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Heterogeneous Responses in Prescriptions to Medicare Part D: A Case Study on Physician Decision-Making and
Antibiotics
Master thesis in Economics
Trent Tsun-Kang Chiang
Nationalekonomiska institutionen
Uppsala Universitet
VT 2015
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Heterogeneous Responses in Prescriptions to Medicare Part D: A Case Study on
Physician Decision-Making and Antibiotics 1
Trent TsunKang Chiang
Faculty Advisor: Prof. Rita Ginja Chiang, T., 2015: Heterogeneous Responses in Prescriptions to Medicare Part D: A Case Study on Physician Decision-Making and Antibiotics. Master thesis in Economics at Uppsala University, 2015, 38pp, 30 ECTS/hp
Abstract: To study the decision-making model behind how physicians making
prescribing decisions, we studied the effects of the introduction of Medicare Part D in
2006 on numbers and characteristics of medications prescribed by physicians. We
identified a significant increase in overall number of medications prescribed due to
Medicare Part D but did not find any effects on the number of antibiotics. The result
suggests there exist factors distinguishing antibiotics from other medications that led to a
change in incentives to prescribe antibiotics, such as costs of antibiotics resistances. . We
also identified the heterogeneity responses to Medicare Part D with respect to physician’s
employment status, primary care relationship and patient’s gender and diagnostic
categories.
JEL Classification: I13, I18. L65, I31
Keywords: Prescriptions, Physician Decision-Making, Antibiotics, Medicare Part D,
Healthcare Reform
Trent Tsun-Kang Chiang, Department of Economics, Uppsala University, Kyrkogårdsgatan 10 B, 4th floor, SE- 751 20 Uppsala, Sweden. [email protected] 1I want to thank Professor Rita Ginja, my primary advisor, and Professor Erik Grönqvist during the entire thesis process for their consultation, wisdom and guidance. I also want to thank Professor Mikael Elinder and Per Engström for their leadership in Master of Economics Program at Uppsala University as well as all my friends and peers who worked on Master’s thesis during Spring term of 2015 (VT2015). Lastly, the thesis will not be possible without the scholarship and sponsorship from Swedish Institute’s Scholarship Awards Program.
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1. Introduction Prescription medicine has been the dominant form of treatments chosen by physicians in
the United States (Mott, 2001). With healthcare and pharmaceutical costs playing a
crucial role in cost-effectiveness and cost-benefits studies for healthcare industry,
prescription drug costs play an increasing important role on policy decisions made by
either government agencies or health insurance organizations (Hart, 1997). Different
from other common goods, demand for prescription medicine is mostly driven not by the
consumers (patients) but physicians, who issue prescriptions to the patients (Carrera,
2013). While there have been proposed models suggesting that physicians take patient
input and suggestions into account in clinical scenarios, sometimes as a result of direct-
to-consumer advertising, there have been no definitive studies showing the size of effects
of patient request in clinical decision making process (Carrera, 2013; Armantier, 2003).
Therefore, a model for decision-making process of physicians is an important component
to understand and make informed policy decisions regarding health care policies in health
insurances, payment schemes, and cost-controls.
Many studies in the past have found that physicians are not perfect agents of patients in
prescribing medicines. Besides patient’s clinical and financial benefits, such as the
insurance status and clinical advantages, physicians were also found be influenced by
financial benefits for themselves, advertising to consumers, advertising to physicians and
probability of non-compliance in prescribing drugs (Liu, 2009; Armantier, 2003). Besides
observing effects on the expenditure on drugs or number of drugs prescribed, some
previous studies have also used generic substitution of brand name drugs in
understanding physicians’ decision-making models (Liu, 2009; Godman, 2013).
However, there exist few studies that examined the heterogeneity of physicians
prescription behavior change in response to changes in the financial status of patients
with different categories of drugs. Of particular public interests are antibiotics, which
may results in negative externalities in the form of possible antibiotics resistances with
every prescription. Using the implementation of Medicare Part D in 2006 in the United
States, we investigate the heterogeneous effects of the policy change on change in
physicians prescribing behaviors between antibiotics and other drugs to determine if
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physicians take the unique negative externality of antibiotics prescription into account
during the prescription decision-making process.
This paper is organized as follows: in section 2, we first introduce the relevant
background policies and institutional models on relevant issues. Then, we introduce a
model on physician prescribing decision. In section 4, we discuss the empirical strategy
used while section 5 presents data. Section 6 discusses the results and section 7 concludes
with relevant policy implications.
2. Background A. The choice of focus on antibiotics Antibiotics are a clinical class of compounds that is effective in treating common
bacterial infections. They are one of the most widely-known healthcare intervention in
the public. Healthcare providers provided 258 million courses of antibiotics in the US in
2010 (833 prescriptions per 1000 persons) (Hicks, 2013). Because of its effectiveness and
popularity, patients often request antibiotics even for mild conditions that may not be
bacterial infections. Prescription for antibiotics is high, especially to persons younger
than 10 years old or older than 65 years old. However, antibiotics prescriptions are often
unnecessary despite medical best practices suggest to only prescribe antibiotics if
confirmed bacterial infections. Doctors often feel pressured by patients to prescribe
unnecessary antibiotics (Bennett, 2010). One qualitative study actually recorded a
physician indicating that “You can’t just say ‘It’s viral, you don’t need antibiotics, go
away,’ because [patients] feel they’re being fobbed off. They feel that their illness is not
being taken seriously.” (Butler, 1998). Some studies have suggested that as much as 50%
of the antibiotics to outpatients in the United States may be unnecessary (Hicks, 2013).
Antibiotics uses contribute significantly to the development of antibiotics resistances
around the world (Hicks, 2013). Antibiotics select mutated bacteria with resistances to
antibiotics to survive and eliminate the competing non-resistant bacteria in patients.
People then share resistant bacteria within the population with any subsequent
interactions with other people. With decades of antibiotics usage, resistance to
erythromycin, a common antibiotic, is 28.3% in the US and higher overseas (72.4% in
Hong Kong) (Bennett, 2010).
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Therefore, it is of interest to examine if there exists heterogeneous effects on antibiotics
prescription relative to other treatments, which may result in the negative externalites in
making clinical decisions for patients. With physicians already found to prescribe
medicines in higher quantity and more expensive drugs to patients with prescription drug
insurance such as Medicare part D, it is crucial to understand how the change in patient’s
payment status affects prescription of antibiotics relative to other medicines (Hu, 2014).
B. Institutional Model for Prescribing Medicines Formulary and Insurances In the US, health plans can influence the usage of prescription drugs by adjusting the
level of cost sharing of the cost and changing the procedures for obtaining prescription
drugs. During late 1990s and early 2000s, many private insurers in the US started to cut
costs on drug expenditures by implementing stringent cost-sharing models, such as a
tiered or incentive-based formularies of benefit design (Carrera, 2013). Governments or
insurers also use formularies and treatment guidelines to limit the usage of prescription
drugs in other countries with different payment systems, such as in Sweden and Germany
(Persson, 2012). The formulary is typically controlled by the health organizations or
contracted pharmacy benefit manager, which provides cost information, such as tiers or
generic substitution information for a specific drug, via computer software to prescribing
physicians (Mott, 2001). Two other common strategies to control drug expenditures in
some European countries, reference pricing and price cap regulations, are less common in
the US, because US government lacks to power to regulate the prices of drugs directly,
except through limited influences from Medicare and Medicaid (Brekke, 2009).
Procedures to obtain drugs may also adjusted in order to discourage or encourage drug
usage. By requiring physicians to obtain prior authorizations from insurances before
prescriptions or requiring the usage of low-cost generic drugs before brand-name drugs,
insurers can also lower the expenditures by reducing the usage of expensive drugs
(Carrera, 2013).
