A Framework for Evaluating Safety-Net and Other Community-Level Factors on Access for Low-Income...

Pamela L. Davidson

Ronald M. Andersen

Roberta Wyn

E. Richard Brown

A Framework forEvaluating Safety-Net andOther Community-LevelFactors on Access forLow-Income Populations

The framework presented in this article extends the Andersen behavioral model of healthservices utilization research to examine the effects of contextual determinants of access. Aconceptual framework is suggested for selecting and constructing contextual (orcommunity-level) variables representing the social, economic, structural, and publicpolicy environment that influence low-income people’s use of medical care. Contextualvariables capture the characteristics of the population that disproportionately relies on thehealth care safety net, the public policy support for low-income and safety-net populations,and the structure of the health care market and safety-net services within that market.Until recently, the literature in this area has been largely qualitative and descriptive andfew multivariate studies comprehensively investigated the contextual determinants ofaccess. The comprehensive and systematic approach suggested by the framework willenable researchers to strengthen the external validity of results by accounting for theinfluence of a consistent set of contextual factors across locations and populations. Asubsequent article in this issue of Inquiry applies the framework to examine access toambulatory care for low-income adults, both insured and uninsured.

For more than three decades, health services re-searchers have investigated the factors that deter-mine an individual’s access to medical care.These factors, conceptualized as predisposing,enabling and need, have been used routinely topredict health services utilization and to identifycharacteristics of people having more or less ac-cess (Andersen 1968, 1995). Thus, the underly-ing theory and supportive empirical evidenceare well established for understanding an individ-ual’s determinants of access. However, multivar-iate models containing only individual-level

variables are limited. Most researchers havefailed to comprehensively account for the effectsof contextual variables reflecting the social, eco-nomic, structural, and public policy environmentin which access occurs. Previously, Andersenand colleagues and other researchers publishedfoundational work in this area, applying a mea-surement model to investigate ‘‘contextual’’ orcommunity-level determinants of access (Ander-sen et al. 2002; Cunningham 1999; Cunninghamet al. 1999; Grumbach, Vranizan, and Bindman1997; Cunningham and Kemper 1998; Long

Pamela L. Davidson, Ph.D., is an adjunct assistant professor, and Ronald M. Andersen, Ph.D., is the Wasserman Professor,both in the Department of Health Services, UCLA School of Public Health. Roberta Wyn, Ph.D., is associate director of theUCLA Center for Health Policy Research. E. Richard Brown, Ph.D., is the director of the UCLA Center for Health PolicyResearch and a professor in the UCLA School of Public Health. Funding for this research came from the Robert Wood John-son Foundation. Address correspondence to Prof. Davidson at the UCLA School of Public Health, Department of HealthServices, 650 C.E. Young Drive South, Campus Box 951772, Los Angeles, CA 90095-1772. Email: [email protected]

Inquiry 41: 21–38 (Spring 2004). � 2004 Excellus Health Plan, Inc.

0046-9580/04/4101–0021

21

and Marquis 1999; Lave et al. 1998; Szilagyi etal. 2000; Gaskin and Hoffman 2000; Laditkaand Laditka 1999; Laditka and Johnson 1999;Bierman et al. 1999; Roblin 1996; Billings, An-derson, and Newman 1996; Bindman et al.1995; Billings et al. 1993; Friedman et al. 1999).

This article delineates a more comprehensiverange of contextual variables and empiricalsources for constructing contextual variables. Asan additional contribution, the study distinguishesthe effects of contextual variables separately forlow-income insured and low-income uninsuredpopulations. The framework suggests the effectsof public policy and financing variables and howtheir influence on provider organizations’ deliveryof health services leads to different access out-comes for the insured and the uninsured.

Contextual variables for a defined geographicarea (e.g., county, metropolitan statistical area[MSA] or state) measure the environment ormilieu in which access occurs (Phillips et al.1998; Andersen and Davidson 2001). The exter-nal validity of findings can be strengthened byapplying a comprehensive framework and a con-sistent set of contextual variables to investigateaccess across locations and populations.

The first objective of this research is to proposea conceptual framework for comprehensively andsystematically explaining how individual andcontextual variables influence access and accessoutcomes. As stated previously, the frameworksuggests contextual variables differentially affectaccess of low-income insured versus low-incomeuninsured populations. Second, the frameworkserves as a guide for constructing contextualvariables hypothesized to influence access. Thepaper concludes by identifying data gaps inmeasuring safety-net and other contextual vari-ables believed to affect access.

Summary of the Framework

Figure 1 presents a framework for evaluating theeffects of safety-net and other community-levelfactors on access and access outcomes. Theframework consists of three broad domains andrelated sub-domains. Individual characteristics,categorized as predisposing, enabling, and needfactors, are the well-established predictors of ac-cess. Community-level variables capture thecharacteristics of the low-income and safety-netpopulations, the structure of the health care mar-ket and safety-net services in a geographic area,

and public policy support for providing servicesto low-income populations, both insured and un-insured. Health care access and outcomes mea-sure potential and realized entry into the medicalcare system, and the results of access in terms ofeffective and efficient medical care delivery.

Individuals' Determinants of Access

Individual-level characteristics are categorized asAndersen’s (1968, 1995) predisposing, enabling,and need variables. The model suggests people’suse of health services is a function of: their pre-disposition to use or not use services, even thoughthese predisposing characteristics are not directlyresponsible for use; enabling conditions that fa-cilitate or impede the use of services; and need,or conditions recognized by lay people or healthcare providers as requiring medical treatment.More than three decades of research on thesefactors have resulted in a substantial body of em-pirical findings that are generalizable across loca-tions and populations. We are not proposingchanges to the set of individual determinants.

