Comparing Patient-Centered Outcomes for Adults and Children With Asthma in High-Deductible Health Plans With and Without Preventive Drug Lists
Conditions: Asthma
Sponsor: Harvard Pilgrim Health Care

 
 
Asthma in Families Facing Out-of-pocket Requirements With Deductibles 
 
 
Protocol 
 
 
NCT03175536 
 
 
Updated August 16, 2018

 
RESEARCH STRATEGY  
 
A. BACKGROUND  
Asthma is a major cause of preventable disease burden (PCORI Methodology Standard RQ-3).  More than 25 million 
people in the U.S. have asthma, including 8% of adults and 9% of children, with significant racial and income disparities 
in prevalence.1-3  Morbidity from asthma is substantial, causing 10.5 million healthcare visits, 479,000 hospitalizations, 
and 3,100 deaths in 2009.1  Low income and racial/ethnic minority patients are more likely to experience adverse 
outcomes, leading to four times as many hospitalizations and five times as many deaths in blacks than whites, and this 
gap has been increasing.4-7 Guidelines recommend controller medications such as inhaled corticosteroids (ICS), 
leukotriene antagonists (LTA), or combination ICS and long-acting β2-agonists (LABA) that are proven to improve asthma 
control and quality of life, prevent exacerbations, and reduce need for oral corticosteroids, emergency department (ED) 
visits, hospitalizations, and deaths due to asthma.8-18 However, many patients do not use daily controller medications, 
with 14-20% of individuals not filling prescribed controller medications once.19  Poor adherence to asthma controller 
medications increases the risk for adverse clinical outcomes.16-18  Increased levels of cost-sharing for asthma medications 
that lead to reduced adherence have also been associated with adverse downstream outcomes such as increased rates 
of asthma exacerbations, oral steroid bursts, and asthma-related ED visits and hospitalizations.20-22 
Patients are increasingly faced with high levels of cost-sharing through high-deductible health plans (HDHPs). 
Compared with traditional plans, HDHPs have lower premiums but subject most services to annual deductibles of at 
least $1000.  HDHP membership more than quadrupled from 2006 to 2015 and 46% of workers now have HDHPs.23  
HDHPs with associated Health Savings Accounts (HSAs) enable enrollees to use pre-tax dollars to pay for medical care.  
To qualify to include an HSA, HDHPs must have federally-regulated minimum annual deductibles (individual/family: 
$1300/$2600), and must include all care under the deductible except for select preventive services.24 As of early 2013, 
over 15 million people had HSA-HDHPs, with rates of uptake increasing rapidly, especially among large employers.25, 26  
HDHPs are also increasingly prevalent among enrollees in the ACA’s health insurance exchanges.27 
Patients experience substantial barriers to accessing needed medications for chronic conditions such as asthma due to 
cost-sharing. Numerous studies suggest that high cost-sharing is associated with lower use of both non-essential and 
essential health care, including medications for chronic diseases.28-33 Among adults reporting difficulty paying medical 
bills, more than half said they had had difficulty paying for prescription drugs.34 Studies in adults have shown that 
increased cost-sharing is associated with decreased use of important asthma medications.20, 35, 36 The few studies in 
pediatric populations have also shown reduction in use of asthma medications with increased cost-sharing.21, 37, 38  
Evidence is limited on the impact of cost-sharing on rescue medications for asthma.  Two studies have found that higher 
out-of-pocket (OOP) costs for bronchodilators were associated with decreased use for adults and children;37, 39 our 
recent work found that higher copayments did not lead to decreased albuterol use for children relative to those without 
a copayment change, but did result in higher OOP costs (Figure 1).40   
Families are increasingly concerned about OOP costs that can strain family budgets and lead to intra-familial trade-
offs.  OOP costs from one family member can lead to delayed/forgone care for other family members,41 and high family 
OOP expenditures have been associated with lower rates of initiation of expensive medications.42  Some data suggest 
that parents prioritize their children’s health care over their own when faced with financial pressures.43, 44  Our research 
found that both adults and children are at increased risk for delayed/forgone care due to cost when faced with high 
deductibles, and that the risk is greater for healthy adults in families with chronically ill children, and for healthy children 
in families with chronically ill adults.45  In another study, we found that having a greater number of children in the family 
increased risk of financial burden among health insurance exchange plan enrollees, the majority of whom had HDHPs.46    

HDHP enrollees are at risk for 
cost-related underuse of 
medications, especially in HSA-
HDHPs.  Our research team was 
among the first to conduct studies 
of modern HDHPs,47-58  including 
more recent studies using the 
large, national dataset that we 
propose to use for the current 
project.59  We found that a switch 
to a HDHP was associated with 
fewer ED visits53 and 
hospitalizations, especially for low 
socioeconomic status (SES) 
enrollees;51 no change in rates of 
well-child visits and screenings for 
breast, cervical, and colorectal 
cancer, all of which were exempt 
from the deductible;48, 54 and 
pronounced reductions in 
colonoscopy among low SES 
members.57, 58 Other early studies 
of HDHPs without HSAs 
demonstrated decreased utilization of both appropriate and inappropriate services,60-62 including prescription drugs.63-67  
HDHP plans are also associated with greater patient out-of-pocket spending for care for families with chronic 
conditions.68  Patients with asthma are at particular risk from deductible costs, as controller and rescue medications are 
expensive branded drugs, costing $140-$307 a month for some inhaled steroids and $30-60 for CFC-free albuterol 
inhalers69-71 without available lower-cost generic substitutes.  HSA-HDHPs have been associated with decreased 
adherence for medications for asthma72, 73 and other chronic conditions.72-75  In survey studies, we found that families in 
HDHPs were more likely than those in traditional plans to report health care-related financial burden and 
delayed/forgone care due to cost for both adults and children.45, 46, 68, 76  In our qualitative studies, families reported 
challenges understanding and using HDHPs.77, 78 
Cost-sharing required by HSA-HDHPs has the potential to exacerbate disparities in access to medications for patients 
with asthma.  Some studies suggest that the impact of increased cost-sharing is greater for low-income populations,79  
who are more likely to experience delayed/forgone care due to cost in HDHPs.76  Low-income, Hispanic, and African-
American populations are more likely to report cost-related underuse of mediations and other health care,45, 80-82 and 
increases in copayments for chronic medications have a greater negative impact on adherence for low-income 
patients.83   However, one of our studies found that both lower and higher-income populations experience reductions in 
utilization in HDHPs.45   
Increasing adoption of Preventive Drug Lists (PDLs) has the potential to preserve use of important asthma 
medications by patients in HSA-HDHPs.  Insurance plans increasingly seek to develop value-based health insurance 
designs (VBID) that include low or no cost-sharing for high-value services.84-87  In 2008, 19% of large employers used 
VBID strategies for medications in their health insurance offerings.88  The strategy of exempting medications from the 
deductible as part of a PDL is becoming increasingly popular among employers and health plans.89  PDLs are lists of 
medications for primary and secondary prevention (including asthma controllers and rescue medications) that are 
exempt from the deductible and available at reduced or no cost.90, 91  Evidence is mixed about the impact of reducing or 
eliminating copayments for medications.  Some studies of diabetes and cardiovascular medications demonstrated 
modest improvements in adherence and reductions in OOP costs,88, 92-97 no change in adverse outcomes,96 and 
attenuation of racial/ethnic disparities.98  In HDHPs without HSAs, when medications have been excluded from the 
deductible and copayments applied instead, we found that prescription drug use for patients with asthma was 
preserved.56  We have also found this pattern in our ongoing national study of diabetes and HDHPs (1U58DP02719).  
However, in other studies, exempting medications from the deductible or eliminating copayments did not improve or 
Figure 1. Impact on OOP costs from a co-pay increase for albuterol inhalers
$0
$5
$10
$15
$20
$25
Jan-07
Apr-07
Jul-07
Oct-07
Jan-08
Apr-08
Jul-08
Oct-08
Jan-09
Apr-09
Jul-09
Oct-09
Jan-10
Apr-10
Jul-10
Oct-10
OOP cost per child dispensed Albuterol
Exposed
Trend (exposed)
Controls
Trend (control)
Exposed group had increased co-pays after a policy change mandating 
branded CFC-free albuterol inhalers. Control group maintained prior 
generic co-pay levels.
Policy change

preserve utilization of medications,99 including asthma medications. 85, 100 PDLs are a unique, readily adoptable VBID 
feature available with HSA-HDHPs with the potential to be substantially more powerful than previously studied VBID 
programs in traditional plans or HDHPs without HSAs because of the greater potential OOP cost differential for enrollees 
in HSA-HDHPs plans.  However, we lack evidence about the impact of PDLs in HSA-HDHPs.  
Addressing gaps in evidence (RQ-1) on strategies to support patients in HDHPs is a priority area for PCORI.101  Most 
studies of HSA-HDHPs or VBID have looked at medications globally or for conditions that are largely asymptomatic, such 
as hypertension.88, 92-97, 99, 102-104 Only a few such studies focus on medications for symptomatic conditions like asthma;56, 
72, 73, 85, 100 of these, none assess downstream effects on asthma-related clinical outcomes or disparities. Many studies of 
cost-sharing effects on asthma medications, especially those focusing on pediatric populations, have relied on weaker 
observational designs.20, 35, 37, 39   Use of populations with a choice of health plan types and a lack of matched comparison 
groups create limitations due to selection bias in some of these studies.20, 21, 35, 37, 39, 73, 100, 105  For some HSA-HDHP or VBID 
studies that include asthma medications, generalizability has been limited by inclusion of populations that come from a 
single employer73, 74, 85, 100 or only include adults.35, 73, 74, 85, 100, 106  The few studies on HSA-HDHPs or VBID that include 
dependents have not explicitly evaluated the impact on children or considered intra-familial effects.65, 75, 102, 103, 107   
Findings of no or only modest changes in asthma medication use in some studies of cost-sharing, HDHPs without HSAs, 
or VBID may reflect relatively small copayment changes, 39, 40, 75, 85, 100 and may underestimate experiences with HSA-
HDHPs and PDLs in which cost differentials are much greater.   
B. SIGNIFICANCE  
The proposed project will improve the quality of evidence about HDHPs by being one of the first studies to examine 
the effect of free preventive drugs in HSA-HDHPs on asthma medication use and adverse asthma outcomes. PDLs are 
especially relevant to asthma patients, given their frequent need for medications that can have short and long-term 
impact on symptoms and morbidity.  Our focus on asthma is unique among studies of chronic conditions in HSA-HDHPs 
and PDLs because asthma affects both adults and children, requires both preventive and symptomatic medications, and 
does not have available generic lower-cost alternatives in key medication classes. 
As one of the first studies to explore adult-child differences and intra-familial tradeoffs that occur with increased 
financial pressures in HSA-HDHPs, this project will provide data on outcomes that affect parents and caregivers in 
families with one or more members with asthma.  Most HDHP and VBID research has focused on individual adults.  
However, many people obtain insurance as a family, and over 60% of HDHP enrollees are in family plans.56  Health care 
decisions involve trade-offs to balance family health care needs and costs against a family budget.  Some studies of 
HDHPs have included data on dependents,65, 75, 102, 103, 107 but these have not addressed child-specific adherence or 
outcomes.  We lack data to know if worrisome findings from adult-focused studies apply to the same extent in children. 
Findings from the proposed research will have immediate practical uses for patients, families, clinicians, employers, 
health insurers, and policy makers who are keen to find strategies to obtain insurance coverage with lower premiums 
while avoiding cost barriers to needed asthma care (RQ-3). 108 109  This project will provide crucial, patient- and family-
centered evidence about health insurance strategies that are already being delivered in real-life settings. Opportunities 
are readily available to implement policy changes stemming from our findings.  Given that HSA-HDHPs are standardized 
to meet federal policies for HSAs, and that PDLs covering a set of common, important medications have been already 
implemented by some insurers nationally,51, 89-91 our findings can easily be generalized and translated into practice in 
other settings.  If HSA-HDHPs with PDLs promote asthma medication adherence and better health outcomes, employers 
and health insurers could quickly shift to such designs. Findings can inform the health insurance choices of families with 
asthma as they seek affordable coverage that does not create cost barriers to obtaining needed care.109    
This research focuses on outcomes that are increasingly of concern to patients with asthma, their families, and other 
stakeholders, namely cost barriers to obtaining needed asthma care (RQ-6).  Patients with asthma desire relevant 
information about the potential health and financial consequences of the multiple, complex insurance benefits options 
before them.  Forward-looking employers, health insurers, and policy makers who wish to minimize unintended 
consequences of HSA-HDHPs are considering nuanced VBID strategies like PDLs to improve outcomes among patients 
with chronic conditions. Our research will evaluate these two “natural experiments” – employer-mandated switches to 
HSA-HDHPs and addition of PDLs that exempt key asthma medications from cost sharing – which increasing numbers of 
patients are facing.  Letters of support from the Asthma and Allergy Foundation of America (AAFA), the Northeast 