Previous literature has focused on if physicians prescribe differently for patients with
different insurances systems, and the result is affirmative. Glied et al. (2002) found that
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physicians’ prescription pattern responds to the insurance status (if belongs to a Health
Management Organization, HMO or traditional-fee-for-service plans) of the majority of
their patients but less to the status of individual patients.
Physicians Physicians issue prescription to patients they treat or see. Prescription denotes a specific
molecule and dosage, either by brand name or molecular (generic) names, for the
specified patient (Carrera, 2013). In issuing the prescription, physicians generally take
patient’s symptoms, medical information into account in finding the appropriate
medication to prescribe. However, there also exist a number of other factors that
physicians may consider in prescription decision-making process other than medical
knowledge and patient’s symptoms.
Substantive amount of literature has detailed the effects of pharmaceutical firms’
marketing efforts on physician’s prescribing choices. It is commonly recognized that
physicians’ prescribing decisions are affected by pharmaceutical detailing, sampling or
other marketing efforts (e.g. sponsored academic conferences) (Fischer, 2010; Campo,
2005; Epstein, 2014). There is also a large amount of literature showing that physicians
take patient’s payment methods, such as health insurance status into account in
prescribing treatments. Physicians are more likely to prescribe more expensive, brand
name pharmaceuticals when patients insured, relative to the cheaper, generic equivalents
of the drug (Lundin, 2000). In a setting where patients face no marginal costs for
prescribing more medicines, physicians were found to prescribe more expensive
medicines to elderly patients in Japan (Iizuka, 2007). Physicians who also have direct
financial incentives themselves in dispensing drugs were found to prescribe more drugs in
Taiwan (Liu, 2009). However, using qualitative evidences, Campo (2005) concluded that
physicians generally do not pay large attention to patient’s financial status, to a higher
degree when large portions of patient’s costs are covered by insurances. Hart (1997), on
the other hand, concluded that drug costs can be an important factor in physician’s
prescribing decision.
In the American context, physicians generally have no financial incentives themselves in
prescribing medicines. However, many studies have indicated that physicians still take
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patients’ financial status into account when prescribing medicines. Inpatient physicians
were found to prescribe more in response to drugs costs in a simulated survey (Hart,
1997). Epstein et al. (2014) found that patient’s formulary information plays a more
important role in prescribing decision when physicians have access to information
technology platforms that provides an easier access to formulary information. Without
the use of information technology, formulary information plays a smaller role in
physicians’ decision-making process. Hu, Decker and Chou (2014) found that the
expansion of Medicare part D to include prescription drug medicines in 2006 resulted in a
statistically significant 35% increase in prescription medicines after the reform.
Pharmacists Patients with prescriptions are required to go to a pharmacist in order to have the
prescription filled. While pharmacists cannot change prescriptions, pharmacists can
suggest a generic substitution of brand-name drugs to patients without prescribing
physicians’ approval, which is also appreciably accepted by physicians (Godman, 2013).
In fact, both physicians and pharmacists believe that pharmacists are responsible for
reviewing a patient’s health plan and its formulary in order to choose cost-saving
alternatives (Carrera, 2013). Pharmacists can also substitute the prescribed drug with
similar and less expensive, but not molecularly identical, drugs to patients, with the
approval from the prescribing physician (“therapeutic interchange”). Pharmacists are also
likely the source of patient’s drug price information, besides price references or
physicians (Mott, 2011). However, generic substitution, in which pharmacist supply a
generic version of a prescribed multi-source drug molecule, does not require physician
approvals.
Medicare and Medicare Part D Medicare is a national social insurance managed by Centers for Medicare and Medicaid
Services, a part of US Federal government, for elderly citizens in the US that are more
than 65 years old. Before 2006, Medicare had only 2 traditional fee-for-service parts,
part A and B, and a managed care component, part C. Medicare Part A is the “hospital
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insurance” that covers mostly inpatient hospital services ranging from lab tests to doctors
visits to hospice cares. Medicare Part B is a supplemental program that covers services
that are not covered by part A, ranging from costs associated with outpatient services to
ambulance costs to preventive care.
Both part A and B do not have prescription drug coverage and only covers inpatient and
outpatient healthcare services except in extremely limiting circumstances in part B (Hu,
2014). Thus, Medicare patients had to obtain prescription drug insurances from other
sources, such as the employer, Medicare Part C, or state programs prior to 2006. On
January 1, 2006, Medicare part D was introduced to cover prescription drugs. Two types
of private insurance plans of part D were introduced for patients to voluntarily enroll: a
Prescription Drug Plan (PDP) and a Medicare Advantage-Prescription Drug Plan (MA-
PD) that covers both healthcare services and prescription drugs.
The introduction of part D decreased the number of Medicare beneficiaries without any
drug coverage from 19% in 2002 to 10% in July 2006 (The Henry J. Kaiser Family
Foundation, 2010). Medicare Part D increased the number of annual prescriptions by
30% and the expenditure for prescription drugs by 40% for both normal elderly
population and elderly population in poor health. (Kaestner and Khan, 2012). Other
studies have similarly concluded that the introduction of Medicare Part D increased the
total monthly drug spending among enrollees by $13-41, depending on the number of
previously drug spending (Zhang, 2009). Yin (2008) concluded that Medicare part D
resulted in a modest increase in drug usage and reduced the average out-of-pocket drug
expenditures among Medicare beneficiaries.
3. Model Hu, Decker and Chou (2014) described a model for physician decision-making:
(1)
in which physician maximize his/her utility function in treating patient i as described
above. Di represents the drug treatment the patient received while Ti represents other
non-pharmaceutical patient received. Pd and pt is the unit price for a unit of drug and
other treatment, respectively. k is the fraction of out-of-pocket price for patients for drugs
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and other treatments, respectively, after the insurance or other payment discounts. Ai is
the effort by physician on treating the patient, which may include things such as lifestyle
recommendations. Physician’s efforts on patients’ health Ai are typically not observable
by patients. C(Ai) thus represent the cost to physician to make such efforts. Finally, F(.)
is a “health production function”, in which patient produced health with a combination of
drug, other treatments and physician efforts. The unit health of patients is worth m for the
physician.
To account other facets of the physician’s decision to prescribe medicine found in
previous literatures, we decided to modify the above model and propose the following: 2
(2)
in which physician prescribed patient i non-antibiotics drug Di ,antibiotics Bi and other
treatment Ti. Physician has a negative utility with prescribed antibiotics Bi since
antibiotics prescriptions help develop global resistance to antibiotics with a probability qb
and unit cost of resistance cb. In addition, physicians also take patient’s preference Ii of
drugs, antibiotics and treatments into account. Ii can be negative when physician prescribe
in disagreement with patient’s preference of treatments and drugs, or positive when both
patients and physicians agree on the treatment, antibiotics and medications prescribed. Ii
can also be zero if patient do not express specific preferences to patients. Notably, the
patients can request unnecessary antibiotics from the physician and if physician refuses, it
will produce a negative Ii while, if physician comply, Ii would be positive. Physicians
receive positive utility when they agree to prescribe such medicines, in which they
receive a unit “agreement” worth of n. In addition, physicians usually have imperfect
2 We recognize that this is a simplified model where health is treated as a static stock in the model in one time period. Alternatively, we can write the dynamic model as follows:
in which s is the current time period and Fs-1 is the health stock from the previous period, and rS is the discount factor for the effectiveness of current antibiotics due to current antibiotic resistance. However, we do not currently understand the detailed mechanism of antibiotics resistances. For simplicity, we choose to use our static model in equation 2.
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information on specific financial information of patients, kd and kt. qb and cb are
estimated by individual physicians from current scientific literatures without definitive
magnitudes, but the sign of qb and cb should definitively be larger than zero, as the
positive link between antibiotics usage and resistances is scientifically sound.3 Thus, we
can also conclude that physicians are imperfect agent for patients, as they do not have
perfect information on patients’ financial/insurance status. The optimal level of
physicians’ effort level Ai happens when the marginal benefit of added efforts equals to
zero.
