Effect of Gaining Insurance Coverage on Smoking Cessation in Community Health Centers: A Cohort Study
Effect of Gaining Insurance Coverage on Smoking Cessation in Community Health Centers: A Cohort Study
Steffani R. Bailey 2
Megan J. Hoopes 1
Miguel Marino 2 3
John Heintzman 2
Jean P. O'Malley 3
Brigit Hatch 1 2
Heather Angier 2
Stephen P. Fortmann 0
Jennifer E. DeVoe
DPhil 1 2
0 Kaiser Permanente Center for Health Research , Portland, OR , USA
1 OCHIN, Inc. , Portland, OR , USA
2 Department of Family Medicine, Oregon Health & Science University , Portland, OR , USA
3 School of Public Health, Oregon Health & Science University , Portland, OR , USA
BACKGROUND: Community health center (CHC) patients have high rates of smoking. Insurance coverage for smoking cessation assistance, such as that mandated by the Affordable Care Act, may aid in smoking cessation in this vulnerable population. OBJECTIVE: We aimed to determine if uninsured CHC patients who gain Medicaid coverage experience greater primary care utilization, receive more cessation medication orders, and achieve higher quit rates, compared to continuously uninsured smokers. DESIGN: Longitudinal observational cohort study using electronic health record data from a network of Oregon CHCs linked to Oregon Medicaid enrollment data. PATIENTS: Cohort of patients who smoke and who gained Medicaid coverage in 2008-2011 after ≥ 6 months of being uninsured and with ≥ 1 smoking assessment in the 24-month follow-up period from the baseline smoking status date. This group was propensity score matched to a cohort of continuously uninsured CHC patients who smoke (n = 4140 matched pairs; 8280 patients). INTERVENTION: Gaining Medicaid after being uninsured for ≥ 6 months. MAIN MEASURES: 'Quit' smoking status (baseline smoking status was 'current every day' or 'some day' and status change to 'former smoker' at a subsequent visit), smoking cessation medication order, and ≥ 6 documented visits (yes/no variables) at ≥ 1 smoking status assessment within the 24-month follow-up period. KEY RESULTS: The newly insured had 40 % increased odds of quitting smoking (aOR = 1.40, 95 % CI:1.24, 1.58), nearly triple the odds of having a medication ordered (aOR = 2.94, 95 % CI:2.61, 3.32), and over twice the odds of having ≥ 6 follow-up visits (aOR = 2.12, 95 % CI:1.94, 2.32) compared to their uninsured counterparts. CONCLUSIONS: Newly insured patients had increased odds of quit smoking status over 24 months of follow-up than those who remained uninsured. Providing insurance This study is registered as an observational study at clinicaltrials.gov (#NCT02355132).
smoking cessation; insurance coverage; Medicaid; community health centers; J Gen Intern Med 31(10); 1198-205 DOI; 10; 1007/s11606-016-3781-4 © Society of General Internal Medicine 2016
coverage to vulnerable populations may have a significant
impact on smoking cessation.
An estimated 42.1 million individuals in the US are current
cigarette smokers,1 and approximately 443,000 Americans die
from smoking-related illnesses each year.2 Between 2005 and
2013, the overall prevalence of smoking decreased from 20.9
to 17.8 %1; even if this rate of decline continues, adult
smoking rates will remain substantially higher than the Healthy
People 2020 target goal of ≤ 12 %.3 Furthermore, several
subgroups experience marked disparities: those who live
below the federal poverty level (FPL) have higher smoking rates
(29.2 %) than those at or above the FPL (16.2 %)1; among
adults under age 65, 29.6 % of uninsured and 30.1 % of
Medicaid recipients reported smoking in 2012 compared to
15.2 % of those with private insurance.4 Additionally, lack of
insurance coverage for smoking cessation services has been
associated with a lower likelihood of smoking cessation
assistance and fewer quit attempts compared to insured
Community health centers (CHCs) provide primary care to
medically underserved and vulnerable populations regardless
of ability to pay.7 Most CHC patients are uninsured or
Medicaid recipients, and have incomes below the FPL.8 CHCs are
well positioned to identify patients who smoke and to provide
them with smoking cessation assistance. Although
evidencebased guidelines for delivering smoking cessation treatments
(medications and counseling) in primary care settings
constitute standard care9–11 and increase the likelihood of smoking
cessation,9 delivery of these services among healthcare
providers remains low.12–15 Given the number of smokers served
in primary care9 and particularly in CHCs, the medical costs of
smoking-related illnesses,2 and the availability of
interventions that are among the most cost-effective in healthcare,16
smoking cessation assistance should be a priority for all
In January 2014, the Affordable Care Act (ACA) mandated
that Medicaid provide insurance coverage for smoking
cessation medications.17 A recent study found that prior to 2014,
only a small percentage of patients with Medicaid utilized
smoking cessation medications.18 The United States is
currently in a position to considerably expand the number of
people who can get help to quit smoking via the ACA. Policy
leaders hope this coverage will result in increased smoking
cessation assistance, leading to an ultimate decrease in
smoking prevalence.17 Previous research found smoking prevalence
dropped by 10 percentage points (38 to 28 %) after the
introduction of new Medicaid-covered tobacco dependence
treatment,5 and states with the most generous tobacco
treatment Medicaid coverage had the highest quit rates.6 This
previous evidence relied on cross-sectional survey
designs,19,20 which are subject to limitations (e.g., response
bias, low completion rates), or clinical trials that enrolled
treatment-seeking patients.21,22 The relationship between
insurance coverage and smoking cessation may be different
among CHC patients with varying degrees of readiness to quit
and among those who recently gained Medicaid insurance.