The literature on predisposing characteristicshas shown decreased access to medical care forpeople with lower educational attainment, the un-employed, ethnic minority groups, especially re-cent immigrants who speak a language differentfrom the mainstream providers, and people withcertain health beliefs (Hulka and Wheat 1985;Guendelman 1991; Portes, Kyle, and Eaton1992; Becker and Maiman 1983; Mechanic 1979;Tanner, Cockerham, and Spaeth 1983; Forrestand Whelan 2000; Davis, Collins, and Hall,1999; Leigh et al. 1999). Enabling characteristicsassociated with under-utilization of medical careinclude lower household income, having no reg-ular source of medical care, and no health insur-ance coverage (Andersen and Newman 1973;Manning et al. 1987). Furthermore, economi-cally and socially disadvantaged people are morelikely to experience medical symptoms that maynot be treated in a timely and effective mannerdue to less access (Aday and Andersen 1974; An-dersen, Kravits, and Anderson 1975; Aday, An-dersen, and Fleming 1980; Aday et al. 1985).

After testing and validating results over severaldecades, definitive information has accumulatedon the individual predictors of access. Empiricalresearch has shown individual characteristics ac-count for an estimated 20% to 25% of the vari-ance explained, depending on the access-related

Inquiry/Volume 41, Spring 2004

22

variable and characteristics of the study popula-tion. Yet, a substantial portion of unexplainedvariance remains.

Similar to the approach Andersen and col-leagues have used to validate ‘‘individual’’ pre-dictors of access, we suggest applyinga comprehensive conceptual framework includ-ing a uniform set of variables to test and validatethe ‘‘community’’ determinants of access. Such astrategy will lead policy researchers to the gener-alizable community determinants affecting ac-cess across populations and locations, includingfactors that may be altered by policy change.From this research, new strategies will emergefor developing evidence-based policy and man-agement interventions to improve access, anda more effective use of public resources.

Community Determinants of Access

Community variables capture the social, eco-nomic, structural, and public policy environmentin which access occurs. Specifically, the frame-work (Figure 1) suggests variables measuringthe characteristics of the safety-net population(insured and uninsured), public policy supportfor low-income and safety-net populations, andthe structure of the health care market and

safety-net services within that market. Other vari-ables that can be considered in the framework af-fect more affluent residents—for instance, thenumber of hospitals and hospital beds per 1,000population, the number of physicians per 1,000population, and economic measures, such as theunemployment rate in a geographic area.

Safety-Net Population

The safety-net population consists of three broadsubgroups of individuals who reside in a geo-graphic location and disproportionately dependon the safety net (IOM 2000): 1) uninsured peo-ple, 2) Medicaid beneficiaries, and 3) vulnerablepopulations.

Geographic variation (defined as differences inaccess by location, for example, state, MSA orcounty) in health insurance coverage has been re-ported for low-income adults and children(Brown, Wyn, and Teleki 2000; Long and Mar-quis 1999; Cunningham and Kemper 1998), forlow-income families (Cantor, Long, and Marquis1998; Zuckerman et al. 1999), and in the percen-tages of uninsured people reporting difficulty ob-taining medical care (Cunningham and Kemper1998). MSAs with higher than average uninsur-ance rates all have large concentrations of immi-

Figure 1. Conceptual framework for evaluating safety net and other community-levelfactors on access and access outcomes.

Access for Low-Income Populations

23

grants, including naturalized citizens and nonciti-zens; these groups are particularly high in Ari-zona, California, Florida, New Jersey, NewYork, and Texas (Brown, Wyn, and Teleki2000). Geographic areas with the highest unin-surance rates also have the lowest rates of em-ployer-based coverage, higher unemploymentand poverty rates, and larger proportions of thepopulation in single-parent households (Brown,Wyn, and Teleki 2000; Holahan, Weiner, andWallin 1998a). In many communities, having ac-cess to employer-sponsored insurance may bemore important than the generosity of publiclysponsored programs in determining the propor-tion of the population that is uninsured. Incomeor poverty level is often the most important de-mographic predictor of uninsurance; however,since race/ethnicity and citizenship are correlatedwith income, sorting out the effects among thecommunity-level variables can be challenging(Marsteller et al. 1998; Weiner and Malakar1999). Multivariate results examining individual-level race/ethnicity generally have found dis-parities in access for nonwhite people. Byevaluating the community effects of race/ethnic-ity (e.g., percentage black or percentage His-panic), researchers will be able to ascertain howresidence in a community can either serve as a re-source or result in a barrier to an individual’smedical care access.

Similarly, among low-income insured resi-dents, we observe geographic variation in thepercentages of individuals covered by programssuch as Medicaid, and the State Children’sHealth Insurance Program (SCHIP). The eligibil-ity criteria for these public programs vary by stateand depend on the availability of federal and statefunds to pay for medical care for low-incomepeople (Norton and Lipson 1998; Brown, Wyn,and Teleki 2000; Bindman et al. 2000; Lillie-Blanton and Lyons 1998; Bronstein and Adams1999; Adams and Bronstein 1999). Because ofthe categorical and episodic nature of Medicaideligibility and changes in state regulations and fi-nancing, individuals cycle on and off insurance,often with long spells of no insurance (Shortand Graefe 2003; Short, Graefe, and Schoen2003; IOM 2000; Kasper, Giovannini, and Hoff-man 2000; Schoen and DesRoches 2000; Brownet al. 1999; Short 1998; Davis 1996; Short andLefkowitz 1992; Short, Cornelius, and Goldstone1990; Short, Cantor, and Monheit 1988). This not

only reduces the continuity of medical care, butalso places greater demand on local safety-netproviders.