If we examine the first order derivative of the above model with respect to non-antibiotics
Di and antibiotics Bi, we can write the following by taking the first-order derivative to
solve for physician’s welfare maximization problem:
(3)
(4)
Using equation 3 and 4, we can solve conditions in which physicians maximize their
welfare:
(5)
In equation 5, ∂F/∂D and ∂F/∂B reflect the marginal health benefits of non-antibiotic
drugs and antibiotics, respectively. ∂I/∂D and ∂I/∂B are the marginal “preference” of
patients on an additional unit of non-antibiotic drugs and antibiotics, respectively.
Using equation 5, we examine a specific scenario: when patients express no preferences
over the treatment or medication prescribed; that is, I(Di,Bi,Ti)=0.
(6)
Thus, the differences between marginal health benefits of non-antibiotics and antibiotics
must equate to the unit cost of antibiotic resistances. Because qbcb is determined by
current public health conditions and not likely to change when drug insurance policy
3 About 50% of antibiotics prescription in US is estimated unnecessary and antibiotics prescription is an important factor in growing antibiotics resistances (Hicks, 2013; US Department of Health and Human Services, 2014; Mott, 2011)
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changes (kd increases or decreases), the physician, who considers the potential cost of
antibiotic resistances would not increase antibiotics prescription in the event of a policy
change to decrease kd, such as the introduction of Medicare Part D.
Lastly, we must note the constraint of the limited amount of financial resources available
for the patient’s to pay for drug out-of-pocket expenditures.4 While patient’s detailed
financial status beyond insurance status is generally observable by the physician, patients
may change the preferred combination of drugs, antibiotics, and treatments because of
their own financial constraints. These preferences are illustrated through the preference
function Ii in our model. Notably, the introduction of Medicare Part D resulted in both an
increase in medications prescribed and a reduction in out-of-pocket drug expenditure
(Yin, 2008). Thus, the lower out-of-pocket cost after policy implementation may lessen
the magnitude of Ii in the model after 2006.
4. Empirical Strategy In this study, we aim to see if physicians behave differently when deciding prescribing
antibiotics against all other drugs, which is similar to antibiotics in other aspects but will
not result in the negative externality of antibiotics resistance. To examine the hypothesis,
we must be carefully in preventing selection bias in which difference was a result from
the unique quality of antibiotics other than antibiotics resistance. Thus, we utilize the
implementation of Medicare Part D as the exogenous policy shock and examine if the
degree in increase in drug prescription were different for antibiotics compared to other
treatments (a difference-in-difference approach coupled with regression-discontinuity
DD-RD). This approach was used by Hu et al (2014) and they found a 35% increase in
drug prescriptions with Medicare Part D. However, they did not examine any specific
prescriptions, such as antibiotics.
4 Patient’s financial constraint can be written as following, but is not typically observable by the prescribing physician:
in which pc is the price level for all other goods, Ci is the consumption of all other goods, and Mi is the budget of patient i, assuming patients won’t borrow for out of pocket healthcare expenditures.
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Difference-in-Difference (DiD or DD) design is a common strategy to test for policy
treatment effects in economics. By estimating the differences in changes of outcomes,
DD can estimate the treatment effects of policy, assuming that both 60-64 and 65-69
year-old patients share similar prescribing patterns prior to 2006. However, the
assumption may not hold if patients’ age within the range of 60-69 affects prescribing
patterns, and if the prescription patterns change discontinuously at age 65, for example,
due to retirement age or any other motives unrelated to life cycle. Thus, we decided to
employ the combined RD-DD model in order to reduce the weaknesses of DD. RD-DD
model compares the difference in changes immediately before and after age 65 before
and after the policy changes. RD-DD model allows us to address the policy confounding
issue in which patients become qualified for all Medicare Programs at age 65 by
assuming that Medicare Part A and B remains time-invariant over the 2006 threshold of
Medicare Part D implementation.
Therefore, we simply estimate the following equation as our main specification:
Outcomeij can be the number of total medications prescribed, antibiotics prescribed to
patient i by physician j or the share of antibiotics in the total number of medications
prescribed. Charlsonindex is a dummy variable that takes value of 1 if the Charlson
comorbidity index, which predicts the 10-year morbidity calculated by the diagnosis, is
larger than 0, and 0 otherwise. Elderly is an indicator variable that takes value of 1 ifa
patient is over age 65 at the time of the visit and thus qualified for Medicare Part D and 0
otherwise. After2006 is whether the visit happens on or after 2006, when Medicare Part D
was available. AgeYears are the years away or from age 65. AgeYears can be modeled in
either a quadratic or a cubic structure. Xi are the control variables for patients such as
race, ethnicity, gender, and major diagnostic category associated with the visit. In some
specifications, we also used ϕj,, which are physician fixed effect as we can track if visits
were treated by the same physician during the same survey year. We focused on
individual’s age between 60-69 at the time of the visit for the band of DD-RD designs
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since people who are either too young or too old have different observable and
unobservable variables compared to patients’ age between 60 and 69. To further ensure
the homogeneity among observations, we also limit our samples to those who are seen by
primary care physicians, i.e. those physicians who specialized either in family medicines
or internal medicines. In addition to the main specification, we also investigated the role
of physician’s employment status; if owning a practice affects prescribing behaviors. For
all specifications, we used simple ordinary least square (OLS) to estimate the coefficients
for variables. To capture the uncertainty in using patient age in years as supposed to days,
which is a continuous variable, we employed age-clustered robust standard error in all
regressions as detailed in Lee and Cards (2008). The conventional standard errors fail to
capture the effect of having the running variable in clustered format and would produce a
smaller standard error. By assuming the group (“clustered”) structure behind the running
variable (age), the estimation in this study have the same coefficient but a different
standard error compared to the conventional standard error. The age-clustered standard
errors also accounted the for the heteroscedasticity and should be appropriate to our
model the conventional heteroscedasticity-consistent robust standard errors.
5. Data We use the National Ambulatory Medical Care Survey (NAMCS) from the National
Center for Health Statistics, a US federal government agency. The survey has been
conducted annually since 1989 and data are available until 2010. We used the data from
2002 to 2004 and from 2006 to 2010 to eliminate the possibility of anticipatory effects in
2005 (Table 1). We used data from 2002 because NAMCS underwent a significant
reform in 2002 and made several changes in its data collection techniques as well as
items collected. The nationally representative sample of non-federally-employed office-
based physicians provided visit-level data in which physicians provided information on
each visit by a single patient during a one-week period. The variables in the dataset
included the four geographical regions of the physician’s practice, if the physician is
located in a metropolitan-statistical-area (MSA), patient’s basic demographic
information, drugs prescribed and patient’s insurance and payment methods. It also
cataloged the patient’s diagnosis code in ICD-9-CM and symptoms. Due to the use of the
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restriction of public-use file, we were not able to pinpoint the exact location or the
birthday of the patient, which requires us to use the age in years instead to control for
eligibility for Medicare Part D. We looked at patients with age from 60-69 on the date of
the visit in NAMCS. To define antibiotics, we use the list of antibiotics drug codes from
US Department of Health and Human Services (HHS) (2014) to generate the count for
numbers of antibiotics prescribed during each visit, as listed in Appendix Table A1.
Table 1 presents the description of the variables used in the analysis, which are all
observations in NAMCS with patient age between 60 and 60, occurred between year
2002-2010 (excluding 2005) that are seen by a physician specialized in either family
medicine or internal medicine.
Table 1. Descriptive Statistics for the Estimation Samples Variables Obs Mean Std.