Business Group on Health (NEBGH), and America’s Health Insurance Plans (AHIP) demonstrate the relevance of our 
proposed research to patients and stakeholders.    
C. APPROACH  
Specific Aims 
Asthma is one of the most common serious chronic diseases of adults and children in the United States, affecting 18.7 
million adults and 6.8 million children.  Despite guidelines and evidence of their effectiveness in preventing adverse 
outcomes such as sick days, ED visits, and hospitalizations, adherence to recommended asthma controller medications is 
low.  Cost is an important barrier to non-adherence to asthma medications.  Employers are increasingly adopting HDHPs 
particularly those that qualify for HSAs, which subject most medications to deductibles rather than copayments as in 
traditional coverage.  HSA-HDHPs can thus lead to forgone care due to cost, including clinically appropriate services such 
as asthma medications.  As a response, value-based insurance designs have been proposed to promote high-value care 
by reducing or eliminating cost-sharing for these services.  One common example is a PDL that can accompany HSA-
HDHPs, which exempts certain chronic medications from the deductible to promote adherence.  PDL’s have become 
increasingly prevalent in HSA-HDHPs offered by employers, and many PDLs include asthma controller and rescue 
medications.  Evidence suggests only a small adherence benefit when VBID reduces out-of-pocket (OOP) costs for 
already low-cost preventive medications in traditional plans without high deductibles.  However, under HSA-HDHPs, 
where OOP costs are higher and the potential reduction by a PDL much more meaningful, we lack evidence on the 
response to PDL cost-sharing reductions among asthma patients.  With the increasing prevalence of HSA-HDHPs, PDLs 
have the potential to improve controller medication adherence for both adults and children, which could improve 
asthma outcomes and reduce downstream costs.  Lower asthma cost burden could mitigate cost-related trade-offs for 
families in HSA-HDHPs who must balance health care and other costs within a family budget.   
In this project, we intend to evaluate the impact of two important developments in the health insurance landscape, HSA-
HDHPs and PDLs, on medication use, clinical outcomes, and patient experiences for adults and children with asthma.  
We will take advantage of a rolling cohort of over 621,000 adults and 310,000 children with asthma who receive 
employer-sponsored coverage from a large national insurer between 2004 and 2017.  We will employ a rigorous quasi-
experimental interrupted time-series (ITS) and difference-in-differences (DiD) designs to examine the effect of being 
switched by an employer from HSA-HDHPs without PDLs to HSA-HDHPs with PDLs compared to controls who remain in 
HDHPs without PDLs.  We will compare changes in medication use and patient-centered clinical outcomes (asthma-
related ED visits and hospitalizations) for adults and children and within families.  Qualitative interviews with patients 
with asthma in HDHPs will provide more in-depth patient-reported data on health care decision making and experiences 
in these plans, which will inform quantitative analyses.  The overarching goal of this research is to provide needed 
evidence – including data gathered directly from patients – on whether pairing PDLs with HSA-HDHPs can mitigate cost 
barriers and improve patient-centered outcomes for adults and children with asthma.  Our Specific Aims are: 
1. To understand health care decision making and experiences of families with asthma under HSA-HDHPs and PDLs 
2. To examine the impact of HSA-HDHPs with and without PDLs on use of asthma controller and rescue medications, 
and on adverse clinical outcomes (asthma-related ED visits and hospitalizations), overall and for vulnerable 
subgroups (low-income and racial/ethnic minority patients) 
3. To examine the extent to which the response to HSA-HDHPs and PDLs is affected by the presence of other family 
members with asthma or other chronic conditions 
4. To examine the impact of HSA-HDHPs with and without PDLs on out-of-pocket (OOP) costs for patients and families 
with asthma   
With input from patients with asthma and other stakeholder partners, the proposed project will focus on patient-
centered outcomes related to asthma care in new insurance designs. This research will meet a pressing need to 
understand the impact of a readily adoptable health insurance innovation that has major potential to improve asthma 
medication adherence and outcomes. Findings will advance the goal of developing and implementing insurance designs 
to promote access to high-value health care services and improve clinical outcomes while avoiding burdensome costs for 
families.   
Overview of Study Protocol (RQ-2)  

This project will employ both qualitative and quantitative methods, based on theoretical models adopted from Levy and 
Meltzer's model of the effect of health insurance on health outcomes,110 and known causal pathways linking asthma 
controller medication adherence to reductions in short-term complications.8-12  
In Aim 1, we will conduct in-depth qualitative interviews with approximately 60 commercially-insured patients with 
asthma or parents of children with asthma who have HDHPs with and without PDLs, or traditional plans.  These 
telephone interviews will explore how patients and their families understand and navigate their HDHP and make health 
care decisions and trade-offs when faced with out-of-pocket costs.  Insights gained from these interviews will shape and 
inform the quantitative analyses in Aims 2-4. 
In Aims 2-4, we will maximize internal validity, reduce selection bias, and address causal inference standards by 
employing cutting-edge study designs and analytic methods that we have used in our prior work (CI-1,4): (1) ITS with 
comparison series design, the most rigorous quasi-experimental design for causal inference;111 (2) a population whose 
employers offer only a single health plan type, minimizing self-selection bias; (3) matching groups on the functional form 
of the baseline trend, recently shown to approximate estimates from RCTs;112 and (4) a rigorous and validated two-level 
propensity score matching approach that matches on both individual and employer baseline characteristics and includes 
family-level characteristics.113 We will follow individuals and families with asthma for 1 year before and up to 3 years 
after an index date when they are required to switch to HSA-HDHPs with or without PDLs, or to remain in HSA-HDHPs 
without PDLs or remain in traditional plans. We will identify two primary intervention groups of interest: 1) patients with 
asthma switched by employers from traditional plans to HSA-HDHPs without PDLs; and 2) patients switched from HSA-
HDHPs without PDLs to HSA-HDHPs with PDLs. Patients in each group will be propensity-matched to patients with 
asthma who remain by employer choice in the same plan.  
Conceptual Model Underlying Study Hypotheses 
The theoretical underpinnings of our study derive from Levy and Meltzer's conceptual model of the effect of health 
insurance on health outcomes,110 and known causal pathways linking asthma controller medication adherence to short-
term complications.8-12  In our model (Figure 2), level of medication cost-sharing for patients with asthma is determined 
by plan type, as follows: 1) HSA-HDHPs without PDLs - full cost-sharing for controller and rescue medications; 2) HSA-
HDHPs with PDLs - no cost-sharing for controller and rescue medications; and 3) traditional plans - three-tiered 
copayments for controller and rescue medications. 

We hypothesize that 
higher cost-sharing in 
HSA-HDHPs without PDLs 
leads to decreased 
adherence to controller 
medications and 
decreased rescue 
medication use (offset by 
increased need for rescue 
medications due to 
suboptimal controller 
adherence).  Reductions 
in controller medication 
use can increase asthma 
exacerbations and short-
term complications such 
as ED visits and 
hospitalizations.  Because 
a patient’s OOP costs 
contribute to total family 
OOP cost burden, which 
can strain family budgets 
and lead patients to avoid 
or defer care and further 
decrease medication use, 
OOP costs are an 
additional outcome of 
interest. We hypothesize 
that lower cost-sharing 
due to PDLs, on the other 
hand, should lead to increased controller medication adherence, less need for rescue medications, fewer asthma 
complications, and lower OOP costs and family cost burden.  
Sources of Data (IR-1) 
The project’s four-year duration will enable us collect data from qualitative interviews on patient-reported experiences 
(Aim 1) that will inform quantitative analyses (Aims 2-4) and will allow us to accrue an adequate sample size of patients 
who experience the “intervention” of switching to an HSA-HDHP with or without a PDL and have one to two years of 
follow up.   
Aim 1.  We will collect patient-reported data through in-depth qualitative interviews with patients with asthma or 
parents of children with asthma who are enrolled in HDHPs (with and without PDLs) or traditional plans.  Interviews will 
be conducted by phone using an interview guide with open-ended questions designed to take approximately 45 
minutes.  Interviews will be audiotaped and transcribed.  There will be two sources of data:   
1)  Interviews of health plan members in non-group and employer-sponsored plans, including those identified from 
national employer accounts from Harvard Pilgrim Health Care (HPHC) which has a partnership with the large 
national health insurer whose data will be used in Aims 2-4.  HPHC is a large non-profit health plan based in New 
England which partners with the national health plan to provide services to members living outside New 
England.   
2) Interviews of subjects recruited through the AAFA, a national non-profit organization founded in 1979 to 
control and prevent asthma and allergic conditions through patient education, public awareness, and 
support for research (see Section F.1).   
Figure 2. Conceptual model illustrating the impact of HSA-HDHPs with and without PDLs 
relative to traditional plans.  Green arrows represent patterns of medication use, with 
wider arrows representing greater use. 
Asthma 
patient
HSA-HDHP with PDL
Rescue 
medication 
use
Controller 
medication 
adherence
↓ Short-term 
complications
↑ Total family 
OOP 
expenditures
Rescue and 
controller 
medications free
Asthma 
patient
Traditional plan
Rescue 
medication 
use
Controller 
medication 
adherence
Short-term 
complications
Total family 
OOP 
expenditures
Co-pays for all 
medications
Asthma 
patient
HSA-HDHP without PDL
Rescue 
medication 
use
Controller 
medication 
adherence
↑ Short-term 
complications
↑↑ Total family 
OOP 
expenditures
Deductible 
applies to all 
medications

