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The below discussion questions need to be answered with citations used in the attached documents. Further use of additional content is okay. Numbers 3-5 are links to documents/citations provided.Name a few characteristics that make data research and analysis in healthcare a separate industry. Think holistically and consider the scientific, policy and social impact perspectives.Explain how processed and analyzed data are used in healthcare. Offer a minimum of 2 examples where data analysis supports/supported health service delivery decision making. Health services analysis as a tool for evidence-based policy decisions: The case of the Ministry of Health and Social Security in MexicoAssessing the burden of diabetes mellitus in emergency departments in the United States: The National Hospital Ambulatory Medical Care SurveySCORE for health data technical package: global report on health data systems and capacity, 2020
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Health Care Manag Sci (2015) 18:475–482
DOI 10.1007/s10729-014-9277-z
Public health capacity in the provision of health care services
Vivian Valdmanis & Arianna DeNicola & Patrick Bernet
Received: 4 November 2013 / Accepted: 16 March 2014 / Published online: 1 April 2014
# Springer Science+Business Media New York 2014
Abstract In this paper, we assess the capacity of Florida’s
public health departments. We achieve this by using
bootstrapped data envelopment analysis (DEA) applied to
Johansen’s definition of capacity utilization. Our purpose in
this paper is to measure if there is, theoretically, enough excess
capacity available to handle a possible surge in the demand for
primary care services especially after the implementation of
the Affordable Care Act that includes provisions for expanded
public health services. We measure subunit service availability
using a comprehensive data source available for all 67 county
health departments in the provision of diagnostic care and
primary health care. In this research we aim to address two
related research questions. First, we structure our analysis so
as to fix budgets. This is based on the assumption that State
spending on social and health services could be limited, but
patient needs are not. Our second research question is that,
given the dearth of primary care providers in Florida if budgets
are allowed to vary is there enough medical labor to provide
care to clients. Using a non-parametric approach, we also
apply bootstrapping to the concept of plant capacity which
adds to the productivity research. To preview our findings, we
report that there exists excess plant capacity for patient treatment and care, but question whether resources may be better
suited for more traditional types of public health services.
V. Valdmanis (*)
Health Policy and Public Health, University of the Sciences,
Philadelphia, PA 19104, USA
e-mail: [email protected]
V. Valdmanis
IESEG, Lille, Paris, France
A. DeNicola
University of Rome, Rome, Italy
P. Bernet
Florida Atlantic University, Boca Raton, FL, USA
Keywords DEA . Bootstapping . Public health clinics .
Capacity utilization
1 Introduction
The health care system in the U.S. is composed of private and
public sectors, each with varying objective functions.
Efficiency analysis has been carried out on the hospital system
focusing on a number of factors, including ownership form,
teaching status, quality of care, economies of scale,
Malmquist productivity measures, specialty hospitals, and
plant capacity. Hollingsworth has summarized the literature
of productivity and efficiency studies in the health care industry [1–3]. One area that is missing in this vast literature is the
efficiency and productivity of public health clinics.
“The mission of the U.S. Public Health Service is to protect, promote, and advance the health and safety of the United
States. According to the U.S. Public Health Service
Commissioned Corps (PHSCC), this mission is achieved
through the rapid response to public health needs, leadership,
and excellence in public health practices, and the advancement of public health service” Each state in the U.S. has its
own public health department which is organized to serve the
needs of the state’s population. The organization form may
vary, but in general terms each county, locality or territory has
a local health department (LHD). LHDs are generally responsible for overall public health initiatives such as epidemiological surveillance and environmental interventions, as well as
direct patient care services. In Florida, county health departments provide most of the public health services throughout
the state. These services include disease control, primary care
and personal health services, environmental initiatives, social/
health education services, and epidemiological studies. We
provide a list of all the services provided by the Florida
Public Health Departments in Appendix 1. Even though there
476
are many services provided by public health departments, the
aspect we study here is direct medical and health care services.