Dev. Min Max
Number of total Medications Prescribed
7035 3.36 2.63 0 8
Number of Antibiotics Prescribed
7035 0.12 0.35 0 3
Age 7035 64.23 2.83 60 69 Charlson Index 7035 0.30 0.53 0 2 Male (%) 7035 43.14% Older than 65 Years Old
7035 46.35%
Pay with Medicare
5295 32.60%
Race White 4911 69.81% African American 592 8.42% Asian 180 2.56% Native Hawaiian or
Pacific Islander 16 0.23%
Native American 112 1.59% Blank 1215 17.27% Diagnostic Categories*
Respiratory System 1111 15.79% Infectious and Parasitic 214 3.04%
14
Diseases Neoplasms 169 2.40% Endocrine, Nutritional
and Metabolic Diseases 2380 33.83%
Diseases of the Blood and Blood-Forming Organs
105 1.49%
Mental Disorders 519 7.38% Nervous System 478 6.79% Circulatory System 2424 34.46% Digestive System 558 7.93% Genitourinary System 491 6.98% Skin-Related 372 5.29% Musculoskeletal and
Connective Tissue 1306 18.56%
Congenital Anomalies 16 0.23% Injury 347 4.93% * A patient can have up to three diagnoses recorded in NAMCS during a single visit to physician’s office.
6. Results
A. Regression Discontinuity in Number of Medications and Antibiotics
We first replicated the results in previous literature indicating an increase in drug usage
after the implementation of Medicare part D. We used a simple regression discontinuity
(RD) design with local linear regression (triangle kernel) to graph any discontinuity in
four different outcomes: 1) numbers of total medications, 2) number of antibiotics, and 3)
share of antibiotics as part of total number of medications for patients with age between
60 and 69 (Figure 1-8). For the graphs below, we have included data from 2005 as well
as data from physicians specialized in all specialties in NAMCS Datasets to maximize the
number of observations available. In each graph, we listed the local linear regression
estimator for the discontinuity and if the local linear estimate at the age cutoff is
statistically significant in the caption. The solid lines are the local linear regression
results after the introductions of Medicare in 2006 while dashed lines represent the period
from 2002 to 2005. Solid filled circles are the averages of the outcome variable post-2006
while pluses are prior to 2006. To allow easier visual inspections on the figures, the
graphs below are the representation of local linear regressions run independently on both
15
sides of the threshold. The RD graphs used to generate the bandwidth can be found in the
appendix Figure A1 to Figure A6. In these figures, the estimate of the effects of the
implementation of Medicare Part D is given by
(
where αA and αB are the sizes of the discontinuity at age 65 after and before 2006,
respectively.
Note. Total Number of Medications Coded, age 60-69. (2002-2005 Estimate: -.074 (0.159); 2006-2010 Estimate: 0.264 (0.156)*)
16
Note. Total Number of Antibiotics, age 60-69. (2002-2005 Estimate:-.0136(0.00967); 2006-2010 Estimate: 0.00734 (0.00837))
Note. Share of Antibiotics, age 60-69. (2002-2005 Estimate:-0.00378(0.00517); 2006-2010 Estimate: 0.00141(0.00390))
17
Examining Figure 1, we can clearly identify a discontinuity in total number of
medications prescribed after Medicare Part D in or after 2006 but not in the samples
before 2006, which is consistent with prior literature (Hu, 2014). However, the number of
antibiotics showed similar prescribing patterns before and after 2006 as indicated in
Figure 2. Using the share of antibiotics in the total number of medications prescribed, we
can also see that the prescribing pattern remains similar prior and after the
implementation of Medicare Part D in 2006 on Figure 3. However, it is worth noting that
there exist decrease in shares for both before and after 2006 groups at age 65 in Figure 3,
which may be a result from the combination of an increase in total number of drugs
prescribed and the constant number of antibiotics prescribed.
Importantly, RD design assumes that the assignment to either side of discontinuity
threshold is as good as in a random experiment. In this study, RD suffers from a
confounding policy discontinuity at age 65: besides becoming eligible for Medicare Part
D, patients who turn 65 would also be qualified for Medicare Parts A and B, which
covers inpatient and outpatient services. Thus, we cannot infer causal relationships from
Figures 1-6 and must look for other strategies in order to identify the effects of Medicare
Part D expansion.
B. RD-DD Design and Results
On Table 2 , Table 3 and Table 4, we present the results from the simple OLS regression
with outcome variable being the number of medications prescribed, number of antibiotics
prescribed and share of antibiotics in the medications prescribed, respectively. Across all
three tables, specification 1 is our main specification without controlling for diagnostic
categories from the model described above. Specification 2 added 14 diagnostic category
dummies as controls to the specification 1. Specification 3 added the physician fixed
effects since NAMCS survey was conducted in one physician’s office to record all visits
to the office during that period and allowed us to identify records of visits to the same
office during single survey year. We changed the age variable structures from the cubic
structure, used in previous three specifications, to quadratic structures in specification 4.
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Specification 5 is the regression with only age controls and without any other covariates,
such as sex, gender, or if physicians are in solo practice.
From Table 2, we can clearly observe that the introduction of Medicare Part D created an
increase in total number of medications prescribed in all specifications. The largest
magnitude of the variable of interest (Elderly*After2006), which indicated the effects of
the implementation of Medicare Part D on the outcome variable, was observed in
specification 2 on Table 2. Due to the many control variables omitted due to
multicollinearity in specification 3 (physician fixed effect), the reduction in significance
can be attributed to the larger standard errors caused by lack of control variables. It is
worth noting that the results for the total number of medications prescribed remain
significant across all 5 specifications, even without any control variables in specification
5. In addition, the magnitude of the effect remains relatively stable ranging from 0.3-0.35
additional medications per visit due to Medicare part D.
From Table 3, we observed that the introduction of Medicare Part D did not result in an
increase in prescriptions of antibiotics. Since numbers of antibiotics are strictly less than
the total number of medications, we can see that the coefficients for antibiotics are
significantly smaller than those on Table 2. However, across all specifications on Table 3,
none of them showed a statistically significant effect. Moreover, in specification 3 on
Table 3, we can see that the magnitude actually became negative controlling with
physician fixed effects. Thus, we can conclude from Table 2 that the number of
antibiotics prescribed, in general, did not increase with the introduction of Medicare Part
D in 2006.
The results from the share of antibiotics in total medications prescribed are presented in
Table 4. Similar to Table 3, Table 4 shows no statistically significant effect at age 65
before or after 20065. These results are consistent with the observation from Part A’s RD
graphical analysis, which indicated that antibiotics did not have a jump in usage after the
introduction of Medicare Part D in 2006, either in absolute terms or in relative terms to
other medications. Furthermore, the lack of discontinuity for the number (and share) of
5 Besides number and share of antibiotics prescribed, we also tested using a dummy variable indicating any antibiotics were prescribed and reached similar conclusions as in Table 3 and 4.
19
antibiotics prescribed after 2006 is consistent with our model’s prediction that physicians
have different decision-making process for prescribing antibiotics and non-antibiotic
medications, such as considering antibiotics resistances. Since the unit cost of antibiotic
resistance was not changed when Medicare Part D was introduced, physicians are not
more likely to prescribe antibiotics due to a change in patient’s financial status, which
decreased kd, the percent of out-of-pocket costs of prescription medicines for patients
with insurances.
