February, 2007 John Billings NYU Center for Health and Public Service Research Robert F. Wagner Graduate School of Public Service SOME THOUGHTS ABOUT MONITORING

February, 2007

John BillingsNYU Center for Health and Public Service ResearchRobert F. Wagner Graduate School of Public Service

SOME THOUGHTSABOUT MONITORING THE

PERFORMANCE OF THE “SAFETY NET”

• Why the focus on “performance” of the safety net?– Some caveats and definitions– Some assumptions

• Some examples of using administrative data to monitor performance

• The limitations of using administrative data

• A few suggestions (unsolicited advice)

WHAT I’M GOING TO TALK ABOUT

• The focus of policy should be on: Assuring optimal health for vulnerable populations

• We need to worry about the resources required to assure optimal health of vulnerable populations

• These resources are the “safety net”

• Because resources are limited, it makes sense to examine the performance of this “safety net”

• But it is important to remind ourselves that this isn’t really a “safety net”– We are flying without a net– No one is particularly safe

SOME CAVEATS AND DEFINITIONS

• Texas is unlikely to enact a universal coverage initiative this year, or next year, or the year after that…

• There are lots of opportunities to improve health of vulnerable populations in addition to buying coverage or subsidizing care

• Therefore, it is critical to have a monitoring capacity

• There is probably not a lot of money around for monitoring things

• But it is critical to recognize the inherent limits of administrative data

SOME ASSUMPTIONS

COMPUTERIZEDHOSPITAL DISCHARGE AND

ED VISIT DATA

Preventable/Avoidable HospitalizationsAmbulatory Care Sensitive (ACS) Conditions

Conditions where timely and effective ambulatory care help prevent the need for hospitalization

• Chronic conditions – Effective care can prevent flare-ups (asthma, diabetes, congestive heart disease, etc.)

• Acute conditions – Early intervention can prevent more serious progression (ENT infections, cellulitis, pneumonia, etc.)

• Preventable conditions – Immunization preventable illness

ACS Admissions/1,000By Zip Code Area Income

New York City - Age 18-64 - 2004

0

5

10

15

20

25

30

35

40

45

50

0% 10% 20% 30% 40% 50% 60%

Adms/1,000

R2 = .622LowInc/HiInc = 3.65

Coef Vari = .536Mean Rate = 16.08

Each represents a zip code

Percent of Households with Income <$15,000

Source: NYU Center for Health and Public Service Research


ACS Admissions/1,000Age 18-64 - 2004

25 to 47 (29)18 to 25 (27)12 to 18 (53)8 to 12 (39)4 to 8 (26)

Unpopulated Areas (3)

New York CityACS Admissions/1,000

Age 18-64 - 2004

NYU EMERGENCY DEPARTMENTCLASSIFICATION ALGORITHM 1.0

Emergent

Primary Care Treatable

ED Care Needed

Not preventable/avoidable

Preventable/avoidable

Non-Emergent

New York CityED Utilization Profile

Adults Age 18-64 - 1998

ED NeededPreventable -

Avoidable7.1%

ED Needed - NotPreventable-

Avoidable18.8%

Non-Emergent39.7%

Emergent - Primary Care Treatable

34.4%

Source: NYU Center for Health and Public Service Research - UHFNYC

Staten Island

Manhattan

Brooklyn

Queens

Bronx

% ED Visits Non-EmergentMedicaid - 1998

45% to 54% (28)42% to 45% (67)40% to 42% (64)20% to 39% (15)Unpopulated Areas (3)

Percent ofNon-Admitted

Emergency Department VisitsThat Are "Non-Emergent"

Medicaid - 1998All Ages

UNDERSTANDING THE CAUSES OFVARIATION IN ACS RATES

AND ED USE

• Theory 1: It’s just New York City

– [Who cares]

– [You’re more or less a different country]


