Pamela L. Davidson
Ronald M. Andersen
Roberta Wyn
E. Richard Brown
A Framework for
Evaluating Safety-Net and
Other Community-Level
Factors on Access for
Low-Income Populations
The framework presented in this article extends the Andersen behavioral model of health
services utilization research to examine the effects of contextual determinants of access. A
conceptual framework is suggested for selecting and constructing contextual (or
community-level) variables representing the social, economic, structural, and public
policy environment that influence low-income people’s use of medical care. Contextual
variables capture the characteristics of the population that disproportionately relies on the
health 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 and
few multivariate studies comprehensively investigated the contextual determinants of
access. The comprehensive and systematic approach suggested by the framework will
enable researchers to strengthen the external validity of results by accounting for the
influence of a consistent set of contextual factors across locations and populations. A
subsequent article in this issue of Inquiry applies the framework to examine access to
ambulatory care for low-income adults, both insured and uninsured.
For more than three decades, health services researchers have investigated the factors that determine an individual’s access to medical care.
These factors, conceptualized as predisposing,
enabling and need, have been used routinely to
predict health services utilization and to identify
characteristics of people having more or less access (Andersen 1968, 1995). Thus, the underlying theory and supportive empirical evidence
are well established for understanding an individual’s determinants of access. However, multivariate models containing only individual-level
variables are limited. Most researchers have
failed to comprehensively account for the effects
of contextual variables reflecting the social, economic, structural, and public policy environment
in which access occurs. Previously, Andersen
and colleagues and other researchers published
foundational work in this area, applying a measurement model to investigate ‘‘contextual’’ or
community-level determinants of access (Andersen et al. 2002; Cunningham 1999; Cunningham
et al. 1999; Grumbach, Vranizan, and Bindman
1997; 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 the
UCLA Center for Health Policy Research. E. Richard Brown, Ph.D., is the director of the UCLA Center for Health Policy
Research and a professor in the UCLA School of Public Health. Funding for this research came from the Robert Wood Johnson Foundation. Address correspondence to Prof. Davidson at the UCLA School of Public Health, Department of Health
Services, 650 C.E. Young Drive South, Campus Box 951772, Los Angeles, CA 90095-1772. Email: Davidson@ucla.edu
Inquiry 41: 21–38 (Spring 2004). Ó 2004 Excellus Health Plan, Inc.
0046-9580/04/4101–0021
21
Inquiry/Volume 41, Spring 2004
and Marquis 1999; Lave et al. 1998; Szilagyi et
al. 2000; Gaskin and Hoffman 2000; Laditka
and Laditka 1999; Laditka and Johnson 1999;
Bierman et al. 1999; Roblin 1996; Billings, Anderson, and Newman 1996; Bindman et al.
1995; Billings et al. 1993; Friedman et al. 1999).
This article delineates a more comprehensive
range of contextual variables and empirical
sources for constructing contextual variables. As
an additional contribution, the study distinguishes
the effects of contextual variables separately for
low-income insured and low-income uninsured
populations. The framework suggests the effects
of public policy and financing variables and how
their influence on provider organizations’ delivery
of health services leads to different access outcomes for the insured and the uninsured.
Contextual variables for a defined geographic
area (e.g., county, metropolitan statistical area
[MSA] or state) measure the environment or
milieu in which access occurs (Phillips et al.
1998; Andersen and Davidson 2001). The external validity of findings can be strengthened by
applying a comprehensive framework and a consistent set of contextual variables to investigate
access across locations and populations.
The first objective of this research is to propose
a conceptual framework for comprehensively and
systematically explaining how individual and
contextual variables influence access and access
outcomes. As stated previously, the framework
suggests contextual variables differentially affect
access of low-income insured versus low-income
uninsured populations. Second, the framework
serves as a guide for constructing contextual
variables hypothesized to influence access. The
paper concludes by identifying data gaps in
measuring safety-net and other contextual variables believed to affect access.
Summary of the Framework
Figure 1 presents a framework for evaluating the
effects of safety-net and other community-level
factors on access and access outcomes. The
framework consists of three broad domains and
related sub-domains. Individual characteristics,
categorized as predisposing, enabling, and need
factors, are the well-established predictors of access. Community-level variables capture the
characteristics of the low-income and safety-net
populations, the structure of the health care market and safety-net services in a geographic area,
22
and public policy support for providing services
to low-income populations, both insured and uninsured. Health care access and outcomes measure potential and realized entry into the medical
care system, and the results of access in terms of
effective and efficient medical care delivery.
Individuals' Determinants of Access
Individual-level characteristics are categorized as
Andersen’s (1968, 1995) predisposing, enabling,
and need variables. The model suggests people’s
use of health services is a function of: their predisposition to use or not use services, even though
these predisposing characteristics are not directly
responsible for use; enabling conditions that facilitate or impede the use of services; and need,
or conditions recognized by lay people or health
care providers as requiring medical treatment.
More than three decades of research on these
factors have resulted in a substantial body of empirical findings that are generalizable across locations and populations. We are not proposing
changes to the set of individual determinants.
