Unpacking the puzzle of Local Healthcare Politics

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UNPACKING THE PUZZLE OF

LOCAL HEALTH CARE POLITICS

David Houvenagle, Ph.D.

[email protected]

August 2010

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Health care reform has been no little deal since the Obama Administration

took office in January 2009. It has become reality with President Obama signing

into law the “Health Care and Education Reconciliation Act of 2010.” In light of

this milestone, a group of health policy scholars recently recognized that health

care is organized and delivered at the local level and declared the local role to be

key for health care reform success:

The ability of each community and its local health care stakeholders todesign and implement payment reforms will be critical to the success of

those reforms. Payment reform is both a national issue because of the

national payers such as Medicare, and a local issue, because negotiations

at the local level reflect market conditions. (Robert Wood JohnsonFoundation, p.1)

Lennarson-Greer (1997) had already concluded over a decade ago that health care

delivery in the U.S. was a local responsibility. She also observed that both urban

studies and health policy scholars had been ignoring this reality and so little is

known about it in terms of social or governmental theory (p.356). 1 Health policy

scholars are expecting policy outcomes out of a local political apparatus that they

have failed to define.

The scholarly ignorance of the local aspect appears to be a product of

adherence to paradigms. Health policy scholars have been entrenched in their

focus on national and state levels and quantitative research methods. Urban

studies scholars have not considered health care to be a central focus of local

governance. However, urban studies has useful political-economic tools that can

be used in this case starting with the premise that politics consists of a tension

1 For Lennarson-Greer used the term “urban health politics” to describe the local aspect. Her

definition of this was “Urban health politics and policies attempt to define and fulfill the

responsibilities of cities for the health of their population, especially in regard to the hazards and

dangers of urban life and the uneven concentration of disease in cities (p.356).

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between political structure and agency. In a personified way, structure and

agency tend to give indication about each other. Given that premise, this article

shows the growth machine thesis as a valid explanation of local health care

politics that is consistent with the older structural findings. Using Louisville

Kentucky as a case, Social Network Analysis and field interviews show that the

local growth coalition with its growth agenda is the group of political decision

makers in local health care.

This article will first review the previous relevant literature on local health

care politics and discuss the growth machine thesis. It will then review the social

network analysis (SNA) and interview findings, draw conclusions and make

suggestions for future research.

LITERATURE REVIEW

Previous scholarly writing and research on local health care politics in the

American context is from the schools of health policy, sociology, and urban

studies and goes no further than suggesting political structure at best. The

majority of this work was in the form of older case studies. While later authors

cited referred to the topic they failed to analyze the local aspect. This section will

review this literature.

Sociological Studies

The sociological authors mostly reached elitist political structure

conclusions. Belknap and Steinle (1963) identified that business interests

dominated hospital board membership (pp.122-123), and that facility

development and improvement processes required “a reasonable unity between

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formal governmental leadership and community leadership in major civic,

economic, social, and cultural groups” (p.89). Elling (a contemporary of Belknap

and Steinle) co-authored a number of qualitative studies repeatedly identifying

economic elites as central in local health care governance. Starr (1982) observed

that physicians historically depended upon the capital and philanthropy of

influential business elites to establish hospitals (pp.152-153).

Two later doctoral dissertations deserve mention. Morone (1982) studied

a number of the citizen-led local health systems agencies (HSA’s) established by

the National Health Planning and Resource Development Act of 1974 and had

found that territorial local politicians had sabotaged them. Plano (1999) vaguely

identified that some interest groups made decisions about hospital placement

within Toronto Canada and Milwaukee, but he did not produce a picture of the

community power structure making the health care decisions. While these studies

contribute to overall knowledge, no one has expounded on their work.

Health Policy Writings

Contemporary to Elling, Conant, and Wilson were health policy

researchers cited in later works. Conant’s (1968) decision-making study of five

communities found the public to be apathetic and ignorant of health issues and

that planning was ad-hoc versus rational-comprehensive in nature (pp.14-18).

Wilson’s (1968) 21-community study found economic leaders were more

influential than political leaders and professionals in health issues (p.98). Both

Conant and Wilson identified several political tendencies 1) incrementalism; 2) a

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status quo; 3) no consistent agenda; and 4) ensconced administrators holding

decision-making power with the backing of supportive civic and economic elites.

Alford (1975) focused on New York City’s health system from a political-

economic view. He suggested that health care institutions “must be understood in

terms of a continuing struggle between major structural interests operating within

the context of a market society.” Alford noted that a tension exists between

bureaucratic reformers and market reformers (pp.1-2). He suggested that popular

control of health care is unlikely, because the public is too unorganized to

negotiate with the structural interests (pp.220-222). Alford’s findings were

unique but like the dissertation writers, there was no expounding on his work.

Ginzburg (1977) was more anecdotal in nature and observed that elites

historically controlled the typical nonprofit community hospital. He observed the

norm of multiple generations of affluent local families sitting on the same local

hospital boards (p.3).

Litman and Robins (1984) devoted one-half of a page to political structure

as it pertained to local health care. Litman and Robins use the term “pluralism” to

describe their view of local politics, but their citations are not about the pluralism

of community power. They referred to pluralism in terms of the autonomy of

doctors, while it indicates popular or democratic control by the community in

urban studies. Their political structure discussion seemed incidental with a focus

on a standard of participatory democracy (p.20).

The later public health authors exemplify Lennarson-Greer’s point about

neglect of the local aspect. Tulchinsky and Varavikova (2000) only discuss the

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local level of health care governance in bureaucratic terms as part of the federal

system (pp.530-532). Gentile-Donnell (2004) related the closing of a

Philadelphia public hospital in terms of formal government but she failed to

discuss community power structure. Bodenheimer and Grumbach (2005)

described local policies in generalities, and only discussed the governance of

community health centers in federal versus local terms (p.60-62). Lastly, Shi and

Singh (2008) declared that “Federal, state, and local governments pursue their

own policies with little coordination of purpose or programs,” but Shi and Singh

failed to give any description of the local governance structure or agency (p.538).

These later health policy authors confirmed Lennarson-Greer’s point.

Urban Studies

A number of urban studies authors produced some relevant work in terms

of this discussion about health care governance. The studies were reflective of the

paradigms of their times evolving from conclusions that were cybernetic to

political-economic in nature.