To build upon previous research, we used electronic health
record (EHR) data from a network of Oregon CHCs to test the
hypothesis that gaining Medicaid coverage after a period of
being uninsured will result in increased quit rates among CHC
patients who smoke, higher rates of ordering smoking cessation
medications, and increased primary care utilization compared
to smokers who remained uninsured. The EHR facilitates
collection of data on patient smoking status at each primary care
encounter, enabling longitudinal assessment and comparison of
smoking status among insured and uninsured patients. After a
freeze on new Medicaid enrollment in the Oregon Health Plan
Standard due to a lack of resources, Oregon reopened
enrollment in 2008 for a randomly selected subset of low-income
adults (ages 19–64 years) who were Oregon residents, US
citizens or qualified non-citizens, not categorically eligible for
Medicaid based on the federal definition, and uninsured for ≥ 6
prior months. This allowed many Oregonians to gain public
health insurance.23–26 During this time period, Oregon’s
Medicaid program also had comprehensive tobacco treatment
coverage (individual and group counseling, nicotine replacement
products, bupropion, and varenicline) comparable to that
mandated by the ACA.27 Therefore, these study findings can be
used to elucidate the potential impact of gaining Medicaid
insurance on the long-term quit rates and rates of smoking
cessation assistance in CHCs, data that cannot yet be
ascertained from post-ACA implementation.
OCHIN EHR. Formerly the Oregon Community Health
Information Network but shortened to BOCHIN, Inc.^ as
other states joined, OCHIN is a nonprofit health information
technology organization providing a centrally hosted and
linked instance of the Epic® EHR to safety net clinic
members. EHR data are managed and warehoused centrally
at OCHIN, including regular validation and cleaning.28 We
used OCHIN EHR data to extract all demographic and
encounter information. The EHR presents a discrete data
field for smoking status, and the OCHIN workflow requires
review of tobacco use status at each primary care encounter. In
this field, smoking status (i.e., current every day, current some
day, former, or never smoker) can be confirmed or modified,
and the reviewed or changed date is saved in the EHR.
Tobacco cessation medications were abstracted from EHR
medication order data.
Medicaid Enrollment Data. We obtained Medicaid
enrollment data from the state of Oregon and linked it to
EHR data using a unique Medicaid patient identifier
available in both data sets. We used these data to identify
Medicaid coverage dates as we have done in prior studies.29,30
We identified all CHC patients aged 19–64 years who gained
Oregon Medicaid coverage between 2008 and 2011 after
being uninsured for ≥ 6 months and who maintained this
insurance for ≥ 6 months. We then identified a cohort of
current smokers among this group who had ≥ 1 visit at a study
clinic with smoking status indicated as ‘current every day
smoker’ or ‘current some day smoker’ within 6 months of
the date they gained Medicaid coverage. From the baseline
smoking status date (the encounter date of initial smoking
assessment), we identified patients with ≥ 1 follow-up
smoking assessment in the 24-month follow-up period (n = 1180
excluded for having no follow-up). Patients were excluded if
they were aged < 19 or > 64 years, pregnant, died, or had
evidence of private insurance or Medicare coverage in the
study period. After exclusions, we had a cohort of 5935
current smokers who gained Medicaid.
