicated Count and Data Integration

Presenters:Loren Hoffmann, System Administrator

WI Statewide HMIS

Ray Allen, Executive DirectorCommunity Technology Alliance

National HMIS ConferenceSponsored by U.S. Department of Housing & Urban Development 2

Topics to be covered:

Types of fields - and data qualityStatistical ConsiderationsAn “unduplicated Count”

OvercountsUndercounts


Data Quality:

General tests:Completeness

NULL vs “something”

ValidityIs the data valid?Is the data “reasonable”?


Data Quality:

Types of data fields:Picklists; yes/noText - numeric, alphanumericDate


Data Quality: Picklists

Validity - must be item from picklistCompleteness - response/no response

Who updates the list?System administrator or userWhat happens to deleted/inactivated items


Data Quality: Date field

Valid date; NULL valueDetermining Validity:

Control by format on entrymmddyyy 10152003mmmddyyyy Oct152004

Is date a valid dateIs it reasonable


Data Quality: Text

Little that can be easily validated on a large scale


Data Quality: Completeness

For a given field, how many NULLS are there?

For the entire databaseFor a specified period of timeFor a given agency/user


Unduplicated Countand the Client identifier

• To generate an unduplicated count (or to merge systems), most HMIS systems create and/or generate a common client identifier.


(UN) Duplicate Counts

How does your system manage the “unique client” or “unduplicated client count”?

You need to know the algorithm usedEvaluate the data elements that are used to generate the count


Un-duplicated Counts Two possible errors

It is not magic or foolproof.Undercount the number of clients:

The system counts two client record entries as a single client when it really is two clientsOvercount the number of clients:The system counts two client record entries as two clients when it really is the same client


Unique Client Count

Two possibilities:


Unique Client Count

Put all the clients in the same room and count them;Make a list of all the clients that you know;


Unique Client Count

Using:Client first nameClient last nameClient date of birthClient gender


Un-duplicated CountsAn example - how many clients?

Using first name, last name, gender, date of birth

William Smith, male, 10-15-1973Bill Smith, male, 10-15-1973William Smith, male, no DOB

Consider: address, race, HH members, etc


Statistical Considerations

Defining the universe:Number of client records in the system vs.Number of ACTIVE client records vs.Number of UNDUPLICATED client records vs.Number of valid responses for a given data element


Defining the Universe:

Example:1200 client records1100 ACTIVE client records1000 UNDUPLICATED clients980 DOB fields have data500 Marital Status fields have data


Defining the Universe:

DOB example - 980 of 1000 had a valid date, therefore:

If 70% of the 980 records are 18+ (adults) then the actual number of adults on the system is between 68% and 72% (margin of error is 2%)Marital status, with only 50% having information, has a margin of error of +-25%


Issue:

What do I do with conflicting answers for the same client? e.g. different race, DOB, or response to a question like :

“Is client homeless?” with both a “yes” and a “no”Or DOB that is different


Coverage of Data

Database statistics vs the “universe”Determine the relevant universe

EX. Emergency SheltersMen’s sheltersWomen’s sheltersFamily unitsDV, etc


Merging Databases

Most HMIS systems are decentralized and will require some form of systems integration and/or data migration to obtain unduplicated counts, service utilization patterns and characteristics of homeless persons served.


11 County Region of Northern California

Population = 7,512,499

Geographic Area = 10,691 (sq. miles)

Equivalent in size to the state of Maryland


BACHICBay Area Counties Homeless Information Collaborative

Mission: To better enable policy makers, service agencies, and funders to understand and service the needs of the homeless within the communityGoals:

Obtain unduplicated regional count of homeless personsIdentify prevalence of cross-county chronic homelessnessUnderstand client movement across continuum boundariesAnalyze service usage across continuumsInform funders about effectiveness of sponsored programs in the regionLeverage HMIS learning and expertise across multiple communities; increase success factors, reduce risk factors


BACHIC

Product: Regional HMIS Data WarehouseOutcomes:

Better planning and resource managementClearer vision of the present and future needs of the homeless

