Topic Position paper on Proposition 8Number of Pages 1 (Dou.docx

Topic: Position paper on Proposition 8
Number of Pages: 1 (Double Spaced)
Number of sources: 3
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Nursing
Language Style: English (U.S.)
Order Instructions: Attached
1.
Position Paper Written Assignment :
A position paper is a document you could present to a legislator
to seek support for an issue you endorse. Present your position
on a current health-care issue in a one-page paper, following the
assignment guidelines below.
You can select your issue topic from newspapers, national news
magazine articles, professional journals, or professional

association literature; and this can be the topic you choose for
your ethical issues debate.
Your position paper should:
•
Be quickly and easily understood.
•
Be succinct and clear.
•
Appear very professional with the legislator’s name and title on
top and your name and your credentials at the bottom.
•
Condense essential information in one, single-spaced page,
excluding the title and reference list pages.
•
Be written using correct grammar, spelling, punctuation, syntax,
and APA format.
•
Clearly describe the issue that you are addressing in the opening
paragraph.
•
Include 3–4 bullet points regarding why you are seeking the
legislator’s vote, support, or opposition. Bullet points should be
clear and concise but not repetitive and should reflect current
literature that substantiates your position.
•
Summarize the implications for the nursing profession and/or
patients.

•
Conclude with two recommendations that you wish to see
happen related to your issue, such as a vote for or against, a
change in policy, or the introduction of new legislation.
•
Use APA format (6th ed.), correct grammar, and references as
appropriate.
The literature you cite must be from peer-reviewed journals and
primary source information. You may use this paper as
preliminary research for your ethical issues debate project that
occurs in weeks 4-7.
Name the dependent and the main independent variables
(identify them separately).
The dependent variable was policy indicator for expansion of
Medicaid in twenty-six states; (they consider also the non- or
late-expansion states, otherwise what would they use to
compare these 26 Medicaid-expansion states to?) Independent
variables were Medicaid spending on prescription drugs. Take a
look and decide whether you need to switch your independent
and dependent variables. What is the outcome here? That would
be your dependent variable.
What is one of the main hypotheses? What is the
treatment/stimulus? State them in your own words.
The hypothesis is to determine the growth of Medicaid drug
spending in Medicaid expansion states. The stimulus is the use
of Medicaid insurance. Hypothesis looks good but you need to
rethink about the stimulus. Stimulus is the same as the
treatment, or the independent variable.
Name the treatment and the comparison groups (identify them
separately). Explain the rationale behind this comparison.
The treatment was medic aid, an upward trend was found in
states that implemented Medicaid expansions under the

Affordable Care Act. Twenty-six states in Columbia
implemented the use of Medicaid expansions. The study used
state level covariates such as unemployment rate, poverty rate,
penetration rate of Medicaid managed care that was measured
by use of percentage of Medicaid enrollees. The rationale of the
study was to compare the uptake of Medicaid in areas under
study. Comment by Gulcin Gumus: I am asking about the
treatment group, not the treatment! These two are related but
not the same. Comment by Gulcin Gumus: I am not asking
about these. I am asking you to explain why they picked the
specific treatment group and comparison group. What is the
rationale behind this comparison.
Calculate the difference-in-differences (DD) estimate based on
the results presented in the first two columns of Exhibit 3
(2011-13 and 2014) for prescription drug spending. Interpret the
DD estimate in a single sentence.
29.86-32.29= -2.43 the result is negative this indicates a
decrease in number of prescription drug spending as a result of
introduction of Medicaid. No this is not it!
e. Consider again the same results as above in part d. provide a
graphical representation of these findings together with the
implied counterfactual. Make sure to label the axes and the
curves.
doi: 10.1377/hlthaff.2015.1530
HEALTH AFFAIRS 35,
NO. 9 (2016): 1604–1607

©2016 Project HOPE—
The People-to-People Health
Foundation, Inc.
Pharmaceutical Spending & Value
By Hefei Wen, Tyrone F. Borders, and Benjamin G. Druss
DATAWATC H
Number Of Medicaid Prescriptions
Grew, Drug Spending Was Steady
In Medicaid Expansion States
Expansions of eligibility for Medicaid under the Affordable
Care Act may have increased the
number of Medicaid drug prescriptions. However, the
expansions did not drive Medicaid
spending on prescription drugs overall in 2014.
In 2014 twenty-six states and the District of
Columbia began to expand Medicaid eligibility
to almost all residents whose household incomes
were at or below 138 percent of the federal
poverty level.
By the end of 2014 an estimated nine million
Americans had gained insurance coverage through
the expansions.

1
In the same year the growth rate of
all prescription drug spending in the United States
reached 13.1 percent, its high-est point since 2001.
2
The 2014 growth rate of Medicaid drug spending
(24.3 percent) was even higher than that of all
prescription drug spend-ing.
3
The concurrent trends
of increasing Medic-aid enrollment and escalating
Medicaid drug spending have led people to partially
attribute the growth in drug spending to Medicaid
expan-sion.
2–4
This may cause concern in states now
contemplating opting into the Medicaid expan-
sion and in those considering whether to contin-ue
their existing expansion programs.
We found significant increases in Medicaid drug
spending (Exhibit 1) and numbers of pre-scriptions
(Exhibit 2) from the preexpansion period (2011–13)
to the postexpansion period (2014). For Medicaid
drug spending, similar up-ward trends were seen

both in states that imple-mented Medicaid
expansions under the Afford-able Care Act (ACA)
in 2014 and in states that expanded eligibility later
or did not expand it (we excluded the District of
Columbia and Virginia from the study sample
because of incomplete-ness and inconsistency in
data reporting). For the number of Medicaid
prescriptions, however, the upward trend was not
seen in states that expanded eligibility after 2014 or
not at all (la-beled “non- or late-expansion states”).
The trend in these states held steady during 2014.
Exhibit 1
Trends in quarterly Medicaid spending on all Medicaid-covered
outpatient prescription drugs
SOURCE Authors’ analysis of data for 2011–14 from the
Medicaid State Drug Utilization Data files of the Centers for