Baltimore - Age 18-64 - 1999

0

10

20

30

40

50

60

0% 10% 20% 30% 40% 50% 60%

% Housholds Income < $15,000

Ad

ms/

1,0

00


R2 = .899LowInc/HiInc = 3.90Mean Rate = 16.93



St. Louis - Age 18-64 - 1999

0

10

20

30

40

50

60

0% 10% 20% 30% 40% 50% 60%


Ad

ms/

1,0

00





Memphis - Age 18-64 - 1999

0

10

20

30

40

50

0% 10% 20% 30% 40% 50% 60% 70%


Ad

ms/

1,0

00





San Diego - Age 18-64 - 1999

0

5

10

15

20

25

30

0% 10% 20% 30% 40% 50%


Ad

ms/

1,0

00





HOUSTON MSA - Age 18-64 - 2002

0

10

20

30

40

50

60

70

80

0% 10% 20% 30% 40% 50% 60% 70%


Ad

ms/

1,0

00




ACS Admissions/1,000By Zip Code Area IncomeDenver - Age 18-64 - 2002

0

10

20

30

0% 5% 10% 15% 20% 25% 30% 35%


Ad

ms/

1,0

00





Portland, OR - Age 18-64 - 1999

0

10

20

30

40

0% 10% 20% 30% 40% 50%


Ad

ms/

1,0

00




SOUTH CAROLINAED Utilization Profile

Adults Age 18-64 - 1997

ED NeededPreventable -

Avoidable7.1%

ED Needed - NotPreventable-

Avoidable18.8%

Non-Emergent31.9%Emergent - Primary

Care Treatable42.3%


Preventable/Avoidable ED Use/1,000By Zip Code Area Income

Austin Metro Area - Age 0-17 - 2000

Preventable/Avoidable ED Visits Per CapitaChildren - Age 0-17 - 2001

300 to 546 (17)200 to 300 (13)120 to 200 (16)60 to 120 (14)12 to 60 (15)

Low Population Area* (4)

Austin Metro Area

UNDERSTANDING THE CAUSES OFVARIATION IN ACS RATES

AND ED USE

• Theory 1: Who cares? It’s just New York

• Theory 2: It’s really pretty complicated– Coverage barriers– Resource supply/capacity– Economic barriers– Provider performance– Quasi-economic barriers

• Transportation• Child care• Lost wages

– Barriers to social care– Limitations in community social capital– Limitations in personal social capital– Education, motivation, confidence, health beliefs– Physician practice style (Wennberg et al), etc, etc

ACS Admissions/1,000Zip 10016 and Citywide Rates

New York City - Age 0-17 – 1982-2001

0

5

10

15

20

25

30

35

40

45

50

55

60

65

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01

Adms/1,000

New YorkCity

Zip 10016

Source: SPARCS - NYU Center for Health and Public Service Research - UHFNYC


New York City - Age 18-64 - 2002

0

5

10

15

20

25

30

35

40

45

50

0% 10% 20% 30% 40% 50% 60%

Adms/1,000

R2 = .622LowInc/HiInc = 3.65

Coef Vari = .536Mean Rate = 16.08


Percent of Households with Income <$15,000


Staten Island

Manhattan

Brooklyn

Queens

Bronx

% ED Visits Non-EmergentMedicaid - 1998



Emergency Department VisitsThat Are "Non-Emergent"

Medicaid - 1998All Ages

Staten Island

Manhattan

Brooklyn

Queens

Bronx

% ED Visits Non-EmergentSelfpay/Uninsured - 1998



Emergency Department VisitsThat Are "Non-Emergent"Selfpay/Uninsured - 1998

All Ages

ACS Admissions/1,000By Zip Code Area IncomeMiami - Age 18-64 - 1999

0

10

20

30

40

50

0% 10% 20% 30% 40% 50% 60% 70%


Ad

ms/

1,0

00



Each represents a predominantly Cuban-American zip code

ACS Admissions/100,000By Ward Code and Deprivation Index

London, UK - Age 15-64 - 2001/2-2002/3

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60 70 80

Deprivation Index

Ad

ms/

10

0,0

00

Each “♦” represents a ward

Note: All data are for 2001/2 and 2002/3

R2 = .387HighDI/LowDI = 2.10Mean Rate = 881.0

ACS Admissions/1,000Low and High Income Areas

Admissions Per 1,000New York City MSA – Age 40-64

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04

Adms/1,000

Low IncomeAreas

High IncomeAreas


ACS Admissions/1,000Low and High Income Areas

Admissions Per 1,000New York City – Age 0-17

0.0

10.0

20.0

30.0

40.0

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04

Adms/1,000

Low IncomeAreas

High IncomeAreas


$50,000,000

WHAT’S GOING ON HERE?