The literature on predisposing characteristics
has shown decreased access to medical care for
people with lower educational attainment, the unemployed, ethnic minority groups, especially recent immigrants who speak a language different
from the mainstream providers, and people with
certain health beliefs (Hulka and Wheat 1985;
Guendelman 1991; Portes, Kyle, and Eaton
1992; Becker and Maiman 1983; Mechanic 1979;
Tanner, Cockerham, and Spaeth 1983; Forrest
and Whelan 2000; Davis, Collins, and Hall,
1999; Leigh et al. 1999). Enabling characteristics
associated with under-utilization of medical care
include lower household income, having no regular source of medical care, and no health insurance coverage (Andersen and Newman 1973;
Manning et al. 1987). Furthermore, economically and socially disadvantaged people are more
likely to experience medical symptoms that may
not be treated in a timely and effective manner
due to less access (Aday and Andersen 1974; Andersen, Kravits, and Anderson 1975; Aday, Andersen, and Fleming 1980; Aday et al. 1985).
After testing and validating results over several
decades, definitive information has accumulated
on the individual predictors of access. Empirical
research has shown individual characteristics account for an estimated 20% to 25% of the variance explained, depending on the access-related
Access for Low-Income Populations
Figure 1. Conceptual framework for evaluating safety net and other community-level
factors on access and access outcomes.
variable and characteristics of the study population. Yet, a substantial portion of unexplained
variance remains.
Similar to the approach Andersen and colleagues have used to validate ‘‘individual’’ predictors of access, we suggest applying
a comprehensive conceptual framework including a uniform set of variables to test and validate
the ‘‘community’’ determinants of access. Such a
strategy will lead policy researchers to the generalizable community determinants affecting access across populations and locations, including
factors that may be altered by policy change.
From this research, new strategies will emerge
for developing evidence-based policy and management interventions to improve access, and
a more effective use of public resources.
Community Determinants of Access
Community variables capture the social, economic, structural, and public policy environment
in which access occurs. Specifically, the framework (Figure 1) suggests variables measuring
the characteristics of the safety-net population
(insured and uninsured), 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. Other variables that can be considered in the framework affect more affluent residents—for instance, the
number of hospitals and hospital beds per 1,000
population, the number of physicians per 1,000
population, and economic measures, such as the
unemployment rate in a geographic area.
Safety-Net Population
The safety-net population consists of three broad
subgroups of individuals who reside in a geographic location and disproportionately depend
on the safety net (IOM 2000): 1) uninsured people, 2) Medicaid beneficiaries, and 3) vulnerable
populations.
Geographic variation (defined as differences in
access by location, for example, state, MSA or
county) in health insurance coverage has been reported for low-income adults and children
(Brown, Wyn, and Teleki 2000; Long and Marquis 1999; Cunningham and Kemper 1998), for
low-income families (Cantor, Long, and Marquis
1998; Zuckerman et al. 1999), and in the percentages of uninsured people reporting difficulty obtaining medical care (Cunningham and Kemper
1998). MSAs with higher than average uninsurance rates all have large concentrations of immi23
Inquiry/Volume 41, Spring 2004
grants, including naturalized citizens and noncitizens; these groups are particularly high in Arizona, California, Florida, New Jersey, New
York, and Texas (Brown, Wyn, and Teleki
2000). Geographic areas with the highest uninsurance rates also have the lowest rates of employer-based coverage, higher unemployment
and poverty rates, and larger proportions of the
population in single-parent households (Brown,
Wyn, and Teleki 2000; Holahan, Weiner, and
Wallin 1998a). In many communities, having access to employer-sponsored insurance may be
more important than the generosity of publicly
sponsored programs in determining the proportion of the population that is uninsured. Income
or poverty level is often the most important demographic predictor of uninsurance; however,
since race/ethnicity and citizenship are correlated
with income, sorting out the effects among the
community-level variables can be challenging
(Marsteller et al. 1998; Weiner and Malakar
1999). Multivariate results examining individuallevel race/ethnicity generally have found disparities in access for nonwhite people. By
evaluating the community effects of race/ethnicity (e.g., percentage black or percentage Hispanic), researchers will be able to ascertain how
residence in a community can either serve as a resource or result in a barrier to an individual’s
medical care access.
Similarly, among low-income insured residents, we observe geographic variation in the
percentages of individuals covered by programs
such as Medicaid, and the State Children’s
Health Insurance Program (SCHIP). The eligibility criteria for these public programs vary by state
and depend on the availability of federal and state
funds to pay for medical care for low-income
people (Norton and Lipson 1998; Brown, Wyn,
and Teleki 2000; Bindman et al. 2000; LillieBlanton and Lyons 1998; Bronstein and Adams
1999; Adams and Bronstein 1999). Because of
the categorical and episodic nature of Medicaid
eligibility and changes in state regulations and financing, individuals cycle on and off insurance,
often with long spells of no insurance (Short
and Graefe 2003; Short, Graefe, and Schoen
2003; IOM 2000; Kasper, Giovannini, and Hoffman 2000; Schoen and DesRoches 2000; Brown
et al. 1999; Short 1998; Davis 1996; Short and
Lefkowitz 1992; Short, Cornelius, and Goldstone
1990; Short, Cantor, and Monheit 1988). This not
24
only reduces the continuity of medical care, but
also places greater demand on local safety-net
providers.