To identify a matched control group of smokers who did not
gain Medicaid, we considered patients who were continuously
uninsured throughout the 24-month follow-up period and met
the current smoker criteria (n = 9371 after excluding 6344
smokers with no follow-up). For this uninsured comparison
group, the baseline smoking status date was set as the earliest
date between 2008 and 2011 at which each patient had an
EHR record indicating current smoking status; the follow-up
period was 24 months from this date. Because the insured and
uninsured groups differed in multiple characteristics, a
propensity score matching approach was used to balance potential
confounders between groups and reduce bias.31 We used
logistic regression models to generate propensity scores based
on baseline and pre-baseline characteristics including
demographics, insurance history, utilization, smoking history (e.g.,
years smoked at baseline, number of pre-baseline smoking
assessments), comorbidities, and health center characteristics.
We completed a one-to-one nearest neighbor match on
propensity scores within a caliper restriction,32 and assessed
covariate balance for both the full sample and the propensity
score-matched sample by computing standardized differences
of each variable. We considered covariates with absolute
standardized difference ≥ 0.1 in the propensity
scorematched sample to have residual imbalance; none of the
variables met this criterion in the matched sample. The final
study sample included 4140 matched pairs (8280 patients).
Additional detail on propensity score matching and covariate
balance is provided in Online Appendix Figure 1. Online
Appendix Table 1 compares characteristics of the propensity
score matched sample to smokers excluded for having no
follow-up smoking assessment (N = 7524).
Our primary outcome was ‘quit’ smoking status after the
baseline assessment, coded as a binary yes/no variable. A
person was identified as ‘quit’ if baseline smoking status was
‘current every day’ or ‘some day’ and status changed to
‘former smoker’ at a subsequent visit. We also assessed
prevalence of having a smoking cessation medication ordered (yes/
no), and analyzed quit smoking status stratified by whether
medication was ordered. Medications included bupropion,
varenicline, and all nicotine replacement products. As a proxy
for utilization of care, we also examined quit smoking status
stratified by number of follow-up visits (≥ 6 vs. < 6 follow-up
visits, as six was the median in this study population). Our
independent variable was insurance status: comparing patients
who gained Medicaid to those who remained continuously
We described patient characteristics of both the original and
propensity-matched samples. We then described distributions
of follow-up visits and smoking assessments, usual source of
care (defined as having ≥ 75 % of study encounters at the same
clinic), prevalence of smoking cessation medication orders,
and changes in smoking status within each group during the
study period. Between-group differences were tested using
generalized estimating equations (GEE) to account for
clustering among propensity score-matched pairs.33 We computed
adjusted odds ratios of having a smoking cessation medication
ordered, having ≥ 6 follow-up visits, and quitting smoking
using GEE accounting for correlation among propensity
score-matched pairs. Models were adjusted for having a usual
source of care in the follow-up period; we did not further
adjust for any baseline covariates because none were found
to have residual imbalance between the groups after
propensity score matching (absolute standardized difference < 0.1,34
see Table 1). We also included each patient’s primary CHC as
a fixed effect to adjust for potential differences between health
centers. Odds ratios for quit status by medication order and
quit status by visit strata were obtained using an interaction
term for medication order by study group, and ≥ 6 visits by
study group, respectively. For our GEE models, we assumed a
compound symmetry correlation structure and applied a
robust sandwich variance estimator to account for possible
misspecification.35 All analyses were conducted using SAS
software, version 9.4 (SAS Institute Inc., Cary, NC). The study
was approved prior to data collection by the Oregon Health &
Science University Institutional Review Board.
After propensity score matching, the gained Medicaid and
uninsured groups were balanced on all baseline demographic
variables (absolute standardized difference < 0.1, Table 1).
The sample was 49 % male, had a median age of 42 years at
baseline, and 81 % had incomes ≤ 100 % of FPL.
Approximately 15 % of patients were Hispanic or non-white race.