Another reason we focus on the direct care aspect of public
health clinic production is that either (a) they may not be
needed with the expansion of private health insurance under
the Affordable Care Act (ACA) and the possibility that individuals previously treated in the public health clinics enter the
private market if they can afford and purchase health care
insurance in the market exchange, or (b) these services provided by the public health clinics are needed as substitutes to
the private market because of supply or demand issues. If
there is a reduced or increased need for direct health services
the efficient allocation of these funds would require information on current capacity and from this information it could be
inferred that direct health services provided by public health
clinics should be expanded of contracted. This could permit
decision makers to discern if expanded public health funding
from the ACA should be allocated for community and clinical
prevention services, public health infrastructure and public
health training, [4]. Even though the data set we use is based
on one state, Florida, policy changes from any type of health
care reform can affect a wider variety of providers in which
their budgets are determined by a governmental agency.
Although the US healthcare delivery system is generally
organized about private organizations and practices, people
without insurance cannot easily access care; particularly for
non-emergent conditions. Given the high proportion of people
living without health insurance, personal health care has become more prominent in the service mix of public health
agencies [5], and free clinics [6]. Higher than average rates
of uninsured in Florida may be associated with more demand
for personal health services from LHDs. In addition to a lack
of insurance complicating the access to private health care
services, Walls et al. [7] reported that for families who are
poor, for African-Americans, for families headed by a woman,
and for families with more children often seek care at alternative sites such as LHDs. Also affecting states such as Florida is
the number of undocumented workers who are not eligible for
insurance programs under the ACA, which has state officials
concerned as to where these individuals can receive care [8]. It
has also been estimated by Katz [9] that 23 million will remain
uninsured under the ACA which may be exacerbated in
Florida where the governor and legislature have refused federal money to expand Medicaid, the health insurance program
for the poor.
Even as the ACA is implemented with individual mandates
for private insurance, public health departments will likely
still be needed due to the dearth of primary care providers
[10]. It has been reported that estimates demonstrate that
Florida faces a worsening physician shortage [11] particularly
in rural areas and poor inner cities [12]. Federal legislation has
been proposed to increase the number of primary care providers (PCPs), a specialty in especially short supply, but
V. Valdmanis et al.
increases in the number of these physicians will not be realized until 2024 [13]. This supply limitation in private physician markets may also impact public health clinics as a viable
substitute for diagnostic and personal care.
Another aspect of the ACA is the focus on public health
providers. Provisions include an expansion in funding for US
public health care of 9.2 billion dollars including a creation of
new sites for the medically underserved including the expansion of preventive services, primary care, oral care, behavioral
services enabling more care at existing sites [14]. Despite the
social and public role public health plays in the entirety of the
U.S. health care system, no systematic study has been carried
out evaluating the productivity of this sector, and since public
health clinics are important in providing care to vulnerable
populations, it is also relevant to assess system capacity [15].
Studies have been carried out in other countries such as
Nigeria [16], China [17], and Spain [18] In the U.S., studies
have been conducted on specific aspects of public health, such
as gauging the public health impact of health promotion
interventions using the REAIM framework [19], effectiveness
and efficiency of HIV programs [20], public health diabetes
initiatives at one county level public health system [21, 22]
and free clinics operating in Virginia [6]. These studies among
others in the literature have provided information applying a
micro-scale—disease specific focus. Glasgow et al. [19] indicate that the relationships among dimensions of public health
are lacking.
In this paper, we assess the plant capacity for health care
services provided by the 67 county public health clinics operating in Florida. Rather than focus only on DEA efficiency, we
incorporate the Johansen’s [23] definition of plant capacity
which states: “capacity is the maximum amount that can be
produced per unit of time with the existing plant and equipment, provided that the availability of variable factors of
production is not restricted”. We use this approach for two
reasons. First, even though expenditures expansion for public
health services are proposed, we assume that budgets may be
fixed, at least for the time being. Alternatively, even if budgets
can be expanded, the question still arises: is there enough staff
to care for patients? The second purpose of our paper is to
analyze the capacity of public health clinics operating in
Florida under various conditions to assess which factor is
more constricting: expenditures in terms of budget or full time
equivalency staff. Reasons for the budget constraint relate
how best to allocate public money and the staffing constraint
reflects the shortage of medical personal. By assessing productive capacity in these ways, we provide an approach that
can be generalized to other settings providing public health
care services. We also note that the capacity analysis performed here is not meant to represent a production technology
in the assembly line definition. Rather given the best practice
efficiency of the public health clinics in our sample we wish to
determine if resources are being underutilized so that more
Plant capacity public health services
patients or clients could be treated without additional variable
inputs. The policy question we address in this paper is given
the production technology is there enough capacity to treat
additional patients without relying on increased budgets from
the ACA or increased private sector physician markets which
may or may not be in practice in a timely fashion.