Sponsored by the Charles and Helen Schwab Foundation


HMIS Implementations by County

ServicePoint- All locally hosted

except for ContraCosta and Monterey

Legacy System

MS Access

Metsys

Deloitte


RHINO Data CollectionRegional Homeless Information Network

BACHIC group agreed to the collection of All Universal Data ElementsAll Program-Specific Data Elements

What each county has agreed to forward RHINO

All Universal & Program Elements except for Protected Personal Information (PPI)Exception: Year of Birth, Program Entry & Exit Dates and ZIP Code


Data Warehouse and CountiesRegional HMIS Data Warehouse

San FranciscoMetsys System

San Mateo Daisy/HOPE

System

ServicePoint Stand-aloneLocally Hosted System

ServicePoint HostedSystems

Other Local Systems


RHINO DesignEncryption(SSH2)

County HMIS System

Data Entry TransformationExtraction

Regional HMIS Data Warehouse

BACHICReports

INTERNETINTERNET

Standard Format


Project PhasesVision

AutomatedReal-TimeFlexibleAccurate

Phase I Phase II Phase III

HardwareSoftwareTesting

ValidationOf Design

Pilot ofSanta Clara

County

ImplementationOf Select

Diverse Counties

All otherCounties

Phase IV

Gro

wth


Design of SystemMinimize counties’ efforts

Especially ongoing duties/obligationsSecurity, privacyMultiple diverse HMIS systems

Different stages of implementationsDifferent data formats

Reporting Flexibility so as not to limit future reporting choices

Work flowProcesses and procedures for resolving exceptions

Linux Server

SQL Server

DAT72 Backup

Reg

iona

l HM

IS D

ata

War

ehou

se


Transference of DataData from counties will be CSV format (min. requirement)Minimum encryption (128 bit) using SSH2 (Secure Shell Version 2)

Regional HMIS Data Warehouse

County HMISSystems

Double firewall for increased security

Future use of OpenSSL

(Open Source software)


Regional Unique Identifier

Required for de-duplication of customers within 11 counties.Information from personal identifiable data elements.Uses a hash algorithm to encrypt ID.Key is created by 11 counties, unknown to data warehouse team.Can not be reverse engineered, is one way encryption.


Data Integrity

Before data is merged, it will be checked for the following:

Each record/entity ID is uniqueRequired data elements have some valueDate formats are correct and values are reasonableCode values conform to HMIS Standard

Ex. Gender: Male=0, Female=1All data elements can be linked back to a unique person identifier within submitted data set


Reporting Scope Status GridItem As of 12/04

Counties (CoC’s) 11

Agencies 85

Emergency Shelters 47

Emergency Beds 8600

Etc…

DemographicsTotal client population:

AgeRaceEthnicity

Adult client populationGenderIncome SourcesDisabilities

Family income groupTop 10 last permanent zip codes

Contra Costa3%

Marin2%

Monterey3%

Napa10%

San Francisco19%

San Mateo13%

Santa Clara11%

Santa Cruz8%

Solano7%

Sonoma9% Alameda

15%

Regional Homeless Population by County


Reporting % Veteran Status

Chronically Homeless Yes No Grand Total

Yes 63% 45% 49%

No 38% 55% 51%

Grand Total 100% 100% 100%

DemographicsFamilies w/ childrenSingleVeteransChronically Homeless

Migration and Service AccessLast permanent zip outside countyClients receiving shelter or other services in multiple counties

Program EffectivenessReason for leavingDestination

• 15% of Adult Client Population are Veterans

Age Group% of Total Client

Population

17 and under 27%

18 – 30 17%

31 – 50 40%

51 – 61 8%

62 and over 2%

Unknown 7%


Contact InformationCommunity Technology Alliance115 East Gish Road, Suite 222San José, CA 95112

Ray AllenExecutive Director

(408) 437-8800(408) 437-9169 (fax)

e-mail: [email protected]


Questions and Answers

Understanding Unduplicated Count and Data … HMIS Conference September 14th and 15th, 2004 Chicago, IL Sponsored by the U.S. Department of Housing and Urban Development 1 Understanding

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