Medicare and
Medicaid Services. NOTES Dollar amounts were converted to
December 2014 values based on the national monthly Consumer
Price
Index. “Expansion states” are the twenty-six states that began to
expand eligibility for Medicaid in 2014. “Non- or late-
expansion
states” are the four states that began expansion after January 1,
2015, and the nineteen states that have not expanded eligibility
(we
excluded Virginia, which has not expanded eligibility, and the
District of Columbia, which expanded it in 2014, from the study
sample
because of incompleteness and inconsistency in data reporting).
1 6 0 4 H e a lt h A f fa i r s S e p t e m b e r 2 0 1 6 3 5 : 9
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Exhibit 2
Trends in quarterly Medicaid prescriptions for all Medicaid-
covered outpatient prescription drugs

Medicare and
Medicaid Services. NOTE “Expansion states” and “non- or late-
expansion states” are explained in the Notes to Exhibit 1.
A more rigorous difference-in-differences esti-
mation (Exhibit 3) was consistent with the trend
comparisons and confirmed that the ACA Med-icaid
expansions may have increased the number of
Medicaid prescriptions, but the expansions per se are
unlikely to be the major driving force behind the
growth in drug spending.
Study Data And Methods
We used sixteen waves of quarterly state-aggregate
data, for the period 2011–14, on Med-icaid spending
on prescription drugs from the Medicaid State Drug
Utilization Data files of the Centers for Medicare
and Medicaid Services (CMS).

5
Exhibit 3
Our outcome variables were quarterly Medic-aid
spending on all covered outpatient prescrip-tion
drugs per state resident and quarterly num-bers of
those prescriptions. We also estimated per enrollee
Medicaid drug spending and pre-scriptions.
Medicaid drug spending was mea-sured as the pre-
rebate amount reimbursed by Medicaid only.We
converted the nominal spend-ing values to real
values as of December 2014 based on the Consumer
Price Index.
As noted above, twenty-six states and the Dis-trict
of Columbia implemented Medicaid expan-sions
under the ACA during 2014. Twenty-two of the
states and the District of Columbia imple-mented
the expansions in full compliance with the Medicaid
state plan amendment provision of
Estimated effects of Affordable Care Act expansions of
Medicaid eligibility on Medicaid drug spending and number of
prescriptions per state resident
Difference-in-differences

Adjusted for state and Adjusted for state and quarter
Difference quarter fixed effects fixed effects and covariates
2011–13 2014 Amount 95% CI Amount 95% CI Amount 95% CI
Spending per quarter per resident ($)
Non- or late-expansion states 29.86 33.07 3.21*** [1.28, 5.15]
Ref —
a
Ref —
a
Expansion states 32.29 37.03 4.75** [0.02, 9.51] 1.58 [−1.18,
4.33] 0.81 [−2.85, 4.47]
Number of prescriptions per quarter per resident
Non- or late-expansion states 0.41 0.41 0.002 [−0.01, 0.02] Ref
—a Ref —a
Expansion states 0.47 0.53 0.06*** [0.02, 0.11] 0.06*** [0.02,
0.10] 0.07*** [0.03, 0.11]
Medicare and Medicaid Services. NOTES
“Expansion states” and “non- or late-expansion states” are
explained in the Notes to Exhibit 1. Covariates are listed in the
text. Dollar amounts were converted to
December 2014 values based on the national monthly Consumer
Price Index. 95% confidence intervals (CIs) were calculated
based on state-clustered standard errors.
a
Not applicable. **p < 0:05 ***p < 0:01

S e p t e m b e r 2 0 1 6 3 5 : 9 H e a lt h A f fa i r s 1 6 0 5
Foundation, Inc.
HealthAffairs.org.
Pharmaceutical Spending & Value
the ACA. The remaining four states used a section
1115 waiver to waive certain statutory require-
ments for Medicaid and redirect Medicaid funds into
premium assistance programs of qualified health
plans in the ACA health insurance Mar-ketplaces.
Our key independent variable was the policy
indicator for expansion of Medicaid in the twen-ty-
six states in 2014. We also provide separate
estimates for the twenty-two state expansions under
the state plan amendment provision and the four
state expansions under the section 1115 waiver
(online Appendices A1 and A2).
6

The preexpansion period and the states that
expanded after 2014 (Alaska, Indiana, Montana, and
Pennsylvania) or did not expand served as the
comparisons. State-level covariates were the
following: unemployment rate; poverty rate;
penetration rate of Medicaid managed care,
measured by the percentage of Medicaid enroll-ees
in comprehensive managed care plans; and an “early
adopter” indicator for partial imple-mentation of
Medicaid expansions in the period 2011–13.
7
We used a quasi-experimental difference-in-
differences design with state and quarter two-way
fixed effects to account for unobserved state
heterogeneity and national secular trends in
Medicaid drug spending and prescriptions.
8
All
estimates were population-weighted and state-
clustered to correct for the heterogeneous policy
effect and within-state serial correlation in our
difference-in-differences context.
9
We performed sensitivity analyses to exclude
sofosbuvir (Sovaldi), a major driver of Medicaid
drug spending growth in 2014,

10
and to add the
group-specific linear trends to account for the
potential heterogeneous trajectory in Medicaid drug
spending and number of prescriptions be-tween the
expansion states and the non- or late-expansion
states that might have emerged before the
expansions (for results of the sensitivity an-alyses,
see Appendix A3).
6
Our study had several limitations. One was the
fact that the study data included only four quar-ters
of postexpansion data. Another was that there may
be inconsistency in state reporting of new Medicaid
enrollees under the expansions and the increased
federal matching rates avail-able to new enrollees.
In addition, our analysis was aggregated at the state
level, which did not allow us to distinguish the new
enrollees after expansion from existing enrollees.
Study Results
We found significant increases from the pre-
expansion period (2011–13) to the postexpan-sion
period (2014) in the amount of Medicaid
1 6 0 6 H e a lt h A f fa i r s S e p t e m b e r 2 0 1 6 3 5 : 9
drug spending per resident in the twenty-three non-

or late-expansion states ($3.21 per quarter) and in
the twenty-six expansion states ($4.75 per quarter)
(Exhibit 3).When we compared the pre-post
spending changes between the two groups of states,
our difference-in-differences estimates indicated that
the difference was not significant. The denominator
of the outcome was the num-ber of state residents,
which remained stable over the short term. Our
estimates thus confirm that state implementation of
the ACA Medicaid expansions did not affect total
Medicaid drug spending.
We found no discernible change over time in the
number of Medicaid prescriptions per resident in the
non- or late-expansion states (Exhibit 3). However,
there was a significant increase in prescriptions
(0.06 per resident per quarter) in the expansion
states. This relative increase implies that the new
Medicaid enrollees after expansion may have had a
considerable level of demand for prescription drugs.
Appendix Exhibit A4 provides additional evi-
dence for the effect of the ACA Medicaid expan-
sions on Medicaid drug spending and number of
prescriptions on a per enrollee basis.
6
The find-ings
suggest that, on average, Medicaid enroll-ees in the
expansion states may have been pre-scribed drugs at
a rate no different from those in the non- or late-
expansion states, but the drugs prescribed for
enrollees in the expansion states may have been less