Vulnerable subgroups represent a third cate-gory of the safety-net population. Vulnerablepopulations are those who are economicallydisadvantaged and whose social or health charac-teristics increase their risks and need for assis-tance. Vulnerable subgroups include: adults andchildren with disabilities; the frail elderly and dis-abled Medicare beneficiaries; low-income chil-dren, pregnant women and adolescents; thementally ill; substance abusers; HIV/AIDS pa-tients; and the homeless (IOM 2000; Holahanand Liska 1997a; Baxter and Mechanic 1997;Davidson 2004).

Thus, the size and characteristics of the safety-net population vary by geographic area, andthese variations affect access and access out-comes. When large groups of low-income peoplereside in a geographic area, whether they arelow-income insured, uninsured, or vulnerablesubgroups, they must compete for limited safety-net services and resources. In such high-demandcommunities, resources may be quickly stretchedfar beyond what the tax base can reasonably sup-port through special programs or subsidies topublic hospitals and clinics (Holahan, Weiner,and Wallin 1998a.). Unlike privately insured pa-tients, low-income uninsured people and thosewith Medicaid or other public coverage have lit-tle financial leverage, few enabling resources,and largely depend on subsidies to obtain carewhen medical symptoms arise. Providers maynot have the incentive or capacity to respond totheir demands and needs.

Community-level variables can be constructedto estimate the magnitude of low-income in-sured, uninsured, and vulnerable subgroups ex-pressed as a percentage of the total populationresiding in a geographic area. Table 1 providesexamples of community-level variables affectingaccess, and includes safety-net population vari-ables, variable definitions, geographic unit ofobservation, and suggested data sources for con-structing each. The table also shows percent-ages of low-income uninsured (e.g., nonelderlyuninsured, employed, and uninsured), insured(e.g., Medicaid and SCHIP enrollees), and vul-nerable populations (e.g., female single-headedhouseholds, AIDS incidence rates, immigrantnoncitizens, homeless).


24

Tab

le1.

Con

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itydeterm

inan

tsof

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andaccess

outcom

es

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ainan

dindicator

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sGeograp

hicun

itof

observation

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mples)

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25

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hicun

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observation

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mples)

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aged

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pay

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rate

s

Th

en

um

erat

or

incl

ud

esex

pen

dit

ure

sav

aila

ble

tod

irec

tly

sub

sid

ize

safe

ty-n

eth

osp

ital

sin

ag

eog

rap

hic

area

.T

he

den

om

inat

or

isth

eto

tal

po

pu

lati

on

of

un

insu

red

per

son

s,0

–6

4,

resi

din

gin

the

geo

gra

ph

icar

ea.

Sta

teC

MS

-64

An

nu

alR

epo

rt

DS

Hp

aym

ent

rate

sD

ata

no

tav

aila

ble

MS

A,

cou

nty

Dat

aso

urc

eis

no

tav

aila

ble

;w

ou

ldre

qu

ire

tele

ph

on

ein

terv

iew

sw

ith

stat

ean

dlo

cal

hea

lth

offi

cial

san

dh

osp

ital

asso

ciat

ion

s.


27

Tab

le1.

(con

tinu

ed)

Dom

ainan

dindicator

Variabledefinition

sGeograp

hicun

itof

observation

Datasources(exa

mples)

Sta

tean

dlo

cal

gra

nts

,an

dg

ran

tsfr

om

loca

lch

arit

ies

and

fou

nd

atio

ns

Th

en

um

erat

or

wo

uld

be

the

exte

nt

of

gover

nm

ent

and

phil

anth

ropic

fun

din

gto

pro

vid

ech

arit

yca

rein

ad

efin

edg

eog

rap

hic

area

.T

he

den

om

inat

or

wo

uld

be

the

nu

mb

ero

flo

w-i

nco

me

un

insu

red

per

son

sre

sid

ing

inth

eg

eog

rap

hic

area

.

Sta

te,

MS

A,

cou

nty

Co

mp

arab

led

ata

are

no

tco

llec

ted

syst

emat

ical

ly.

Healthcare

market

Ph

ysi

cian

sup

ply

Th

ev

aria

ble

mea

sure

sth

en

um

ber

of

no

n-f

eder

ally

emp

loy

edM

Ds

wh

op

rov

ide

tota

lp

atie

nt

care

per

1,0

00

po

pu

lati

on

ina

geo

gra

ph

icar

ea.

MS

A,

cou

nty

Am

eric

anM

edic

alA

sso

ciat

ion

(AM

A)

Ph

ysi

cian

Mas

ter

Fil

esco

nta

ined

inth

eA

rea

Res

ou

rce

Fil

e(A

RF

)

Nu

mb

ero

fh

osp

ital

bed

sp

erca

pit

aT

he

nu

mer

ato

ris

the

tota

ln

um

ber

of

ho

spit

alb

eds

ina

geo

gra

ph

icar

ea.