Vulnerable subgroups represent a third category of the safety-net population. Vulnerable
populations are those who are economically
disadvantaged and whose social or health characteristics increase their risks and need for assistance. Vulnerable subgroups include: adults and
children with disabilities; the frail elderly and disabled Medicare beneficiaries; low-income children, pregnant women and adolescents; the
mentally ill; substance abusers; HIV/AIDS patients; and the homeless (IOM 2000; Holahan
and Liska 1997a; Baxter and Mechanic 1997;
Davidson 2004).
Thus, the size and characteristics of the safetynet population vary by geographic area, and
these variations affect access and access outcomes. When large groups of low-income people
reside in a geographic area, whether they are
low-income insured, uninsured, or vulnerable
subgroups, they must compete for limited safetynet services and resources. In such high-demand
communities, resources may be quickly stretched
far beyond what the tax base can reasonably support through special programs or subsidies to
public hospitals and clinics (Holahan, Weiner,
and Wallin 1998a.). Unlike privately insured patients, low-income uninsured people and those
with Medicaid or other public coverage have little financial leverage, few enabling resources,
and largely depend on subsidies to obtain care
when medical symptoms arise. Providers may
not have the incentive or capacity to respond to
their demands and needs.
Community-level variables can be constructed
to estimate the magnitude of low-income insured, uninsured, and vulnerable subgroups expressed as a percentage of the total population
residing in a geographic area. Table 1 provides
examples of community-level variables affecting
access, and includes safety-net population variables, variable definitions, geographic unit of
observation, and suggested data sources for constructing each. The table also shows percentages of low-income uninsured (e.g., nonelderly
uninsured, employed, and uninsured), insured
(e.g., Medicaid and SCHIP enrollees), and vulnerable populations (e.g., female single-headed
households, AIDS incidence rates, immigrant
noncitizens, homeless).
Table 1. Constructing community determinants of access and access outcomes
Domain and indicator
Variable definitions
Geographic unit
of observation
Data sources (examples)
Safety-net population
Percent low-income,
nonelderly uninsured
Percent low-income,
employed, and uninsured
Percent Medicaida
Percent female single-headed
households
State, Metropolitan
Statistical Area
(MSA), county
Bureau of the Census, March Current
Population Survey, National Health
Interview Survey, California Health
Interview Survey
State, MSA, county
March Current Population Survey,
National Health Interview Survey,
California Health Interview Survey
State, MSA, county
Bureau of the Census, March Current
Population Survey, National Health
Interview Survey, California Health
Interview Survey
State, MSA, county
Bureau of the Census, March Current
Population Survey
State, MSA, county
Centers for Disease Control and
Prevention (CDC)
25
Access for Low-Income Populations
AIDS incidence rates
The numerator for the variable includes
those with no reported coverage of
any kind during the year, who are
also nonelderly (<65 years) and
low-income (<250% federal
poverty level [FPL]) in a
defined geographic population.
The denominator includes
the total area population.
The numerator for the variable includes
low-income (<250% FPL), employed
persons who are also uninsured. The
denominator is the total population in
the defined geographic area.
The number of individuals ages 0–64,
who are both low-income and
respond that they had Medicaid
any time in the past 12 months, is
calculated for each geo-area. The
denominator is the total population in
the defined geographic area.
The numerator for the variable includes
the number of unmarried females
(age 181) with children in the
household. The denominator includes
the total population of households
with children residing in a defined
geographic area.
The numerator for the variable includes
the number of individuals testing
positive with HIV/AIDS in the state,
MSA, or county population. The
denominator includes the total
population for the defined geographic
area.
Geographic unit
of observation
Domain and indicator
Variable definitions
Percent immigrant
noncitizens
<5 years in U.S.
5 to 10 years in U.S.
>10 years in U.S.
The numerator for the variable includes
the number of immigrant noncitizens
present in the state or MSA
population. The denominator
includes the total population for the
defined geographic area.
The numerator for the variable includes
the percent of homeless individuals in
a defined geographic area. The
denominator includes the total
population for the defined
geographic area.