expensive than those pre-scribed for enrollees in the
other states.
Discussion
Our study provides some of the first empirical
evidence concerning the implications of the ACA
Medicaid expansions for prescription drug utili-
zation. On one hand, we found that state expan-sions
did not affect Medicaid drug spending as a whole or
per resident. This finding suggests that Medicaid
expansion per se is unlikely to be the primary driver
of the record-high drug spending growth in 2014.
On the other hand, we found that implemen-tation
of the expansions may have been associ-ated with a
relative increase in the numbers of Medicaid
prescriptions overall or per resident. We also found a
relative decrease in Medicaid drug spending per
enrollee that was associated with the implementation
of the expansions.
A possible explanation for the lack of signifi-cant
impact of the ACA Medicaid expansions on
Medicaid drug spending growth in spite of the rising
number of prescriptions is that expansion states,
facing the potential fiscal impact of ex-pansions and
emerging specialty drugs, may have taken proactive
approaches to contain costs
Foundation, Inc.

HealthAffairs.org.
for prescription drugs. There are three reasons for
this explanation.
First, according to the annual budget survey of
Medicaid officials for fiscal years 2014 and 2015 by
the Henry J. Kaiser Family Foundation and Health
Management Associates, nineteen ex-pansion states
but only nine nonexpansion states have implemented
pharmacy manage-ment initiatives, such as prior
authorization pro-grams, preferred drug lists,
pharmacy benefit carve-outs, incentives to use
generic drugs, and reduced reimbursements for
certain drug ingredients.
11
These cost-containment
strategies may affect Medicaid drug spending not
only on the new enrollees after expansion but also
on existing enrollees.
Second, many states have also taken actions to
increase enrollment in risk-based managed care.
These actions include making enrollment in
managed care mandatory for new enrollees after
expansion, expanding voluntary or mandatory
enrollment to additional groups eligible for man-

aged care, and establishing managed care pro-grams
in new regions.
Finally, the expansion states generally had a
considerable rate of managed care penetration in
their Medicaid programs before the expansions
(approximately 70 percent of enrollees in these
states were in managed care in 2013). This might
have helped mitigate the impact of the expan-sions
on Medicaid drug spending.
Conclusion
Our study used timely and comprehensive Med-
icaid administrative data and provides some of the
first empirical evidence for the impact of the ACA
Medicaid expansions on Medicaid drug spending
and number of prescriptions. Our findings suggest
that state implementation of the expansions may
have increased the number of Medicaid drug
prescriptions but had no sig-nificant immediate
impact on drug spending growth. ▪
NOTES
1 Centers for Medicare and Medicaid
Services. Medicaid and CHIP: De-
cember 2014 monthly applications,
eligibility determinations, and en-
rollment report [Internet]. Balti-more
(MD): CMS; 2015 Feb 23 [cited 2016 Jul
14]. Available from: http://
www.medicaid.gov/medicaid-chip-

program-information/program-
information/downloads/december-2014-
enrollment-report.pdf
2 IMS Institute for Healthcare Infor-
matics. Medicines use and spending
shifts: a review of the use of medi-cines
in the U.S. in 2014. Parsippany (NJ): The
Institute; 2015.
3 Martin AB, Hartman M, Benson J, Catlin
A, National Health Expendi-ture
Accounts Team. National health
spending in 2014: faster growth driven
by coverage expansion and prescription
drugs. Health Aff (Millwood).
2016;35(1):150–60.
4 Truffer CJ, Wolfe CJ, Rennie KE. 2014
actuarial report on the finan-cial outlook
for Medicaid [Internet].
Baltimore (MD): Centers for Medi-care
and Medicaid Services; 2014 [cited 2016
Jul 27]. Available from:
https://www.medicaid.gov/ medicaid-
chip-program-information/by-
topics/financing-and-
reimbursement/downloads/ medicaid-
actuarial-report-2014.pdf
5 Medicaid.gov. State Drug Utilization
Data [Internet]. Baltimore (MD): Centers
for Medicare and Medicaid Services;
[cited 2016 Jul 14]. Avail-able from:

https://www.medicaid
.gov/medicaid-chip-program-
information/by-topics/benefits/
prescription-drugs/state-drug-
utilization-data.html
6 To access the Appendix, click on the
Appendix link in the box to the right of
the article online.
7 Sommers BD, Kenney GM, Epstein AM.
New evidence on the Affordable Care
Act: coverage impacts of early Medicaid
expansions. Health Aff (Millwood).
2014; 33(1):78–87.
8 Wooldridge JM. Econometric analy-
sis of cross section and panel data.
2nd ed. Cambridge (MA): MIT
Press; 2010.
9 Bertrand M, Duflo E, Mullainathan S.
How much should we trust dif-ferences-
in-differences estimates? Q J Econ.
2004;119(1):249–75.
10 Liao JM, Fischer MA. Early patterns of
sofosbuvir utilization by state Medicaid
programs. N Engl J Med.
2015;373(13):1279–81.
11 Smith VK, Gifford K, Ellis E, Rudowitz
R, Snyder L. Medicaid in an era of health
and delivery system reform: results from

a 50-state Medicaid budget survey for
state fiscal years 2014 and 2015 [Inter-
net]. Menlo Park (CA): Henry J. Kaiser
Family Foundation; 2014 Oct [cited 2016
Jul 14]. Available from:
https://kaiserfamilyfoundation
.files.wordpress.com/2014/10/ 8639-
medicaid-in-an-era-of-health-delivery-
system-reform3.pdf
S e p t e m b e r 2 0 1 6 3 5 : 9 H e a lt h A f fa i r s 1 6 0 7
Foundation, Inc.
HealthAffairs.org.
HSA 4700
Fall 2018
Assignment 5
Locate the following article using FAU electronic library
resources to answer the questions