Th

ed

eno

min

ato

ris

the

tota

lp

op

ula

tio

nsi

zein

the

area

.

MS

A,

cou

nty

Am

eric

anH

osp

ital

Ass

oci

atio

nd

ata

(nu

mer

ato

r);

Cen

sus

Bu

reau

(den

om

inat

or)

HM

Op

enet

rati

on

HM

Oto

tal

enro

llm

ent

isd

ivid

edb

yth

eto

tal

po

pu

lati

on

ina

defi

ned

geo

gra

ph

icar

ea.

MS

A,

cou

nty

Inte

rstu

dy

Med

icai

dp

enet

rati

on

2002

indic

ators

are

pro

vid

edfo

rM

edic

aid

enro

llm

ent,

man

aged

care

enro

llm

ent,

and

per

cen

tin

man

aged

care

.

Sta

teC

MS

htt

p:/

/ww

w.c

ms.

hh

s.g

ov

/m

edic

aid

/man

aged

care

/mm

css0

2.a

sp

Med

icai

dm

anag

edca

rep

enet

rati

on

Th

en

um

erat

or

isth

en

um

ber

of

ind

ivid

ual

sen

roll

edin

Med

icai

dm

anag

edca

rein

fed

eral

lyq

ual

ified

com

mu

nit

yh

ealt

hce

nte

rsin

ag

eog

rap

hic

area

.T

he

den

om

inat

or

isth

eto

tal

nu

mb

eren

roll

edin

Med

icai

din

the

sam

ear

ea.

MS

A,

cou

nty

Bu

reau

of

Pri

mar

yH

ealt

hC

are

Un

ifo

rmD

ata

Sy

stem

HM

Oco

mp

etit

ion

Co

mp

etit

ion

ind

exis

calc

ula

ted

by

sub

-tr

acti

ng

fro

m1

the

sum

of

the

squ

ared

per

cen

to

fto

tal

HM

Om

ark

etsh

are

for

each

HM

Oo

per

atin

gin

ag

eog

rap

hic

area

.A

val

ue

of

1in

dic

ates

sev

eral

nea

rly

equ

alco

mp

etit

ors

;a

val

ue

clo

seto

0in

dic

ates

am

on

op

oly

.

MS

A,

cou

nty

Inte

rstu

dy


28

Tab

le1.

(con

tinu

ed)

Dom

ainan

dindicator

Variabledefinition

sGeograp

hicun

itof

observation

Datasources(exa

mples)

Safety-net

services

Nu

mb

ero

fF

QH

Cs

per

cap

ita

for

low

-in

com

ep

op

ula

tio

n

Th

en

um

erat

or

incl

ud

esth

en

um

ber

of

FQ

HC

sin

ag

eog

rap

hic

area

.T

he

den

om

inat

or

isth

en

um

ber

of

low

-in

com

e(�

20

0%

FP

L)

inth

ear

ea.

MS

A,

cou

nty

Bu

reau

of

Pri

mar

yH

ealt

hC

are

Un

ifo

rmD

ata

Sy

stem

Per

cen

to

utp

atie

nt

dep

artm

ent

vis

its

inp

ub

lic

ho

spit

als

Th

en

um

erat

or

isth

en

um

ber

of

ou

tpat

ien

td

epar

tmen

tv

isit

sin

the

pu

bli

ch

osp

ital

inth

eg

eog

rap

hic

area

.T

he

den

om

inat

or

isth

esi

zeo

fth

elo

w-i

nco

me

po

pu

lati

on

(�2

00

%F

PL

)in

the

geo

gra

ph

icar

ea.

MS

A,

cou

nty

Am

eric

anH

osp

ital

Ass

oci

atio

nd

ata

(nu

mer

ato

r);

Cen

sus

Bu

reau

(den

om

inat

or)

Per

cen

to

utp

atie

nt

dep

artm

ent

vis

its

ina

teac

hin

gh

osp

ital

Th

en

um

erat

or

isth

en

um

ber

of

ou

tpat

ien

td

epar

tmen

tv

isit

sin

the

teac

hin

gh

osp

ital

inth

eg

eog

rap

hic

area

.T

he

den

om

inat

or

isth

esi

zeo

fth

elo

w-i

nco

me

po

pu

lati

on

(�2

00

%F

PL

)in

the

geo

gra

ph

icar

ea.

MS

A,

cou

nty

Am

eric

anH

osp

ital

Ass

oci

atio

nd

ata

(nu

mer

ato

r);

Cen

sus

Bu

reau

(den

om

inat

or)

Vo

lum

eo

fse

rvic

esp

rov

ided

by

loca

lh

ealt

hd

epar

tmen

ts

Th

en

um

erat

or

isth

ev

olu

me

and

typ

eo

fse

rvic

esp

rov

ided

by

the

loca

lh

ealt

hd

epar

tmen

tin

ag

eog

rap

hic

area

.T

he

den

om

inat

or

isth

esi

zeo

fth

elo

w-i

nco

me

po

pu

lati

on

inth

ear

ea.