Table 2. Results for Total Number of Medications NAMCS 2002-2004, 2006-2010
Number of Medicines
(1) (2) (3) (4) (5)
Elderly*After2006 0.350** 0.385** 0.298* 0.341** 0.322** (0.132) (0.129) (0.154) (0.130) (0.112) Age Cubic Cubic Cubic Quadratic Cubic Covariates# Yes Yes Yes Yes No Diagnostic Categories
No Yes Yes No No
Physician Fixed Effect
No No Yes No No
Observations 5,373 5,371 5,371 5,371 5,371 Robust standard errors, clustered by age of the individuals, in parentheses
*** p<0.01, ** p<0.05, * p<0.1 # Covariates includes (Visit Month, year, race, ethnicity, sex, Charlson Index, e-medical record and solo
practice, months from Dec.31,2001)
Table 3. Results for Total Number of Antibiotics, NAMCS 2002-2004, 2006-2010 Number of Antibiotics (1) (2) (3) (4) (5) Elderly*After2006 0.009 0.006 -0.001 0.009 0.009 (0.016) (0.014) (0.020) (0.016) (0.017) Age Cubic Cubic Cubic Quadratic Cubic Covariates# Yes Yes Yes Yes No Diagnostic Categories No Yes Yes No No Physician Fixed Effect
No No Yes No No
Observations 5,373 5,371 5,371 5,371 5,371 Robust standard errors, clustered by age of the individuals, in parentheses
*** p<0.01, ** p<0.05, * p<0.1 # Covariates includes (Visit Month, year, race, ethnicity, sex, Charlson Index, e-medical record and solo
practice, months from Dec.31,2001)
20
Table 4. Results for Share of Antibiotics, NAMCS 2002-2004, 2006-2010 Share of Antibiotics (1) (2) (3) (4) (5) Elderly*After2006 -0.004 0.005 -0.009 -0.004 -0.003 (0.007) (0.005) (0.007) (0.007) (0.007) Age Cubic Cubic Cubic Quadratic Cubic Covariates# Yes Yes Yes Yes No Diagnostic Categories
No Yes Yes No No
Physician Fixed Effect
No No Yes No No
Observations 5,373 5,371 5,371 5,371 5,371 Robust standard errors, clustered by age of the individuals, in parentheses
*** p<0.01, ** p<0.05, * p<0.1 #Covariates includes (Visit Month, year, race, ethnicity, sex, Charlson Index, e-medical record and solo
practice, months from Dec.31,2001)
C. Robustness Checks
To verify the robustness of our results above, we check the results for varying
bandwidths for the DD-RD designs. We first verify the robustness for the RD-DD
bandwidth selections within close range to age 65. Table 5 presents results from varying
bandwidths with the identical specifications from specifications 1 and 2 on Table 2.
According to Lee and Lemieux (2010), regression discontinuity bandwidths need to
balance the noise created by having too few observations and the heterogeneity in
observations between the two ends of the selected bandwidth.
From Table 5, we can see that the statistically significant effects of the introduction of
Medicare Part D remains significant when including larger or smaller bandwidths in
specifications 1 to specification 5. The results also hold when controlling for diagnostic
category dummies in specification 3-5. With a small age bandwidths, however, the
significance of the effects of the policy was reduced in specification 5, which may be a
result of smaller number of observations available in a more limited sample, which in
term increased the possibility of been affected by noise in the sample. Lastly, we also use
the number of antibiotics as outcome variable with various age bandwidths. To our
surprise, we observed a marginally significant effect in specification 6 when we expand
the RD sample bandwidth from 60-69 to 58-71. However, the significant result
disappeared when we regress with 59-70 year-old patients in specification 7 on Table 5.
The result indicated that while Medicare Part D may also have an effect on the number of
antibiotics prescribed, it is marginally significant and relatively weaker than the effects
21
on the total number of medications prescribed. To further understand the reason behind
the marginally significant results in specification 6 and 7, we examine the distribution of
the observations between ages 58-71 in our dataset, with and without 2005 data as listed
in appendix Table A2. We find that there are no outliers or aberrations in the numbers of
observations across the age spectrum except the natural decline in number of
observations as people age. Thus, the significant results in specification 6 can be a result
of the inclusion of the high number (400) of observations at age 58 post-2006, relative to
other age in the dataset.
On Figure 4, we can see that after expanding the RD bandwidth, there still exist little
evidence in any discontinuity at age 65 post-2006.
Table 5. Regressions with Different RD Bandwidths (1) (2) (3) (4) (5) (6) (7) Outcome Variable Total Number of Medications Prescribed # of Antibiotics RD Bandwidth 58-71 61-68 58-71 61-68 62-67 58-71 59-70 Elder*After2006 0.358*** 0.428** 0.386*** 0.452** 0.307* 0.0255* 0.0200 (0.0906) (0.147) (0.0893) (0.148) (0.135) (0.0132) (0.0149) Age Cubic Cubic Cubic Cubic Cubic Cubic Cubic Diagnostic Categories
No No Yes Yes Yes Yes Yes
Observations 7,539 4,328 7,534 4,323 3,260 7,534 6,434 R-squared 0.081 0.077 0.126 0.118 0.159 0.171 0.164
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
22
Figure 4. Antibiotics Prescription across age 65, individuals with age 58-71.
Table 6. Regressions Including 2005 Observations –Age 60 to 69
(1) (2) (3) (4) (5) (6)
Outcome Variable Total Number of Medications
Total Number of Antibiotics
Share of Antibiotics
Elderly*After2006 0.273** 0.201* -0.000256 0.00475 -0.00721 -0.00460 (0.107) (0.103) (0.0153) (0.0145) (0.00525) (0.00508) Diagnostic Categories Control
Yes No Yes No Yes No
Observations 5,977 5,977 5,977 5,977 5,977 5,977 Robust standard errors, clustered by age of the individuals, in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Secondly, we includE the 2005 observations in our data and repeated the regressions with
varying RD bandwidth as presented in table 6. However, we observe surprising results as
the treatment effects of Medicare Part D decreased across all specifications. The
23
reduction in treatment effects after including 2005 observations is surprising as Hu et al.
(2014) showed an opposite direction of effects (increase in effects) after including 2005
observations in the sample, which they attributed to the anticipatory effects of Medicare
Part D introduction by patients. Patients may conserve drug usage in 2005 in order to
qualify for Medicare Part D on Jan.1, 2006 (Hu, 2014). The anticipatory effect would
lead to the overestimation of Medicare Part D and resulted in a larger magnitude of
treatment effect after including 2005 data. Alpert (2012) found that Medicare Part D
introduction induced anticipatory effects, when patients delayed receiving chronic drug
prescriptions in 2005 but not acute drugs. Nevertheless, the inclusion of 2005 data still
indicated a positive jump in number of medications prescribed due to Medicare Part D.
One possible explanation for the decrease in magnitude of the coefficients may be due to
our dataset for regressions contains only physicians specialized in either family or
internal medicines in office visit (non-hospital) settings, which generally prescribe less
specialized, higher-priced medications than specialized physicians in other fields (e.g.
cardiologists or dermatologists).
While we do not have a clear explanation for the reason behind the drop in magnitude,
we still find that our results significant and valid despite the reduction in treatment effects
after including 2005 data. Further research may be warranted in order to examine the
effects of Medicare Part D on the prescribing decision-making process in 2005.
To further check the robustness of our regressions, we also generated a series of placebo
cutoffs on age and years of policy implementation as shown in Table 7. Using the main
specification similar to those of specification 1 on Table 2 with 2005 data, we can
conclude that our results are robust against placebo age cutoffs (age 64) from
specification 1 and 2 as well as placebo year of policy implementation (2007) from
specifications 3 and 4.
24
Table 7. Placebo Cutoffs for ages and Policy Year# Panel A: Placebo for Treatment Age Number of Medications
Prescribed Number of Antibiotics
Prescribed (1) (2) Age64*after2006 0.191 0.00431 (0.111) (0.0115) Observations 5,977 5,977 R-squared 0.111 0.011 Panel B: Placebo for Years of Policy Implementation
(3) (4) Elderly*After2007 0.141 0.00913 (0.111) (0.0168) Observations 5,977 5,977 R-squared 0.111 0.011
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
#Physicians in the samples specialized in either family or internal medicine. .
D. Effects from the Employment Status of Physicians
To further understand the factors behind the reasons for the discrepancy with previous
literature on the anticipatory effects of 2005 observations, we investigated the role of
physician’s ownership of the practice in prescribing medicines. Sun (2006) reported that
physicians who do are not the owner of their own practices prescribed 1.5 times more
antibiotics in upper respiratory infection cases compared to those who do.