Swanson’s (1972) “cybernetic” political model consisted of seven

variables where different variable formulations explain government’s

involvement in health care on various levels. His variables are: 1) system output,

2) system stress (political contention, controversy, and conflict), 3) system inputs,

4) power structure, 5) political ideologies, 6) political focus, and 7) system change

processes (pp.443-454). Local health politicians then theoretically utilize

different mixtures of the variables to achieve desired policy results. Swanson

seemed to adapt a general process model to a particular policy realm and did not

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identify general structural or agency tendencies, and there was no further

expansion upon his work.

Dye (1973) stated, “In nearly every community, decision-making in health

and hospital matters is firmly lodged in the hands of leaders of the local medical

associations” (p.479). Dye did not empirically substantiate this statement and did

not repeat it in later editions of his textbook.

Schussler (1994) explored corporatism in local health care politics through

the process of the state of Kentucky awarding the Humana Corporation the

management contract for the University of Louisville Hospital. In this case, a

locally based proprietary health care corporation and government had a public-

private partnership to deliver indigent care (pp.1-2).

Britton and Ocasio (2007) explored hospital and orphanage location in

Chicago between 1848 and 1916. Britton and Ocasio noted that hospitals were

placed in locations to create greater visibility of the “material and symbolic

benefits provided by these facilities.”

Within the limited body of literature on local health politics in the United

States, an elitist political structure is the most consistent theme, but this is still an

incomplete picture of the local situation. More recent urban studies findings have

evolved with the discipline to a political economic framework asking the question

“for what are the actors motivated,” which suggests agency in addition to political

structure (See Vogel, 1992 for a further discussion of urban political economy).

THE GROWTH MACHINE THESIS

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As noted earlier, the growth machine thesis is an established urban

political economy theory of urban governance. Molotch (1976) asserts in his

theory “that the political and economic essence of virtually any given locality in

the present American context is growth.” Growth unifies categorically defined

local elite stakeholders in their quest for constant expansion of the city in the

areas of population, land development and commerce (1976, pp.309-310). These

elites informally comprise a growth coalition to make political decisions for the

city. The local public only weighs in on local issues when the local elites have

agreed to disagree on those particular issues (Logan and Molotch, 2007, p.51).

The three categories of growth coalition stakeholders are listed in table 1.

They have the most to lose or gain from land-use decision-making (1976, p. 314).

The first category consists of parochial/local businesspeople (especially property

owners) and those in “locally-oriented financial institutions who need local

government in their daily money-making routines.” The second category of

stakeholders includes lawyers, syndicators and realtors who make their incomes

and revenues servicing the property owners and investors. The third category of

stakeholders consists of parties whose fortunes are tied to the overall growth of a

metropolitan area (i.e., the daily newspaper, universities and utilities). Molotch

observes that these stakeholders mobilize, legitimize and sustain local pride and

growth momentum into “particular kinds of policy decisions” (1976, pp.314-316).

Since the growth machine thesis suggests central or key relationships

within a community, testing for its presence is a matter of measuring and

evaluating relationships. The

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(Table 1 about here)

growth machine thesis offers a set of nominal variables for social network

analysis (SNA) by which relationships in a local network can be interpreted.

METHOD

Research Questions

Given that the previous literature stopped at identifying political structure

and did not make any conclusions about agency, the latter was left open for

exploration. Given that the urban political economy view holds that there is a

tension between structure and agency, the question became: does the structure tell

something about the agency? Initial research indicated a growth machine

hegemony on all of the local hospital boards in Louisville, so the general question

became does the growth machine thesis explain both the structure and agency of

health care politics for the community as a whole? Evolving out of this were at

least two overall research questions:

1) Is there a growth machine in Louisville?

2) Does it shape local health care decision-making?

For all practical purposes, the question about the existence of a growth machine in

Louisville, Kentucky had been answered. Three previous studies (Whitt 1988),

(Savitch and Vogel, 2004), (Savitch et al 2008) had identified the existence of a

growth machine in Louisville.

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The research design for answering the questions was a disciplined-

configurative case study that used Social Network Analysis (SNA)2 3 and elite

interviewing of individuals. This case study design applies a specific theory as a

test and compares the test results with the theoretical propositions Otherwise this

study is similar in design to one by Moore and Whitt (2000) that also combined

SNA and interviews to explore gender in community leadership roles in

Louisville, KY.

SNA is a formalized, quantitative approach for studying networks (Adam

and Kriesi 2007, p.130). It “is useful for investigations of kinship patterns,

community structure, interlocking directorships and so forth” (Scott, 1991, p.2).

Knoke and Kuklinski (1982) define a network “as a specific type of relation

linking a defined set of persons, objects or events” (p.12). SNA results are both

graphical and numerical in form to show connections and strengths or relationship

respectively.

Whitt (1989) first suggested the use of SNA to study questions suggested

by the growth machine thesis. He proposed that it could identify structural

channels and connections (pp.104-105). In light of that suggestion, SNA methods

were used to explore necessary subsidiary questions of the structural aspects that

included:

• Is there a coherent network in Louisville health care?

2 The SNA results were generated from an initial dataset of 8240 cases from a snowball sample of

board members and officer rosters of hospital and other organizations in the Louisville area

between 2005 and 2006. It is assumed that the sample used to create the dataset is representative

of the community and the health care actors.3 The centrality rankings of individuals and organizations were the same whether n= 7515 or

n=4438. The dataset likely could have been reduced further with similar ranking results.

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• If there is a coherent network, then what individuals are central in the

network and thus more powerful?

• What organizations are central in the network?

• Is there any detectable elite clique/inner circle that has high centrality?

• Where do the sectors of the growth coalition fit in relation to the whole

network?

These questions should answer the structural matter of whether health care

governance is connected to the growth machine and to what extent.

The use of SNA to determine a community-wide structure has been

conducted on a limited basis. There are two prior, known examples of the SNA

analysis of Multi-Dimensional Scaling (MDS) used to study community elites.

Laumann and Pappi (1973) used MDS to identify the network of a German city’s

elites and followed up with interviews. Galaskiewicz (1979) used MDS to

analyze the organizational networks in a Midwestern city. So, there has been a

track record for SNA to examine networks at the community level.

Whitt (1989) suggested that network analysis of a community can identify

organizational connections, but field interviews are needed to uncover intentions

(pp.104-105). In light of this suggestion—and given the SNA results, the specific

interview topics were:

1) Can the validity of the SNA results be affirmed?

2) Is there a growth agenda?