below: Wen, H., Borders, T. F., & Druss, B. G. (2016). Number
of Medicaid Prescriptions Grew,
Drug Spending Was Steady in Medicaid Expansion States.
Health Affairs, 35(9), 1604-1607.
a. Name the dependent and the main independent variables
(identify them separately).
b. What is one of the main hypotheses? What is the
treatment/stimulus? State them in your
own words.
c. Name the treatment and the comparison groups (identify them
separately). Explain the
rationale behind this comparison.
d. Calculate the difference-in-differences (DD) estimate based
on the results presented in the
first two columns of Exhibit 3 (2011-13 and 2014) for
prescription drug spending. Interpret the
DD estimate in a single sentence.
e. Consider again the same results as above in part d. Provide a
graphical representation of these
findings together with the implied counterfactual. Make sure to

label the axes and the curves.
Intervention: Tobacco Control
Tobacco Smoke Exposure and Health-Care
Utilization Among Children in
the United States
Ashley L. Merianos, PhD, CHES
1
, Cathy Odar Stough, PhD
2
,
Laura A. Nabors, PhD, ABPP
1
, and E. Melinda Mahabee-Gittens, MD, MS
3
Abstract
Purpose: The purpose of this study was to assess patterns of
health-care utilization among children who potentially had
tobacco
smoke exposure (TSE) compared to those who were not
exposed.
Design: A secondary data analysis of the 2011 to 2012 National
Survey on Children’s Health was performed.
Setting: Households nationwide were selected.

Participants: A total of 95 677 children aged 0 to 17 years.
Measures: Sociodemographic characteristics, TSE status, and
health-care visits were measured.
Analysis: Multivariable logistic regression models were
performed.
Results: A total of 24.1% of children lived with smokers.
Approximately 5% had home TSE. Participants who lived with a
smoker
were significantly more likely to have had a medical care visit
(odds ratio [OR] ¼ 1.22, confidence interval [CI] ¼ 1.21-1.22)
and
were more likely to seek sick care or health advice at an
emergency department (OR ¼ 1.23, CI ¼ 1.23-1.24) but were
less likely
to have had a dental care visit (OR ¼ 0.82, CI ¼ 0.82-0.83) than
those who did not live with a smoker. Similar findings were
found
among participants who had home TSE.
Conclusion: TSE is a risk factor for increased use of pediatric
medical care. Based on the high number of children who
potentially
had TSE and received sick care or health advice at an
emergency emergency department, this setting may be a venue
to deliver
health messages to caregivers.
Keywords
secondhand smoke, tobacco use, health-care utilization,
pediatrics
Purpose

Tobacco smoke exposure (TSE) has been consistently associ-
ated with an increased prevalence of childhood morbidity
including increased bronchiolitis, asthma exacerbations,
respiratory infections, and sudden infant death syndrome.
1
Yet, in 2011 to 2012, 24.7 million US children were exposed
to tobacco smoke.
2
TSE-related illnesses may contribute to
increased demand for health-care services and they represent
a great proportion of preventable childhood morbidity.
1
Thus,
the American Academy of Pediatrics
3
(AAP) identifies tobacco
use as a pediatric disease due to the harm to children caused by
use and TSE. Further, the AAP encourages implementing
initiatives during all health-care visits in order to decrease TSE
and related harms.

Research on the association between TSE and health-care
utilization has produced inconsistent findings, suggesting a
complex relationship. Studies have reported caregiver smoking
and TSE exposure are associated with an increased number of
physician visits for children with asthma,
4
respiratory symp-
toms,
5
emergency department visits for respiratory symptoms,
6
and hospital admissions.
7
In contrast, TSE has been associated
with a decreased number of preventive care visits,
8
health-care
visits for asthma,
9
and hospital admissions for asthma.
4

Fur-
ther, some research has not found differences between TSE and
number of primary care visits, emergency visits, or hospital
1
Health Promotion and Education Program, School of Human
Services,
University of Cincinnati, Cincinnati, OH, USA
2 Division of Behavioral Medicine and Clinical Psychology,
Cincinnati Children’s
Hospital Medical Center, Cincinnati, OH, USA
3
Division of Emergency Medicine, Cincinnati Children’s
Hospital Medical
Center, College of Medicine, University of Cincinnati,
Cincinnati, OH, USA
Corresponding Author:
Ashley L. Merianos, PhD, CHES, School of Human Services,
University of
Cincinnati, PO Box 210068, Cincinnati, OH 45221, USA.
Email: [email protected]
American Journal of Health Promotion
2018, Vol. 32(1) 123-130
ª The Author(s) 2017
Reprints and permission:
sagepub.com/journalsPermissions.nav

DOI: 10.1177/0890117116686885
journals.sagepub.com/home/ahp
mailto:[email protected]
https://us.sagepub.com/en-us/journals-permissions
https://doi.org/10.1177/0890117116686885
http://journals.sagepub.com/home/ahp
http://crossmark.crossref.org/dialog/?doi=10.1177%2F08901171
16686885&domain=pdf&date_stamp=2017-01-30
admissions.
8
For these reasons, examining patterns of health-
care utilization in a national sample of children who live with
smokers and have home TSE is warranted.
The aim of the present study was to compare patterns of
health-care utilization among children who were potentially
exposed to tobacco smoke compared to those who were not
exposed using a nationally representative sample of children
aged 0 to 17 years. We hypothesized that children who live
with smokers or have home TSE use more health-care services
than children who do not live with smokers or do not have
home TSE.

Methods
Design
The data for this study are from the 2011 to 2012 National
Survey on Children’s Health (NSCH), and the present study’s
analyses were performed in 2015. This survey was conducted
by the US Centers for Disease and Control Prevention’s
National Center for Health Statistics, with funding provided
from the US Department of Health and Human Services’
Maternal and Child Health Bureau.
10
The purpose of the survey
was to provide national and state-specific prevalence estimates
for a range of children’s health and well-being indicators in
combination with information on the child’s family context and
neighborhood environment.
10
Sample
The 2011 to 2012 NSCH was a telephone survey conducted
between February 2011 and June 2012. It consisted of a total

sample of 95 677 children from birth through 17 years of age,
with approximately 1 850 interviews collected per state. A list-
assisted random digit dial sample of landline telephone num-
bers and an independent random digit dial sample of cell phone
numbers were called to find households with children 0 to
17 years from each of the 50 states including the District of
Columbia. The cell phone sample was new for survey admin-
istration, and landline and cell phones make up the complete
sample. Prior research indicates that answering machines and
caller ID have contributed to a decline in response rates of
conducting telephone surveys and that individuals are substi-
tuting landline telephones with cell phones.
11,12
Thus, individ-
uals have a greater frequency of answering their cell phones
compared to a landline phone; the inclusion of cell phones may
have increased NSCH response rates. If more than 1 age-
eligible child lived in the household, 1 child was randomly
selected to be included in the study sample. Interviews lasted