Unfortunately, without access to quality of care data we are
unable at this juncture to account for patient care differentials
such as patient time with a provider. We state this at the outset
to ensure research integrity and to alert readers to this caveat as
we present our results.
We also expand on the plant capacity measures used in the
past by applying the bootstrapping techniques advocated by
Kneip et al. [24]. This analysis will enable us to compare our
findings with the more straightforward descriptive studies of
public health clinics as well as to regress environmental factors on the clinic plant capacity measures to illuminate the
county needs based on urban (inner city) and the percent of the
population below poverty (poor) the two factors that could
require more public health clinic services. In the next section,
we provide a description of the plant capacity measure approach and the bootstrapping model we employ. In Section 3
we describe the data and results and conclude the paper with
discussion and policy implications in Section 4.
1.1 Model and methods
We apply the data envelopment approach (DEA) as described
by Färe et al. [25] and the application of Johansen’s definition
of plant capacity described by Färe et al. [26]. This approach
was first applied to the health care setting in hospitals [27],
used by Ferrier et al. [28] to assess possible hospital closure
and by Valdmanis et al. [29] in order to estimate hospital
capacity and capability of intensive care units.
To review, this is a two-step process. In the first step we
assess technical efficiency under variable returns to scale in
which case all inputs can vary. In the second step, we assess
technical efficiency with only the fixed variables included in
the production set. Dividing the efficiency measure using the
first step by the efficiency measure in the second step; we can
derive the plant capacity measure. One of the main benefits of
this approach is that we can also control for inefficiency,
resulting in a measure that accounts for how much outputs
can be efficiently expanded given plant capacity. In this way,
we do not account for expansion that would need to increase
the use of inefficient inputs.
In order to assess efficiency and productivity of public
health departments operating in the state of Florida, we use
DEA. To briefly review, this method is non-parametric and
determines efficiency as the maximum amount of outputs
produced given level of inputs. This measure of the output
based efficiency is derived using linear programming techniques to find the best practice public health clinics that are in
477
turn used to define the best practice frontier. The best practice
public health departments are those that produce a maximum
level of outputs given inputs. Any public health department
that is not on the frontier is deemed as inefficient and the
measure of inefficiency is the radial distance between the
hospital and the best practice frontier. In the case of multiple
inputs and outputs, the technical efficiency of each observation (TEn, n=1,…,N) relative to the best-practice technology
can be calculated by determining the proportion λ which is the
radial distance of the observed inputs used that is technologically required to produce the observation’s given level of
outputs:
T E n ðx; yÞ ¼ maxfðλ : λyn ∈LðxÞg
where TEn(x, y) is the Farrell [30] output-oriented measure of
technical efficiency. L(y) denotes the observed level of outputs produced by all the observations in the sample.
To define plant capacity given efficiency, we must solve
two linear programming problems (given below). In addition
to solving for plant capacity, we also apply bootstrapping
techniques as described by Simar and Wilson [30] so that
we can regress the capacity findings on an array of environmental variables that may be of interest for policy purposes.
Following Simar and Wilson [31], we compute a bootstrapped
DEA technique for two different models—(1) with all inputs
variable and (2) with some inputs fixed. The ratio of the two
efficiency scores is our measure of plant capacity.