3) Is there a particular political mode of operation?

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4) Why was there a disconnect between local public and private health

care organizations shown in the SNA results?

SNA Dataset

The dataset for the SNA was a snowball sample collected in 2005-2006.

The sample starting point was the hospital organizations, and the collection

spanned out into the metropolitan area to have a clear representation of the major

public and private organizations in Louisville-Jefferson County and the adjacent

Indiana and Kentucky Counties. The sample was inclusive of board and officer

rosters taken from a variety of publicly available sources based on published

rankings, organization websites, federal income tax statements, annual reports,

business databases, and the Kentucky Secretary of State business services

database.4 The data was cleaned and encoded into a form readable by the

UCINET 6 software package. The initial database had 8240 cases, which was

pared down to 4438 after initial centrality results rendered a large number of cases

to be insignificant with a Bonacich eigenvector score of 0.

Field Interviews

Interviews were conducted with 22 central individuals. These individuals

had high centrality scores, which meant that they were structurally in positions of

power. The object was to interview all the top informants as substantiated by the

SNA analyses, but not all were available or willing. The reality was that the

informants ended up being some of the most central, but also others were selected

4 The paper Business First runs a ranking of leading Louisville area firms and organizations every

week by size of business or volume of business. The paper runs a different sector every week.

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based on having been identified by earlier informants as authoritative and

knowledgeable.

SNA RESULTS

The pertinent SNA analyses were 1) clique analysis (several values of N)

on the adjusted dataset of 4438 cases,5 2) Bonacich centrality of organizations and

individuals, and 3) Multi-dimensional scaling (MDS) (non-metric) of the

relationships among the different growth coalition interest groups. Graphical

analyses of individuals and organizations in Louisville using UCINET were not

meaningful due to the large dataset size.

Centrality Analysis Results

In SNA, centrality is a relational matter, and the most central individual or

individuals in a network holds the most power. Hanneman and Riddle (2005)

state, “An individual does not have power in the abstract, they have power

because they can dominate others—ego’s power is alter’s dependence”(chapter

13). There are two centrality metrics that are used in SNA: “Freeman Degree”

and “Bonacich Power.” The Bonacich measure is more precise than the Freeman

measure and was selected.6 The Bonacich power measure of centrality is

mathematically stated as cj = Σ jr ij(α +β c j) and accounts for the number of

connections an actor has and the numbers of connections the connections have.

Bonacich’s equation gives “each actor an estimated centrality equal to their own

degree plus a weighted function of the degrees of the actors to whom they were

connected” (Hanneman and Riddle, chapter 13). The values of Bonacich’s

5 UCINET 6 can be instructed to look for cliques of different sizes.6 The Freeman Degree measure merely counts the number of connections of a point and says

nothing about how significant a point’s connections are. The Bonacich measure does.

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measure are called “eigenvalues” have a nature similar to eigenvectors in factor

analysis but are interpreted in a basic fashion (higher score = more centrality).

Centrality Rankings of Organizations in Louisville

Table 2 displays the gradient of Bonacich rankings of Louisville

organizations. The development sector has hegemony. The most central “Greater

Louisville Inc,” (GLI) the local chamber of commerce is an extreme outlier as it is

603 % more central than the next most central entity. 7 Louisville Metro

Government was ninth on the list. The Metro United Way and the now-defunct

“getCare Health Network” were anomalies in the list as charitable concerns.8 The

health care organizations and typical growth coalition entities ranked high in

centrality.

To correct the skewness, the GLI cases were removed from the dataset.

Table 3 displays that eight entities dropped out of the rankings, including

Louisville Metro Government and Norton Healthcare. However, the Frost Todd

Brown law firm (FBT) and Jewish Hospital Health Services (JHHS) increased in

centrality and became the outliers,9 which indicated inherent centrality and

organizational interlock. The now-defunct getCare Network stayed in the top 20,

suggesting participation by highly central individuals. The changes indicate that

GLI is a “knot” or “cut-point” (Scott, pp.110-111) or central exchange connecting

numerous entities in the network. This adds support to the existence of growth

coalition interests and their hegemony.

7 Mean = 1.799 Std Deviation = 8.6548 They are redistributive or charitable organizations and of a different nature than the rest. The

getCare Network was an organization spear-headed by the Louisville-Metro Health Department

and primarily funded by a Robert-Wood-Johnson Foundation “Communities in Charge” grant to

provide free or reduced medical services to uninsured individuals.9 Mean =0.488, Standard Deviation = 5.827

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Centrality Rankings of Individuals

The Bonacich centrality for individuals further strengthened the picture of

hegemony of the growth coalition as attorneys, and individuals from utilities,

finance and education dominate the top 20 as shown in Table 4. The highest-

ranking elected politician is ranked sixth.



Like the organization scores, the individual Bonacich scores evidenced

positive skewing of the data, but with a less steep gradient .

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There thus appears

to be correlation between membership or leadership in GLI and membership on a

hospital board.

With the GLI cases removed from the dataset, the individual rankings

displayed in table 4 followed the organization ranking patterns. M1476 gained in

value and 18 of the 20 in this second list are attorneys at the Frost Brown Todd

(FBT) law firm. While GLI is most central in the network, many FBT members

have centrality by firm membership. The more central FBT members have more

known civic involvement. GLI proved to be a central knot or cut-point connection

in the network.

Clique Analysis

In SNA, a clique is a group of individuals who are not a sub-group of any

other group. In graphical terms it is also “a sub-set of points in which every

possible pair of points is directly connected by a line and the clique is not

contacted in any other clique” (Scott, 117). The point of clique analysis in this

10 Mean =1.799, Standard Deviation = 8.654

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research was to identify whether the most central and powerful individuals in the

database had any evident, exclusive associations with each other.

Clique analysis was conducted on the dataset of 4438 cases to determine

social alignment among the most central actors.11 There were 28 cliques with an

average eigenvalue of 12 or higher which was the approximate “floor” of table 4.

Two cliques stood out, the clique listed in table six (average 16.156) and the

second clique listed in table seven (average eigenvalue -15.665). The

predominant clique consisted primarily of health care executives and physicians,

and growth coalition types who had been documented to have sat on hospital

boards. Eight members in the second clique were unique to it from the

predominant clique so there was little redundancy between the two cliques.