State, MSA, county
March Current Population Survey,
National Health Interview Survey,
California Health Interview Survey
City, county, MSA
Data source not available
Medicaid generosity index is calculated
based on each state’s individual
decisions regarding where to set
eligibility levels in a given year for its
own population given
congressionally mandated
minimums. The index is an aggregate
of income eligibility levels for the
state, weighted for each particular age
group’s proportion within an applied
standardized population.b
Constructed indicator showing 1997
Medicaid per capita payments for all
Medicaid recipients and separately
for American Indians/ Alaskan
Natives
State
National Governors Association;
variable constructed by the UCLA
Center for Health Policy Research
State
Centers for Medicare & Medicaid
Services (CMS) – CMS 2082 and
CMS 64 forms
Calculations were provided by
the Indian Health Service
http://www.ihs.gov/
nonmedicalprograms/lnf/
medicaidpc.htm
Refer also to: Finance Working Group
Report: The Lewin Report on
Medicaid Payments to Hospitals and
Related Issues
Percent homeless
Data sources (examples)
Low-income population support
Medicaid generosity indexa
Medicaid payments
per enrollee
Inquiry/Volume 41, Spring 2004
26
Table 1. (continued)
Table 1. (continued)
Geographic unit
of observation
Domain and indicator
Variable definitions
Managed care payments
per enrollee
Table 9C in the Uniform Data
System, ‘‘Managed Care Revenue
and Expenses,’’ provides a breakdown
of revenues and expenses by payer
category Medicaid, Medicare, other
public, private, and total. The
numerator can be constructed from
this table. The denominator is the
number of patients served at the federally
qualified health centers (FQHCs)
located in a defined geographic area.
Another option would be state
indicators of average monthly
Medicaid managed care rates
(Holahan, Rangarajan, and
Schirmer 1999).
State, MSA, county
Bureau of Primary Health Care
Uniform Data System
Data sources (examples)
The numerator is annual dollars
provided to a community health
center to subsidize care for
low-income uninsured populations
served. The denominator is the
number of low-income uninsured
persons residing in the defined
geographic location.
The numerator includes expenditures
available to directly subsidize safety-net
hospitals in a geographic area.
The denominator is the
total population of uninsured
persons, 0–64, residing in the
geographic area.
Data not available
Federally qualified
health centers in a
state, MSA, county
Bureau of Primary Health Care
Uniform Data System
State
CMS-64 Annual Report
MSA, county
Data source is not available; would
require telephone interviews with
state and local health officials and
hospital associations.
Safety-net support
Per capita expenditures
awarded by HRSA to
FQHCs
DSH payment rates
27
Access for Low-Income Populations
Disproportionate share
hospital (DSH) payment
rates
Domain and indicator
State and local grants,
and grants from local
charities and foundations
Variable definitions
Geographic unit
of observation
Data sources (examples)
The numerator would be the extent of
government and philanthropic
funding to provide charity care in
a defined geographic area. The
denominator would be the number of
low-income uninsured persons
residing in the geographic area.
State, MSA, county
Comparable data are not collected
systematically.
The variable measures the number of
non-federally employed MDs who
provide total patient care per 1,000
population in a geographic area.
The numerator is the total number of
hospital beds in a geographic area.
The denominator is the total
population size in the area.
HMO total enrollment is divided by the
total population in a defined
geographic area.
2002 indicators are provided for Medicaid
enrollment, managed care enrollment,
and percent in managed care.
The numerator is the number of
individuals enrolled in Medicaid
managed care in federally qualified
community health centers in a
geographic area. The denominator is
the total number enrolled in Medicaid
in the same area.
Competition index is calculated by subtracting from 1 the sum of the squared
percent of total HMO market share for
each HMO operating in a geographic
area. A value of 1 indicates several
nearly equal competitors; a value
close to 0 indicates a monopoly.
MSA, county
American Medical Association (AMA)
Physician Master Files contained in
the Area Resource File (ARF)
MSA, county
American Hospital Association data
(numerator); Census Bureau
(denominator)
MSA, county
Interstudy
State
CMS http://www.cms.hhs.gov/
medicaid/managedcare/mmcss02.asp
MSA, county
Bureau of Primary Health Care Uniform
Data System
MSA, county
Interstudy
Health care market
Physician supply
Number of hospital beds
per capita
HMO penetration
Medicaid penetration
Medicaid managed care
penetration
HMO competition
Inquiry/Volume 41, Spring 2004
28
Table 1. (continued)
Table 1. (continued)
Domain and indicator
Variable definitions
Geographic unit
of observation
Data sources (examples)
Safety-net services
Number of FQHCs
per capita for
low-income population
Percent outpatient
department visits in
public hospitals
Percent outpatient
department visits in a
teaching hospital
a
MSA, county
Bureau of Primary Health Care Uniform
Data System
MSA, county
American Hospital Association data
(numerator); Census Bureau
(denominator)
MSA, county
American Hospital Association data
(numerator); Census Bureau
(denominator)
State, MSA, county
State and county health departments
Similar variables can be constructed for percentages of Healthy Families enrollees and/or children enrolled in the State Children’s Health Insurance Program.
Since each state sets different eligibility levels for different ages, the standardizing ensures the differences in the index are not based on differences due to age structure of the population, but
rather due to variations in the eligibility index within an age group. The index measures how likely a person of a certain age is to be eligible for Medicaid in a particular state relative to other states.
b
29
Access for Low-Income Populations
Volume of services
provided by local
health departments
The numerator includes the number of
FQHCs in a geographic area. The
denominator is the number of
low-income (200% FPL) in the
area.
The numerator is the number of
outpatient department visits in the
public hospital in the geographic
area. The denominator is the size of
the low-income population (200%
FPL) in the geographic area.
The numerator is the number of
outpatient department visits in
the teaching hospital in the
geographic area. The denominator
is the size of the low-income
population (200% FPL) in the
geographic area.