To review, we apply an output oriented DEA with variable
return to scale (VRS) by solving
θbit ¼ maxθλ θ
s:t: xit ≥ X t λ
θ yit ≤ Y t λ
l 0λ ¼1
λ≥0
i ¼ 1; 2…; n;
i ¼ 1; 2…; n;
ð1Þ
Where b
θi and D are the Farrell [29] and Shepard’s [32]
distance functions, n is the number of DMUs, Yt is a s×n
matrix of s outputs, X is a r×n matrix of r inputs, λ represent a
n×1 vector of weights which allows to obtain a convex
combination between inputs and outputs and 1’ is a vector
of ones.
In the second step, we measure the maximum amount of
outputs that can be produced given the fixed inputs and
keeping the variable inputs unconstrained. We accomplish
this by solving the second linear programming problem:
θbit ¼ maxθλ θ
s:t: x f it ≥ X f t λ
θ yit ≤ Y t λ i ¼ 1; 2…; n;
l 0λ ¼1
λ≥0
i ¼ 1; 2…; n;
ð2Þ
478
V. Valdmanis et al.
By dividing the solution from Eq. (1) by the solution in
Eq. (2) we arrive at our measure of plant capacity which is
consistent with Johansen’s definition.
However, we do not know if the results from (1) and (2) are
real, or merely an artifact. This uncertainty is because the true
frontier, according to Simar and Wilson [31] cannot be derived
from single point estimates via DEA and therefore bias arises.
To overcome this problem and to obtain unbiased results,
we use bootstrapping techniques, based on the idea of the Data
Generating Program (DGP). These unbiased results can then
be estimated using the given sample to generate a set of
bootstrap samples from which parameters of interest can be
calculated..
In this way, the estimates of the unknown true values of b
θ
0
and b
θit can be generated through the DGP process via a series
of bootstrap estimations.
Thus, for the generic unit i, we first compute the bias term:
N
X
*
b
θi ; ∀i ¼ 1; …; n; ; ;
θ i;b −b
BIAS b
θi ¼ B−1
ð3Þ
b¼1
where b
θit the bootstrapped technical efficiency and B is is the
number of bootstrap replications. So, the bias-corrected DEA
efficiency score is given as:
B
X
c
*
b
b
θi ¼ 2b
θi −B−1
θi;b
θi ¼ θbi −BIAS b
ð4Þ
b¼1
Using this analysis, the results of the bootstrapped DEA are
obtained from 2000 iterations using the FEAR software library linked to the statistical package R. It is also noted that
the same iterations were used for both the output based model
with fixed and variable inputs and the output based model
with just the fixed inputs i.e., (1) and (2) so that the ratios are
derived using the same bootstrap.
Unlike the typical bootstrapped DEA, our plant capacity
measures are both greater than and less than 1, therefore, we
do not need to use a censored or truncated regression approach
in assessing the effect environmental variables may have the
public health centers’ capacity to treat patients. Rather, we
simply employ ordinary least squares (OLS) since we do not
have any a priori hypotheses that an alternative regression
technique is preferable. The use of OLS is consistent with
McDonald’s [33] contention that if there is no truncation or
censoring of the results, then OLS is appropriate in the second
stage analysis using DEA findings.
2 Data
We use the data provided by the Florida State Department of
Health for each county’s public health center. For inputs we
specify expenditures by service category (minus labor costs)
as well as full time equivalent (FTE) labor inputs for the
treatment and health services. The output is defined as the
number of clients served by these sections of the public health
center. As for output heterogeneity counts are typically included by some type of clustering such as by payer group, by
case mix, by age group et cetera. Therefore, the use of number
of individuals by whether they were treated in the health
services or in the treatment services which are defined in
Table 1.
The inputs used in this study are also strictly allocated to
the health care and treatment services mitigating the concern
of double counting inputs.
The descriptive statistics for the inputs and outputs we use
for the bootstrapped DEA and plant capacity measures are
given in Table 2. We also note a negative and statistically
significant correlation (−0.30, p|t|
Intercept
Percent urban
Percent below poverty level
1.18
−2.0
0.39
8.2
−2.06
0.57
0.001
0.05
0.58
N=67
F~4.50 Pr|t|
Intercept
Percent urban
Percent below poverty level
0.68
0.56
2.12
2.8
3.26
1.83
0.007
0.002
0.07
N=67
F~5.32 Pr
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