Returning to the construct that a clique is a group of individuals that is not

a sub-group of any other group, we can conclude that there is support for an elite-

group of individuals that relate to each other exclusively within this network. In

both central cliques, growth coalition sectors are present alongside health care

figures, supporting both elite and growth coalition hegemony.

Analyses of the Network in Terms of Sectors

At this point, it has been established that 1) there is a hegemony of growth

machine and healthcare figures in Louisville in terms of centrality scores, and 2)

11 Given the gargantuan size of the network and the fact that it was a snowball sample versus

survey, it cannot be determined whether these cliques were strong cliques or weak cliques (Scott,

p.118).

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there is evidence that the central growth machine and health care figures have

exclusive relationships amongst themselves in the form of coherent cliques, but

despite the strength of the centrality and clique analysis results, where the growth

coalition fits in the overall picture of the community structure remained a

curiosity. This is because Molotch’s theory is framed in terms of specific interest

groups/sectors. To assess whether different interest groups (especially those of

the growth coalition) relate to each other a dataset of 747012 was modified to

include a classification of each individual person’s livelihood.

This modification reframed the dataset in terms of 28 nominal variables,

which allowed for meaningful Multi Dimensional Scaling analysis.13 Strict

adherence to Logan and Molotch’s list of 12 growth coalition categories would

have meant a large, imprecise “other” category14 and so other livelihood names

were used for precision. 15,16

Bonacich Eigenvalue Results of Sectors.

12 This was the original dataset of n=8240 where non-human beings were removed. (Corporations

as entities were listed as officers in these cases.) Even though it was obvious from previous

analyses that the larger set contained had 41 percent of essentially irrelevant actors, the larger data

set should lend more credibility to such an analysis of the overall community picture. The added

benefit of using the larger dataset also can inform about what sectors are outside of or inside the

center of the community network. 13 For a discussion of network mapping of the sectors see Author (2008). Traditional network

graphing in the form of a map added little meaning to the results other than the growth machine

sectors were in the mainstream but affirmed that public health and health related businesses wereoutliers from the network.14 With a large general “other” category that included all other different entities, much would

likely be missed in analysis. Creating other unique categories seems to be in harmony with the

growth machine thesis as the specific categories appear to be locally dependent.15 There was an initial data problem in that the UCINET DL formatting routine did not read 377

cases. Reducing the sector names to one word reduced the missing cases down to 3.16

The sector names are as follows: Accountants, Advertising, Colleges, Branch (Corporate

Capitalists of Multi-locational firms), Development, Doctors, Elite (Social Organization),

Foundation (Charitable organization), Government, HRB (Health-Related Business), Health care

(other than hospitals), Hospitals, Insurance, Law, Media, MH (Mental Health), National (Home

Office of a Corporation based in Louisville), Nonprofit, Other, Politicians, PI (Property Investing),

Public (Health organizations), Finance (Real Estate Financing), Retailers, Sports, Technology,

Arts (Museums, Theaters, Expositions), Utilities

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The Bonacich eigenvalues in the sector analysis suggest why FBT

subsumed GLI’s centrality, and why M1476 (an attorney) gained centrality when

GLI cases were removed. The “law” sector had a total eigenvalue score of

141.380 out of a sum or (Σ ) of 149.229, which was 94.7 percent of the value.17

The “development” sector had an eigenvalue of 2.283. The “hospitals” sector was

third with an eigenvalue of 2.208. The distribution of eigenvalues was, positively

skewed given that the mean was 5.330 and the standard deviation was 26.189.

Local attorneys are central in the “development” sector and are the foremost

central sector in the network of health care politics in Louisville, Kentucky.

Multi-Dimensional Scaling (MDS)

MDS has broad applications and is an alternative to factor analysis

(Wasserman and Faust, p.288). Its general goal is “to detect meaningful

underlying dimensions that allow the researcher to explain observed similarities

or dissimilarities (distances) between the investigated objects”

(http://statsoft.com/textbook/stmulsca.html). Borgatti (1997) 18states that the

purpose of MDS is “to provide a visual representation of the pattern of

proximities among a set of objects.” It is used to “provide a visual representation

of a complex set of relationships that can be scanned at a glance”

17 The reader is referred to the idea that the Bonacich measure is akin to Factor Analysis. Only so

much probability is generated and therefore 149.229= 100 percent of the possible value or

probability. It is clear that the lawyers in Louisville as a group are overwhelmingly the most

powerful group of individuals in town.18 Borgatti is also one of the architects of the UCINET software package. There are also two

versions of this page on the Internet. The other is at

http://www.analytictech.com/networks/mds.htm.

18

http://statsoft.com/textbook/stmulsca.html

http://www.analytictech.com/networks/mds.htm

http://www.analytictech.com/networks/mds.htm

http://statsoft.com/textbook/stmulsca.html



(http://www.analytictech.com/borgatti/mds.htm). Therefore, MDS goes one-step

further than factor analysis to give a valued, graphical depiction of a network. 19

Figure 1 is the initial result from the UCINET MDS routine. The result

shows that the public health and health-related business sectors are extreme

outliers in the network—they are distinctly isolated from the other points with no

connection. In contrast, the other sectors are densely compacted or tightly

clustered into a small space.

(Figure 1 about here )

Figure 2 is a magnification of the compacted cluster in Figure 1. The

“neighborhood” of sectors overall are close to each other and could be called a

cluster in and of itself. However, within the MDS results there appears to be a

tighter cluster composed of hospitals, development, law, property-investing, non-

profit, and finance. Mental health, public health and health-related businesses

were extreme outliers.

On the other hand, in the center of the map, hospitals and development

sectors are practically on top of one another with law and finance being the other

closest, adjacent sectors. There are no clean or neat dimensions evidenced in the

19 MDS has both numerical and graphical components that can add additional understanding to the

network of actors. Its output “is a set of estimated distances among pairs of entities, which can beexpressed as coordinates in one-, two-, or higher dimensional space” (Wasserman and Faust,

p.288). Its graphical depiction is a “Shepard’s diagram” similar to a Cartesian plane.

(http://www.statsoft.com/textbook/stmulsca.html)

Borgatti’s guidelines for interpreting a MDS map are as follows. First, “All that matters in an

MDS map is which point is close to which others.” Second, the axes are “meaningless” and “that

the orientation of the picture is arbitrary.” Furthermore, Borgatti notes that the two keys to

interpretation are noting the “clusters” or groups of points that are closer to each other than toother points and the “dimensions” that are “item attributes that seem to order the items in the map

along a continuum”(http://www.analytictech.com/borgatti/mds.htm).