on average 33 to 34 minutes and were conducted in English,
Spanish, or 1 of 4 Asian languages. The respondent was iden-
tified by the interviewer as a parent or guardian with knowl-
edge of the child’s health status and health-care. The interview
completion rate among known households with children was
54.1% for the landline sample and 41.2% for the cell phone
sample.
13
The research ethics review board of National Center
for Health Statistics approved data collection procedures. Ver-
bal informed consent for survey participation was obtained
after informing respondents of the voluntary and confidential
nature of the survey. Analyses were conducted for the total
95 677 children from birth to 17 years of age.
Measures
1. We investigated 5 health-care visit outcome variables
using a yes/no scale:
a. Medical care visit was derived from the question
‘‘During the past 12 months, did [sampling child]

see a doctor, nurse, or other health-care professional
for any kind of medical care including sick child
care, well-child checkups, physical examinations,
and hospitalizations?’’
b. Preventive medical care visit was derived from the
question ‘‘During the past 12 months, did [sampling
child] see a doctor, nurse, or other health-care pro-
vider for preventive medical care such as physical
examination or well-child checkup?’’
c. Specialty care visit was derived from the question
‘‘Specialists are doctors like surgeons, heart doc-
tors, allergy doctors, skin doctors, and others who
specialize in one area of health-care. During the
past 12 months, did [sampling child] see a specialist
(other than a mental health professional)?’’
d. Dental care visit was derived from the question
‘‘During the past 12 months, how many times did
[sampling child] see a dentist for any kind of dental

care, including checkups, dental cleaning, X-rays, or
filling cavities?’’
e. Preventive dental care visit was derived from the
question ‘‘During the past 12 months, how many
times did [sampling child] see a dentist for preven-
tive dental care, such as checkups and dental
cleanings?’’
2. Usual place for sick care or health advice for the sampling
child was investigated using the question ‘‘Is there a place
that [sampling child] usually goes when (he/she) is sick or
you need advice about (his/her) health?’’ If respondents
answered ‘‘yes,’’ they were asked the following question:
‘‘Is it a doctor’s office, emergency department, hospital
outpatient department, clinic, or some other place?’’
The 2 main TSE variables were household smokers and
home TSE. The presence of household smokers was assessed
with the question ‘‘Does anyone in your household use cigar-
ettes, cigars, or pipe tobacco?’’ Home TSE was assessed with

the question ‘‘Does anyone smoke inside the child’s home?’’
and was only asked of respondents who answered ‘‘yes’’ to the
question on household smokers. If caregivers answered ‘‘yes’’
to both questions, the child was considered positive for both
household smokers and home TSE.
124 American Journal of Health Promotion 32(1)
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Covariates considered were the sampling child’s gender,
age, and race/ethnicity (white, black, Hispanic, and multira-
cial), mothers’ education (less than a high school graduate,

high school graduate, and more than high school), household
composition (2-parent biological or step families, single
mother, and other family type), household poverty status mea-
sured as a ratio of family income to federal poverty level (FPL;
<100%, 100%-199%, 200%-399%, and >400%), and insurance
type (public, private, and no insurance).
Analysis
NSCH data were collected through a complex sample design
involving unequal selection probabilities of children within
households and stratification of households within states. We
applied sampling weights to adjust for potential nonresponse
biases and account for noncoverage of nontelephone house-
holds. Resulting estimates are generalizable to all US nonin-
stitutionalized children aged 0 to 17 years, since the weighting
procedure includes a raking adjustment to parallel each US
state’s weighted survey responses to selected demographic
characteristics of the state’s noninstitutionalized population
17 years and younger. Bivariate associations between whether
there was a household smoker and sociodemographic charac-

teristics were tested with w2 analyses. Similar analyses were
performed between home TSE status and sociodemographics.
Then, multivariable regression analyses were performed to
examine whether (1) living with a household smoker or (2)
having home TSE predicted health-care utilization. Specifi-
cally, a series of multivariable logistic regression models with
a step-wise selection procedure were performed to derive the
odds ratios (OR) and covariate-adjusted prevalence of exposure
for each type of health-care visit outcome (ie, any medical visit,
preventive medical care visit, specialty care visit, any dental
care visit, and preventive dental care visit) and usual place for
sick care or health advice (eg, doctor’s office, emergency
department). All data were conducted by using SPSS version
23.0.
Results
Child gender had near equal distribution: 51.2% were males
and 48.8% were females. The majority of sampling children
were white (52.5%) followed by Hispanic (23.0%), black
(13.5%), and multiracial (10.3%). Two-thirds of the children
lived in a biological, 2-parent home (65.6%), 19.0% lived with
a single mother, 8.8% lived in a step family, 2-parent home,

and 6.7% had other family household composition. Most moth-
ers of sampling children completed more than high school
(63.8%), 21.9% were high school graduates, and 14.3% did not
graduate from high school. Based on FPL, 22.4% had a family
income less than 100% FPL, 21.5% were 100% to 199% FPL,
28.5% were 200% to 399% FPL, and 27.8% had a family
income more than 400% FPL. More than half had private health
insurance (57.4%), 37.1% had public health insurance (eg,
Medicaid, Children’s Medicaid), and 5.6% were currently
uninsured. A total of 24.1% of the 95 677 children lived with
smokers. Approximately 5% had home TSE.
In the past 12 months of survey completion, a total of 88.1%
children had any medical care visit, 84.4% had a preventive
medical care visit, 22.6% had a specialty care visit, 77.5% had
any dental care visit, and 77.2% had a preventive dental care
visit. Most sampling children (91.4%) had a usual place for sick
care or health advice; 76.6% usually went to a doctor’s office
for sick care or health advice, 2.4% usually went to a hospital
emergency department, 2.4% usually went to a hospital out-
patient department, 18.4% usually went to a clinic or health
center, and 0.1% usually went to a retail store or minute clinic.
Sociodemographic characteristics in relation to house-
hold smokers and home TSE are described in Table 1.
Child’s gender, age, race/ethnicity, household composition,
mother’s education, household poverty status, and insur-
ance type significantly differed based on household smo-
kers and home TSE.