Sta

te,

MS

A,

cou

nty

Sta

tean

dco

un

tyh

ealt

hd

epar

tmen

ts

aS

imil

arvar

iable

sca

nbe

const

ruct

edfo

rper

centa

ges

of

Hea

lthy

Fam

ilie

sen

roll

ees

and/o

rch

ildre

nen

roll

edin

the

Sta

teC

hil

dre

n’s

Hea

lth

Insu

rance

Pro

gra

m.

bS

ince

each

stat

ese

tsdif

fere

nt

elig

ibil

ity

level

sfo

rdif

fere

nt

ages

,th

est

andar

diz

ing

ensu

res

the

dif

fere

nce

sin

the

index

are

not

bas

edon

dif

fere

nce

sdue

toag

est

ruct

ure

of

the

popula

tion,

but

rath

erdue

tovar

iati

ons

inth

eel

igib

ilit

yin

dex

wit

hin

anag

egro

up.T

he

index

mea

sure

show

likel

ya

per

son

of

ace

rtai

nag

eis

tobe

elig

ible

for

Med

icai

din

apar

ticu

lar

stat

ere

lati

ve

tooth

erst

ates

.


29

Similar to the Brown et al. study (2004), whichappears in this issue of Inquiry and applies theconceptual framework, other researchers willneed to apply an interactive model to better un-derstand how population, delivery system, andpolicy and financing variables influence accessof individuals with and without health insurancecoverage. A major consideration is the appro-priate unit of observation for constructingcommunity-level variables. The research andevaluation questions guide the selection of con-textual variables and unit of observation. Ideally,safety-net population variables are constructed atthe local level (e.g., county, sub-county, censustract or census block) to capture differences in so-cial and economic conditions in a geographic ar-ea. But reliable data are not always available toquantify the characteristics and extent of vulner-able populations. For example, evaluating accessto medical care among homeless people is prob-lematic because they are transient, having no per-manent residence in a geographic location. Onthe other hand, AIDS incidence rates are avail-able at the county level and can be used to quan-tify and compare the size of this vulnerablesubgroup residing in geographic areas and the ul-timate effects on access and access outcomes.

In summary, the framework suggests thatsafety-net population variables influence accessdepending on the percentages of low-income in-sured, uninsured, and vulnerable subgroups. Inlarge part, the size of the uninsured populationis determined by such factors as the generosityof public programs, the extent of employer-spon-sored health insurance, and the general economicand market conditions in the community. Further,the magnitude of the low-income population re-siding in a geographic location translates to moreor less competition for limited safety-net servicesfor individuals seeking medical care access. Thenext section provides the rationale for including‘‘support’’ variables in the conceptual framework(i.e., the resources available to directly and indi-rectly pay for medical care for low-income popu-lations [Figure 1]).

Public Policy Support for Safety-Netand Low-Income Populations

Essentially, support for the medically indigentvaries on a community-by-community basis andis influenced broadly by federal legislation andmore directly by state and local health policy

and financing (Rajan 1998; Cunningham andKemper 1998).

Medicaid eligibility and payment levels are es-tablished based on congressionally mandatedminimums and each state’s individual decisions.Medicaid has been a major source of funding forsafety-net providers, but the stated goal of Med-icaid managed care was to reduce expendituresover time by gradually reducing capitation pay-ments as a percentage of annual per capita costs(Siegel 1996). A twofold variation has been re-ported in what states pay managed care plans, at-tributed to fee-for-service reimbursement levelsand demographics (Holahan, Rangarajan, andSchirmer 1999). States vary considerably in thenumbers of people they cover and the amountsthey spend on services (Holahan and Liska1997b). Some states have higher health risksand others have more expensive resource utiliza-tion patterns, not necessarily related to the mix ofhealth problems (Bronstein and Adams 1999;Adams and Bronstein 1999). One study by Co-hen (1993) found that low Medicaid fees hamperaccess to office-based physicians and encourageuse of hospital outpatient departments and emer-gency rooms. Although support for Medicaidlanguished in the 1990s, more recent data showthat the growth in Medicaid capitation expendi-tures between 1998 and 2001 averaged 18%(Holahan and Suzuki 2003). These increases inMedicaid managed care expenditures have beenaccompanied by a 2002 initiative launched bythe Bush administration that will add a projected1,200 new and expanded health center sites, andincrease the number of people served annuallyfrom about 10 million to an estimated 16 millionby 2006 (HRSA 2002).

Medicaid eligibility levels influence access tomedical care for both the low-income insuredand uninsured populations by influencing thesubsequent size of the uninsured population ina geographic area. The same is true for other fed-eral and state-funded government programs, suchas SCHIP. Historically, Medicaid payments havebeen used not only to cover program beneficia-ries but also to subsidize uncompensated carefor the uninsured and to support the safety net.Not-for-profit and public hospitals and federallyqualified community and migrant health centershave used Medicaid and Medicare payments tosubsidize care for vulnerable subgroups not en-rolled in the programs (Davidoff et al. 2000;


30

Davis, Collins, and Hall 1999; Gaskin and Had-ley 1997; Lillie-Blanton and Rowland 1996).

Medicaid disproportionate share hospital(DSH) expenditures are available to supportsafety-net services in each state (CMS-64 AnnualReport).1 Federal DSH payments through Medic-aid and Medicare have been the primary methodfor states to directly subsidize safety-net hospi-tals, paying for nearly 30% of unreimbursed care,with state and local subsidies covering another60% of this care (Fagnani and Tolbert 1999;Fishman and Bentley 1997). But DSH paymentsare often determined through complex mecha-nisms (Holahan and Liska 1997b), not directlyrelated to provision of services and care for thepoor (Gaskin 1999), and some states have beencriticized for using DSH payments in ways Con-gress never intended (Fagnani and Tolbert 1999).This has prompted Congress to revise the DSHprogram three times since 1991 (Coughlin andLiska 1998; Holahan and Liska 1997b).