The numerator is the volume and type
of services provided by the local
health department in a geographic
area. The denominator is the size
of the low-income population in
the area.
Inquiry/Volume 41, Spring 2004
Similar to the Brown et al. study (2004), which
appears in this issue of Inquiry and applies the
conceptual framework, other researchers will
need to apply an interactive model to better understand how population, delivery system, and
policy and financing variables influence access
of individuals with and without health insurance
coverage. A major consideration is the appropriate unit of observation for constructing
community-level variables. The research and
evaluation questions guide the selection of contextual variables and unit of observation. Ideally,
safety-net population variables are constructed at
the local level (e.g., county, sub-county, census
tract or census block) to capture differences in social and economic conditions in a geographic area. But reliable data are not always available to
quantify the characteristics and extent of vulnerable populations. For example, evaluating access
to medical care among homeless people is problematic because they are transient, having no permanent residence in a geographic location. On
the other hand, AIDS incidence rates are available at the county level and can be used to quantify and compare the size of this vulnerable
subgroup residing in geographic areas and the ultimate effects on access and access outcomes.
In summary, the framework suggests that
safety-net population variables influence access
depending on the percentages of low-income insured, uninsured, and vulnerable subgroups. In
large part, the size of the uninsured population
is determined by such factors as the generosity
of public programs, the extent of employer-sponsored health insurance, and the general economic
and market conditions in the community. Further,
the magnitude of the low-income population residing in a geographic location translates to more
or less competition for limited safety-net services
for individuals seeking medical care access. The
next section provides the rationale for including
‘‘support’’ variables in the conceptual framework
(i.e., the resources available to directly and indirectly pay for medical care for low-income populations [Figure 1]).
Public Policy Support for Safety-Net
and Low-Income Populations
Essentially, support for the medically indigent
varies on a community-by-community basis and
is influenced broadly by federal legislation and
more directly by state and local health policy
30
and financing (Rajan 1998; Cunningham and
Kemper 1998).
Medicaid eligibility and payment levels are established based on congressionally mandated
minimums and each state’s individual decisions.
Medicaid has been a major source of funding for
safety-net providers, but the stated goal of Medicaid managed care was to reduce expenditures
over time by gradually reducing capitation payments as a percentage of annual per capita costs
(Siegel 1996). A twofold variation has been reported in what states pay managed care plans, attributed to fee-for-service reimbursement levels
and demographics (Holahan, Rangarajan, and
Schirmer 1999). States vary considerably in the
numbers of people they cover and the amounts
they spend on services (Holahan and Liska
1997b). Some states have higher health risks
and others have more expensive resource utilization patterns, not necessarily related to the mix of
health problems (Bronstein and Adams 1999;
Adams and Bronstein 1999). One study by Cohen (1993) found that low Medicaid fees hamper
access to office-based physicians and encourage
use of hospital outpatient departments and emergency rooms. Although support for Medicaid
languished in the 1990s, more recent data show
that the growth in Medicaid capitation expenditures between 1998 and 2001 averaged 18%
(Holahan and Suzuki 2003). These increases in
Medicaid managed care expenditures have been
accompanied by a 2002 initiative launched by
the Bush administration that will add a projected
1,200 new and expanded health center sites, and
increase the number of people served annually
from about 10 million to an estimated 16 million
by 2006 (HRSA 2002).
Medicaid eligibility levels influence access to
medical care for both the low-income insured
and uninsured populations by influencing the
subsequent size of the uninsured population in
a geographic area. The same is true for other federal and state-funded government programs, such
as SCHIP. Historically, Medicaid payments have
been used not only to cover program beneficiaries but also to subsidize uncompensated care
for the uninsured and to support the safety net.
Not-for-profit and public hospitals and federally
qualified community and migrant health centers
have used Medicaid and Medicare payments to
subsidize care for vulnerable subgroups not enrolled in the programs (Davidoff et al. 2000;
Access for Low-Income Populations
Davis, Collins, and Hall 1999; Gaskin and Hadley 1997; Lillie-Blanton and Rowland 1996).
Medicaid disproportionate share hospital
(DSH) expenditures are available to support
safety-net services in each state (CMS-64 Annual
Report).1 Federal DSH payments through Medicaid and Medicare have been the primary method
for states to directly subsidize safety-net hospitals, paying for nearly 30% of unreimbursed care,
with state and local subsidies covering another
60% of this care (Fagnani and Tolbert 1999;
Fishman and Bentley 1997). But DSH payments
are often determined through complex mechanisms (Holahan and Liska 1997b), not directly
related to provision of services and care for the
poor (Gaskin 1999), and some states have been
criticized for using DSH payments in ways Congress never intended (Fagnani and Tolbert 1999).
This has prompted Congress to revise the DSH
program three times since 1991 (Coughlin and
Liska 1998; Holahan and Liska 1997b).