A series of multivariable logistic regression models, while
adjusting for covariates, indicated that children who lived with
a smoker were more likely to have had a preventive visit (odds
ratio [OR] ¼ 1.10, confidence interval [CI] ¼ 1.09-1.10), a
specialty visit (OR ¼ 1.01, CI ¼ 1.00-1.01), or a medical care
visit including sick care, checkups, or physical examinations
(OR ¼ 1.22, CI ¼ 1.21-1.22). Children who lived with a smo-
ker were less likely to have had a dental care visit (OR ¼ 0.82,
CI ¼ 0.82-0.83) or preventive dental care visit (OR ¼ 0.81, CI
¼ 0.80-0.81; Table 2). Overall, children who lived with a smo-
ker were more likely to have a usual place for sick care or
health advice (OR ¼ 1.03, CI ¼ 1.03-1.03); specifically, chil-
dren were significantly more likely to have usual care at the
following places: a doctor’s office (OR ¼ 1.05, CI ¼ 1.05-
1.06),
hospital emergency department (OR ¼ 1.23, CI ¼ 1.23-1.24),
hospital outpatient department (OR ¼ 1.01, CI ¼ 1.00-1.01), or
retail store or minute clinic (OR ¼ 1.53, CI ¼ 1.50-1.55).
Children who lived with a smoker were less likely to report
a clinic or health center (OR ¼ 0.92, CI ¼ 0.92-0.92) as a
usual place for sick care or health advice.
Multivariable logistic regression analyses indicated that
children who had home TSE were more likely to have had a
medical care visit (OR ¼ 1.35, CI ¼ 1.34-1.35) or a preventive
care visit (OR ¼ 1.32, CI ¼ 1.31-1.32). Children who had home

TSE were less likely to have had a specialty care visit
(OR ¼ 0.92, CI ¼ 0.91-0.92), a dental care visit (OR ¼ 0.77,
CI ¼ 0.76-0.77), or a preventive dental care visit (OR ¼ 0.73,
CI ¼ 0.73-0.74; Table 3). Overall, children who had home TSE
were less likely to have a usual place for sick care or health
advice (OR ¼ 0.90, CI ¼ 0.90-0.91); children were signifi-
cantly less likely to have usual care at a clinic or health center
(OR ¼ 0.85, CI ¼ 0.85-0.86). Children who had home TSE
were more likely to have usual care at the following places: a
doctor’s office (OR ¼ 1.06, CI ¼ 1.05-1.06), a hospital emer-
gency department (OR ¼ 1.40, CI ¼ 1.38-1.40), a hospital
outpatient department (OR ¼ 1.19, CI ¼ 1.18-1.20), or a retail
store or minute clinic (OR ¼ 1.30, CI ¼ 1.26-1.34) as usual
places for sick care or health advice.
Merianos et al. 125
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Discussion
Among a nationally representative sample, approximately one-

quarter of children lived with a smoker corresponding to a
weighted total of 17.6 million children and approximately
5% had home TSE equivalent to 3.6 million children. Com-
pared to the 2007 NSCH, self-reported rates of TSE have
decreased over the past several years from 19.1 million chil-
dren who lived with a smoker (26.2%) and 5.5 million children
who had home TSE (7.6%).14 Although self-reported NSCH
TSE rates have slightly decreased, recent research that assessed
TSE using serum cotinine, a metabolite of nicotine that is an
optimal assessment of TSE,
15
found that 15 million children
aged 3 to 11 years and 9.6 million children aged 12 to 19 years
were exposed to tobacco smoke.
2
These higher rates, compared
to the present study’s results, are not surprising since caregivers
typically do not report their child’s accurate level of TSE.
6,16,17
Thus, it is important to note that children who live with a
smoker, despite reporting no one smokes inside the home, are

still at risk of exposure.
We found a considerable difference between self-reported
rates of smokers in the home compared to home TSE. This
association suggests that home TSE rates may actually be
higher than the rates self-reported by caregivers, given that
the home is the most common source of TSE for children.
18
Additionally, prior evidence suggests that the majority of
nonsmokers who live with a smoker are exposed to TSE.
19
As smoke-free policies have increased in public places and
work places in recent years, private settings such as homes
and cars are becoming greater sources of exposure.
18
The
prevalence of home smoking bans has increased over the past
2 decades, but there has been a disproportionately slower
decline in home TSE since less than half of households with
a smoker have adopted voluntary smoke-free home rules.
20

Thus, efforts are still widely needed to promote voluntary
smoke-free policies in the home and to encourage smoking
cessation among caregivers.
As hypothesized and similar to previous research,
4,5
chil-
dren who lived with a smoker and who had home TSE were
more likely to have had any medical care visit including sick
Table 1. Sociodemographic Characteristics of Children 0 to 17
Years Old by Household Smokers and Home TSE in the United
States, 2011 to
2012.
Sociodemographic Characteristics
Household Smokers Home TSE
Lives With Nonsmoker
(n ¼ 72 617), n (%)a
Lives With Smoker
(n ¼ 22 137), n (%)a P Value
No Home TSE
(n ¼ 90 125), n (%)a
Home TSE
(n ¼ 4623), n (%)a P Value

Child gender
Female 35 262 (76.1) 10 651 (23.9) <.001 43 710 (95.2) 2199
(4.8) <.001
Male 32 276 (75.7) 11 463 (24.3) 46 314 (95.0) 2423 (5.0)
Child age
0-9 years old 38 316 (76.4) 11 557 (23.6) <.001 48 182 (96.7)
1687 (3.3) <.001
10-17 years old 34 301 (75.2) 10 580 (24.8) 41 943 (93.1) 2936
(6.9)
Child race/ethnicity
White 47 101 (73.9) 14 217 (26.1) <.001 58 472 (94.8) 2843
(5.2) <.001
Black 6731 (75.0) 2132 (25.0) 8073 (91.0) 790 (9.0)
Hispanic 10 033 (81.7) 2637 (18.3) 12 312 (98.1) 358 (1.9)
Multiracial 7598 (73.5) 2840 (26.5) 9872 (94.9) 566 (5.1)
Household composition
2-parent biological 53 788 (80.3) 12 295 (19.7) <.001 64 155
(97.1) 1924 (2.9) <.001
2-parent stepfamily 3854 (59.1) 2696 (40.9) 5891 (90.4) 658
(9.6)
Single mother 10 290 (71.0) 4800 (29.0) 13 759 (91.5) 1331
(8.5)
Other family type 4296 (67.6) 2227 (32.4) 5841 (91.2) 681 (8.8)
Mother education
Less than high school 4183 (70.5) 2505 (29.5) <.001 6019
(92.9) 669 (7.1) <.001
High school graduate 10 002 (64.2) 6046 (35.8) 14 599 (91.4)
1447 (8.6)
More than high school 53 419 (82.0) 11 147 (18.0) 62 785
(97.3) 1781 (2.7)
Household poverty status