A survey of 40 states examining Medicaid DSHand supplemental payments showed that while theoverall size of the DSH program did not growfrom 1993 to 1997, the composition of DSH rev-enues and expenditures changed substantially. Ahigher share of DSH funds was paid to local hos-pitals and relatively less was retained by the states(Coughlin, Ku, and Kim 2000). The BalancedBudget Act of 1997 reductions in Medicaid andMedicare payments were expected to lead to fur-ther reductions in DSH funds and related bad debtand charity pools (Norton and Lipson 1998;Coughlin and Liska 1998). States have respondedin a variety of ways. In Indiana, the financial sta-bility of urban public Level I trauma centers hasbeen described as tenuous due to diminishedDSH funds and tax dollars and high proportionsof uninsured and underinsured patients (Selzeret al. 2001). In California (1997–99 data), publichospitals were able to increase their profits frompediatric and neonatal intensive care units (ICUs),and DSH hospitals located in markets with highHMO penetration reduced their operating lossesin nursery and pediatric services (McCue 2002).In Colorado, legislators responded to the reduc-tions in DSH payments by creating a Medicaid re-financing strategy so that major teaching hospitalscould enhance their teaching programs and at thesame time meet the needs of low-income patients(Vancil and Shroyer 1998).

Managed care capitation in a competitive com-

mercial market also restricts hospitals’ ability toshift costs to private payers. At the same time,subsidies from local and state governments topay for indigent care are declining, making it moredifficult for safety-net organizations to care for theuninsured (Fagnani and Tolbert 1999; Norton andLipson 1998; Cunningham and Tu 1997). Increas-ingly, communities have relied on local charityand foundation funding to create a patchwork ofservices and programs to provide care for vulner-able populations (Brown and Dallek 1990; Davis,Collins, and Hall 1999; Felland and Lesser 2000).In addition to state and local grants, Medicaid, andMedicare, other funding for community healthcenters is available through private insurance, pa-tient fees, and annual grants from the Health Re-sources and Services Administration (HRSA).Many safety-net hospitals would not be econom-ically viable without large public subsidies be-cause of the gap between operating revenue andoperating expenses. The size of the gap variessubstantially among geographic areas, as doesthe extent to which it is covered by local, state,or federal funding sources (Meyer et al. 1999).

Many are concerned that care for uninsuredpeople and the viability of the safety net are injeopardy. This is due to welfare reform, changingeligibility levels, restrictions on entitlement ofnoncitizens, changes in Medicaid payments,Medicaid managed care competitive contracting(Lipson and Naierman 1996; Norton and Lipson1998; Ellwood and Ku 1998; Baxter and Feldman1999; IOM 2000), and most recently the down-turn in the U.S. economy and diversion of federalfunds to other priorities. The Institute of Medi-cine (IOM 2000) and others have emphasizedthe importance of community attitudes in shapinggovernment’s response to providing access forthe low-income uninsured, although few empiri-cal studies were found on the topic (Baxter andFeldman 1999; Baxter and Mechanic 1997).

As suggested in the framework (Figure 1),community-level variables can be constructed tooperationalize support variables as ‘‘low-incomepopulationsupport’’ and‘‘safety-net support’’vari-ables. Table 1 suggests support variables, vari-able definitions, geographic unit of observation,and data sources for constructing each. Examplesof support variables include Medicaid eligibilitylevels and Medicaid payments. Medicaid eligibil-ity levels, which benefit the low-income insuredpopulation, are measured by constructing a


31

‘‘Medicaid generosity index,’’ using state-leveldata from the National Governors Association(Brown et al. 2004). Medicaid payments per re-cipient and/or Medicaid managed care paymentsper recipient can be constructed to measure re-sources expended through HRSA-funded feder-ally qualified health centers. State indicators ofMedicaid spending per enrollee excluding long-term care can be constructed using data fromthe Centers for Medicare and Medicaid Services(CMS-2082 and CMS-64 Annual Report).

The framework suggests both Medicaid bene-ficiaries and low-income uninsured individualsstand to benefit in areas where state Medicaidpayments are higher—the latter because safety-net providers have greater flexibility to subsidizecare for the uninsured. However, when Medicaidmanaged care has greater penetration in a marketarea and payments per recipient are lower, safety-net providers will have fewer resources to subsi-dize care for low-income uninsured individuals,leading to measurable declines in access for theuninsured, as well as for Medicaid beneficiaries.

Safety-net support variables, including grantsawarded annually by HRSA to the federally qual-ified health centers, can be constructed as dollarsper capita using the Bureau of Primary HealthCare Uniform Data System. Medicaid DSH ex-penditures are available for each state. However,tracking the funds distributed at the MSA orsmaller geographic area is more challenging, re-quiring telephone interviews with state and localhealth officials and hospital associations. No sys-tematic data have been collected on funding pro-vided to safety-net providers from state and localgovernment grants and/or grants from local char-ities and foundations to subsidize care for the un-insured. The majority of research results onsafety-net support variables have emerged fromqualitative studies (Holahan, Weiner, and Wallin1998a; Katz and Thompson 1996; Ullman, Bruen,and Holahan 1998; Meyer et al. 1999; Rajan 1998;Coughlin and Liska 1998; Gold, Sparer, and Chu1996; Summer 1998; Norton and Lipson 1998;Baxter and Feldman 1999; Lipson and Naierman1996; Baxter and Mechanic 1997; Steinberg andBaxter 1998), with few multivariate studies to cor-roborate findings. Efforts to strengthen the resultsusing multivariate methods will require furthermethodological work to construct quantifiable in-dicators for support variables. Collecting andmonitoring this information could prove to be ex-

pensive and time consuming. Nevertheless, policyresearchers should creatively and rigorously at-tempt to construct support variables so data-driv-en policy and management interventions can bebased more fully on evidence.