19

http://www.analytictech.com/borgatti/mds.htm

http://www.statsoft.com/textbook/stmulsca.html







MDS map. At best, the closeness of the law, hospitals, and development sectors

reflect their high Bonacich centrality results.

(Figure 2 about here)

Validity of MDS results

The validity of MDS results is indicated by the “stress” score. Stress is

either an indication of goodness of fit (www.statsoft.com/textbook/stmulsca.html)

or “a measure of badness of fit” (Hanneman and Riddle, chapter 13). The stress

score was 0.002 in 50 iterations. Borgatti noted that a map with “non-zero stress”

is distorted.” Borgatti suggests that any stress score under 0.1 is “excellent and

anything over 0.15 is unacceptable”

(http://www.analytictech.com/borgatti/mds.htm). Therefore, the stress score of

0.002 means that the fit is excellent and the results are valid.

It can be concluded from the MDS results that the “development” and

“hospitals,” “law” and “finance” sectors are closely connected to each other along

the lines of the measures of centrality in the network. There is cohesion among

certain sectors within the network while other sectors are outsiders of the

network. 20

Section Conclusion

Overall, the SNA results support a conclusion that a coherent political

network of elite relationships exists in governing Louisville health care apart from

elected government. The Bonacich centrality results identified growth coalition

20As mentioned above, there have been few studies of a community power structure using MDS.

The above cited studies (Laumann and Pappi and Galaskiewicz) were identified as utilizing this

method at the community level. The MDS results here have similarity to Galaskiewicz’s (1979)

smallest space analysis MDS (SSA-1) on an anonymous Midwestern community which suggests

that these results can be generalized.

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hegemony both in terms of analysis of individual and organizational networks—

there was no branch plant representation in the elite group of individuals. The

clique analysis buttressed the centrality results by identifying that the two most

central cliques consist of local health care and growth coalition types. In terms of

the overall community network, the MDS results showed hospitals are closely

related to the law firms, and economic development organizations.21 Under the

premises of SNA, a reasonable conclusion is the relatively few elite actors at the

top of a steep eigenvalue gradient are the controlling parties making decisions in

the network.

FIELD INTERVIEWS

As discussed earlier, the SNA results suggested individual informants who

were central in the network of power in health care. Available and willing

central individuals were interviewed as well as other informants who were

identified to be authoritative. The interview questions sought to verify validity of

the SNA results, as well as explore areas of policy and structure. This section will

discuss those issues in addition to examining the role of local government, and the

role of doctors.

Who do the Central Players Say Move and Shake

Individuals

The informants identified health care movers and shakers in Louisville as

coming from one of four groups: health administrators, educational figures,

political figures, and board chairs of hospital organizations. Overall, individuals

21 Furthermore some sectors are distinctively not in this network, and some sectors (especially

doctors) are distinctively on the fringe of the central power.

21



who were both community elites and executives were identified to be the movers

and shakers.

Organizations 22

There was a short list of key organizations. The leading three leading

hospital organizations (Norton, Jewish, and Baptist) were the most identified

leading organizational actors. The University of Louisville was identified next in

terms of frequency. Informants repeatedly identified the proprietary

organizations: Humana23, Kindred and Rescare24 as a third echelon and not

involved with the top tiers of actors. Given the list, informants saw the hospital

organizations as the main organizational players in local health care politics.

Sectors

Some informants framed answers in terms of sectors and acknowledged

that a great disconnect existed between the public and the private sectors.

Informants most frequently identified the Family Health Centers and the

Louisville Metro Health Department as the main public sector actors. The non-

profit hospital organizations were again identified as the primary movers and

shakers. Informants did not recognize the proprietary actors (Kindred and

Humana)25 as involved in local health care politics. Humana was identified as the

22

One respondent also said “I may be using the wrong term, the unions or the company, Ford,GE, UPS, are major players in this, and then you get into the health care companies themselves . . .

like Humana, Ventas.”23 As noted elsewhere Humana was first a Louisville-based proprietary health care organization

that then evolved into health insurance and then divested itself of hospitals. It is a hometown

company that wields sizeable influence.24 Rescare is a Louisville-based proprietary organization that primarily specializes in running

residential care facilities across the United States. The residential facilities historically have been

called “group homes.”25 Kindred’s home office is in Louisville, and they have one hospital in Louisville.

22



exception when it had owned three Louisville hospitals and had the management

contract for the University of Louisville Hospital for 13 years.

Where are the Doctors?

The informant comments affirmed the MDS results. Informants

consistently did not name doctors and only answered when asked the specific

question: How have you seen the role of doctors in the political realm?

The informants consistently confirmed that the physicians as a group in

Louisville are marginally involved in the local politics and do not wield power or

influence over community health care goals. Despite the existence of the Greater

Louisville Medical Society,26 informants did not recognize strong organization

among physicians at the community level and they are quiet unless they have

strong opinions on a particular issue.

However, informants consistently noted that physicians wield political

power at the institutional level. Physicians are economic drivers as they direct

business to the hospitals. One informant said that doctors did not have a strong

say until it was discovered that hospitals could “construct a lot of profit under the

guise of doctors’ offices” and that doctors are cheerleaders for a hospital getting

new equipment already in use at other hospitals. Another informant said a

specialist making significant revenue for a hospital has more pull and can extract

concessions.27

26 Historically, the Jefferson County/Greater Louisville Medical Society has facilitated a process

whereby a physician can apply for credentials at all the desired facilities in town through its

auspices.27 Informants repeatedly described physicians as independent business people who sell their time

and can influence placement of facilities. However, a change in the trend of doctors as

independent business people was noted by a number of informants as hospital organizations have

been hiring physicians (especially family practice doctors) as employees. The employed

23



On the other hand, some informants noted the Louisville Medical Center

as an exception in terms of physician political power and influence because of the

University of Louisville Medical School. The University of Louisville Medical

School dean is an agenda-setter. Medical school department chairs wield

influence over the placement and operation of indigent care services. However,

the responding informants said the Medical School’s influence stays within the

downtown Medical Center and does not extend to the larger community.28

Where is Greater Louisville Inc?

Like the doctors, Greater Louisville Inc (GLI) was not automatically

identified as a mover and shaker .29 This supports the SNA results when the

dataset was adjusted for skewness.