Aims 2-4 will use 14 years (2004-2017) of enrollment and benefits data, insurance claims, standardized cost data, 
geocoded census data, and credit report SES variables from a large U.S. commercial health plan obtained through 
Optum. We will have detailed benefit information for ~80% of employer groups obtaining coverage from the health 
plan. For employers with missing deductible levels, we have developed an imputation algorithm that has 97.0% 
sensitivity and 96.2% specificity (MD-2).  The health plan links insurance claims to de-identified credit report data, 
including novel indicators of individual-level SES (household income and net worth) and imputed information on 
ethnicity based on member surname (MD-2, see ‘Methods to monitor and address missing data’, below). Data from this 
insurer have been used in published health services research studies72, 114, 115 and the study team has experience 
examining a range of health care outcomes using these data. 59, 116-121  The research team’s extensive experience using 
these data will enable us to efficiently create analytic cohorts and study variables, building upon previously developed 
methods. We will leverage existing health plan data from 2004-2014 that the study team has used in other projects, and 
add data through 2017 in December 2017 (see Optum letters). 
Description of Comparators (RQ-5)  
This project will assess the impact of the “intervention” of being switched to an HSA-HDHP with a PDL, relative to a 
control group that was not switched and remained in an HSA-HDHP, and of being switched to an HSA-HDHP without a 
PDL relative to a control group that was not switched and remained in a traditional plan.  Comparator groups include: 
HSA-HDHP with PDL: enrollment in an HSA-qualified HDHP with a PDL that exempts asthma controller and rescue 
medications from the deductible, after being switched by an employer from an HSA-HDHP without PDL or from a 
traditional plan. There is limited data on the effectiveness of HSA-HDHPs with PDLs on asthma outcomes, despite an 
estimated prevalence of 19% among HSA-HDHP enrollees based on the national health plan to be studied.  Other VBID 
programs have shown modest increases in adherence to chronic medications.88, 92-97   
HSA-HDHP without PDL:  enrollment in an HSA-qualified HDHP without a PDL, after being switched by an employer from 
a traditional plan.  Those who enroll in HSA-HDHPs without PDLs and do not switch will also be used as a control group 
for those who switch from HSA-HDHPs without PDLs to HSA-HDHPs with PDLs.  Evidence in adult populations suggest a 
reduction in adherence to asthma medications for those enrolling in HSA-HDHPs without PDLs.72, 73  In 2015, the 
prevalence of HSA-HDHPS among covered workers  was 15%.23 
Traditional plan: enrollment in a plan with no or a low (<$500) deductible through an employer who offers only one 
plan. Fifty-four percent of covered workers have traditional plans, with a baseline proportion of days covered (PDC) for 
asthma medications of 46%.74    
Study Population (PC-2) (See section D below for further detail) 
The Aim 1 study population will consist of adults with asthma and parents of children with asthma. Eligible participants 
will be those who are currently enrolled in employer-sponsored and non-group health insurance plans (high-deductible 
plans with and without a PDL, or traditional plans).  Participants will be drawn from two different populations. First, we 
will recruit members of a health plan in order to ensure representation of patients through their membership in HSA-
HDHPs with and without PDLs. Second, we will recruit additional patients with asthma and their caregivers through 
AAFA in order to ensure representation of patients who have other health insurance carriers. 
The health plan population will be identified through enrollment and claims data from HPHC among non-group plans 
and large national employer accounts. HPHC offers HSA-HDHPs with the same standardized, federally mandated benefits 
as the national health plan used in Aims 2-4, as well as a PDL containing similar medications, including asthma controller 
and rescue medications.  Eligible patients will include adults aged 18-64 with a diagnosis of asthma and/or a child with 
asthma.  Using methods from our previous asthma studies,19, 40, 122 an asthma diagnosis will be defined as having at least 
one inpatient, ED, or outpatient claim in the prior year with a diagnosis of asthma based on the following International 
Classification of Diseases, 9th edition (ICD-9) codes (up through 2015): 493.XX and ICD-10 codes (2015 and after): J45.2x, 
J45.3x, J45.4x, J45.5x, J45.90x, J45.990, J45.991, J45.998.  We will select patients who have been enrolled in an 
employer-sponsored or non-group market HSA-HDHP with or without a PDL, or a traditional plan without a high 
deductible, for the prior year.  We will identify other family members with asthma sharing the same insurance plan.  
Among those eligible in each sub-group outlined in Table 1 below, we will randomly select up to 140 from each cell to 
send a recruitment mailing/email, for a total of up to 660 patients or parents invited to participate.   

The AAFA population will be recruited through postings to AAFA’s Asthma Online Community, Educational Support 
Group, email listserv, Facebook page, newsletters, flyers, and/or other communication channels. These posts will include 
a description of preliminary eligibility criteria (i.e. have a diagnosis of asthma or are parents of children with asthma who 
currently have an employer-sponsored or non-group commercial plan).  
Both the HPHC recruitment mailing/email and AAFA recruitment messaging will invite potential participants to complete 
a pre-screening questionnaire to confirm eligibility, either by phone or online via REDCap, a secure web application for 
data capture. The questionnaire will assess insurance plan type, asthma diagnoses in the family, as well as other 
sociodemographic characteristics. The study team will schedule interviews with eligible participants, selecting 
participants to ensure diversity of socioeconomic status, race/ethnicity, geographic location and asthma severity. 
Interviews will be conducted to reach the target distribution of sub-groups outlined in Table 1 below with equal 
numbers of each sub-group coming from HPHC and AAFA, or until thematic saturation is reached. If the team is 
particularly interested in one category, we may oversample from that sub-category. The totals below are approximate.  
We will offer a gift card incentive of $50 for completed interviews. 
Table 1: Targeted study sample for interviews 
Traditional plan 
HDHP 
Total 
With PDL 
Without PDL 
Adult with asthma 
6 
6 
6 
18 
Parent of child with asthma 
6 
6 
6 
18 
Adult with asthma who also has a child with asthma 
6 
6 
6 
18 
Total 
18 
18 
18 
54 
 
For Aims 2-4, the study population will be drawn from 14 years of data from a large U.S. commercial health plan with 
membership in all 50 states and annual enrollment of ~30 million members.  The eligible population will consist of adults 
aged 18-64 years and children aged 4-17 years with asthma in a baseline year while insured in a traditional plan; all 
subjects must have continuous insurance coverage with pharmacy benefits for at least 24 consecutive months.  Study 
subjects must have spent a year in a traditional health plan with no or low deductibles prior to the switch to an HSA-
HDHP without a PDL, or have spent a year in an HSA-HDHP without a PDL before switching to an HSA-HDHP with PDL, 
and then remain enrolled for at least one-year post-switch. Control group members spend a year in a traditional plan or 
a HSA-HDHP without PDL then remain that plan for at least another 12 months.  As study groups for secondary analyses, 
we will also identify enrollees switching from traditional plans to HSA-HDHPs with PDLs for comparison with those 
remaining in traditional plans. We will also identify enrollees with other plans types, not just HSA-HDHPs, which appear 
to have PDLs in that they have lower cost-sharing for asthma medications compared to other non-preventive 
medications. Based on preliminary findings from qualitative interviews as part of Aim 1 of the project, we have learned 
the cost of asthma medications is a problem not only for enrollees in HSA-HDHPs but in other types of HDHPs and in 
traditional plans with low deductibles as well, so we will also identify enrollees who are switched from having no PDL to 
having a PDL.  Each of these groups will be matched with controls who start in the same type of plan and remain in that 
type of plan without gaining a PDL. 
 
Thus, to be included in the study, all subjects will have at least 12 months in both the Pre and Post periods.  We selected 
a follow-up period of up to three years because we hypothesized that patients in HSA-HDHPs with PDLs might 
experience benefits that take longer than one year to manifest.  A primary purpose of the pre period is to ensure precise 
matching between groups on both the level and trend in key baseline characteristics (e.g., employer characteristics, 
demographics, utilization patterns, etc.).  Our difference-in-difference analyses will include annual-to-annual 
comparisons (baseline to follow-up year 1, baseline to follow-up year 2, etc.).  This will allow intuitive comparisons such 
as “relative to controls, adherence among HSA-HDHP members with PDLs increased by 4% in follow-up year 1 compared 
to baseline, but by follow-up year 2, adherence had increased by 11% compared to baseline.”  We have used this 
approach in a variety of studies49, 57, 58, 123 and find that it translates well to clinical and policy audiences.  For analyses of 
outcomes in the second and third year post-switch, we will only include study group patients and their matched controls 
who have had that length of follow up.  We will compare and report differences in baseline characteristics for the 
subgroups of study cohort members that remain enrolled for each additional year. 

We will exclude those over age 64 who are eligible for Medicare.  We will identify members with asthma during a 12-24-
month period prior to the index date using the same claims-based algorithm used to identify HPHC members with 
asthma for the Aim 1 interviews, with the inclusion of ICD-9 codes 493.XX to identify a diagnosis of asthma through 
October 2015.  This broad definition will include patients with intermittent asthma as well as those with persistent 
asthma, for whom controller medications are recommended.  Subgroup analyses will focus on the subset of patients 
who meet the HEDIS definition of persistent asthma (≥ 1 ED or inpatient visit with asthma as the principal diagnosis, ≥4 
outpatient visits with any asthma diagnosis, or ≥4 asthma drug dispensing events in the baseline year). 
Eligible employers are those that offer only one plan type in a given benefit year: 1) traditional HMO/PPO/POS plans 
(deductibles <$1000, copayments of <$50 for most services, tiered copayments for medicines); 2) HSA-HDHPs without 
PDLs; or 3) HSA-HDHPs with PDLs.  We will select “full-replacement” employers that replace a traditional plan with an 
HSA-HDHP without PDL, or replace an HSA-HDHP without PDL with an HSA-HDHP with PDL, for all employees at a given 
point in time; matched comparison employers will include those that keep all employees in their prior plan. Including 
only full-replacement employers is a key strategy used in multiple previous studies to reduce member self-selection 
bias.47-59, 124 The index dates for HSA-HDHP enrollees will be the dates of the employer-mandated plan switch. Matched 
controls will have contemporaneous enrollment in traditional plans also mandated by their employers; index dates will 
be assigned to be the same as matched intervention enrollees. We will also include matched controls who remain in 
HSA-HDHPs without PDLs.  To reduce bias due to dropout, we will censor both HDHP and control patients together when 
either one drops out, and weight the remaining population to retain its baseline demographic and clinical profile.125  We 
estimate that there will be 621,481 adults and 310,741 children with asthma from 2004-2017 who will meet our 
inclusion criteria: 18,000 asthma patients in HSA-HDHPs without PDLs, 4,222 in HSA-HDHPs with PDLs, and 910,000 in 
traditional plans. 
Outcomes 
Aim 1 qualitative interviews will assess patient and family experiences across a number of domains related to asthma 
health care decision making and outcomes in HDHPs (RQ-6, PC-3).  These domains have been selected based on patient 
concerns and interests expressed in our prior surveys, qualitative interviews, and focus groups with HDHP members and 
asthma patients;45, 68, 77, 126 suggestions from AAFA co-investigator Meryl Bloomrosen; and postings from readers of Dr. 
Wu’s asthma blog (url: http://asth.ma) (RQ-6).127 Domains and interview questions will be refined based on input from 
the project’s Stakeholder Advisory Board and Patient and Family Research Council.  Given the limitations of claims data 
for understanding the perceptions and experiences of patients, these patient-reported qualitative outcomes will provide 
complementary, in-depth insight into decision making about health and financial outcomes that matter most to 
patients. See Appendix for the draft interview guide.  Area to be explored will include: 
1. Understanding and perceptions of insurance benefits (understanding cost-sharing requirements, especially for 
asthma medications, awareness of having a PDL, perceptions about PDL usefulness for asthma medications) 
2. Impact of cost barriers on health care use for asthma, especially medications (decision making around delaying 
or forgoing care due to cost and for which types of services, patient-defined adverse consequences related to 
asthma care and disease burden) 
3. Financial burden of asthma care (OOP cost burden from asthma care, other unmet medical or non-medical 
needs due to paying for asthma care, other patient-defined adverse consequences, strategies to reduce OOP 
barriers for asthma care, experiences discussing cost barriers with providers) 
4. Intra-familial trade-offs (impact on asthma care needs, other family members’ medical and non-medical needs) 
Interviews will also explore non-financial barriers to adherence faced by families with asthma, such as low parental 
expectations for symptom control, concern about side effects, competing household priorities, and lack of routines for 
medication administration.128  We will also explore the relative importance of financial vs. non-financial barriers to 
adherence to asthma medications. 
The primary study outcomes for Aims 2-4 are the claims-based measures of asthma medication use, outcomes, and OOP 
costs listed below, measured at the individual level.  Our project focuses on these patient-centered, clinically-relevant 
outcomes (RQ-6), and will not focus on total costs to employers and the health care system.   
1. Adherence to controller medications (Aims 2-3):  We will assess adherence to guideline-recommended asthma 
controller medications, including ICS, LTA, or ICS-LABA, which have been shown to reduce asthma morbidity and 
adverse outcomes.8  These medications are subject to the deductible in HSA-HDHPs without PDLs, free in HSA-

HDHPs with PDLs, and subject to copayments in traditional plans.  We will measure: 1) PDC, the proportion of 
days that members have their medication available per month. Proportion of days covered is calculated based 
on the days’ supply in each pharmacy fill, spread over the days following the fill. Previous studies have used 
proportion of days covered as a valid measure of adherence that is associated with better clinical outcomes (IR-
4).19, 129 2) medication discontinuation, defined as experiencing a gap of more than 60 days in availability of a 
controller medication following a period of at least 180 days of continuous treatment (i.e. with no gap in 
availability greater than 60 days).  Claims-based measures such as these have been shown to be valid measures 
of adherence (IR-4).130 
Because PDLs are not standardized nationally, there is some variation among health plans in the specific types of 
drugs included.  From a review of several other insurers’ and employers’ PDLs, those that include asthma 
medications cover both controllers and beta agonists/rescue medications.90, 131-133  However, some of these PDLs 
include a few less commonly used inhaled corticosteroids which are not included in the PDL that we propose to 
study.  To address this variation, in addition to measuring adherence to asthma controller medications generally, 
we will also measure adherence to the specific controller medications included in the PDL and adherence to the 
sub-set of controller medications that are not included in the PDL (Aerospan, Arnuity ellipta, Asmanex HFA, 
Aerobid, Azmacort). 
 