Marginally more patients in the group that gained Medicaid
had a history of Medicaid coverage prior to the 6 months of
being uninsured (43.7 vs. 39.0 % of uninsured). Chronic
conditions were common in both groups: overall, 15.5 % of
patients had diagnosed hypertension, 10.2 % had asthma/
COPD, 6.2 % had diabetes, and 9.7 % had ≥ 2 active chronic
conditions as of the baseline date. Approximately 23 % of
patients had smoked for > 20 years at baseline, and 96 % were
Patients who gained Medicaid had more primary care
visits (median = 7 vs. 5 for uninsured) and more follow-up
smoking assessments (median = 4 vs. 3, respectively, Table 2)
over the 24-month follow-up. Over one-quarter of patients
who gained Medicaid had a smoking cessation medication
ordered (26.9 % vs. 11.5 % of uninsured, p < 0.001), and
16.6 % quit smoking in the study period vs. 13.3 % of
uninsured, p < 0.001.
Figure 1 presents adjusted odds ratios (aOR) of quit
status and having a smoking cessation medication ordered,
as well as the odds of quitting stratified by whether or not
medication was ordered and whether or not a patient had
≥ 6 follow-up visits. The newly insured had 40 %
increased odds of quitting compared to their uninsured
counterparts (aOR = 1.40, 95 % CI: 1.24, 1.58; 16.6 %
vs. 13.3 %, respectively). The odds of having medication
ordered were almost three times higher for patients who
gained Medicaid relative to the uninsured cohort (aOR =
2.94, 95 % CI: 2.61, 3.32), and the newly insured had
twice the odds of having ≥ 6 follow-up visits than the
continuously uninsured (aOR = 2.12, 95 % CI: 1.94, 2.32).
Among patients without a smoking medication ordered, the
gained Medicaid group had significantly higher odds of
quitting compared to the group of uninsured smokers
(aOR = 1.23, 95 % CI: 1.06–1.41); among those with
medication ordered, the odds of quitting was also higher
for those who gained Medicaid, but the difference was not
Smoking status at
Every day smoker
Some day smoker
Bold = absolute standardized difference ≥ 0.1
*Additional variables were included in the model to achieve optimal prediction of the propensity score: primary language, number of quit records
prebaseline, number of departments in primary health center, number of primary care departments in primary health center, number of adult patients in
primary health center, whether primary health center provides multidisciplinary care
†Chi square test
‡Includes Spanish language speakers regardless of recorded ethnicity
quite significant in this smaller group (aOR = 1.29, 95 %
CI: 0.99, 1.67). Among those with more follow-up visits,
the odds of quitting were 22 % higher for those who
gained Medicaid (aOR = 1.22, 95 % CI: 1.05–1.42); there
were no significant between-group differences in quit rates
among patients with < 6 follow-up visits.
To further explore the relationships between quitting
smoking and smoking cessation assistance, we conducted
withingroup analyses to determine: 1) whether having a smoking
cessation medication order documented was associated with
higher odds of quitting compared to no documented smoking
cessation order; and 2) whether patients with ≥ 6 follow-up
visits had higher quit rates compared to patients with < 6 visits.
As expected, having a smoking cessation medication order
resulted in higher odds of quitting smoking for both groups
(newly insured: aOR = 2.00, 95 % CI: 1.69–2.37; uninsured:
aOR = 1.90, 95 % CI: 1.50–2.41), and patients with more
visits had higher odds of quitting than patients with fewer
visits (newly insured: aOR = 2.86, 95 % CI: 2.36-3.45;
uninsured: aOR = 2.60, 95 % CI: 2.16–3.12).
To our knowledge, this is the first study to use longitudinal
EHR data from CHCs to examine the impact of gaining
Medicaid coverage on ordering of smoking cessation
medications and quit status. Over 24 months of follow-up, patients
who gained Medicaid received smoking medication orders
and quit smoking at higher rates than those who remained
uninsured. The newly insured also had more follow-up visits.