In Table 8, we can clearly identify that physicians who do not owns the practice are more
likely to prescribe more medicines after the introduction of Medicare Part D. Physicians
who do not own the practice may either be an employee or a contractor of the practice.
Similar to Sun (2006), Medicare Part D only has statistically significant effects on
physicians who do not own the practice in specification 1 and specification 2 on Table 8.
The fact is surprising since we controlled for if physician is a solo practitioner, the use of
electronic medical record and diagnostic categories as a proxy for the type of the primary
care physicians (all physicians in the sample specialized either in family or internal
medicine) in all specifications on Table 8.
25
Table 8. Physician’s Ownership and Prescribing Patterns#
Number of Medications Prescribed
(1) (2) (3) (4)
Ownership Physician is not the Owner of the Practice
Physician is the Owner of the Practice
Elderly*After2006 0.401** 0.312* 0.240 0.335 (0.149) (0.170) (0.233) (0.228) Diagnostic Categories Controls
No Yes No Yes
Observations 2,614 2,610 2,759 2,761 R-squared 0.122 0.163 0.120 0.157 Number of Antibiotics Prescribed
(5) (6) (7) (8)
Elderly*After2006 0.0283 0.0197 -0.00614 -0.00611 (0.0301) (0.0246) (0.0220) (0.0175) Diagnostic Categories Controls
No Yes No Yes
Observations 2,614 2,610 2,759 2,761 R-squared 0.012 0.161 0.025 0.189
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
#Physicians in the samples specialized in either family or internal medicine.
Similar to our prior findings, Medicare Part D did not have a significant effect on number
of antibiotics, regardless of physicians’ employment status, as shown in specification 5 to
8 on Table 8.
Previous literature has shown that physicians working for either private clinics or private
hospitals are more likely to prescribe cheaper generic drugs compared to those who
works for public sectors in Taiwan (Liu, 2009). Healthcare market competition has also
driven up the number of prescribed antibiotics in Taiwan (Bennett, 2010). Table 8 shows
that the introduction of Medicare Part D has only significant effects in increasing the total
number of prescribed medications for primary care physicians who do not own the
practices. However, physicians in the US, unlike those in Taiwan, are less likely to own
a pharmacy and have less financial incentives tied to prescribed medications.
Past literatures have attributed the higher rate of prescription of physicians who do not
owns the practice to peer pressure, legal concerns or the physician’s desire to validate the
26
reason for an office visit (Sun, 2006). Our findings in Table 8 showed that physicians
who do not owns the practice are also more likely to respond to the introduction of
Medicare Part D than physicians who do not.
E. Physician being the Primary Care Physician (PCP) of the Patient
Sun (2006) also found that physicians who are also the primary care physician (PCP) of
the patient visiting are more likely to prescribe antibiotics in upper respiratory infection
cases. The generally long-term relationship between PCP and patient may result in
patients’ higher willingness to request specific medicines or allows physicians greater
knowledge regarding patient’s financial status. To see if PCPs are also more susceptible
to the influence of drug insurance expansion, we presented the results on Table 9 below.
From specification 1 on Table 9, we observe that only physicians who are also PCP of the
patients have statistically significant policy effect from Medicare Part D’s
implementation. We need be cautious about interpreting the results when a physician is
not PCP as the sample sizes were very small (658) and thus had large standard errors and
may have inaccurate estimates in specification 2 and 4. However, Medicare Part D still
did not have statistically significant effects on the number of antibiotics, even only
looking at PCP physicians.
Table 9. Prescription Effect of Being the Primary Care Physician of the Patient#
(1) (2) (3) (4) Outcome Variable Total Number of
Medications Total Number of Antibiotics
Primary Care Physician of the patient?
Yes No Yes No
Elderly*After2006 0.337** 0.473 0.0121 -0.0356 (0.144) (0.398) (0.0191) (0.0500) Observations 4,715 658 4,715 658 R-squared 0.151 0.203 0.155 0.303
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
# Sample includes only physicians specialized in family or internal medicine.
27
F. Prescribing Differences with Different Diagnosis categories
It is also possible that physicians treating patients with certain diagnoses prescribe
differently in response to the implementation of Medicare Part D. To see the differences
in prescribing behavior, we first return to specifications 2 on Table 2 and Table 3. By
examining the statistically significant diagnostic category variables in Table 10, we
identified two groups of diagnosis categories that are statistically significant in the total
numbers of medications and antibiotics. For respiratory diseases, nervous system diseases
and genitourinary system diseases (“Group A”), the dummies for these diagnoses are
statistically significant and positive for both medications and antibiotics. However, for
endocrine, nutritional and metabolic diseases, mental disorders, musculoskeletal system
and connective tissue diseases, circulatory diseases and injuries (“Group B”), the
dummies for these diagnoses are statistically significant and positive for total number of
medications but negative for total number of antibiotics. Lastly, we will classify
diagnostic categories that do not belongs to either group A or B into “group C”, which
includes infectious diseases, neoplasms, blood-related diseases, digestive system diseases,
skin-related diseases and congenital anomalies.
To further investigate the characteristics and effects of these groups of diagnostic
categories, we examine the prescribing behavior change in patients that have a diagnosis
in any of the diagnostic categories in all three groups in Table 11 below.
Table 10. Comparison Between Effect of Different Diagnostic Categories
(1) (2) Outcome Variable Total Number of
Medications Total Number of
Antibiotics Elderly*After2006 0.385** 0.00623 (0.129) (0.0138)
Group A: Significantly Positive in both Specifications
Respiratory System Diseases 0.609*** 0.310*** (0.124) (0.0190) Nervous System Diseases 0.408** 0.0334* (0.152) (0.0156) Genitourinary System Diseases 0.316*** 0.179*** (0.0903) (0.0202) Group B: Significantly Negative in Antibiotics but Significantly Positive
28
in Number of Medications Endocrine, Nutritional and Metabolic Diseases and Immunity Disorders (B)
0.567*** -0.0518***
(0.132) (0.0140) Musculoskeletal System and Connective Tissue Diseases (B)
0.436*** -0.0528***
(0.131) (0.00791) Mental Disorders (B) 0.629*** -0.0852*** (0.135) (0.0192) Injury and Poisoning (B) 0.315* -0.0358* (0.142) (0.0161) Circulatory System Diseases (B) 0.829*** -0.0485*** (0.0655) (0.0101) Group C: Insignificant in at least One of the Two Specifications
Digestive System Diseases (C) 0.645*** -0.00154 (0.141) (0.0148) Blood and Blood Forming Organs (C) 0.527 -0.0126 (0.301) (0.0511) Skin-Related Diseases (C) 0.286 0.102*** (0.202) (0.0228) Congenital Anomalies (C) -0.0466 -0.0944*** (0.642) (0.0272) Infectious Diseases (C) 0.283 0.0684 (0.263) (0.0505) Neoplasms (C) -0.196 -0.0507** (0.246) (0.0208) Observations 5,371 5,371 R-squared 0.154 0.166
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
Table 11. Sub-Sample Analysis with Different Diagnostic Groups (1) (2) (3) (4) (5) (6) Outcome Variable Number of
Medications
Number of Antibiotic
s
Number of Medication
s
Number of Antibiotic
s
Number of Medication
s
Number of Antibiotic
s Group A A B B C C Elderly*After2006
0.251 -0.00665 0.131 0.0128 0.401* 0.0524**
(0.285) (0.0790) (0.177) (0.0153) (0.211) (0.0204) Observations 1,534 1,534 3,768 3,768 1,062 1,062 R-squared 0.123 0.037 0.111 0.012 0.152 0.043
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
29
On Table 11, we observed no statistically significant effects in either group A and B for
the total number of medications and antibiotics prescribed. However, we observed
statistically significant effects for both outcome variables with patients been diagnosed
with diseases in group C, which also had the a relatively low number of observations.