2. Use of asthma rescue medications (Aims 2-3): We will assess use of albuterol and levalbuterol inhalers, the 
primary asthma rescue medications prescribed to patients, which are subject to the deductible in HSA-HDHPs 
without PDLs, free in HDHPs with PDLs, and subject to copayments in traditional plans.  All albuterol and 
levalbuterol inhalers are brand-name drugs containing 200 puffs each.  We will exclude nebulized versions as 
these are predominantly used by younger children and do not come in standardized doses. As in our prior 
study,40 we will measure the standardized number of inhalers dispensed per month based on National Drug 
Code (NDC) in pharmacy claims.  Given the inter-relationship between rescue and controller medication use, we 
will also measure the ratio of controller medications to total asthma medications, a metric for which higher 
values are associated with better asthma control and lower rates of asthma-related ED visits.134, 135  
3. Use of spacers and nebulizer machines (Aims 2-4): Based on input from patients and stakeholders, and with 
additional input from questions from added questions in the qualitative interview guide in response, we will 
assess changes in the number of spacers and nebulizer machines received by study patients, based on claims 
data.  We will measure changes in receipt of spacers and nebulizer machines after a switch to an HSA-HDHP vs. 
remaining in a traditional plan to see if changes in the cost-sharing for these devices leads to a decrease in 
receipt and an increase in OOP costs to families. 
4. Asthma-related ED visits and hospitalizations (Aims 2-3): As a measure of potentially avoidable short-term 
complications, we will measure asthma-related ED visits and hospitalizations that have a primary ICD-10 
diagnosis code for asthma.   
5. OOP health care costs (Aim 4): We will calculate consumer price index (CPI)-adjusted OOP costs for asthma 
medications and other health services, summing deductible, co-pay, and coinsurance amounts, aggregated into 
annual measures.  Financial burden due to OOP costs will be defined as annual family OOP costs as a percent of 
family income, with cutpoints of >5% and >10% to indicate financially burdensome OOP costs.136-139 
Predictors and Covariates 

Table 2 shows the main predictors and covariates 
we will use in the proposed study for propensity 
matching to create study cohorts (CI-1, 4-5), 
predicting or adjusting outcomes, or stratifying 
analyses.  Our primary predictor variables include 
study period and insurance type. Study period 
indicates the one-year period before or up to three 
years after the index date. Insurance type includes: 
1) HSA-HDHP without PDL; 2), HSA-HDHP with PDL; 
and 3) traditional plan.  
Other co-variates include asthma severity in the 
baseline period using the HEDIS definition for 
persistent asthma (see above under Study 
Population), and using chronic oral corticosteroid 
therapy (180+ days in the prior year) or any use of 
omalizumab in the baseline year to define severe 
asthma.  To estimate co-morbidity, we will use the 
well-established Johns Hopkins Adjusted Clinical 
Groups (ACG) scores140, 141 which use ICD-10 codes 
in the baseline period to calculate a standardized 
morbidity score in a specified time period.  We will 
measure the presence of other chronic conditions 
from claims data in the baseline year using the 
Johns Hopkins ACG system.  We will also measure 
whether enrollees have COPD as a co-morbidity, as 
there can be overlap with COPD and asthma, and 
use of similar medications.  
A unique innovation of our study is the availability 
of data linkages (IR-2) with consumer data from a 
major credit reporting company linked to insurance 
claims from our data vendor, Optum. Consumer 
data elements include total household income, net 
worth, and race/ethnicity.   We will combine self-reported race/ethnicity data from credit card and loan applications 
with data from Optum on surname analysis for identifying Hispanic or Asian ethnic groups and geocoding to derive 
race/ethnicity from census data.143 We will also use census-based indicators for neighborhood education and poverty 
levels.144-147 Other variables used to propensity-match cohorts and adjust for confounding will include sex and age; 
state/region of residence; individual versus family plan; baseline number of outpatient visits, presence of an inpatient 
hospitalization, and total expenditures; employer size (number of employees); and average employer baseline 
expenditures per capita (CI-1, 4-5).   
Using methods from our prior family-level studies,50, 68 we will aggregate individual member data to create family-level 
co-variates that will be applied to each subject and used for individual-level propensity matching and analyses.  Patients 
with individual coverage will be considered a family of one.  Family-level variables include: number of family members; 
mean age of children in the family; mean age of adults; baseline mean family ACG score; number of family members 
with asthma; and number of asthma and other medications used by the family, and number of ED visits and 
hospitalizations in the baseline year.  
Our study population includes health plan members working for employers that either switched to an HSA-HDHP 
without PDL or HSA-HDHP with PDL, or remained in a traditional plan or an HSA-HDHP without PDL, respectively, for all 
employees. While this minimizes member-level selection, employer-level selection could still bias results. We will 
therefore use a validated and rigorous employer- and individual-level propensity score classification/matching approach 
(CI-1, 4-5)148-151 that is superior to a single-level matching technique.113 For employer-level propensity matching, 
potential predictors of an employer HSA-HDHP switch in our propensity score models will include employer size 

category; baseline level and trend of total per capita health care expenditure quintile; baseline level and trend of 
outpatient, ED, and hospital visits quintile; median employee age; median employee ACG score; and percentage of 
employees who are women, in family plans, and living in lower SES neighborhoods (Table 2). We will perform this 
employer-level match separately for each intervention group of interest (HSA-HDHPs with and without PDLs) in order to 
generate each comparison with the control group remaining in traditional plans or HSA-HDHPs without PDLs. 
Intervention and control groups will be matched based on their baseline characteristics when both were in the same 
type of plan.  We will set p<0.05 as a cutoff for including predictors and use backwards selection to choose a 
parsimonious model, testing for significant effects on remaining predictors when one is eliminated. Following methods 
in our ongoing work, we will stratify employers based on resulting propensity scores into quartiles. We will then perform 
individual-level propensity matching within each quartile (below) to develop a multiple control groups for each 
intervention group. 
Within employer propensity quartiles, we will create closely matched control groups using individual-level propensity 
score matching (CI-1, 4-5),148 with family-level covariates included in the matching.  Using individual and family-level 
covariates from the baseline period, we will match four contemporaneous control group patients to each intervention 
group patient within a standard propensity score caliper (0.2 of the standard deviation of the logit of the pooled 
baseline propensity score).152  Use of a comparison group matched at both employer- and individual-level will generate 
study cohorts with nearly identical employer/member demographics and baseline health care trends, offering a further 
degree of control for any potential changes that may influence specific study outcomes.57, 111, 153-156 
Methods to monitor and address missing data (MD- 1-3, 5) 
We have identified the following 5 variables as exhibiting data missingness (MD-1) in our dataset for this study: 
▪ 
Variable 1: Geocoded poverty level 
▪ 
Variable 2: Geocoded education level 
▪ 
Variable 3: Member-level income level 
▪ 
Variable 4: Member-level net worth 
▪ 
Variable 5: Member-level deductible Level 
Missing poverty, education, income, and net worth data: We will use 3 methods to accommodate missingness (MD- 2-
3): (a) multiple imputation, (b) modeling missingness as a member-level characteristic, and (c) excluding members with 
missing data.  If findings differ, we will present results using all 3 methods in manuscripts or attached appendices.  We 
will use SAS procedures PROC MI and PROC MIANALYZE to impute the missing individual level covariates and conduct 
the statistical analyses under the assumption of missing at random. The SAS procedure PROC MI will impute the missing 
variables (including individual net worth, income level, poverty level and education level) using other available variables 
such as age, gender, census block level income, poverty, education etc. Then we will use PROC MIANALYZE to analyze 
the imputed data. We will also explore certain Bayesian imputation techniques to handle missing data in case missing is 
not at random and compare the results.  If the degree of missingness for member-level credit report data is found to be 
too great, we will still be able to use geocoded poverty and education census block-level data, for which missingness has 
been less than 1% in the study team’s prior work using this data source. 
Missing Deductible Level Data:  Through our data vendor, Optum, we will have detailed benefit information for ~80% of 
employer groups obtaining coverage from the health plan.  For smaller employers (with approximately 100 or fewer 
employees), we will use a benefits type variable that is not missing for smaller employers.  For larger employers with 
missing deductible levels, we will take advantage of the fact that health insurance claims data are the most accurate 
source for assessing out-of-pocket obligations among patients who utilize health services.  Our claims data contain an in-
network/out-of-network deductible payment field.  For patients who use expensive or frequent services, the sum of 
their yearly deductible payments will add up to clearly identifiable exact amounts such as $500.00, $1000.00, $2000.00, 
etc.  When several members have these same amounts, it provides strong evidence that the employer offered such an 
annual deductible level.  It will also be possible to detect employers that offer choices of deductible levels when multiple 
employees have deductibles at two or more levels, such as 20 employees with an annual amount of $1000.00 and 12 
employees with $500.00.  For employers with at least 10 workers, we therefore will sum each employee's in-network 
deductible payments and number of claims over the enrollment year and plan to assess other key characteristics such as 
percentage with Health Savings Accounts.  On a randomly selected half of the employer data set that contains our 
calculated employer characteristics (such as the percentage of patients with deductible levels between $1000-$2500) as 

well as actual deductible amounts, we will use a logistic model that predicts the 5-level outcome of deductible 
<=$250/$250-$499/$500-$999/$1000-$2499/>$2500 based on multiple aggregate employer characteristics such as the 
first and second most common whole number deductible value, the percentage with Health Savings Accounts, the 
median deductible payment, the percentage of employees using services, the employer size, the percentage of 
employees with deductible levels between $100-$250/$250-$500/ $500-$1000/ $1000-$2500/ >$2500, etc.  This 
predictive model will output the probability that employers had deductibles in the 5 categories (summing to 1) and we 
will assign the employer to the level that has the highest probability.  If we detect employers that have 10 or more 
employees with whole number deductible levels both above and below $500 (e.g. $250.00 and $1500.00), we will assign 
the employers' category as "choice."  If 100% of employees have Health Savings Accounts, we will also overwrite any 
previous assignment to classify the employer as a high-deductible employer.  We will test the predictive model on the 
other half of the sample for which we have actual deductible levels.  We have previously used a similar algorithm and, at 
employers with 75-100 workers, we found sensitivity and a specificity of over 96% (MD-5). 
 
Analysis plan (IR-3; HT-2) 
Aim 1: to understand health care decision making and experiences of families with asthma under HSA-HDHPs and PDLs. 
We will analyze qualitative data in iterative cycles of content analysis in the manner described by Patton. In the first, 
inductive phase of analysis, two coders will independently read initial transcripts multiple times in their entirety, and 
then analyze them through a process of open coding aimed at identifying broad topics of discussion. Through discussion 
with the larger study team, coders will identify codes of interest, and will structure subsequent interviews to explore 
these topics in greater depth. Over time, coders will refine codes and organize them into a codebook. When interviews 
begin to yield little new information (i.e., saturation), we will systematically apply codes to all transcripts using NVIVO.  
In the second, deductive phase of analysis, we will consider data code-by-code to identify areas of convergence and 
divergence by insurance type (e.g., perceptions of medication affordability between participations with high-deductible 
versus traditional health plans). We will also consider data across codes by our three participant categories (i.e., patient, 
parent, patient and parent). We will describe emergent codes thematically, selecting representative quotations to 
illustrate key themes. We will then re-read transcripts to test identified themes to check for “dissenting views,” or 
exceptions to overall themes, that might require additional analysis or discussion. To conduct “member checking,” we 
will present our themes to AAFA partners, using facilitated discussion to elicit and incorporate their views in our analysis. 
Findings will inform quantitative analyses for Aims 2-4.  Outcomes of interest identified in the interviews will be added 
to Aim 2-4 where possible with claims data.  Findings related to intrafamilial trade-offs will be used to refine analyses for 
Aim 3, and findings related to financial burden will be used to refine analyses for Aim 4. 
Aim 2: to examine the impact of HSA-HDHPs with and without PDLs on use of asthma controller and rescue medications, 
and on adverse clinical outcomes (asthma-related emergency department (ED) visits and hospitalizations), overall and 
for vulnerable subgroups (low-income and racial/ethnic minority patients).   
Hypothesis (see Figure 2): Enrollees with asthma in HSA-HDHPs without PDLs will have worse adherence to controller 
medications, greater need for rescue medications, and higher rates of asthma-related ED visits and hospitalizations, 
with widening of income and racial/ethnic disparities; the addition of a PDL will improve use of controllers and rescue 
medications, reduce ED and hospitalization rates, and lessen disparities. 
Analyses will compare changes in outcomes from baseline to up to three years of follow-up among 1) asthma patients 
switched to HSA-HDHPs without PDLs from traditional plans vs. matched patients whose employers remain in traditional 
plans; and 2) patients switched to HSA-HDHPs with PDLs from traditional plans vs. matched controls remaining in HSA-
HDHPs without PDLs.  Sensitivity analyses will compare patients switched to HSA-HDHPs with PDLs from traditional plans 
vs. those who remain in traditional plans to examine the combined impact of both increasing the deductible and gaining 
a PDL.  We will use separate regression models to compare year-to-year changes for each intervention group relative to 
its matched control group, rather than including all patients in a single model with multiple interaction terms. For 
secondary analyses of enrollees in the larger set of plan types that include PDLs, we will compare enrollees who are 
switched from plans with no PDL to plans with a PDL vs. those who remain in plans without a PDL.  These analyses will 
match and adjust for different baseline plan types and deductible levels.   