Health Care Market and Safety-Net Services

Figure 1 shows that the final categories of com-munity-level variables are ‘‘health care market’’and ‘‘safety-net services,’’ which influence ac-cess in a geographic area. This section explainsthe rationale for including delivery system varia-bles in the conceptual framework. These marketand delivery system factors influence the medicalcare access of the general population, as well asthose who depend on safety-net services (Figure1). Health care market variables present the gen-eralized depiction of the delivery system in a de-fined geographic area (e.g., physician supply,number of hospital beds per capita, and managedcare penetration and competition). While themajority of middle-income and affluent Ameri-cans use mainstream medical care providers, thesafety net serves a disproportionate number ofpoor and uninsured people (IOM 2000; Cunning-ham and Tu 1997; Lefkowitz and Todd 1999).

As discussed previously, the Institute of Medi-cine defines core safety-net providers (IOM 2000)as those who organize and deliver a significantlevel of health care and other related services touninsured, Medicaid, and other vulnerable pa-tients. In most communities, there is a subset of‘‘core safety-net providers’’ that show two distin-guishing features: 1) either by legal mandate or ex-plicitly adopted mission, they maintain an ‘‘opendoor,’’ offering access to services for patients re-gardless of their ability to pay; and 2) a substantialshare of their patient mix is uninsured, Medicaid,and other vulnerable patients.

The core safety-net providers delivering careto low-income populations include a varyingmix of public hospitals, urban teaching hospitals(Gaskin 1999; Gaskin and Hadley 1997; Siegel1996; NAPH 2001), not-for-profit hospitals witha charitable care mission (Baxter and Feldman1999), physician offices (Forrest and Whelan2000), federally qualified and other communityhealth centers (Davis, Collins, and Hall 1999;NACHC 2001), and local health departments(Wall 1998; Martinez and Closter 1998). Forrestand Whelan (2000), in a recent study of accessin different provider organizations using three


32

national databases, concluded that: 1) expandingcommunity health centers likely will improve ac-cess to primary care for vulnerable populations;2) enhancing access to physicians’ offices isneeded to bolster the safety net; and 3) the greaterservice intensity and poorer continuity for pri-mary care in hospital outpatient departments raiseconcerns about the suitability of these clinics asprimary care sites.

Market dynamics influence access as well.Intense price competition arising from com-mercial managed care penetration and the rap-id growth of Medicaid managed care havecontributed to an increasingly hostile healthcare environment. Competitive bidding forMedicaid contracts became increasingly attrac-tive to the for-profit sector, particularly inareas with high managed care penetration.When commercial contractors are grantedMedicaid contracts, safety-net providers standto lose market share (Holahan, Weiner, andWallin 1998b; Gaskin 1998; Davis, Collins,and Hall 1999; Siegel 1996). Higher commer-cial HMO penetration has been associated withlower patient volumes in minority hospitals(Gaskin 1997), and smaller profit margins withless excess in operating budgets to subsidizecare for uninsured people (Cunningham 1999;Center for Studying Health System Change1998, 1999; Lillie-Blanton and Rowland1996; Cunningham et al. 1999).

However, due to declines in financial perfor-mance, evidence has shown commercial plans areexiting the Medicaid market in many states(McCue et al. 1999; Gray and Rowe 2000).Health plans also have been created by safety-net organizations, consisting of community healthcenters, public hospitals, and urban teaching hos-pitals (Gray and Rowe 2000; Baxter and Feldman1999; McCue et al. 1999; Holahan, Rangarajan,and Schirmer 1999). Unfortunately, Gray andRowe (2000) recently concluded even the moststable safety-net health plans live on the edge offinancial disaster—in 1997, 60% lost money,8% broke even, and only 32% generated a surplus.

Urban Institute researchers have used datafrom case study interviews to develop typologiesthat characterize the network of safety-net pro-viders at the community level (Holahan, Weiner,and Wallin 1998a; Norton and Lipson 1998). Butthese are largely descriptive case studies thatshould be supplemented with multivariate studies

to ascertain the extent to which the structure ofprovider networks affect safety-net performanceand access. The Figure 1 framework can be usedto construct a consistent set of quantifiable vari-ables reflecting the health care market and safety-net services in a geographic area. As noted earlier,health care market variables include physiciansupply, hospital beds per capita, HMO penetrationand competition, and Medicaid managed carepenetration. Suggested safety-net services vari-ables are the number of federally qualified healthcenters per capita for the low-income population,percentage of outpatient department visits in pub-lic and teaching hospitals, and services utilized inlocal health departments. However, no systematicdatabase exists on the extent of services providedto low-income patients by private physician offi-ces. Physician supply is at best a crude indicator;a better indicator would be the number of primarycare physicians who accept new Medicaid pa-tients, but such a variable currently is not readilyavailable. At the moment, these data would be ex-pensive to construct and beyond the scope of thepresent study. In the future, as states and associa-tions within states become more sophisticated incollecting industry data, structural informationmay become more available on HMOs, hospitals,health systems, and physician supply.