It is likely that both increased access to medications and to
primary care influenced the improved quit rates for several
reasons. Regardless of insurance status, smoking cessation
medication orders were associated with an almost twofold
higher quit rate. Higher medication order rates; however, do
not tell the whole story because newly insured patients who
did not have a smoking cessation medication ordered still had
significantly higher quit rates than the uninsured. This,
coupled with the findings that the odds of quitting were higher
among patients with more visits, and that the insured cohort
had more visits than the uninsured, suggests that increased
access to care via insurance coverage is also a significant
contributing factor. Increased access to primary care could
result in more opportunities for receipt of other smoking
cessation assistance, such as smoking cessation counseling
or referral for such services; however, even among patients
with higher utilization, the newly insured had higher odds of
quitting than the uninsured. Although we were unable to
capture counseling/referrals, we hypothesize that this could
be indicative of more Medicaid-covered smoking cessation
counseling services provided during these visits. This finding
supports other research that has shown a combination of
counseling and medications to be most efficacious for
successful smoking cessation.9,36
Our findings among a population of CHC patients are
similar to those reported in cross-sectional and
treatmentbased studies in other primary care settings. Further, they are
consistent with studies that have limited analyses to the
associations between insurance coverage and smoking cessation
assistance among patients with smoking-related diseases.37–39
A recent study reported that Medicaid spent over $39 billion
on smoking-related medical services in 2010; Medicare and
Medicaid together covered approximately half of the $170
billion spent annually on smoking-attributable healthcare
among adults in the US, and the majority of these costs are
associated with inpatient services.40 Our results suggest that
Medicaid coverage can increase the odds of smoking cessation
*p value from patient type parameter estimate from bivariate GEE
model with clustering effect of propensity score-matched pair
†Wilcoxon signed rank test
‡ ≥ 75 % of study encounters at the same clinic
§Includes bupropion, varenicline, and nicotine replacement therapy
within the primary care setting, which could significantly
decrease rates of smoking-related diseases and their associated
healthcare costs in the long-term.
Increased access to Medicaid via the ACA expansion, along
with improved Medicaid coverage for tobacco treatment, may
lead to increased primary care utilization and access to
smoking cessation assistance. Patients who gained Medicaid had
more primary care visits in the follow-up period than patients
that remained uninsured; increased visit rates were associated
with greater smoking status changes. Insurance coverage and
access to primary care act interdependently to improve
population health outcomes41–43; our study provides additional
evidence supporting this link. Given the longstanding, major
impacts of tobacco on mortality and morbidity, an increase in
quit rates in a high-risk population could have major benefits
on health outcomes and total healthcare costs.40
Because we used EHR data, we only had follow-up
smoking status for those patients who returned to the clinic, and
therefore cannot determine the quit status of non-returning
patients. Non-returning patients differed from the study
sample on a number of baseline demographics (Online
Appendix Table 1); the results of the study may not
generalize to this group. Although we used propensity
score matching to balance a range of baseline factors,
there could be residual confounding due to unmeasured
variables that could account, in part, for the observed
differences. Our study was limited to an Oregon
population of established CHC patients; thus, our results might
not generalize to patients in other states or to adults who
do not access healthcare services regularly. We did not
assess if bupropion was prescribed for smoking cessation
or depression; however, we used propensity scores to
match patients on psychiatric diagnoses, including
depression. It should be noted that we were only able to assess
whether medications were prescribed, not whether the
patients filled the prescriptions and took the medications.
Finally, as mentioned previously, we were not able to
reliably capture counseling services or referrals in the
EHR during the study period. We hypothesize that the
higher quit rates among the newly insured not prescribed
medications were due to increased access to other
smoking cessation services; future research is warranted.
Returning CHC patients who smoked and gained Medicaid
insurance coverage after being uninsured had increased visit
rates and were significantly more likely to have a smoking
cessation medication prescribed and to quit smoking over 24
months of follow-up than those who remained uninsured.
These findings suggest that expanding Medicaid coverage
could lead to a substantial decrease in smoking rates among
vulnerable populations, thus reducing downstream detrimental
effects caused by this life-threatening health behavior.
Acknowledgements: Contributors: The authors gratefully
acknowledge the OCHIN network and the OCHIN Practice-Based Research
Network. The corresponding author affirms that she has listed
everyone who contributed significantly to the work represented in
Funders: This study was supported by grants R01HL107647 from
the National Heart, Lung, and Blood Institute, K23DA037453 from the
National Institute on Drug Abuse, and R01HS024270 and
K08HS021522 from the Agency for Healthcare Research and Quality.
Prior presentations: The information in this paper was presented on
5 March 2016 at the 22nd Annual Society for Research on Nicotine &
Tobacco Meeting in Chicago, IL, and on 2 May 2016 at Oregon Health
& Science Research Week in Portland, OR.
Corresponding Author: Steffani R. Bailey, PhD; Department of
Family MedicineOregon Health & Science University, Portland, OR,
USA (e-mail: ).
Compliance with Ethical Standards:
Conflicts of Interest: Stephen P. Fortmann has received prior funding
from Astra-Zeneca as a co-investigator on an observational study of
hypertriglyceridemia in patients with diabetes. All other authors
declare that they do not have a conflict of interest.
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