<100% 8924 (66.3) 5832 (33.7) <.001 13 032 (90.4) 1721 (9.6)
<.001
100%-199% 11 379 (68.6) 5 634 (31.4) 15 649 (92.9) 1364 (7.1)
200%-399% 22 400 (77.4) 6298 (22.6) 27 644 (96.8) 1053 (3.2)
�400% 29 914 (87.8) 4373 (12.2) 33 800 (98.8) 485 (1.2)
Insurance type
Public 16 832 (66.0) 10 246 (34.0) <.001 24 379 (91.5) 2695
(8.5) <.001
Private 52 344 (82.9) 10 208 (17.1) 61 043 (97.6) 1507 (2.4)
No insurance 2642 (70.6) 1338 (29.4) 3636 (93.9) 344 (6.1)
Abbreviation: TSE, tobacco smoke exposure.
a
n refers to raw scores and percentages are weighted.
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care, checkups, or physical examinations in the past year.
Greater use of any medical care may be related to the fact that
children with TSE are more likely to experience a variety of
health conditions and illnesses.
21,22
Further, it is particularly
concerning that children with TSE are less likely to have a

usual place of care due to recent efforts to increase the presence
of patient-centered medical homes. Lack of a usual place of
care also limits the opportunities for medical providers to mon-
itor changes in these children’s health over time. When chil-
dren with TSE do have a regular place of care, emergency
departments and retail store/minute clinics were the most likely
sources of care, suggesting these settings may be suitable
venues for providing interventions for these families.
Children who lived with a smoker and who had home TSE
were significantly more likely to seek sick care or health
advice at an emergency department. Research indicates that
there are high rates of biochemically validated TSE in chil-
dren who present to the pediatric emergency department.
6
Given the high acceptability of tobacco-related interventions
among caregivers who smoke in this setting,
23
the emergency
department may be an optimal venue for delivering interven-

tions to decrease child TSE and increase caregiver quit
attempts.
24,25
Contrary to our hypothesis, children who lived with a smo-
ker and who had home TSE were less likely to have had a
dental care visit including checkups, X-rays, or fillings in the
past year. This association is concerning, given children with
TSE are at greater risk of dental caries.
26
Further, smoking
cessation interventions at dental visits are not widespread.
27,28
Taken together, efforts are needed to increase dental visits
among children who have TSE and to increase smoking cessa-
tion counseling among smokers during dental visits.
Table 2. Adjusted Prevalence Health-Care Visits According to
Household Smokers in Children 0 to 17 Years Old in the United
States, 2011 to
2012.
Household Smokers
Health-Care Visits Multivariable Regression
a

No, n (%)b Yes, n (%)b OR 95% CI
Any medical care visit
Child lives with nonsmoker 7086 (11.6) 65 435 (88.4) Ref Ref
Child lives with smoker 2655 (12.5) 19 438 (87.5) 1.22c 1.21-
1.22
Preventive medical care visit
Child lives with nonsmoker 10 339 (15.1) 61 772 (84.9) Ref Ref
1.10
Specialty care visit
Child lives with smoker 17 049 (79.2) 5059 (20.8) 1.01c 1.00-
1.01
Any dental care visit
0.83
Preventive dental care visit
Child lives with smoker 5490 (27.8) 15 447 (72.2) 0.81
c
0.80-0.81
Has usual place for sick care or health advice
Child lives with nonsmoker 4019 (8.4) 68 473 (91.6) Ref Ref
1.03
Doctor’s office as usual place for sick care or health advice

Child lives with smoker 5396 (25.3) 15 461 (74.7) 1.05
c
1.05-1.06
Hospital emergency department as usual place for sick care or
health advice
Child lives with nonsmoker 68 130 (97.9) 864 (2.1) Ref Ref
Child lives with smoker 20 315 (96.8) 542 (3.2) 1.23c 1.23-1.24
Hospital outpatient department as usual place for sick care or
health advice
Child lives with smoker 20 244 (97.4) 613 (2.6) 1.01
c
1.00-1.01
Clinic or health center as usual place for sick care or health
advice
Child lives with smoker 16 640 (80.7) 4217 (19.3) 0.92c 0.92-
0.92
Retail store/minute clinic as usual place for sick care or health
advice
Child lives with smoker 20 833 (99.9) 24 (0.1) 1.53c 1.50-1.55
Abbreviations: CI, confidence interval; OR, odds ratio; Ref,
referent.
aStep-wise regression controlling for mother education,
household composition, poverty level, insurance, child gender,
child age, and child race/ethnicity.
bn refers to raw scores and percentages are weighted.

c
P < .001.
Merianos et al. 127
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Limitations
There are several factors that may limit the generalizability of
the study results. For instance, data are based on self-report,
and as such social desirability may have influenced information
provided by caregivers who might have been very sensitive to
reporting if they smoked in the home. The NSCH may have
resulted in sampling bias that influenced parameter estimates
due to the data collection procedures. Although the NSCH may
not be truly representative of the US population due to the low
capture rate, the NSCH does provide information consistent
with the overall survey’s purpose to provide estimates of child
data for key health indicators and generate information about
children, their families, and neighborhoods. Further, the phras-

ing of the home TSE question may have also influenced social
desirability bias (eg, ‘‘inside the child’s home’’ vs ‘‘in your
home’’). Based on the self-report nature of the TSE questions,
underreporting or overreporting may have occurred.
29,30
Bio-
chemical validation of results would provide a more precise
measure of TSE. Due to self-report, caregivers may have not
known the differences between what type of place (eg, doctor’s
office vs clinic or health center) they go most often for their
child’s medical care. Data from behavioral observations,
reports from another family member, or biochemical validation
of the child’s TSE status would provide a way to verify infor-
mation provided by caregivers. The NCHS does not measure
the child’s smoking status, which may confound results in the
older age group. The NCHS is cross-sectional in nature. Evi-
dence on the impact of TSE over the course of children’s
development would provide more information on health-care
utilization. Finally, analyses were based on single items or

