# Presentation: 502561

## USING ADMINISTRATIVE DATATO MONITOR ACCESS, IDENTIFY DISPARITIES, AND ASSESS PERFORMANCEOF THE SAFETY NET

**John Billings**
**September, 2003**

- NYU Center for Health and Public Service Research

## WHAT ARE“ADMINISTRATIVE DATA”

**Computerized records**
**Gathered for some “administrative” purpose**
**Bill paying/reimbursement**
**Record keeping**
**Typically containing information about individuals**
**Demographics**
**Utilization of services**
**Other events (birth, death, etc.)**

## SOME EXAMPLES OF“ADMINISTRATIVE DATA”

**Birth/death records**
**Hospital admission/discharge abstracts**
**Emergency department billing records**
**Medicare and Medicaid claims files**

## ADVANTAGES OF“ADMINISTRATIVE DATA”

**They’re already there**
**They’re electronic [computerized]**
**They can be relatively inexpensive to analyze [**_**sometimes**_**]**
**They can tell you a lot about what is going on [**_**sometimes**_**]**

## DISADVANTAGES OFADMINISTRATIVE DATA

**They can be “dirty” (caution is required)**
**Some data elements are a lot better than others**
**A good test is whether anyone will go to jail for bad data, or there is some other good reason to get it right**
**They seldom tell the whole story (often raising more pesky questions)**
**Not everyone is willing to share (which may be required)**
**You’re probably going to be dealing with bureaucrats not particularly interested in being helpful**

## USING BIRTH RECORDSTO MONITOR BIRTH “OUTCOMES”

**Late/no prenatal care**
**Low birth weight (adjusted for gestational age)**
**Preterm birth**

## Percent Late/No Prenatal CareNew York City1997-8

- Each “■ “ represents zip code area

- R2 = .435

- Source:  NYU Center for Health and Public Service Research

## Staten

- Island

- Coney
- Island

- Source:  NYU Center for Health and Public Service Research

## USING HOSPITAL DISCHARGE DATAPreventable/Avoidable HospitalizationsAmbulatory Care Sensitive (ACS) Conditions

_**ACS 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/1,000By Zip Code Area Income Baltimore - Age 0-17 - 1999

- Adms/1,000

- R2 = .595
- LowInc/HiInc = 2.24
- Mean Rate = 9.53

- Each    represents a zip code

- Percent of Households with Income <$15,000

- Source:  NYU Center for Health and Public Service Research

## ACS Admissions/1,000By Zip Code Area Income Baltimore - Age 40-64 - 1999

- Adms/1,000

- R2 = .893
- LowInc/HiInc = 4.08
- Mean Rate = 26.69

- Each    represents a zip code

- Percent of Households with Income <$15,000

- Source:  NYU Center for Health and Public Service Research

## Atlanta Metro Area

- ACS Admissions/1,000
- Age 40-64 - 1999

- Source:  AHRQ/HCUP - NYU Center for Health and Public Service Research

## Atlanta Metro Area

- ACS Admissions/1,000
- Age 40-64 - 1999

- Source:  AHRQ/HCUP - NYU Center for Health and Public Service Research

## ACS Admissions/1,000By Zip Code Area Income New York City - Age 18-64 - 2000

- Adms/1,000

- R2 = .613
- LowInc/HiInc = 3.18
- Mean Rate = 10.64

- Each    represents a zip code

- Percent of Households with Income <$15,000

- Source:  NYU Center for Health and Public Service Research

**Low income zip codes**
**with large differences**
**in ACS rates**

- 10035

- 11239

## USING EMERGENCY DEPARTMENT DATATO MONITOR THE SAFETY NET NYU ED CLASSIFICATION ALGORITHM

**USING EMERGENCY DEPARTMENT DATA****TO MONITOR THE SAFETY NET**** NYU ED CLASSIFICATION ALGORITHM**

**Emergent**

**Primary Care Treatable**

**ED Care Needed**

**Not preventable/avoidable**

**Preventable/avoidable**

**Non-Emergent**

- Source:  NYU Center for Health and Public Service Research

## Preventable/Avoidable ED Use/1,000By Zip Code Area Income Baltimore - Age 18-64 - 2000

- ED Visits/1,000

- R2 = .783
- LowInc/HiInc = 3.77
- Mean Rate = 80

- Each    represents a zip code

- Percent of Households with Income <$15,000

- Source:  AHRQ/HCUP - NYU Center for Health and Public Service Research

## Preventable/Avoidable ED Use/1,000By Zip Code Area Income Austin Metro Area - Age 0-17 - 2000

**Austin Metro Area**

- Source:  NYU Center for Health and Public Service Research

## SOME CAUTIONS FOR USINGADMINISTRATIVE DATA

**The data can be “dirty” (see above)**
**If a number is way high or way low, it’s probably wrong**
**Unless it’s not**
**(Some disparities are huge)**
**Don’t expect final answers**
**Avoid the easy explanation – this stuff is complex**