A survey of 40 states examining Medicaid DSH
and supplemental payments showed that while the
overall size of the DSH program did not grow
from 1993 to 1997, the composition of DSH revenues and expenditures changed substantially. A
higher share of DSH funds was paid to local hospitals and relatively less was retained by the states
(Coughlin, Ku, and Kim 2000). The Balanced
Budget Act of 1997 reductions in Medicaid and
Medicare payments were expected to lead to further reductions in DSH funds and related bad debt
and charity pools (Norton and Lipson 1998;
Coughlin and Liska 1998). States have responded
in a variety of ways. In Indiana, the financial stability of urban public Level I trauma centers has
been described as tenuous due to diminished
DSH funds and tax dollars and high proportions
of uninsured and underinsured patients (Selzer
et al. 2001). In California (1997–99 data), public
hospitals were able to increase their profits from
pediatric and neonatal intensive care units (ICUs),
and DSH hospitals located in markets with high
HMO penetration reduced their operating losses
in nursery and pediatric services (McCue 2002).
In Colorado, legislators responded to the reductions in DSH payments by creating a Medicaid refinancing strategy so that major teaching hospitals
could enhance their teaching programs and at the
same 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 to
shift costs to private payers. At the same time,
subsidies from local and state governments to
pay for indigent care are declining, making it more
difficult for safety-net organizations to care for the
uninsured (Fagnani and Tolbert 1999; Norton and
Lipson 1998; Cunningham and Tu 1997). Increasingly, communities have relied on local charity
and foundation funding to create a patchwork of
services and programs to provide care for vulnerable populations (Brown and Dallek 1990; Davis,
Collins, and Hall 1999; Felland and Lesser 2000).
In addition to state and local grants, Medicaid, and
Medicare, other funding for community health
centers is available through private insurance, patient fees, and annual grants from the Health Resources and Services Administration (HRSA).
Many safety-net hospitals would not be economically viable without large public subsidies because of the gap between operating revenue and
operating expenses. The size of the gap varies
substantially among geographic areas, as does
the extent to which it is covered by local, state,
or federal funding sources (Meyer et al. 1999).
Many are concerned that care for uninsured
people and the viability of the safety net are in
jeopardy. This is due to welfare reform, changing
eligibility levels, restrictions on entitlement of
noncitizens, changes in Medicaid payments,
Medicaid managed care competitive contracting
(Lipson and Naierman 1996; Norton and Lipson
1998; Ellwood and Ku 1998; Baxter and Feldman
1999; IOM 2000), and most recently the downturn in the U.S. economy and diversion of federal
funds to other priorities. The Institute of Medicine (IOM 2000) and others have emphasized
the importance of community attitudes in shaping
government’s response to providing access for
the low-income uninsured, although few empirical studies were found on the topic (Baxter and
Feldman 1999; Baxter and Mechanic 1997).
As suggested in the framework (Figure 1),
community-level variables can be constructed to
operationalize support variables as ‘‘low-income
population support’’ and ‘‘safety-net support’’ variables. Table 1 suggests support variables, variable definitions, geographic unit of observation,
and data sources for constructing each. Examples
of support variables include Medicaid eligibility
levels and Medicaid payments. Medicaid eligibility levels, which benefit the low-income insured
population, are measured by constructing a
31
Inquiry/Volume 41, Spring 2004
‘‘Medicaid generosity index,’’ using state-level
data from the National Governors Association
(Brown et al. 2004). Medicaid payments per recipient and/or Medicaid managed care payments
per recipient can be constructed to measure resources expended through HRSA-funded federally qualified health centers. State indicators of
Medicaid spending per enrollee excluding longterm care can be constructed using data from
the Centers for Medicare and Medicaid Services
(CMS-2082 and CMS-64 Annual Report).
The framework suggests both Medicaid beneficiaries and low-income uninsured individuals
stand to benefit in areas where state Medicaid
payments are higher—the latter because safetynet providers have greater flexibility to subsidize
care for the uninsured. However, when Medicaid
managed care has greater penetration in a market
area and payments per recipient are lower, safetynet providers will have fewer resources to subsidize care for low-income uninsured individuals,
leading to measurable declines in access for the
uninsured, as well as for Medicaid beneficiaries.
Safety-net support variables, including grants
awarded annually by HRSA to the federally qualified health centers, can be constructed as dollars
per capita using the Bureau of Primary Health
Care Uniform Data System. Medicaid DSH expenditures are available for each state. However,
tracking the funds distributed at the MSA or
smaller geographic area is more challenging, requiring telephone interviews with state and local
health officials and hospital associations. No systematic data have been collected on funding provided to safety-net providers from state and local
government grants and/or grants from local charities and foundations to subsidize care for the uninsured. The majority of research results on
safety-net support variables have emerged from
qualitative studies (Holahan, Weiner, and Wallin
1998a; Katz and Thompson 1996; Ullman, Bruen,
and Holahan 1998; Meyer et al. 1999; Rajan 1998;
Coughlin and Liska 1998; Gold, Sparer, and Chu
1996; Summer 1998; Norton and Lipson 1998;
Baxter and Feldman 1999; Lipson and Naierman
1996; Baxter and Mechanic 1997; Steinberg and
Baxter 1998), with few multivariate studies to corroborate findings. Efforts to strengthen the results
using multivariate methods will require further
methodological work to construct quantifiable indicators for support variables. Collecting and
monitoring this information could prove to be ex32
pensive and time consuming. Nevertheless, policy
researchers should creatively and rigorously attempt to construct support variables so data-driven policy and management interventions can be
based more fully on evidence.