Due to the low number of observations available, it is impossible for us to further divide
samples in group C to smaller groups in order to see which diagnostic groups are behind
the statistically significant effect of Medicare Part D on total number of drugs and,
specifically, antibiotics. We note that group C included infectious diseases, a group that
may be more elastic to antibiotics and other medications than other types of diagnoses.
However, since only 160 out of the 1024 observations in group C were patients
diagnosed with infectious diseases, we cannot conduct further analysis due to the low
number of samples available. Further investigations may be necessary to understand if
physicians treating patients with specific diagnostic categories are more likely to
prescribe a higher amount of antibiotics due to the implementation of Medicare Part D.
G. Gender and Prescribing Behavior Table 12. Gender and Prescribing Decision
(1) (2) (3) (4) Outcome Variable Total Number of Medications Total Number of Antibiotics Gender Female Male Female Male Elderly*After2006 0.415*** 0.425 0.0144 -0.00980 (0.100) (0.258) (0.0222) (0.0173) Observations 3,053 2,318 3,053 2,318 R-squared 0.147 0.183 0.188 0.161
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
Multiple studies have shown that women were more likely to have a higher amount of
antibiotics prescription (Sun, 2006). Indeed, gender may play a role in respect to the
tendency of patients to seek health care resources or indicate their preferences during the
visit to physician’s office, which will lead to higher amount of drugs been prescribed
after the implementation of Medicare Part D.
Table 12 shows the effects of Medicare Part D on the number of medications and
antibiotics on the female and male patients separately. We observe that the increase in
30
total numbers of medications prescribed after the implementation of Medicare Part D in
2006 was primarily driven by female patients with a statistically significant coefficient in
specification 1 on Table 126. In specification 2, male patients have the equal amount of
magnitude but a large standard error, which is surprising due to the relatively large
amount of male observations available. This may be a result from a higher variance in the
number of medications prescribed to male patients than to female patients.
H. Do patients in the sample become sicker after the implementation of
Medicare Part D? Table 13. Charlson Index Before and After 2006
(1) (2) (3) (4) Outcome Variable Charlson Comorbidity Index Dummy (>0) Elderly*After2006 0.00893 0.0173 -0.00686 0.0155 (0.0204) (0.0197) (0.0203) (0.0188) Observations 5,373 5,371 5,977 5,977 R-squared 0.018 0.305 0.016 0.303 2005 Data No No Yes Yes Diagnostic Category Controls
No Yes No Yes
Robust standard errors, clustered by age of the individuals, in parentheses *** p<0.01, ** p<0.05, * p<0.1
One of the underlying assumptions in our DD-RD design is that the trend before and after
the Medicare Part D’s implementation would stay the same in the absence of the policy
intervention in 2006. The assumption has to hold in order for our findings regarding the
increased number of total number of medications prescribed due to Medicare Part D to be
valid, as well as our finding about the lack of a significant increase or decrease regarding
antibiotics. However, Medicare Part D’s introduction may also induce a increase in
tendency for more patients to seek health care in a physician’s office due to the lower
costs and may change the demographics of patients in our sample. If sicker patients,
which normally requires a higher amount of medications than less sick patients, were
6 We also conducted regressions in which we used the interaction of female and elderly*after2006. However, given that the coefficient it’s not statistically significant for both antibiotics, we cannot reject the null hypothesis that Medicare Part D introduction has equal effect on men and women.
31
more likely to be included in our sample after 2006 than before, it could be an alternative
explanation to our previous findings than the direct effect from Medicare Part D.
To see if patients became “sicker” and thus requires a higher amount of prescriptions
after 2006, we used a dummy variable of positive Charlson comorbidity index as the
outcome variable and the exact same specification from table 2 and 3 on Table 13, with
the exception of using Charlson index as a control variable.
On Table 13, we observe that patients were not statistically sicker or less sick before and
after the Medicare implementation in 2006 in a variety of specifications. The result
ensured the robustness of our model and indicated that the indirect effects of Medicare
Part D on patient’s behavior via patient’s finance are negligible. Medicare Part D
implementation ‘s effect on total number of medications prescribed resulted primarily
from its influences on physician’s prescribing decision-making process.
7. Discussion and Conclusion In this study, we first seek to construct a model on physician’s decision-making model in
prescribing medications in an office-based setting with a physician specialized either in
either family medicine or internal medicine. We based our model on Hu et al. (2014) but
added patient preferences and antibiotics resistance costs into the decision-making
process. While there have been evidence showing that physicians do take patient’s
involvement and preferences into account, it is worth noting that patient’s input into
physician’s prescribing decisions are complicated by the existence of direct-to-consumer
(DTC) advertising, when pharmaceutical advertisements target patients and suggest
patients to ask their physicians for specific medications (Carrera, 2013). Campo (2005)
found that many physicians held negative views on DTC campaigns and rather appreciate
more patient inputs, may instead feel threatened by patient’s involvement in prescribing
decisions. Thus, our model is limited in interpreting individual physician’s variation in
the decision-making process but rather try to show a general model that can be used in
policy studies. We had special interests in antibiotics, a common class of drugs that have
societal negative externalities with every prescription in the form of the development of
antibiotics resistances. By incorporating antibiotics resistances into our model, we tried to
32
see if physicians consider antibiotics differently than other medications when making
prescriptions, potentially due to risk in increasing antibiotic resistances.
Using NAMCS data, we reached several important findings by exploiting the
implementation of Medicare Part D in 2006 in the US. First, we replicated the result in
previous literature in showing that the introduction of Medicare Part D in 2006 increased
the total number of prescribed medications during every visit. The replicated results are
robust, controlling for diagnostic categories, physician fixed effects and region fixed
effects in different specifications. We employed a regression-discontinuity-and-
difference-in-difference approach, which estimated the changes in the number of
medications prescribed prior and after the policy change in 2006.We observed an
unexpected drop in magnitude of the effect after including observations from 2005,
which is contradictory to anticipatory effects found in previous literature. While we could
not identify the exact reason for the drop, it is possible that it was due to the selection of
only family medicine and internal medicine physician visits in our samples. We also find
evidence that the increases in prescribed medications in response to Medicare Part D
were caused directly by the policy and not by changing the patient populations who visits
the clinic.
Secondly, we find that despite a statistically significant increase in overall number of
medications prescribed due to Medicare Part D implementation, Medicare Part D did not
have significant effects in the number of antibiotics prescribed in most specifications. We
also tested the share of antibiotics as a part of the total number of medications prescribed
and reached similar conclusions. The results suggest that our theoretical model in whch
prescription decisions of physicians are not independent of the patient’s insurance status
and that physicians respond to factors other than patient’s health status. But physicians
may take antibiotics resistances into account when they prescribe medications and treat
antibiotics differently than other non-antibiotic medications. Therefore, we see no
changes after the introduction of Medicare Part D in 2006 in either the absolute number
of antibiotics prescribed or share of antibiotics in total number of medications prescribed.
We had some statistically significant effects on antibiotics when we expanded the RD
bandwidth to 58-71, but the effect are not robust in other RD bandwidths. Therefore, we
concluded that, while unlikely, even if there’s a corresponding increase in antibiotics
33
prescribed due to Medicare Part D, the effects are most likely to be smaller than the
effects on the total number of medications. Our findings, however, cannot eliminate the
possibility that particular type(s) of prescribed medications caused the increase in number
of medications prescribed after Medicare Part D was introduced.