• It’s an improvement in clinical medicine (e.g., asthma)

• Changes in composition of NYC’s low income population

• It’s related to changes in the factors that contribute to health disparities– Coverage expansion (???)– Supply expansion (???)– Service improvement: greater “competition for patients”– Changes in social context– Etc, etc, etc…

1. It isn’t anything

2. It is something:

Change in ED Visits/1,000New York City

Medicaid FFS – ADC/HR Girls Age 6mos-14yrs1994-1999

94 95 96 97 98 99

Asthma

Injuries

% Change (Log Scale)

-33% -

-20% -

-50% -

+25% -

+50% -

+100% -

ACS - NoAsthma


Change in Percent of ED Visits Resulting In Admission New York City

Medicaid FFS – ADC/HR Girls Age 6mos-14yrs1994-1999

94 95 96 97 98 99

Asthma

Injuries

% Change (Log Scale)

-33% -

-20% -

-50% -

+25% -

+50% -

+100% -

ACS - NoAsthma


ACS Admissions/1,000Low Income Areas

New York MSAs - Age 0-17

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02

Adms/1,000


New York City

Buffalo

Rochester

Syracuse

ACS (W/o Asthma) Admissions/1,000Low Income Areas

California MSAs and New York City - Age 0-17

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02

Adms/1,000


Los AngelesSan DiegoSan Francisco

New York City

Oakland

USING MEDICAID CLAIMS DATATO MONITOR PROVIDER PERFORMANCE

OUR APPROACH

• We examined fee-for-service paid Medicaid claims

• Patients are linked to their primary care provider– Linking based on primary care visits (not ED or specialty care)

– Patients with 3+ primary care visits linked to provider having the majority of primary care visits [“predominant provider’]

– Patients with fewer than 3 visits examined separately

• Performance of providers for their patients is then examined

GETTING BEYOND ADMINISTRATIVE DATAIN MONITORING THE SAFETY NET

So If “Provider Performance” Matters…What Factors Influence “Provider Performance?

• Hours of operation (?)

• “Cycle time” (?)

• Wait time for appointment (?)

• Language barriers (?)

• Doctor-patient interaction [respect, courtesy, communication] (?)

• Staff-patient interaction [respect, courtesy, communication] (?)

• Content of care: doctor skill (?)

• Content of care: patient education on self-management (?)

• Staffing mix (MD type, nurse practitioner, etc.)

• Staffing mix (use of medical residents)

• Patient “outreach” (?)

• Easy telephone access (?)

• MIS systems [notification that patient is in ED] (?)

• Etc, etc, etc.

Factors That Matter to Patients“I Would Recommend This Place to My Friends”


• Things that matter most– The facility is pleasant and clean– I saw the doctor I wanted to see– The office staff were respectful and courteous– The doctor was respectful and courteous

• Things that matter somewhat– The office staff explained things in a way I could understand– The location is convenient for me– I waited a short time to see the doctor– It is easy to get an appointment when I need it

• Things that don’t seem to matter as much– The doctor spent enough time with me– The doctor/nurse/office staff listened to me carefully– It is easy to get advice by telephone– The hours are convenient

• Most patients wait a considerable amount of time before heading to the ED

• But they are unlikely to have contacted the health care delivery system before the visit

• Convenience is the leading reason for ED use

• Many are not well-connected to the health system

Source: NYU/UHF survey of ED patients in 4 Bronx hospitals - 1999

FINDINGS FROM INTERVIEWS OFED PATIENTS

• It is critical to know…– Are things getting better or worse?– What are the biggest problems?– Where are the biggest problems?

• Support evidence-based policy making - Use data to:– Identify the areas and populations in greatest need– Understand the nature and characteristics of that need– Assess impact of interventions– Learn from natural experiments– Get answers for some of things we don’t know

• Oh, and talk to patients once in a while– They know what they want better than you do– It is important to understand what’s driving their use patterns

FINAL THOUGHTS ABOUTMONITORING THE SAFETY NET

Documents

February, 2007 John Billings NYU Center for Health and Public Service Research Robert F. Wagner Graduate School of Public Service SOME THOUGHTS ABOUT MONITORING