Health Care Market and Safety-Net Services
Figure 1 shows that the final categories of community-level variables are ‘‘health care market’’
and ‘‘safety-net services,’’ which influence access in a geographic area. This section explains
the rationale for including delivery system variables in the conceptual framework. These market
and delivery system factors influence the medical
care access of the general population, as well as
those who depend on safety-net services (Figure
1). Health care market variables present the generalized depiction of the delivery system in a defined geographic area (e.g., physician supply,
number of hospital beds per capita, and managed
care penetration and competition). While the
majority of middle-income and affluent Americans use mainstream medical care providers, the
safety net serves a disproportionate number of
poor and uninsured people (IOM 2000; Cunningham and Tu 1997; Lefkowitz and Todd 1999).
As discussed previously, the Institute of Medicine defines core safety-net providers (IOM 2000)
as those who organize and deliver a significant
level of health care and other related services to
uninsured, Medicaid, and other vulnerable patients. In most communities, there is a subset of
‘‘core safety-net providers’’ that show two distinguishing features: 1) either by legal mandate or explicitly adopted mission, they maintain an ‘‘open
door,’’ offering access to services for patients regardless of their ability to pay; and 2) a substantial
share of their patient mix is uninsured, Medicaid,
and other vulnerable patients.
The core safety-net providers delivering care
to low-income populations include a varying
mix of public hospitals, urban teaching hospitals
(Gaskin 1999; Gaskin and Hadley 1997; Siegel
1996; NAPH 2001), not-for-profit hospitals with
a charitable care mission (Baxter and Feldman
1999), physician offices (Forrest and Whelan
2000), federally qualified and other community
health centers (Davis, Collins, and Hall 1999;
NACHC 2001), and local health departments
(Wall 1998; Martinez and Closter 1998). Forrest
and Whelan (2000), in a recent study of access
in different provider organizations using three
Access for Low-Income Populations
national databases, concluded that: 1) expanding
community health centers likely will improve access to primary care for vulnerable populations;
2) enhancing access to physicians’ offices is
needed to bolster the safety net; and 3) the greater
service intensity and poorer continuity for primary care in hospital outpatient departments raise
concerns about the suitability of these clinics as
primary care sites.
Market dynamics influence access as well.
Intense price competition arising from commercial managed care penetration and the rapid growth of Medicaid managed care have
contributed to an increasingly hostile health
care environment. Competitive bidding for
Medicaid contracts became increasingly attractive to the for-profit sector, particularly in
areas with high managed care penetration.
When commercial contractors are granted
Medicaid contracts, safety-net providers stand
to lose market share (Holahan, Weiner, and
Wallin 1998b; Gaskin 1998; Davis, Collins,
and Hall 1999; Siegel 1996). Higher commercial HMO penetration has been associated with
lower patient volumes in minority hospitals
(Gaskin 1997), and smaller profit margins with
less excess in operating budgets to subsidize
care for uninsured people (Cunningham 1999;
Center for Studying Health System Change
1998, 1999; Lillie-Blanton and Rowland
1996; Cunningham et al. 1999).
However, due to declines in financial performance, evidence has shown commercial plans are
exiting the Medicaid market in many states
(McCue et al. 1999; Gray and Rowe 2000).
Health plans also have been created by safetynet organizations, consisting of community health
centers, public hospitals, and urban teaching hospitals (Gray and Rowe 2000; Baxter and Feldman
1999; McCue et al. 1999; Holahan, Rangarajan,
and Schirmer 1999). Unfortunately, Gray and
Rowe (2000) recently concluded even the most
stable safety-net health plans live on the edge of
financial disaster—in 1997, 60% lost money,
8% broke even, and only 32% generated a surplus.
Urban Institute researchers have used data
from case study interviews to develop typologies
that characterize the network of safety-net providers at the community level (Holahan, Weiner,
and Wallin 1998a; Norton and Lipson 1998). But
these are largely descriptive case studies that
should be supplemented with multivariate studies
to ascertain the extent to which the structure of
provider networks affect safety-net performance
and access. The Figure 1 framework can be used
to construct a consistent set of quantifiable variables reflecting the health care market and safetynet services in a geographic area. As noted earlier,
health care market variables include physician
supply, hospital beds per capita, HMO penetration
and competition, and Medicaid managed care
penetration. Suggested safety-net services variables are the number of federally qualified health
centers per capita for the low-income population,
percentage of outpatient department visits in public and teaching hospitals, and services utilized in
local health departments. However, no systematic
database exists on the extent of services provided
to low-income patients by private physician offices. Physician supply is at best a crude indicator;
a better indicator would be the number of primary
care physicians who accept new Medicaid patients, but such a variable currently is not readily
available. At the moment, these data would be expensive to construct and beyond the scope of the
present study. In the future, as states and associations within states become more sophisticated in
collecting industry data, structural information
may become more available on HMOs, hospitals,
health systems, and physician supply.