We will begin by performing descriptive analysis using the full dataset of all enrollees with asthma and their family 
members to examine predictors and trends over time concerning insurance coverage type, asthma care utilization, and 
costs for asthma care. Then among the targeted study groups, we will compare baseline characteristics between study 
groups using chi-square, t-tests, and Poisson or quantile regression.157 We will next display each outcome as a monthly 
time series of rates or counts, comparing trends over time in the groups of interest. We will use both ITS and DiD 
frameworks for analyses.  Presuming our matching approach will generate similar baseline trends in the intervention 
and control groups, we will use ITS to display results and DiD to convey effect estimates in a more intuitive manner than 
ITS estimates.  Controlling for potential confounders, we will use generalized linear models (GLMs) to model the 
independent effect of switching to each of the two types of HSA-HDHPs (with or without PDLs) on the likelihood of each 
outcome, assessed by interacting insurance type and study period variables in models.  Data from the same patients in 
successive years are correlated, as are data from individuals from the same family. Extended GLMs - generalized 
estimating equations (GEE) and generalized linear mixed models (GLMM) - are appropriate methods to adjust for this 
correlation158, 159 and to examine changes in outcomes between baseline and follow-up.  
Asthma controller medications are intended to be taken regularly, so we will examine controller medication adherence 
using ITS. We will test the statistical significance of level or trend changes following insurance plan type switch using 
GLM models, adjusting for seasonality and first-order autocorrelation between sequential monthly measurements using 
the empirical sandwich estimator. Our analytic model will compare the odds ratio of having the primary therapy in the 
category available each day before and after the coverage switch. Our key independent variables will be: month, period 
(before or after the benefit switch), and month after the plan switch, adjusting for the same covariates as above.  In 
addition to modeling changes in adherence to controller medications generally, we will address the variation across 
health plans in the types of controller medications included in PDLs by conducting sub-analyses to examine changes in 
adherence to the specific controller medications that are included in the PDL and those that are not included in the PDL 
(Aerospan, Arnuity ellipta, Asmanex HFA, Aerobid, Azmacort).  For patients using medications that are not included in 
the PDL, we will examine the degree to which patients change to medications that are included on the PDL.   
For analyses of rescue medication use, we will focus on albuterol and levalbuterol inhaler users.  We will limit the time 
period of interest to 2009-2017, as albuterol inhalers, the primary asthma rescue medication, became available only as 
higher-priced branded CFC-free medications as of 2009 due to the FDA ban on generic CFC-containing inhalers.  The 
standardized number of rescue inhalers dispensed will be modeled as count data in difference-in-differences models.  
ITS models will be used to model changes in level and trend of monthly rates of rescue inhalers dispensed, as in our 
previous study.40  Because use of controller medications can affect need for rescue medications, we will add controller 
medication adherence as a co-variate; sub-analyses will stratify by controller medication use in the baseline period.  We 
will also model the ratio of controller medications to total asthma medications.  For analyses of asthma-related ED visits 
and hospitalizations, outcomes can be binary, counts, or continuous.  We will use logistic GEE models to estimate the 
effect of switching to each type of HSA-HDHP on binary outcomes such as any asthma-related hospitalization. We will 
use negative binomial regression to model the effect for count outcomes such as ED visits. We will select the conditional 
mean and variance functions based on the actual data, using a log link with a Gamma error distribution.160, 161 To inform 
final model specification, we will employ specification tests including the Pregibon Link test for non-linearity162 and a 
modified Park test to select the conditional variance function.161  
In all statistical models, we will include key individual-level predictors from Table 2 to adjust for potential confounding. 
To determine the impact of HSA-HDHP with and without PDLs on vulnerable populations (RQ-4) and test for 
heterogeneity of treatment effects among vulnerable populations (HT-1-4), we will first perform stratified analyses, 
comparing outcomes between intervention and control subgroups defined by the binary measures of the risk factors of 
interest (low income, minority race/ethnicity, moderate-severe asthma, presence of other chronic conditions (including 
co-morbid diagnosis of COPD; we will also conduct analyses limited to ages 4-50 to remove the effect of COPD co-
morbid diagnoses), high ACG morbidity).  We hypothesize that these vulnerable subgroups will have worse adherence, 
more frequent adverse clinical outcomes, and higher OOP costs.  We will use three-way interaction terms (insurance 
type * study period * subgroup) to test for statistical differences between subgroups in the impact of the change to an 
HSA-HDHP with PDL vs. remaining in a traditional plan.  Heterogeneity of treatment effect analyses for these key 
variables will be reported along with overall findings from the combined population.  Given that we will be conducting 
sub-group analyses to test for heterogeneity of treatment effect based on race/ethnicity and income using three-way 
interaction terms, we elected to separate the analyses of heterogeneity of treatment effect by age group into Aim 3 
(described below), rather than add a fourth interaction term to the analyses in Aim 2.  In Aim 2 analyses, we will include 

both adults and children together, with age included as a co-variate.  Given that health plan cost-sharing policies in HSA-
HDHPs and PDLs usually apply across enrollees regardless of age, and given that health plans, employers, policy makers, 
and families must make decisions about HSA-HDHPs and PDLs for both adults and children together, we felt it was 
important to evaluate the impact at the overall population level.   
Aim 3: to examine the extent to which the response to HSA-HDHPs and PDLs is affected by the presence of other family 
members with asthma or other chronic conditions.   
Hypothesis: Adults with asthma will disproportionately experience negative effects of HSA-HDHPs on adherence and 
outcomes relative to children with asthma (HT-2), as will patients who have other family members with asthma or 
other chronic conditions; differences will be mitigated by the addition of a PDL. 
Aim 3 will focus on intra-familial trade-offs in response to cost-sharing changes in HSA-HDHPs and PDLs, and on adult-
child differences in response to HSA-HDHPs and PDLs.  Analyses will use the same population, outcomes, study group 
comparisons, and modeling strategies as Aims 1 and 2 except that we will perform stratified analyses, comparing 
outcomes between intervention and control groups stratified by adult/child status (RQ-4).  To statistically test for 
heterogeneity of treatment effect for adults vs. children (HT-1-3), we will use three-way interaction terms (insurance 
type * study period * adult/child) to test for statistical differences between adults and children in the impact of the 
change to an HSA-HDHP with PDL vs. remaining in an HSA-HDHP without PDL, and the change to an HSA-HDHP without 
PDL vs. remaining in a traditional plan.  Analyses will be done at the individual level, but will include family-level 
variables listed in Table 2 as predictors of interest.  Separately for adults and children, we will test the extent to which 
having another family member with asthma, another chronic condition, or high baseline family ACG morbidity modifies 
the impact of HDHPs and PDLs on study outcomes for an individual asthma patient. The primary predictor of interest will 
be the interaction between the family-level variable, study period, and study group.   
We will conduct secondary analyses on the subset of families in the study population that have both an adult and child 
with asthma insured in the same family plan, and will assess whether adults and children within the same family are 
differentially impacted by HDHPs and PDLs by comparing outcomes between intervention and control groups in 
separate strata for adults and children (HT- 1-2, IR-5).   
Aim 4: to examine the impact of HSA-HDHPs with and without PDLs on out-of-pocket (OOP) costs for patients with 
asthma.   
Hypothesis: Enrollees with asthma in HSA-HDHPs without PDLs will have higher OOP costs and increased financial 
burden as measured by the percent of income spent on OOP costs; the addition of a PDL will diminish these adverse 
outcomes. 
Analyses will be similar to those of Aim 2, but for OOP cost outcomes.  Our primary analyses of changes in OOP costs will 
use a DiD analytic framework. We will follow the same approaches as used in analyzing ED visits and hospitalizations, 
employing two-part models/zero-inflated negative binomial models to account for zero costs. We will use GEE or GLMM 
models to examine changes in outcomes between baseline and follow-up to model the independent effect of switching 
to each of the two types of HSA-HDHPs (with or without PDLs) on the likelihood of having financial burden (OOP cost 
greater than 5% of income).   
 
Hypothesized Effect Size for Intervention on Main Patient-Centered Outcome  
Based on prior studies of chronic medications, we hypothesize that we will find a decrease of 5-9% in the PDC for 
controller medications after switching to HSA-HDHPs without PDLs relative to traditional plans,74  and an increase in the 
number of fills of 1-4% for those who switch to HSA-HDHPs with PDLs relative to HSA-HDHPs without PDLs.100 
Sample Size and Power  
Aim 1: We will conduct qualitative interviews with approximately 60 patients or parents or patients.   
Aims 2-4: We estimate that there are 1,695 patients with asthma eligible for our study in 2004 in our dataset (12% of 
HSA-HDHP members), increasing to 2,449 by 2011 (17% of HSA-HDHP members). Projecting current trends, we expect a 
total of 22,500 HSA-HDHP members with asthma by 2017. Preliminary numbers suggest a third of the cohort will be 
children under age 18.  Based on detailed estimates from our health insurance partners, ~19% of HSA-HDHP members 
have a PDL. We will have an abundant number of traditional plan members, more than seven times the number in HSA-

HDHPs.  Table 3 displays power calculations for pairs of regression models we plan to run for three primary outcomes: 
controller medication adherence (PDC) (Aims 2-3), and adverse clinical outcomes (rate of asthma-related ED visits or 
hospitalizations) (Aims 2-3), and OOP costs (Aim 4), both overall and for stratified analyses by adult/child (power 
calculations for children are displayed, as they are the smaller group).  Power calculations, which assume 80% power 
and 0.05 alpha level, were performed using PASS software.163 
Table 3: Power calculations 
 
Contrast of Interest 
Least detectable absolute difference 
Intervention Group 
Control 
Overall 
Children 
Controller medication adherence (PDC)* 
 
 
HSA-HDHP with PDL (n=4,222) 
HSA-HDHP without PDL (n=16,888) 
2.0% 
3.5% 
HSA-HDHP without PDL (n=18,000) 
Traditional plan (n=72,000) 
1.0% 
1.7% 
Asthma related ED visits or hospitalizations** 
 
 
HSA-HDHP with PDL (n=4,222) 
HSA-HDHP without PDL (n=16,888) 
1.2% 
2.0% 
HSA-HDHP without PDL (n=18,000) 
Traditional plan (n=72,000) 
0.6% 
1.0% 
OOP costs*** 
 
 
 