Most informants saw GLI driving research and economic development

agendas that involved health care but did not see GLI having a consistent role in

health care. Two informants cited the Boyle Report 30 of 1996 (commissioned by

GLI’s predecessor organization) as a source for understanding Louisville’s health

care agenda in economic development terms, but that was the extent of GLI

involvement in health care governance.

physicians are required to refer within the hospital organization and therefore hospital

organizations are controlling the referrals under the guise of an established continuum of care.

One informant declared that this is a ‘de facto’ closed staff system in place in the various hospitals

and the doctors are ceasing to be independent business people with mobility throughout the city to

practice in different hospitals.

28 One respondent suggested that as part of an overall agenda by the University of LouisvilleMedical School to build the “U of L” brand, it is looking to be a full competitor with Jewish,

Norton, and Baptist. There is some news coverage to that extent where U of L is contemplating

outpatient facilities outside the downtown Howington, (2007 July 12), but no other informant

confirmed this.29 Some informants identified the Health Enterprises Network, which is a “network” of GLI, but

few spontaneously mentioned GLI or the Health Enterprises Network as a player.30 The formal title of this was Economic Development Strategy for the Greater Louisville Region.

24



The informant responses reflect SNA indications that GLI as an

organization is a central cut point or juncture of relationships as opposed to a

central actor in health care politics. This is despite the face value evidence that

GLI has members who are active in health care politics including hospital

executives.

Where Are Local Government and the Mayor among the Players?

Like the doctors and GLI, informants did not consistently name the Mayor

of Louisville and Municipal/Metro Government as actors in health care politics.

This also validates the SNA centrality results. The question asked to elicit this

information was usually worded as: In your opinion, what is the Mayor’s and

Metro Government’s31 primary role in the health care system?

The Mayor

Informants generally saw the current Louisville Mayor, Jerry Abramson as

more involved in economic development issues pertaining to health care and not

in redistributive issues. He is a passive, but well informed behind-the scene

player who supports and cheers on development. Some informants said that the

Mayor will talk to executives in an effort to influence, but he does not wield

control over the hospital organization agendas. One informant said that there does

not appear any “mechanism” set up for the Mayor to be involved in identifying

health needs in the community.

Involvement by Metro Government as an Entity32

31 The term “Metro Government” is being used in lieu of the 2003 consolidation of Louisville and

Jefferson County, Kentucky governments.32 Louisville renamed its health department to be the “Department of Health and Wellness.”

25



Overall, the informants saw that beyond the Mayor and the head of the

health department, Adewale Troutman M.D., the Louisville Metro (Consolidated)

Government is a minor, if not just a passive, player in health care politics. One

informant said,

I don’t think there are even positions established that the designation or

authority to really take that on beyond Dr. Troutman, and if there are positions, they’re relative weak positions, I am not talking individuals; I’m

talking positions that have somewhat perfunctory leadership roles.

A number of informants viewed Dr. Troutman as a credible and influential

opinion leader in the area of health disparities, and that the Mayor backs him.

Informants did not see the Louisville Metro Council as a central player in health

care politics.33 The bottom line is that the municipal government was not a

significant player in local health care politics.

Section Summary

This section reviewed the informants responses regarding the structure of

local health care politics in Louisville. Elected government and local elected

politicians are not the movers and shakers. Overall, the informants confirmed that

local health care politics is not a matter of formal government and controlled by a

small group of actors outside government.

What is the Agenda of the Local Players?

The question about the political agenda of the players did not have to be

asked in every interview, but in many cases the question was: Do you think that

33 Informants saw GLI as being the driver of the 2006 indoor smoking ban ordinance and the

Metro Council as only the official ratifying body. Informants saw the Mayor and the University of

Louisville president as the primary negotiators of the QCCT.

26



there is any particular agenda overall in this political realm? Informants gave a

variety of views.

Some informants saw the city agenda as a combination of the growth

agendas of the different hospital organizations. The different organizations have

their own strategic plans and cooperate when it does did not threaten the

organizational interests.34 A common theme among informants was that the

hospital organizations are competitors first and collaborators second. In this

context, basic market logic appears to determine the political positions. Overall,

a theme from most of the informants with an opinion on this was that the different

organizations do not want the competitors to have an advantage, and will go to

court to maintain parity in the marketplace.

Two examples of this cooperation were the joint venture of Norton Health

care, Jewish Hospital, and the University of Louisville to govern the University of

Louisville Hospital as University Medical Center Inc (UMC), and the formation

of the Louisville Medical Center Development Corporation (LMCDC). Despite

being competitors Norton and Jewish had a mutual desire to see Columbia HCA

leave town and so cooperated on governing a third hospital in the Louisville

Medical Center. The governance agreement lasted from 1996 until 2007 when

the University of Louisville terminated it for what some informants said were

development purposes.35

The LMCDC was formed about the same time that34 One example of this was cooperation by the hospital organizations to make a jointannouncement in May 2007 that all facilities would be smoke-free. Some informants said that this

was a way of preventing turnover and transience of employees from one organization to another in

order to smoke at work.35 Informants said that initially Jewish and Norton spent significant money to revitalize the

infrastructure of University Hospital, and that they could maintain a status quo in the area of

medical specialties. The board of directors had members from Jewish, Norton, and the University

of Louisville. However, University Hospital got stronger over time with the investment, and

27



Norton and Jewish entered into the UMC agreement that and backed away from it

about the same time they left UMC.36 Informants indicated that Norton and

Jewish decided that cooperation in this case stopped being in their interests.37

A third example of the informal cooperation around the development

agenda surrounded the closing of Southwest Hospital by Norton Healthcare and

the moving of the Certificate of Need (CON) to a new hospital that was opened in

a developing area of eastern Louisville in 2009. Informants suggested that

Norton, Jewish, and Baptist Healthcare all agreed on the need for growth and did

not challenge each others plans in front of the pertinent state planning body.

Some informants explicitly identified the health care politics agenda in

Louisville as economic development.38 Some informants also said that the agenda

includes national and international prestige, especially given the Abiocor

Artificial Heart implantation trials and the first hand transplants. Besides the

branding efforts, health care is recognized as a “hot issue” in the Louisville

economy and thus of political importance. One informant said,

conflict started when there was a push to upgrade certain specialty infrastructure that would

compete with the specialties of Norton and Jewish. Some informants reported that in the last

several months that Norton and Jewish participation, the conflict was so great that there was no

quorum so the University Medical Center board of directors could not meet. A dynamic in this

conflict was the University of Louisville has a state mandate to facilitate economic, development

and informants said that it is now the main leader in the health care development agenda.36 Norton and Jewish are still members of the LMCDC now “ Nucleus” but they are neither central

members nor do they give as much money to the organization as they previously did.37 In light of the detachment among the organizations within the Medical Center, a number of

informants questioned whether there was a future in cooperation in Louisville health care.