Table 3. Adjusted Prevalence of Health-Care Visits According
to Home TSE Among Children 0 to 17 Years Old in the United
States, 2011 to
2012.
Home TSE
Health-Care Visits Multivariable Regression
a
No, n (%)b Yes, n (%)b OR 95% CI
Any medical care visit
No home TSE 9071 (11.7) 80 391 (88.3) Ref Ref
Home TSE 669 (13.3) 3937 (86.7) 1.35c 1.34-1.35
Preventive medical care visit
No home TSE 13 211 (15.5) 76 241 (84.5) Ref Ref
Home TSE 942 (17.1) 3626 (82.9) 1.32c 1.31-1.32
Specialty care visit
Home TSE 3626 (80.3) 986 (19.7) 0.92c 0.91-0.92
Any type of dental care visit
Home TSE 1244 (27.4) 3285 (72.6) 0.77c 0.76-0.77
Preventive dental care visit
Home TSE 1273 (28.5) 3241 (71.5) 0.73
c
0.73-0.74

Has usual place for sick care or health advice
No home TSE 5240 (8.4) 84 718 (91.6) Ref Ref
Home TSE 459 (12.1) 4159 (87.9) 0.90c 0.90-0.91
Doctor’s office as usual place for sick care or health advice
Home TSE 1255 (26.8) 3044 (73.2) 1.06
c
1.05-1.06
Hospital emergency department as usual place for sick care or
health advice
No home TSE 84 304 (97.7) 1242 (2.3) Ref Ref
Home TSE 4135 (95.4) 164 (4.6) 1.40c 1.38-1.40
Hospital outpatient department as usual place for sick care or
health advice
Home TSE 4167 (96.8) 132 (3.2) 1.19
c
1.18-1.20
Clinic or health center as usual place for sick care or health
advice
Home TSE 3346 (81.2) 953 (18.8) 0.85c 0.85-0.86
Retail store/minute clinic as usual place for sick care or health
advice
Home TSE 4293 (99.9) 6 (0.1) 1.30c 1.26-1.34
Abbreviations: CI, confidence interval; OR, odds ratio; Ref,
referent; TSE, tobacco smoke exposure.

aStep-wise regression controlling for mother education,
household composition, poverty level, insurance, child gender,
child age, and child race/ethnicity.
bn refers to raw scores and percentages are weighted.
c
P < .001.
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Highlight
questions. Although questions were specific and easy to under-
stand, use of standardized measures might have provided more
accurate information.
Significance
Our results indicate that TSE is a risk factor for increased use
of
medical care. Based on the high number of children who lived
with a smoker or were exposed to tobacco smoke inside the
home and received sick care or health advice at an emergency
department, this setting may be a potential venue for health
messages to inform caregivers about the dangers of TSE for
children. The AAP and prior research recommends screening

and documenting TSE as standard care during health-care vis-
its.
3,31,32
Moreover, the practice of screening all caregivers for
tobacco use and for child TSE may provide an ideal way for
health professionals to begin discussions about child TSE at
‘‘teachable moments’’ during pediatric health-care visits when
the caregiver is focused on child health. These visits may be
opportunities when caregivers are very open to education about
risks of TSE and benefits to reducing child exposure to tobacco
smoke. Physicians should consider using minimal counseling,
which is a state-of-the-art, brief intervention that lasts less than
3 minutes and has been proven to increase tobacco abstinence
rates.
33
Future research on the longitudinal effects of TSE on
child health and the impact of interventions to reduce TSE will
provide further information about health risks for children and
ideas about ways to mitigate these risks through health messa-

ging and prevention programming.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial
support for
the research, authorship, and/or publication of this article: This
study
was funded by a grant from the National Institutes of Health
Eunice
Kennedy Shriver National Institute of Child Health and Human
Devel-
opment: R01HD083354 (to Dr Mahabee-Gittens).
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So WHAT? Implications for Health
Promotion Practitioners and
Researchers
What is already known on this topic?
TSE causes physical health consequences in children
including respiratory symptoms, increased infections,
and exacerbated asthma. Few studies have examined
whether TSE translates into more frequent pediatric
health-care utilization.
What does this article add?
TSE contributes to increased use of health-care services.
Settings with high volume of children with TSE, including
emergency departments, are potential outlets for health
messages to inform caregivers about the dangers of child
TSE.
What are the implications for health promotion
practice or research?
Offering smoking cessation interventions to caregivers in
health-care settings with high volume of children with TSE

is needed. The practice of screening all caregivers for
tobacco use and child TSE during these visits may provide
an ideal way for health professionals to begin discussions
about child TSE at ‘‘teachable moments’’ during health-
care visits when the caregiver is focused on child health.
Merianos et al. 129
http://www.childhealthdata.org/learn/facts
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AC. A smoking cessation intervention plus proactive quitline
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JS. A
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http://www.cdc.gov/nchs/slaits/nsch.htm
http://www.cdc.gov/nchs/slaits/nsch.htm
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#35626 Topic: Article Assignments
Number of Pages: 1 (Double Spaced)
Number of sources: 3
Writing Style: APA
Type of document: Article Critique
Academic Level:Undergraduate
Category: Healthcare
Language Style: English (U.S.)
Order Instructions: Attached
I have an assignment which consist of two different articles. I
will provide instructions for both articles.
For article 1: Tobacco Smoke Exposure and Health-care
Utilization among children in the U.S.

Instruction: PLEASE READ!
This is an article critique assignment for a research method
class. Attached is the article.
Please critique this article implying research method strategies.
DO NOT summarize the article but to provide a CRITICAL
EVALUATION that goes above and beyond of what is already
in the article, and be specific.
Basically, in short 4-5 sentences find any potential biases due to
sampling or non-sampling errors (Non-response errors, coverage
error, poulation etc..) that are in the article. See how they
experiment the study using telephones or other types if surveys
used to see if there should be an alternative or an error.Is
underestimated or overestimated? Is there an alternative
sampling strategy that would minimize or eliminate some of
these biases?
The 2nd article: Number of medicaid prescription grew, drug
spending was steady in medicaid expansion states. It is 5
questions that you would use the article to answer them. I will
attach it as well. One or two sentences is fine for each. I will
understand if you cant do question #5 cause it's graphing, I'll
figure it hopefully.
Thank you very much!

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