HSA-HDHP with PDL (n=4,222) 
HSA-HDHP without PDL (n=16,888) 
$47 
$80 
HSA-HDHP without PDL (n=18,000) 
Traditional plan (n=72,000) 
$23 
$40 
*Baseline rate estimated at 46% 74    ** Baseline rate estimated at 8.4 per 100 patients with asthma164   ***Baseline OOP costs estimated at $740 
based on data from current studies in the same data set. 
Generalizability and Limitations  
This study has several limitations. We include in our sample only members offered no choice in health plan selection 
(exogenous insurance choice).  By doing so, we limit generalizability but minimize individual-level selection bias, the 
major threat to internal validity. Employer selection is still possible, but we minimize this via the two-stage propensity 
score matching approach. Propensity score matching cannot control for selection on unobserved characteristics, but our 
planned approach of matching on the baseline trend of outcomes of interest approximates the estimates of randomized 
controlled trials.112  Dropout is a problem in longitudinal studies in insured populations. In our analyses, we will track and 
document drop out from disenrollment and changing plans, and will compare baseline pre-index characteristics of 
members who have one, two or three years of follow up. Furthermore, we will censor propensity-matched members of 
our study and comparison groups together if one drops out, and adjust analyses by weighting the members present 
during a given period of interest based on the baseline distributions of their demographic and health characteristics.  If 
drop out in the third year of follow-up ends up being substantial in the proposed study population, we will limit the 
follow-up period to 2 years. 
There are multiple factors that affect adherence to asthma medications and asthma outcomes in addition to insurance 
type and cost barriers. We are able to measure many of these factors using claims (e.g. baseline disease severity, age, 
gender, income, race/ethnicity) but not others (e.g. attitudes, provider factors, competing demands).  Claims data also 
do not allow measurement of important asthma outcomes such as symptom days, missed school/work days, or lung 
function.  The Aim 1 qualitative study will be able to contribute these additional dimensions of non-financial barriers to 
adherence and asthma outcomes, however.  Despite its limitations, claims data permit population-level measurement of 
important and commonly used downstream indicators of asthma complications such as asthma-related ED visits and 
hospitalizations which we have experience measuring in our prior asthma studies.19, 40, 122, 128, 165, 166  The use of geocoding 
and surname analysis limits our ability to determine member race/ethnicity at the individual level, but provide excellent 
population-level estimates when used together.143, 167-172   We will assess the degree of missingness for these fields in our 
study, but do not anticipate that it will be problematic given that annual missingness of geocoded SES variables in the 
study team’s prior work using this data source has been less than 1% (MD-1). 
Sharing of asthma medications within a family may affect the accuracy of measurement of an individual’s medication 
use based on claims data.  However, only 16% of children share or borrow asthma medications, usually 
bronchodilators.173  Unlike other studies of cost-sharing and medication use in the literature, our study can capture this 
effect by measuring family-level medication use and OOP costs.  Utilization of medications and other health services in 
HSA-HDHPs may be affected by the amount of funds in a member’s HSA, which can be used to pay deductible costs.  
Data on HSA balances is not available for all members, however.   

Because our interest is assessing clinically relevant effects of an economic intervention, the proposed project does not 
examine the impact of HSA-HDHPs and PDLs on total costs to employers or the health care system.  The impact of 
HDHPs with PDLs on total costs is important to the decision making of employers and payers, and while beyond the 
scope of this project, our findings will inform future studies to address this topic using datasets with premium and full 
cost data.  
Prior studies on inhaled asthma rescue medications have found that utilization of these medications is relatively inelastic 
to price changes,39, 174 raising the possibility of a “no effect” study.  In our study that showed no significant impact on 
inhaler use from an increase in co-payment,174 we did not include HDHPs, so the cost increase was not large.   In the 
study by Jena et al, which found a modest decrease in inhaler use with increased out-of-pocket costs, the increases in 
cost-sharing took the form of both co-payments and deductibles.  It is possible that the larger cost-sharing changes 
associated with HSA-HDHPs and PDLs (ranging from $0 in PDLs to $140-$307 for some inhaled steroids and $30-60 for 
albuterol inhalers69-71) could have a larger impact on medication use.    Studying the impact of HSA-HDHPs and PDLs with 
respect to asthma rescue inhalers will still be important even if our study finds no effect on inhaler utilization, as there 
are likely to be significant changes in out-of-pocket costs for rescue inhaler medications even if use is unchanged.39, 174 
The cost of asthma medications can cause financial burden for families (e.g. having to borrow money or cut back on 
other necessities),126 so studying the impact of HSA-HDHPs and PDLs on out-of-pocket costs for asthma rescue inhalers is 
important even if the utilization analyses produce a “no effect” study. 
D. PATIENT POPULATION (see Section C - Study Population above for further details) 
For this study of patients with asthma, we expect the patient populations will be similar to that of commercially-insured 
populations with asthma nationally in which asthma is more prevalent among African-Americans and among women 
overall (although more prevalent among boys for children) (Table 4).1, 3 
The Aim 1 population will consist of adult patients with asthma or parents of children with asthma identified through 
HPHC and through AAFA.  We will reach out to up to 660 HPHC members divided equally among those with HSA-HDHPs 
with and without PDLs or with traditional plans, and stratified to equally represent adults with asthma, parents of 
children with asthma, and adults with asthma who also have a child with asthma.  Potential participants will include 
adults aged 18-64; those 65 and over will not be included because they are eligible for Medicare, which may cover the 
costs of some health care services.  These patients will represent a range of geographic locations, both genders, 
different race/ethnicities and levels of socioeconomic status, and asthma severity.  To ensure adequate representation 
of patients with lower incomes and lower educational attainment, we propose to oversample HPHC members who live 
in low socioeconomic status zip codes where more than 20% of households are below the poverty level or where more 
than 25% of adults did not complete high school,175   such that approximately one quarter to one third of the sample live 
in low socioeconomic status zip codes.  A pre-screening questionnaire will be used to confirm eligibility and to ensure 
equitable distribution of subgroup membership prior to scheduling interviews. We will conduct interviews with 
approximately 27 eligible HPHC members (approximately 6 from each subgroup).   
 
Table 4: Estimated Final Racial/Ethnic and Gender Enrollment Table (for interview population for Aim 1 plus 
claims-based population for Aims 2-4) 
Race 
Male (N) 
Female (N) 
Total (N) 
American Indian/Alaska Native 
-- 
-- 
-- 
Asian 
1,745 
1,675 
3,420 
Black/African American 
4,537 
7,522 
12,059 
Hawaiian/Pacific Islander 
-- 
-- 
-- 
White 
40,138 
50,032 
90,170 
Multirace 
2,443 
3,072 
5,515 
Ethnicity 
Male (N) 
Female (N) 
Total (N) 
Hispanic (Latino/Latina) 
5,235 
6,457 
11,692 

Non-Hispanic 
43,628 
55,844 
99,472 
 
We will also recruit commercially-insured patients with asthma and parents of children with asthma through AAFA.  
Through pre-screening of interested patients who respond to AAFA’s recruitment outreach, we will select approximately 
27 patients or parents to represent the same subgroups based on insurance plan type and which family member has 
asthma.  Our goal is to include interviews with patients representing a range of incomes and educational levels, as well 
as those with varying levels of asthma severity, control, and engagement with their illness.  In addition to recruiting 
participants from AAFA’s Asthma Online Community, email listserv, Facebook page, and newsletters, AAFA will be able 
to conduct outreach beyond its currently engaged community members to attract interview participants via 
intermediaries such as AAFA’s Chapters and Educational Support Groups, the school community  and school nurses, and 
faith based and local community organizations with whom AAFA has established partnerships from current and prior 
projects  (e.g. a project with CDC focusing on the safety-net adult asthma population which involved community and 
faith-based entities and Federally Qualified Health Centers).  Through our prescreening questionnaire of interested 
patients, we will be able to further ensure that patients selected for interviews are representative of the asthma 
population.  If after completing interviews with HPHC members, we find that particular sub-groups are not well 
represented, we will use information from the pre-screening questionnaire to identify and include patients with these 
characteristics (RQ-3-4).  Pre-screening will allow us to ensure participation of patients with a range of asthma severity 
in order to explore how differences in severity affect health care decision making and impact of OOP costs.  Children will 
not themselves be research participants, although their experiences will be reflected through the participation of their 
parents.  
We will overcome barriers to enrollment by providing patient participants with a $50 gift card to thank them for their 
time in completing the 45-minute interview.  Working with AAFA will also help us overcome barriers to enrollment, as 
AAFA has trust and experience with outreach among this patient population. Patients recruited through HPHC and AAFA 
will be prescreened by online questionnaire or by study staff with experience interviewing patients for qualitative 
studies. 
Through our strategy of recruiting patients through a health plan and AAFA, we expect that patient participants will 
reflect a range of plan types, geography, asthma severity, and socioeconomic status. The inclusion of the health plan 
population enables us to guarantee that the study population includes patients who have an HSA-HDHP with a PDL, 
since patients from the AAFA population may not know if their plan includes a PDL.  The standardization of HSA-HDHPs 
and the similarity of HPHC’s PDL to other insurers’ PDLs (including the one to be studied in Aims 2-4) will enhance 
generalizability.  In inclusion of the AAFA population will ensure a more diverse population across the country with 
commercial insurance from other carriers.  Most patients are likely to know if they have a high-deductible plan, although 
they may not be aware if their plan is an HSA-HDHP or if it includes a PDL.  Nonetheless, they will still be able to provide 
relevant data on their experiences in a plan with a high-deductible and cost-sharing for asthma medications.   
The patient population for Aims 2-4 will consist of patients with asthma enrolled in a large U.S. commercial health plan 
from 2004-2017.  We will capitalize on one of the largest and most geographically diverse observational datasets of 
commercially-insured asthma patients, including adults and children from all 50 states over 14 years.  The size and 
geographic reach of this health plan will allow our study to be representative of a wide range of sociodemographic 
groups, geographic locations, and delivery systems.  Our study will analyze retrospective de-identified data routinely 
collected by the health plan, and the study team will not contact or recruit patients for these Aims. We will 
systematically identify eligible patients using claims-based algorithms that have been used in multiple studies to identify 
patients with asthma.19, 40, 122  The characteristics and numbers of patients whose claims data will be analyzed are 
described above in Tables 2 and 3. 
Our study will specifically examine potential disparities for low-income and racial/ethnic minority populations in 
response to high levels of cost sharing in high deductible plans, based on prior evidence that these groups may be 
adversely affected (RQ-3).51, 57, 98  We will identify patient subgroups based on income and race/ethnicity using our 
dataset’s credit report information and imputed information on ethnicity based on member surname and neighborhood 
SES data.  We will use claims-based measures employed in other studies to identify patient subgroups based on asthma 
severity, other chronic conditions, and morbidity who may be likely to be adversely affected by high cost-sharing(RQ-
3).19, 40, 50, 54, 176, 177  