However, a number of informants saw cooperation as inevitable. The actors are connecting in

other ways. For example, Jewish and the University of Louisville had cooperated in creating a

cardiovascular research institute. One informant said that Norton and the University of Louisville

were looking for other ways to cooperate despite the break-up of the governance consortium of UMC. The organizations operating in the downtown Louisville Medical Center are still working

together in some form.

38 It was interesting that a number of actors acknowledged that this was the agenda, but also voiced

criticism of this agenda as it led to inequities and was not a solid basis for Economic

Development.

28



Health care has been seen not only because of the services it provides, but

as a way to provide lost manufacturing jobs so it is a critical service to the

quality of life to the people, but it’s also an economic cluster that isvalued by our community. So I see that city government and the Mayor

driving that role as well.

One informant said, “Our local government has a vested interest in the healthcare

system continuing to grow.”

The Political Modus Operandi

When it came to the modus operandi of the health care movers and

shakers, the general answer was that hospital executives and not board members

were the primary negotiators in local health care politics.

39

The chief executive

officers (CEO’s) and executive teams are the prime negotiators, because health

care has evolved into a highly complex industry.

The hospital executives talk both formally and informally. Some

informants said the hospital organization executives have conversations under the

auspices of the Kentucky Hospital Association (KHA). The executives of the

different organizations are on various KHA committees and have conversations in

a legal context that cannot be construed as fixing the market. However, other

informants reported that the executives of the different organizations have a

history of talking informally among themselves. The executives of the three

hospital organizations have a history of engaging in quid pro quo with regard to

different facility projects (i.e., we will not challenge you in front of the Certificate

of Need board on your particular project if you do not challenge us on our

project).

39 Informants reported that board members were not involved in outside informal negotiations with

board members from other hospitals on behalf of the respective hospital organizations. One

informant was emphatic that there are no such conversations that take place at “Fund for the Arts”

meetings or at work places.

29



The Lack of Network Connection between the Private and Public Sectors

As noted earlier, the MDS results showed a dramatic disconnect between

public health agencies and the private/non-profit organizations. As illustrated in

figures 1 and 2, the mental health and public health sectors were not part of the

network in the UCINET-generated Multidimensional Scaling (MDS) results in

figures 3 and 4. Why was this? The consistent answer from informants was that

the public sector agencies were required by federal law to be representative of the

community—to include consumers.

DISCUSSIONUpon investigating what constitutes the political structure of health care in

Louisville and talking to available individuals within that structure, the essential

conclusion is that when it comes to the local context the movers and shakers are

elite, locally dependent business interests embedded within the community. The

available informants also consistently confirmed that the agenda of these movers

and shakers is growth. Recognizing that, 1) a U.S. city, as a geopolitical entity is

subject to the political and regulatory structure of the state and national

governments, and 2) if any local health care policy can be locally shaped and

driven in Louisville, it is growth and development driven by the configuration of

actors called a growth machine.

The actors in their political dealings with each other have focused on what

growth and development can occur in terms of the interest of the actors. There

were obvious examples of the actors being united by growth opportunities, and

there was no evidence that the public was part of the discussions. The movers and

shakers in health care in Louisville are not doctors, nor local government nor the

30



general public, but a small, elite group that has the markers of the growth

coalition.

These growth machine findings build upon the older elitist findings on

local health care politics. Congruent with Wilson and Conant’s findings the non-

profit hospital organization executives in Louisville turned out to be the central

actors even though lawyers, developmental, and financial interests were on

hospital boards and appeared to be central in the network of relationships.

These findings also suggest that local non-profit hospitals as a sector could

be added to Molotch’s model of a growth coalition. Like the other growth

coalition livelihoods, hospitals (with a very few exceptions40) are locally

dependent on the area in which they serve for revenue. Local non-profit hospitals

are large employers and serve as an avenue of growth. Hospital organization

executives integrate into the local business community and align with the business

interests of the community. They serve on the boards of banks, charities and

other local institutions and become engage in elite relationships with other elite

business and economic figures.

Given the types of actors and the types of agenda, it appears that the local

responses to health care reform in Louisville within the larger political structure

of the Kentucky and federal levels of government will be along the lines of the

market concepts of growth and development. These elite actors and the agenda of

growth and development will be the political objects for study as to how the local

level responds to health reform. A possible market action in this case is

40 The Mayo Clinic and the Cleveland Clinic are two examples of hospitals that primarily draw

patients from outside their localities and regions, and which would be exceptions.

31



oligopolistic mergers of local non-profit hospital organizations. A related

question to explore pertains to the type of local market condition that engenders

proprietary ownership of former nonprofit hospitals and its relationship to the

local growth machine. (Are local economies where health care is a non-basic

sector more likely to have the presence of proprietary ownership?) More research

is needed to test for generalization of these findings in the United States context.

The results of this study could provide markers for researchers to identify local

health political structures and agendas in other communities to assist health care

payment reform efforts to achieve desired outcomes and impacts.

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Table 1

The political players of the local growth coalition

____________________________________________________________

The major players__________________________________________________

Property Investing

Development

36



Real Estate Financing

Politicians

Local Media

Utilities

____________________________________________________________

The Auxiliary Players_______________________________________________

Universities

Theaters, Museums, Expositions

Professional Sports

Organized Labor

Self-employed professionals

Small retailers

_____________ _____________________________________________

(Logan and Molotch, 1987, pp.62-85)