Health Care Access and Outcomes
Access indicators measure potential and realized
entry into the primary care system. Potential access is indicated by the enabling variables. More
enabling resources provide the means for, and
increase the likelihood that use will take place
(Andersen and Davidson 2001). Indicators of potential access include having a regular source of
medical care and health insurance coverage (Figure 1). Realized access is the actual use of services. Realized access dependent variables focus
on entry into the primary care system, such as
actual utilization of physician services, delay in
seeking treatment, and late or no prenatal care
(Figure 1). In this issue of Inquiry, Brown and
colleagues (2004) investigate the community
and individual determinants of access to ambulatory care predicting potential (having a regular
source of care) and realized (at least one doctor
visit in the past 12 months) access among lowerincome adults residing in 54 large urban areas.
Access outcomes measure the extent to which
effective and efficient access is achieved once the
33
Inquiry/Volume 41, Spring 2004
individual enters the system. The Institute of
Medicine Committee on Monitoring Access to
Medical Care defined access as: the timely use
of personal health services to achieve the best
possible health outcomes (IOM 1993). This definition relies both on the use of health services
and health outcomes as yardsticks for judging
whether access has been achieved (Andersen
and Davidson 2001). The resulting measures
are referred to as effective access. Indicators of
efficient access are similar to measures of effective access with the added emphasis on measuring resources used to influence outcome.
Efficiency is achieved by improving health outcomes, while minimizing resource requirements
(Andersen and Davidson 2001). Examples of
access outcomes include preventable hospitalization rates and relative risks (Billings, Anderson,
and Newman 1996; Bindman et al. 1995; Gaskin
and Hoffman 2000), hospital use rates for ambulatory care sensitive conditions (Billings et al.
1993; Friedman et al. 1999; Laditka and Laditka
1999; Bierman et al. 1999), and hospital use rates
for high-risk and complicated deliveries (Laditka
and Johnson 1999).
Discussion and Conclusions
This paper suggests a framework for analyzing
the community determinants of access. The
framework builds on the extensive research pioneered by Andersen and colleagues over the past
several decades. Similar to the approach they
used to validate the ‘‘individual’’ predictors of access, this paper suggests applying a comprehensive conceptual framework including a uniform
set of variables to test and validate the ‘‘community’’ determinants of access. The framework
suggests community variables differentially affect access of low-income insured versus low-income uninsured populations. The framework
serves as a guide for constructing contextual variables hypothesized to influence access. Data
sources are suggested and guidance provided on
how to construct contextual variables including
the most appropriate geographic unit of observa-
tion. The paper discusses data gaps in measuring
safety-net and other community variables believed to influence access.
In the research literature, ‘‘safety-net population’’ characteristics have been found to be the
most widely measured and analyzed in multivariate studies. The majority of research results on
public policy ‘‘support’’ variables have emerged
from descriptive and case study methods, with
few multivariate studies to corroborate findings.
Without a doubt, most data gaps exist for the
‘‘support’’ variables because funding to support
safety-net providers is complex and varies for
each safety-net provider, community, and state.
Other than Medicaid funding, systematic data
may not be readily available on other state and
local government and private funding sources.
Nevertheless, strategies are suggested for operationalizing support variables to advance the research (Table 1). The framework suggests that
more comprehensive measures of ‘‘health care
market’’ and ‘‘safety-net services’’ can be constructed to evaluate access for low-income insured, uninsured, and vulnerable populations.
The adverse effects of market competition and
managed care penetration on access, whether
commercial or Medicaid managed care, have been
reported in the literature. However, the most recent MSA-level study examining 22 major U.S.
cites reported that higher HMO penetration rates
were associated with fewer access problems (Hendryx et al. 2002). One unique contribution of the
Hendryx et al. study was that it included ‘‘social
capital’’ variables in the analysis, showing that
people who live in MSAs featuring higher levels
of social capital report fewer access problems.
The comprehensive and systematic approach
suggested by the framework will enable policy
researchers to strengthen the external validity of
results by accounting for the influence of a consistent set of contextual factors across locations and
populations. A subsequent article in this issue of
Inquiry applies the framework to investigate access 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 the
Robert Wood Johnson Foundation to the UCLA Center for Health Policy Research as part of the Safety Net
Assessment Project (SNAP). Drs. E. Richard Brown of
34
UCLA, John Billings of New York University, and Joel
Cantor of Rutgers University were principal investigators of the SNAP. The authors are grateful to the Inquiry reviewers who critiqued earlier versions of this
Access for Low-Income Populations
manuscript. Their comments contributed to major improvements in the article, especially related to the effects of contextual variables on low-income insured
and uninsured populations.
1 The Quarterly Medicaid Statement of Expenditures
for the Medical Assistance Program (Form CMS-
64) is the accounting statement which states, in accordance with 42 CFR 430.30(c), must submit each
quarter under title XIX of the Social Security Act
(the Act). It shows the disposition of Medicaid grant
funds for the quarter being reported and previous
fiscal years, the recoupment made or refunds received, and income earned on grant funds.
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