E. RESEARCH TEAM AND ENVIRONMENT  
The proposed project will be conducted primarily at the Department of Population Medicine (DPM), which resides 
within the Harvard Pilgrim Health Care Institute (HPHCI), and is a department of Harvard Medical School.   
1. The research team has the capability to accomplish the goals of the proposed research. Our multidisciplinary study 
team has a strong track record that brings together expertise in health services research, health policy, asthma care, and 
health economics; experience studying asthma, HDHPs, medication adherence, health care decision making, and 
financial burden for adults, children, and families; capability conducting patient surveys, qualitative research, and 
rigorous claims-based analyses; a successful history of collaboration between study team members and with health 
plans including HPHC and the large national insurer whose claims data will be studied; partnership with Meryl 
Bloomrosen and AAFA; and the input of a Stakeholder Advisory Board and Patient and Family Research Council 
(described below in Section F.2).   
Alison Galbraith, MD, MPH, Principal Investigator, is a health services researcher and general pediatrician who has 
conducted formative studies of the impact of new health insurance designs such as HDHPs and VBID-like policies on 
health care decision making, health care utilization, health outcomes, and financial burden; she has conducted some of 
the first family-level studies of health insurance and cost-sharing.45, 46, 50, 54, 68, 76-78  Her research on asthma includes 
studies of asthma care quality, disparities, and cost barriers using claims-based and survey methods.40, 126, 128, 166, 178 
J.Franklin Wharam, MB, BCh, BAO, MPH, Co-Investigator, is a general internist and leading national expert in the effects 
of HDHPs on appropriate care for patients with cancer, diabetes, and mental illness, including studies of VBID-like 
arrangements. 47-49, 51-53, 55-58  He leads five large grants examining HDHPs using the same large national health plan 
population and rigorous methods as in the proposed project.  Dennis Ross-Degnan, ScD, Co-Investigator, is a health 
services researcher and methodologist who is known for his pioneering longitudinal evaluation methods and his studies 
of the impact of health insurance benefit design changes on use of medications and other health care services.153-156, 179-
186  Ann Chen Wu, MD, MPH, Co-Investigator, is a general pediatrician and asthma health services researcher who has 
conducted claims and survey-based studies of asthma medication use, outcomes, and disparities.  Through her 
leadership of multiple studies in the Population-based Effectiveness in Asthma and Lung Disease (PEAL) network of 
multiple health plans, she has experience developing and using claims-based methods to identify members with asthma, 
assess asthma severity, and measure adherence.19, 122, 165 She also writes an award-winning asthma blog (url: 
http://asth.ma) designed for communicating with patients with asthma and parents of children with asthma.   Melissa 
Gilkey, PhD, Co-Investigator, is a behavioral scientist specializing in health communication who has expertise in 
qualitative methods, health communication theory, medical decision making, and patient engagement.187-189 Fang Zhang, 
PhD, Biostatistician and Co-Investigator, has expertise in designing observational research methods to study the impact 
of HDHPs and other insurance policies.49, 51, 52, 56-59, 155, 183  Anna Sinaiko, PhD, Co-Investigator, is a health economist with 
expertise in health insurance, evaluation of consumer response to health insurance benefit design, and claims-based 
analyses.46, 78, 190-196   Meredith Rosenthal, PhD, Co-Investigator, is a health economist with expertise in evaluating the 
impact of market-based health policy reforms to alter consumer behavior.45, 54, 68, 88, 192, 194, 195, 197-202  Meryl Bloomrosen, 
MBA, MBI, Co-Investigator, is Senior VP for Policy, Advocacy and Research at AAFA and has experience engaging patients 
in the research process.203  She is a PCORI Ambassador and leads a PCORI Engagement award entitled, “Training Patients 
with Asthma to Understand and Participate in Patient-Centered Outcomes Research.”   
2. The project’s research environment is well-suited for conducting the proposed study.  DPM’s unique position within 
a health plan has fostered the study team’s experience engaging health plan members and health plan representatives 
in research.  DPM has assembled an extensive collection of data from a large U.S. commercial health plan through 
Optum, and has built the infrastructure, analytic capacity, and familiarity with the data to facilitate high quality, efficient 
research. The HPHC environment enables the research team to identify and recruit interview participants from across 
the country whose benefits allow them to access services through HPHC’s partnership with the large national insurer 
whose claims data will be used in Aims 2-4.   Our partnership with the Harvard School of Public Health brings added 
expertise in health economics which has been instrumental in prior collaborations.  We worked with AAFA to develop 
this proposal as AAFA is an important advocate for patients with asthma and has experience as a PCORI engagement site 
developing innovative new methods of training patients to serve as research advisors.  AAFA is an optimal site for 
implementing the project’s engagement plan and for recruiting patients and parent participants.   
F. ENGAGEMENT PLAN  

1. Planning the Study 
Input from patients, families, and health plan stakeholders has informed the planning of the proposed project.  The 
design of the project and the issues to be addressed have been informed by the study team’s existing relationships with 
health plan partners, including HPHC and the large national insurer whose population will be used in Aims 2-4.  These 
colleagues have identified questions that enrollees and employers have raised regarding HDHPs, PDLs, and strategies to 
promote adherence to important medications while reducing costs.  Input on the salience of the topic to patients and 
suggestions about problem areas to examine in the proposal, such as the high costs of albuterol inhalers, were obtained 
through a solicitation for input about cost-related issues in HDHPs through Dr. Wu’s blog (http://asth.ma).127 
The research team has been fortunate to have the opportunity to partner with AAFA in the planning of the proposed 
project.  AAFA is a national organization that advocates on behalf of patients with asthma through patient education, 
public awareness, and support for research.  AAFA’s work as a PCORI engagement awardee on training asthma patients 
in research has informed the planning of the project’s engagement strategy.  Both the engagement plan and research 
strategy reflect the contributions of Meryl Bloomrosen, Senior Vice President of Policy, Advocacy and Research at AAFA, 
who is a member of the study team and a co-investigator.  Ms. Bloomrosen has been part of discussions about the 
design of the project and has provided input about the proposal to make it relevant to asthma patients and their 
families.  She has suggested useful strategies to strengthen our recruiting strategy for patient participants and advisors 
using AAFA’s multimodal methods of outreach to asthma patients.  Her prior experience with recruiting and involving 
patient and family advisors in AAFA’s Patient and Family Advisory Council has led us to use this approach for our Patient 
and Family Research Council.   
The plan for patient engagement was also informed by our recent research with HPHC members about their perceptions 
related to serving as research advisors.  In this focus group study, preliminary findings show that health plan members 
are interested in serving as research advisors, especially if they have a health condition that makes the topic salient.  
Members expressed the need to have clearly defined and respected roles, to receive education about the research 
process and the study, and to have the opportunity to contribute in ways that fit with their interests, skills, and time 
constraints.  In particular, our plan to create a Patient and Family Research Council was based on our finding that some 
people did not feel that they could make a long-term commitment to a multi-year study but would like opportunities to 
participate in research with short-term or one-time contributions based on their unique background, skills, and 
interests, as exemplified by the quotes below: 
“I think people would tend to play to their strengths and also work within whatever parameters they have around 
time that's available.”   
“So if I would like to participate but I can't come here on a weeknight, I can edit a manuscript. So okay, over e-mail I 
can do that in my time when it's convenient for me and that way still participate. So I think it's definitely important 
to give different levels of participation or engagement for individuals.” 
2. Conducting the Study  
A key foundation of our patient and stakeholder engagement strategy is our partnership with AAFA.  Ms. Bloomrosen, 
who helped develop this proposal, will serve as a co-investigator on the project for all four years.  She will attend 
monthly study team meetings in person or by phone, and will contribute her experiences working with asthma patients 
and caregivers, asthma clinicians, researchers, and policy makers to address the medical and educational needs of 
patients with asthma.  Her input will allow us to refine the engagement plan post-award to incorporate the learning and 
experience from AAFA’s ongoing PCORI engagement award in which they will develop trainings for asthma patients as 
research partners.  AAFA will conduct and videotape patient trainings in 2017 at several regional AAFA sites around the 
country.  Patients or parents trained in these sessions who have HDHPs will be invited by AAFA to participate in our 
project as advisors, either on our Stakeholder Advisory Board or on our Patient and Family Research Council (described 
below).  Ms. Bloomrosen and AAFA staff will review the Aim 1 interview guide to ensure that it is understandable and 
covers relevant aspects of asthma care and patient experiences.   
Conduct of this research will be informed at all stages by input from patients with asthma, their parents, and other 
stakeholders through the project’s Stakeholder Advisory Board (PC-1, 2, 4).  The Board will include: 1) an adult patient 
with asthma; 2) a parent of a child with asthma; 3) a representative of AHIP, the health insurance industry trade 
association; 4) a clinician-researcher who studies and treats patients with asthma; 5) a pediatrician who studies health 
policy and intergenerational family services; and 6) a representative from the NEBGH, an organization serving large 

employers.  We will use AAFA’s existing Asthma Online Community, Educational Support Group, email listserv, Facebook 
page, and print and email newsletters to recruit the patient and parent members of the Board at the start of the project.  
All Board members will be given a descriptive summary of the project before the Board meets, and patient/parent 
members will be offered the opportunity to view AAFA’s online research training video.  The Board will meet with the 
study team three times a year to provide guidance on research questions of interest, target populations, recruitment 
strategies and domains to explore in Aim 1 interviews, outcomes and other measures, interpretation of results, 
presentations, and writing of manuscripts.  The Board will help interpret the qualitative findings from Aim 1 and use 
them to refine the target outcomes and analyses for the quantitative work in Aim 2-4, and will help interpret the 
quantitative findings from Aims 2-4.  The Board will assist the study team develop and implement dissemination 
strategies, especially to their respective stakeholder groups.   
We will work with AAFA to create a Patient and Family Research Council.  The Council will be modeled on the virtual 
Patient and Family Advisory Councils that AAFA has successfully created for other projects. The Council will be a rotating 
cohort of up to 50 patients and parents who will be available for requests for specific feedback or tasks during the 
project. During recruitment of patient/parent advisors at the start of the project, we will offer the opportunity to 
participate in the Council to those interested in being involved with less of a time commitment.  Potential Council 
members will be asked about their background (e.g. type of insurance plan, asthma severity, state of residence), skills 
and interests (e.g. Would they be interested in doing cognitive testing of the interview guide? Do they like numbers and 
would like to review data tables?), and their preferred amount of time and setting for participation (e.g. daytime or 
evening meetings, conference calls, email only).  Upon joining the Council, members will be given a descriptive summary 
of the project and will be invited to obtain training through AAFA’s online video sessions.  Based on their reported 
background and preferences, we will target outreach to Council members as appropriate study activities arise.  Tasks 
would include reviewing and providing input on interview recruitment materials, interview questions, preliminary 
findings, dissemination materials, claims-based outcomes, and research questions for future research.  Members can 
choose to cycle off the Council, and new patients/parents will be recruited by AAFA.  
We are privileged to have the opportunity to work with AAFA, the Stakeholder Advisory Board, and the Patient and 
Family Advisory Council.  We are committed to transparency, honesty, and trust in these collaborations, and strive to 
foster collaborative, mutually beneficial reciprocal relationships. We recognize that open communication is crucial to 
bringing out the unique contributions of all partners and fostering a spirit of collaboration and trust necessary to achieve 
our shared goals of improving asthma care and outcomes and reducing cost burden.  Major study decisions will be 
discussed at Stakeholder Advisory Board meetings, and Board members will be apprised of any key decisions made 
outside of the Board meetings. The contributions of the Board and Council will be acknowledged in all project 
manuscripts and reports, and those interested in playing a more substantial role in the conduct of the study and 
manuscript writing will be offered the opportunity for co-authorship.  
We intend for the Stakeholder Advisory Board and Patient and Family Research Council to serve as forums for co-
learning for all partners. Study investigators will learn about the perspective of the patient and stakeholder communities 
represented on the Board and Council, while the Board and Council members will have the opportunity to learn about 
the research topic and general research processes through participation in AAFA’s web-based research training. 
Additionally, we will convene the Council annually for a webinar in which the research team will provide background 
information about study topics (e.g. use of asthma medications, barrier to adherence, health insurance design) and 
methods, and summarize study findings to date, while Council members can share their experiences, feedback, and 
questions.  We also hope that bringing stakeholder representatives together on the Advisory Board will facilitate co-
learning and relationship building between Board members who share interests but may not have had the opportunity 
to work together before.  
In establishing a partnership with the Stakeholder Advisory Board and Patient and Family Research Council, the research 
team will strive to balance the desire to involve them in key aspects of the project with the desire to be respectful of 
their jobs, families, and other commitments and to adequately compensate them for their efforts. Board members will 
be given a $500 stipend per year to compensate them for attending the three Board meetings each year, pre-meeting 
review of materials. The Patient and Family Research Council provides opportunities for patients/parents to be involved 
in the research process with a flexible, smaller time commitment as determined by their own needs and interests. 
Patients/parents who join the Council will be eligible for a biannual drawing for a $200 gift card for as long as they 
remain on the Council, and will be given a $25 gift card for each completed activity.   

3. Disseminating the Study Results (PC-4) (See also the section below on Dissemination and Implementation Potential)  
AAFA and our patient and stakeholder partners will play a central role in the dissemination of study results, and will help 
us determine which findings will be of greatest interest to patients with asthma and their families, and which channels 
will be most effective in reaching patients living with asthma.  AAFA will use its wide range of existing outreach channels 
(Asthma Online Community, Educational Support Group, email listserv, Facebook and Twitter, print and e-newsletters) 
to provide multi-pronged opportunities to disseminate results to patients in understandable, meaningful ways.  
Additionally, AAFA’s Medical Scientific Council will be involved as a resource for dissemination of findings to the clinical 
and scientific community. The project’s Stakeholder Advisory Board will also be involved in planning dissemination 
strategies.  The Board’s inclusion of representative from diverse stakeholder groups will enable us to develop strategies 
that will effectively reach a wide range of stakeholders and enable findings to be framed to be most relevant to each 
group’s interests.  The Patient and Family Research Council will review and provide input on dissemination materials and 
ensure that findings are communicated in understandable, salient ways. 
 