Table 2

Top 20 Bonacich Eigenvalues Values for Louisville organizations in 2005____

Organization Eigenvalue Score

Greater Louisville Inc……………………………………………………...133.024

Fund for the Arts…………………………………………………………....22.059

Jewish Hospital Healthcare Services……………………………………….18.475

Frost Brown Todd Law Firm……………………………………………….15.096

37



Norton Healthcare……………………………………………………….….12.143

Louisville Development Foundation………………………………………..10.875

University of Louisville Hospital/University Medical Center…………….…8.269

Louisville Medical Center Development Corporation……………………….8.238

Louisville/Jefferson County Metro Government…………………………….8.098

Anthem Health Plans of Kentucky…………………………………………...7.928

Republic Bank and Trust………………………………………….………….7.765

Fund for the Arts Properties Foundation…………………………………….7.305

Metro United Way……………………………………………………………6.786

Leadership Louisville…………………………………………….…………...6.729

Baptist Healthcare System………………………….…………………….…..6.014

Wyatt Tarrant and Combs Law Firm……………….………………………..5.604

Alliance for Life Science Technology Commercialization, Inc………….….5.567

U of L and Jewish Hosp. Cardiovascular Institute……………………….….5.131

Getcare Health Network………………………………………………..…….5.024

Kindred Healthcare……………………………………………………….…..4.797 _________________________________________________________________

_

Table 3

Top 20 organization Bonacich Eigenvalues with GLI removed from dataset

Organization Eigenvalue Score

Frost Brown Todd…………………………………………………..…..…130.022

Jewish Hospital Health Services………………………………………….....51.801

Republic Bank and Trust…………………………………………………....11.995

38



Fund for the Arts……………………………………………………………..7.548

Leadership Louisville…………………………………………………………6.948

Louisville Development Foundation……………………………………….....5.477

Yum Brands…………………………………………………………….…….4.738

Metro United Way……………………………………………………………4.442

Fund for the Arts Properties Foundation……………………………………..3.107

Republic Bank and Trust—Indiana………………………………………..…2.758

Branch Bank and Trust (BB&T)……………………………………………...2.666

Goldberg and Simpson Law Firm…………………………………………….2.596

Indiana University Southeast…………………………………………………2.425

Actors Theatre…………………………………………………………….…..2.309

U of L/Jewish Hospital Cardiovascular Institute……………….…………….2.063

Home of the Innocents………………………………………………………..2.038

Baptist Healthcare System………………………………………….………...1.965

Louisville Zoo Foundation……………………………………………………1.898

Lynch Cox Gilman Maher Law Firm………………………………………...1.702

Getcare Health Network (now defunct)………………………………………1.602

_________________________________________________________________

_

Table 4

Top 20 individual Bonacich eigenvalues in 2005 with their economic sectors

Individual actor_______________________________________Eigenvalue score

M1476 (Attorney) ………………………………………………………..…27.005

M0031 (Healthcare) ………………………………………………………...23.252

M2985 (Utilities)……………………………………………………………22.823

39



M3715 (Healthcare)………………………………………………………....22.777

M2868 (Education)………………………………………………………….22.315

M0044 (Politician)…………………………………………………………..21.642

M3590 (Health Insurance)…………………………………………………..21.392

M1656 (Development)………………………………………………………20.680

W1259 (Healthcare)…………………………………………………………20.654

M3389 (Development)………………………………………………………20.506

M1799 (Finance)…………………………………………………………….19.563

M2919 (Development)………………………………………………………19.115

M3747 (Education)………………………………………………………….19.115

W1084 (Development)……………………………………………………...19.115

M1611 (Attorney)…………………………………………………………...12.925

M0634 (Healthcare)…………………………………………………………12.687

M3559 (Healthcare)…………………………………………………………12.237

M3682 (Healthcare)…………………………………………………………12.156

M2338 (Health-related field)………………………………………………..11.757 _________________________________________________________________

_

Table 5

Individual Bonacich eigenvalues with GLI cases removed from dataset___

Individual actor_______________________________________Eigenvalue score

M1476(Attorney)…………………………………………………….……..29.256

W0497 (Banking)………………………………………………….………..15.593

M0568(Attorney)…………………………………………………………...11.579

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M1552 (Charity)……………………………………………………………11.498

W0207 (Attorney)………………………………………………………….11.160

M0242 (Attorney)…………………………………………………………..11.153

M2633 (Attorney)…………………………………………………………..11.148

M1562 (Attorney)…………………………………………………………..11.080

M2822 (Attorney)…………………………………………………………..11.080

M0061 (Attorney)…………………………………………………………..10.984

M0098 (Attorney)…………………………………………………………..10.984

M0119 (Attorney)…………………………………………………………..10.984

M0127 (Attorney)…………………………………………………………..10.984

M0157 (Attorney)…………………………………………………………..10.984

M0176 (Attorney)…………………………………………………………..10.984

M0273 (Attorney)…………………………………………………………..10.984

M0275 (Attorney)…………………………………………………………..10.984

M0282 (Attorney)…………………………………………………………..10.984

M0301 (Attorney)…………………………………………………………..10.984

M0390 (Attorney)…………………………………………………………..10.984

_________________________________________________________________

_

Table 6

Clique with highest average Bonacich eigenvalue score with sectors.

Actor with corresponding sector

1. M0031 (Healthcare)


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3. M1476 (Attorney)

4. M1542 (Physician)

5. M2004 (Physician)

6. M2357 (Development)

7. M2868 (Education)

8. M3065 (Local Business Executive)




12. M3715 (Healthcare) _________________________________________________________________

_

Table 7

Clique#106 with second highest average eigenvalue score with sectors.

Actor and economic sector

1. M0031 (Health care)

2. M0044 (Politician)

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3. M0093 (Local Professional)

4. M0465 (Local Business Executive)



7. M2868 (Education)


9. M3389 (Development)



12. W1259 (Health care) _________________________________________________________________

_

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Figure 1: Initial MDS result of sectors in the Louisville, KY Metropolitan

Area

5

4

3

2

1

0

- 1

- 2

A ccountantsA dvert isingA rtsB r a n c hCollegesDeve lopmentDoctorsEliteFinanceFoundationG o v e r n m e n t

HR B

Heal thcareHospitalsInsuranceLa wMHMediaNationalNonprof i tOther PIPoliticians

Public

RetailersSportsTechno logyUtilities

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Figure 2: magnification of the cluster of sectors in the Louisville KY area

0.066

0.065

0.064

0.063

0.062

0.061

0.06

0.059

0.058

0.057

0.056

0.055

0.054

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0.05

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0.048

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0.046

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0.039

0.038

Accountants

Advertising

Arts

Branch

Colleges

Development

Doctors

Elite

Finance

Foundation

Government

Healthcare

Hospitals

Insurance

Law

MH

Media

National

Nonprofit

Other

PI

Politicians

Retailers

Sports

Technology

Utilities

Unpacking the puzzle of Local Healthcare Politics

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