Third, we investigated the heterogeneous response in prescriptions to Medicare Part D in
a variety of different subgroups. Our study is similar prior literatures in finding that
physicians who do not own the practices are affected by Medicare Part D. Medicare Part
D did not have significant effects on physicians’ prescribing behavior if they also own the
practice. Being the Primary Care Physician (PCP) of the visiting patient is also a factor in
the increase in prescribed medications, as PCP physicians are more likely to value long-
term relationships with patients and are more likely to be aware of patients’ drug
insurance status and economic situations. Medicare Part D also had statistically
significant effects on female patients but not on the male counterparts. Sun(2006) found
that physicians who do not own the practice, PCP physicians, and female patients receive
a higher amount of antibiotics. However, we did not find the significant effects by
Medicare Part D on the number of antibiotics in the corresponding subgroups.
Lastly, we identified a statistically significant increase in number of antibiotics prescribed
in response to Medicare Part D in patients being diagnosed with diagnosis in infectious
diseases, neoplasms, blood-related diseases, digestive system diseases, skin-related
diseases and congenital anomalies. While these diseases may have a higher elasticity to
antibiotics demands, we lack the necessary data to investigate further into the group of
diseases. Further research can be done in order to identify the response to insurance
expansion in specific therapeutic areas.
Our study has implications for future healthcare insurance policies. In light of the
implementation of Affordable Card Act (ACA) in 2013, the effects in increasing number
of prescribed medications by Medicare Part D introduction may be replicated in the
future as more people acquire drug insurances. Crucially, our findings showed that the
concerns in the probability that an expansion in drug insurances might cause an over-
prescription of antibiotics are not valid. This finding is especially important as we seek to
address the mounting challenges of increasing antibiotics resistances around the world
(Hicks, 2013; Bennett, 2010). Our findings suggest that insurance information may not
34
play a big role in physicians’ decision to prescribe antibiotics, unlike other medications.
Future study can be done to understand how antibiotic prescriptions interact with an
expansion of drug insurances in other cultural or international contexts. While the non-
increase in antibiotic prescriptions is consistent with our proposed model in which
physicians treat antibiotics differently due to the threats of antibiotic resistances, we will
need future investigations, both quantitatively and qualitatively, to clearly identify the
factors that distinguish antibiotics from other medications in physicians’ prescription
decision-making process.
Our study has some fundamental limitations in its scope and interpretations. First, there
have been anecdotal studies suggesting physician’s prescribing decision as being less
rational and complex compared to what is suggested in our model (Campo, 2005; Fischer,
2010). Furthermore, our simplified model did not include the effects of pharmaceutical
marketing effects explicitly. Past studies have suggested that physicians are susceptible to
these efforts and future development of the decision-making model may be needed to
incorporate this aspect of the process. Lastly, our findings do not distinguish other types
of medications except for antibiotics. It remains possible that specific types of
medications or drugs treating drove the significant increase in the number of medications
after the introduction of Medicare Part D. This could be addressed in future projects.
8. References
Alpert, Abby. 2012. “The Anticipatory Effects of Medicare Part D on Drug Utilization.” SSRN Electronic Journal (October). Retrieved (http://dx.doi.org/10.2139/ssrn.2161669).
Armantier, Olivier, and Soiliou Namoro. 2006. “Prescription Drug Advertising and
Patient Compliance: A Physician Agency Approach.” Advances in Economic
Analysis & Policy 6(April).
Bennett, D., Cl Huang, and Tl Lauderdale. 2010. “Health Care Competition and
Antibiotic Use in Taiwan.” Harris School of Policy Studies, 8. Retrieved
(https://home.uchicago.edu/~dmbennett/abx.pdf).
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Brekke, Kurt R., Astrid L. Grasdal, and Tor Helge Holmås. 2009. “Regulation and
Pricing of Pharmaceuticals: Reference Pricing or Price Cap Regulation?” European
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Butler, C. C., S. Rollnick, R. Pill, F. Maggs-Rapport, and N. Stott. 1998. “Understanding
the Culture of Prescribing: Qualitative Study of General Practitioners’ and Patients'
Perceptions of Antibiotics for Sore Throats.” BMJ (Clinical research ed.) 317:637–
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39
Appendix
Table A1. Antibiotic Codes Used in Classification
00007 00009 00028 00062 00103 00125 00194 00340 00345 00349 00391 01017
01044 01046 01053 01054 01189 01196 01242 01315 01630 01635 01640 01685
01865 02047 02070 02102 02116 02146 02161 02987 03059 03081 03109 03138
03220 03283 03331 03425 03430 03741 04156 04157 04180 04235 04240 04264
04528 04531 04586 05117 05156 05190 05207 05232 05233 05245 05690 05955
05983 05985 05988 05993 05995 06097 06125 06127 06128 06130 06131 06133
06162 06196 06204 06224 06238 06839 06883 06963 07015 07067 07561 07888
08030 08081 08113 08130 08132 08150 08252 08268 08373 08468 08496 08557
08574 08640 09182 09379 09433 09569 09611 09752 09846 09878 09892 10340
10350 10355 10363 10364 10705 10800 10820 10845 10875 10905 11553 11651
11655 11657 11658 11660 11665 11667 11669 11905 12967 13350 13355 15490
15495 16472 16475 16480 16482 16485 17150 17270 18325 18645 19050 19263
19460 19465 19698 20140 20175 20215 20218 20490 21250 21385 21795 22233
22328 22340 22670 22935 23047 23125 23150 23185 23195 23215 23220 23221
23222 23223 23225 23228 23230 23305 23500 23603 23605 24228 24435 24440
24465 24848 25070 25075 25130 25575 26460 26795 26800 26825 26940 26960
27835 27840 28205 28258 28260 28280 28285 28320 29078 29315 29838 29843
29888 29897 30025 30035 30575 30725 30850 31020 31045 31050 31055 31060
31075 31645 31650 31870 32020 32423 32430 33068 33092 33155 33355 33400
33410 33425 33430 33780 33805 34085 34090 34950 34970 34975 34990 35595
40310 41785 50036 60115 60120 60125 60295 60335 60485 60500 60505 60780
61085 61185 61295 61410 61415 61470 89015 89027 89028 89029 89059 89075
89076 91015 91017 91059 91067 91068 91069 91070 91094 92004 92006 92013
92029 92031 92109 92110 92111 92112 92140 93038 93088 93093 93098 93166
93179 93214 93230 93301 93303 93338 93360 93387 93416 93417 94037 94129
94139 94146 94169 95028 95037 95149 95167 95187 96070 96087 97001 97004
97045 97132 97163 98029 98040 98061 98066 98082 99001 99014 99022 99073
99135.
Source: US Department of Health and Human Services (DHHS) “Health, United States, 2013 With Special
Feature on Prescription Drugs” May 2014, DHHS Publication No.2014-1232
40
Table A2. Total Number of Observations by Age with and Without 2005 Data
Age in Years
Number of Observations (Before 2006, without 2005 Data)
Number of Observations (Before 2006, with 2005 Data)
Number of Observations (After 2006)
58 169 248 400 59 197 269 350 60 179 243 360 61 173 247 337 62 138 217 345 63 157 213 337 64 167 233 321 65 153 208 319 66 172 220 306 67 150 211 246 68 158 209 253 69 153 203 230 70 137 194 219 71 133 181 257
Total 2,236 3,096 4,280
41
Note. Total Number of Medications Coded, before 2006, age 60-69 (Bandwidth: 2.793; Estimate: -.074 (0.159))
Note. Total Number of Medications Coded after 2006, age 60-69 (Bandwidth: 2.428; Estimate: 0.264 (0.156)*)
42
Note. Total Number of Antibiotics Coded for Visits before 2006, age 60-69 (Bandwidth:1.803; Estimate:-.0136(0.00967))
Note. Total Number of Antibiotics Coded for Visits after 2006, age 60-69 (Bandwidth: 1.719; Estimate: 0.00734 (0.00837))
43
Note. Share of Antibiotics for Visits before 2006, age 60-69 (Bandwidth: 1.596; Estimate:-0.00378(0.00517))
Note. Share of Antibiotics for Visits after 2006, age 60-69 (Bandwidth:1.443; Estimate: 0.00141(0.00390))