Health Care Access and Outcomes

Access indicators measure potential and realizedentry into the primary care system. Potential ac-cess is indicated by the enabling variables. Moreenabling resources provide the means for, andincrease the likelihood that use will take place(Andersen and Davidson 2001). Indicators of po-tential access include having a regular source ofmedical care and health insurance coverage (Fig-ure 1). Realized access is the actual use of ser-vices. Realized access dependent variables focuson entry into the primary care system, such asactual utilization of physician services, delay inseeking treatment, and late or no prenatal care(Figure 1). In this issue of Inquiry, Brown andcolleagues (2004) investigate the communityand individual determinants of access to ambula-tory care predicting potential (having a regularsource of care) and realized (at least one doctorvisit in the past 12 months) access among lower-income adults residing in 54 large urban areas.

Access outcomes measure the extent to whicheffective and efficient access is achieved once the


33

individual enters the system. The Institute ofMedicine Committee on Monitoring Access toMedical Care defined access as: the timely useof personal health services to achieve the bestpossible health outcomes (IOM 1993). This def-inition relies both on the use of health servicesand health outcomes as yardsticks for judgingwhether access has been achieved (Andersenand Davidson 2001). The resulting measuresare referred to as effective access. Indicators ofefficient access are similar to measures of effec-tive access with the added emphasis on measur-ing resources used to influence outcome.Efficiency is achieved by improving health out-comes, while minimizing resource requirements(Andersen and Davidson 2001). Examples ofaccess outcomes include preventable hospitaliza-tion rates and relative risks (Billings, Anderson,and Newman 1996; Bindman et al. 1995; Gaskinand Hoffman 2000), hospital use rates for ambu-latory care sensitive conditions (Billings et al.1993; Friedman et al. 1999; Laditka and Laditka1999; Bierman et al. 1999), and hospital use ratesfor high-risk and complicated deliveries (Laditkaand Johnson 1999).

Discussion and Conclusions

This paper suggests a framework for analyzingthe community determinants of access. Theframework builds on the extensive research pio-neered by Andersen and colleagues over the pastseveral decades. Similar to the approach theyused to validate the ‘‘individual’’ predictors of ac-cess, this paper suggests applying a comprehen-sive conceptual framework including a uniformset of variables to test and validate the ‘‘commu-nity’’ determinants of access. The frameworksuggests community variables differentially af-fect access of low-income insured versus low-in-come uninsured populations. The frameworkserves as a guide for constructing contextual vari-ables hypothesized to influence access. Datasources are suggested and guidance provided onhow to construct contextual variables includingthe most appropriate geographic unit of observa-

tion. The paper discusses data gaps in measuringsafety-net and other community variables be-lieved to influence access.

In the research literature, ‘‘safety-net popula-tion’’ characteristics have been found to be themost widely measured and analyzed in multivari-ate studies. The majority of research results onpublic policy ‘‘support’’ variables have emergedfrom descriptive and case study methods, withfew multivariate studies to corroborate findings.Without a doubt, most data gaps exist for the‘‘support’’ variables because funding to supportsafety-net providers is complex and varies foreach safety-net provider, community, and state.Other than Medicaid funding, systematic datamay not be readily available on other state andlocal government and private funding sources.Nevertheless, strategies are suggested for opera-tionalizing support variables to advance the re-search (Table 1). The framework suggests thatmore comprehensive measures of ‘‘health caremarket’’ and ‘‘safety-net services’’ can be con-structed to evaluate access for low-income in-sured, uninsured, and vulnerable populations.The adverse effects of market competition andmanaged care penetration on access, whethercommercial or Medicaid managed care, have beenreported in the literature. However, the most re-cent MSA-level study examining 22 major U.S.cites reported that higher HMO penetration rateswere associated with fewer access problems (Hen-dryx et al. 2002). One unique contribution of theHendryx et al. study was that it included ‘‘socialcapital’’ variables in the analysis, showing thatpeople who live in MSAs featuring higher levelsof social capital report fewer access problems.

The comprehensive and systematic approachsuggested by the framework will enable policyresearchers to strengthen the external validity ofresults by accounting for the influence of a consis-tent set of contextual factors across locations andpopulations. A subsequent article in this issue ofInquiry applies the framework to investigate ac-cess to ambulatory care for low-income adults,both insured and uninsured (Brown et al. 2004).

Notes

This study was funded by grant no. 036499 from theRobert Wood Johnson Foundation to the UCLA Cen-ter for Health Policy Research as part of the Safety NetAssessment Project (SNAP). Drs. E. Richard Brown of

UCLA, John Billings of New York University, and JoelCantor of Rutgers University were principal investiga-tors of the SNAP. The authors are grateful to the In-quiry reviewers who critiqued earlier versions of this


34

manuscript. Their comments contributed to major im-provements in the article, especially related to the ef-fects of contextual variables on low-income insuredand uninsured populations.

1 The Quarterly Medicaid Statement of Expendituresfor the Medical Assistance Program (Form CMS-

64) is the accounting statement which states, in ac-cordance with 42 CFR 430.30(c), must submit eachquarter under title XIX of the Social Security Act(the Act). It shows the disposition of Medicaid grantfunds for the quarter being reported and previousfiscal years, the recoupment made or refunds re-ceived, and income earned on grant funds.

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