Using Data to Manage and Market Your Program

Marcia Finlayson & Joy HammelUniversity of Illinois at ChicagoAFP & ATF Technical Assistance

Program

Federal Accountability Initiative “If you don’t measure results, you

can’t tell success from failure” (C. Mindel)

“If you CAN demonstrate results, you CAN win public support” (C.Mindel)

Session Objectives To discuss how to effectively use AFP

outcome data in your state program & systems change initiatives

To share examples of state use To review the process for requesting

custom data runs and reports for your state

The System: Data Collection

Initial Application (n=4210) Applicant & AT user demographics, AT Request,

Prior funding experiences, Loan info, decision & terms

Follow-up: Approved at 6 months post (n=816) If received & using AT, Impact on life

Satisfaction with services, Overall impressions of program and its utility

Follow-up: Denied or did not accept at 1 mo. Post (n=338) Reasons for denial/not accept, Follow-up outcomes,

Satisfaction

Ways to Obtain & Use Data

1. Online Public Reports By state or nationally By time period

2. Annual & State Reports

3. Custom Reports From states upon

request

AFP Use Demographics Overall demographics (n=4210 as of

11/4/04) 52.8% male 76.5% White & 17.1% are African-American 91% are primarily English speakers 70% are not working Median monthly income = $2000/month

25% are below $1069/month Fairly evenly distributed urban, suburban, rural

(1/3)

Custom Reports: Data Mining Refers to “mining” or exploring the

data available in much more depth Possible by having UIC download

the data from the system into special software that allows advanced statistical analyses

Allows the development of custom reports and the ability to answer specific questions

Data Mining: Example

Question: How are older adults using AFP and are there differences in AFP

use & outcomes by age?

Findings Age distribution: Range: 6 months

to 95 years 0.5-39: 33% (n=1386) 40+: 60% (n=2512)

40-49: 16.5% (n=693) 50-59: 17.4% (n=733) 60-69: 12.3% (n=516) 70+: 13.5% (n=570)

Not reported/unknown: 7% (n=312)

AFP Use by Age Descriptive information:

Age 2003: Average age: 46.5 years (sd=19.8) 2004: Average age: 45.4 years (sd=22.5)

Find Out about the Program 2003: Referrals primarily through a disability

agency (25.2%) or vendor/dealer (19.7%) 2004: Referrals primarily through a disability

agency (23.5%) or vendor/dealer (27.8%)

**As of November 27, 2003, N=2639**As of November 4, 2004, N=4210

Finding the AF Program

0

5

10

15

20

25

30

35

40

45

40-49 50-59 60-69 70+

VendorProfessionalMailDisability Agency

p<0.0001

Age of applicants

% v

ia m

eth

od

Nature of Requests Among Applicants Aged 40+

Overall, most common single request is for adapted transportation (n=1413), followed by hearing aides (n=881), then mobility equipment (n=303) Most common dual request is for mobility

equipment plus adapted transportation (n=238), followed by computer equipment plus computer access (n=100)

% of Requests for Specific AT, by Age Group

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

40-49 50-59 60-69 70+

Age groups

% o

f re

qu

es

ts

adapted transportation

hearing aides

mobility equipment

p<0.0001

Outcomes of Applications Among 40+ group Overall, 65.6% of all applications

have been approved & 26.8% denied Other outcomes of loan - 7.6%

E.g., withdrawn, approved/not accepted; pending

Average age of: Approved applicants = 60.5 (sd=13.0) Denied applicants = 56.5 (sd=12.1) Other applicants = 56.7 (sd = 10.8)

Loan Decisions by Age Group

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

40-49 50-59 60-69 70+

Age groups

% o

f d

ec

isio

ns

other

denied

approved

p<0.0001

Loan Amounts by Age Group

$0.00

$1,000.00

$2,000.00

$3,000.00

$4,000.00

$5,000.00

$6,000.00

$7,000.00

40-49 50-59 60-69 70+

Age Group

Me

dia

n L

oa

n A

mt

Follow-Up on Approved Loans 474 people 40+ participated in at

least part of an approved follow-up interview Missing data for individual questions

depending on applicability to loan request – up to 30% for some questions

Results must be considered exploratory

Follow-up on Approved Loans Status of AT equipment receipt

(N=337 age 40+) 90.5% had received their AT and were

using it 3.6% had not yet received Remainder (5.9%) had received but not

using (e.g., broken, don’t know how, doesn’t meet needs, etc)= abandoned

No differences by age

Satisfaction with Program for Approved Loans (N=336)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

40-49 50-59 60-69 70+

Age Groups

% o

f re

sp

on

de

nts

satisfied

not satisfied

Follow-up on Approved Loans

Participants reported improvements in: QOL related to AT/EM impact –

88.7% report improvement (N=310); 9.8% stayed the same, 1.1% got worse

60-69 least likely to report improvements (p=0.03) Ability to participate in social & recreational

activities - 77.1% got better (N=284) Ability to complete home/community

management activities - 70.3% got better (N=279)

Ability to control life and life decisions - 63.4% gained control/increased (N=262)

Follow-up Outcomes 67.8% (N=329) report ability to fund AT

they would have been unable to obtain through other sources 70+ least likely to report this outcome (p=0.02)

85.2% who were approved loans and did a follow-up interview would recommend the program to others (*)

86.3% who were denied loans and did a follow-up interview would recommend the program to others (*)

(*) – high rates of missing data (up to 30%)

Outreach to Older Adults

State Specific Examples of Data Use

Additional ways to use AFP Data

Using data to negotiate with lending institutions Comparison of state interest rates to

renegotiate rates in each state Proportion of African Americans using program

to negotiate relationship with lending institution that serves this population

Average loan amount for each repayment schedule (e.g., under 1 year, 5 yr., 10 yr. payback periods)

Relationship between income and loan size to negotiate with bank

Using data to leverage/expand resources for AFP Proportion of low income individuals for

tax exempt program eligibility Characteristics of AFP applications for

people under 18 yrs.of age to pursue grant to supplement funding

Using data to target outreach efforts ID gaps in source of referrals and who’s

applying E.g., coming in from professional referrals versus

other sources Examining how different groups access the

program E.g., looking at referral source in light of applicant

characteristics such as minority status, income status, etc.

Trends in these issues over time E.g, showing how minority outreach & application

rates have increased over time/impact of targeted outreach campaigns

How to request your own custom analyses

Requests Either:

Send us an e-mail: marciaf@uic.edu or hammel@uic.edu

Complete the request form and mail or fax it in

Turn-over time depends on the nature of request and its complexity Typically 5 working days

Questions & Comments