 
DISSEMINATION AND IMPLEMENTATION POTENTIAL  
 
A. Describe the potential for disseminating and implementing the results of this research in other settings (PC-4).  
We will disseminate the results of the project to a broad range of patients, families, providers, health plans, policy 
makers, employers, and researchers who share an interest in improving health care for patients with asthma and 
reducing cost barriers.  Our partnership with AAFA, our Stakeholder Advisory Board, and Patient and Family Research 
Council will help us prioritize findings that are important to them and others like them, and design effective, 
understandable, multi-pronged dissemination strategies to reach out to their constituencies.  See Section F.3 above for 
details about patient and stakeholder engagement in dissemination efforts. 
Summaries of study findings will be disseminated to patients and families through existing AAFA channels such as their 
Asthma Online Community, Educational Support Group, email listserv, Facebook page, and in notices in AAFA’s existing 
print and e-newsletters. Ms. Bloomrosen and AAFA staff, patient and family members of the Stakeholder Advisory 
Board, and Patient and Family Research Council will review materials to make sure the information is understandable to 
lay audiences.   
We plan to disseminate study findings through presentations at research meetings and publications in peer-reviewed 
journals.  Preliminary findings will be presented at meetings of groups such as AcademyHealth, the American Thoracic 
Society, the Pediatric Academic Societies, the Society for General Internal Medicine, and the Health Care Systems 
Research Network.  We will submit manuscripts describing study findings to medical, health services research, and 
health policy journals such as the Journal of the American Medical Association, Health Services Research, American 
Journal of Respiratory and Critical Care Medicine, Journal of Allergy and Clinical Immunology in Practice, Annals of the 
American Thoracic Society, Health Affairs, Pediatrics, and Annals of Internal Medicine.  We will develop a newsletter 
summarizing published results for electronic and print transmission to individuals and organizations suggested by our 
Stakeholder Advisory Board and Patient and Family Research Council.  We will transmit published research findings to a 
wide range of patients, stakeholders, and the general public through traditional news media and social media, for 
example, through the Harvard Medical School and HPHC press offices, and through Dr. Wu’s award-winning asthma blog 
(http://asth.ma) and her Twitter account (@Asthma3Ways) which has over 8,000 followers.  
Study team members will communicate findings to providers at national meetings of groups such as the American 
Thoracic Society, Pediatric Academic Societies, and Society of General Internal Medicine.  We will communicate findings 
to clinicians on AAFA’s Medical Scientific Council, who can serve as conduits to the provider community and suggest 
other avenues to reach providers.  As practicing physicians, Drs. Galbraith, Wu, Wharam, Hartert, and Rosen will be able 
to communicate findings to their provider communities. 
Findings will be disseminated to health plan, employer, and policy communities to inform decision making about 
implementation of HSA-HDHPs and PDLs.  We will communicate study findings to colleagues at the large national health 
plan involved in the study, to HPHC, and to AHIP through Board member Kevin Fahey to inform health plan efforts to 
improve asthma care and refine insurance plan design.  We will be able to communicate results to other health plans 
through the annual meeting of the Health Care Systems Research Network, (formerly known as the HMO Research 
Network), of which HPHC is a longstanding member, so that other health plans can learn from our study.  Through Board 
member Jeremy Nobel from NEBGH, we will disseminate findings to the community of employers who must make 
decisions about the insurance benefit designs they offer to their employees.  Through his experience as the Director of 
PolicyLab at Children’s Hospital of Philadelphia, Board member David Rubin will be able to advise the research team 
about various routes for sharing results, such as meetings with policy makers and key staff, testimony, blogs, and 
conferences.  
Study findings have the potential to be implemented widely into existing health insurance designs. This project will 
provide crucial, patient-centered evidence about health insurance strategies already being delivered in real-life settings 
and their impact on adherence, clinical outcomes, and cost burden for asthma patients.  Given the increasing use of 
PDLs,89-91  our results will be applicable to other health plans, employers, and public payers who are interested in 
adopting effective VBID strategies.204-206  Because federally standardized benefits policies must be met for HSA-HDHPs to 
qualify for HSAs, our findings are relevant to the large numbers of health plans and employers who offer HSA-HDHPs, for 

whom PDLs could be readily incorporated if shown to be effective.  Our project’s inclusion of Mr. Fahey and Dr. Nobel on 
the project Stakeholder Advisory Board will enable us to report our findings to AHIP and NEBGH and assist us in 
developing strategies to communicate our findings to wider health plan and employer communities nationally. 
B. Describe possible barriers to disseminating and implementing the results of this research in other settings.  
One goal of the proposed study is to provide relevant data to stakeholders to find strategies to provide more affordable 
health insurance while also ensuring that asthma patients can obtain needed medications.  Up until now, health plans 
and payers have had to decide whether to invest in strategies like PDLs with limited data on their effectiveness, so our 
study will allow them to know if this investment is worthwhile.  Even if our study shows that HSA-HDHPs with PDLs 
improve adherence to asthma medications and reduce adverse clinical outcomes, employers and health plans may still 
choose not to adopt such strategies if total costs in such arrangements is not reduced.  Further research, beyond the 
scope of this PCORI award, may be needed to establish whether the additional costs to payers for medications on PDLs 
in HSA-HDHPs can be offset by avoidance of costly adverse outcomes.   
Dissemination and implementation of our findings to other health insurers may be a challenge for HDHPs that differ 
from those of the large health insurer studied in this project. However, given the standardization of HSA-HDHPs, with 
federally-regulated policies about which services are subject to the deductible, and the commonalities in the preventive 
medications on many large insurance carriers’ PDLs,90, 91   implementation in other health plans’ HSA-HDHPs should be 
feasible. 
Barriers to the dissemination and implementation of study findings could arise due to the complexity of health insurance 
benefits, and of HDHPs in particular.  Health plans, employers, and public payers may decide not to adopt HSA-HDHPs 
with PDLs if they feel that the added complexity of a PDL would make it hard for patients to understand and use 
appropriately.  For PDLs to be effective, patients must be aware that asthma medications are on the list and free of cost-
sharing; our research will identify areas where there are information gaps and learning effects over time.  Limitations in 
provider awareness of cost barriers for patients in HDHPs and of what medications and services will cost their patients 
could also pose barriers to successful implementation of HDHPs and PDLs.  The Aim 1 qualitative interviews will explore 
knowledge barriers related to potential costs in HDHPs and PDLs on the part of both patients and providers, and how 
patients might discuss costs with their providers to mitigate cost issues for asthma care.  These findings, together with 
input from the Stakeholder Advisory Board and Patient and Family Research Council, will suggest ways to address 
awareness issues to help successfully implement PDLs. 
C. Describe how you will make study results available to study participants after you complete your analyses.  
As part of the interview process in Aim 1, we will ask participants if they would like to receive follow-up information 
about study findings. If so, we will note their preferred contact method (postal address or email), and provide a copy of 
our publications when they become available and a results summary in plain language appropriate for a lay audience, 
which will be reviewed by AAFA and members of our Patient and Family Research Council. Because our data on health 
plan members participating in the quantitative studies (Aims 2-4) will come from fully de-identified existing health plan 
data, we have no way of contacting these participants directly.  However, they may be reached through our other efforts 
to disseminate study results to the asthma community described above.  

 
REPLICATION AND REPRODUCIBILITY OF RESEARCH AND DATA SHARING 
 
A. Describe the ability to reproduce potentially important findings from this research in other data sets and 
populations. 
Our Aim 1 in-depth interviews could readily be replicated with other patient populations who have high-deductible 
plans with PDLs or other strategies to prevent cost barriers to use of high-value care.  Replicating our qualitative work 
among patients with insurance through federal and state-based health insurance exchanges would be particularly 
beneficial and feasible, as commercial health plans are increasingly offering HSA-HDHPs in health insurance exchanges in 
an effort to contain costs. Our interview instrument could also easily be adapted to conduct similar qualitative research 
among patients with other chronic conditions.  Additionally, findings suggested from our qualitative study could be used 
to develop more structured surveys to test the reproducibility of our findings on a larger scale among broader 
populations of patients with asthma and their families in the future, through AAFA or our health plan partners.  
Our claims analyses (Aims 2-4) could readily be replicated in datasets of other health plans, although not on the same 
scale as our proposed analyses, which will be conducted among one of the largest insurers in the U.S. Additionally, 
analyses using other datasets may not yield the same amount of comprehensive, detailed, and robust information for 
such large numbers of patients. However, there may be value in replicating our studies in future years, given that 
insurance designs are constantly evolving, and HDHPs and strategies to help patients cope with cost barriers may change 
in future years.  
The implementation of the ACA and the launch of federal and state-based insurance exchanges has accelerated the 
growth of HDHPs, with expansion into the individual, non-group insurance market due to the ACA’s coverage mandate 
and the lower premiums afforded by HDHPs. It will be crucially important to conduct studies examining how patients 
with asthma fare in HDHPs in exchanges and non-group market, especially given that exchange enrollees are more likely 
to come from vulnerable populations. However, while these potential studies could borrow some of the measures and 
design features of our proposed claims-based studies, they would not be able to offer our study’s ability to mitigate 
selection effects by studying employers who did not offer enrollees a choice of plans.  
B. Describe your data management and sharing plan, including how you will make study data sets available in a 
manner that is consistent with applicable privacy, confidentiality and other legal requirements, if requested. 
Transcripts of Aim 1 individual patient/parent in-depth interviews and the intermediate coded material from interviews 
cannot be shared with requestors external to our study team. These data will be collected from patient/parent 
participants with the promise of confidentiality by agreement with participants as part of the informed consent process.   
This agreement also affirms that the study team will share data only in aggregate form.  Our finalized interview 
instrument will be made available to other researchers upon request, and our qualitative analysis methods will be 
presented in a transparent manner in published papers and on-line appendices should other researchers wish to adopt 
them. 
Unfortunately, the individual-level analytic dataset for Aims 2-4 of our proposed study cannot be made available to 
other researchers by our study team.  The datasets used are proprietary, obtained from Optum. Other researchers may 
contact Optum directly and propose to purchase and analyze the same data. Our study methods will be presented in a 
transparent manner in published papers and on-line appendices, so that others may replicate our analyses. Our study 
variable dictionaries and SAS statistical programs will be made available to other researchers upon request.  
C. Propose a budget to cover costs of your data-sharing plan, if requested.  
Our SAS statistical programs, interview instrument, and methods summaries would be made available free of charge. 
 
 

 
PROTECTION OF HUMAN SUBJECTS 
 
Describe the protection of human subjects involved in your research.  
 
PROTECTION OF HUMAN SUBJECTS 
The proposed study examines insurance design features that can influence quality and cost of asthma care. The study 
will collect qualitative data from patients with asthma or parents of children with asthma through telephone interviews 
(Aim 1) and will use retrospective analysis of insurance claims and linked data to detect differences in health care 
utilization patterns and costs for adults and children with asthma (Aims 2-4). This constitutes human subjects research 
according to federal guidelines. Prior to obtaining data, we will seek Institutional Review Board approval from the 
Harvard Pilgrim Health Care (HPHC) Human Studies Committee. 
A. Risks to Human Subjects 
1. Human Subjects Involvement, Characteristics, and Design 
Our aims include understanding health care decision making and experiences of families with asthma with high-
deductible health plans (HDHPs) and determining the impact of HDHPs and preventive drug lists (PDLs) on controller and 
rescue medication adherence, emergency department visits, hospitalizations, and out-of-pocket costs, overall, for 
vulnerable subgroups, and within families.  
Subjects for Aim 1 will include adults aged 18-64 who have asthma or have a child with asthma, and have commercial 
health insurance plans (high-deductible plans with and without a PDL or traditional plans).  Potential subjects will be 
recruited through Harvard Pilgrim Health Care (HPHC) and through our partnership with AAFA.  Potential subjects from 
HPHC will be identified from enrollment and claims data, and invited to participate through a mailing and/or email.  
Enrollees will be given the option to opt out of the study by calling a toll-free number, and the study team will contact by 
phone those who do not opt out to invite them to participate. To recruit subjects through AAFA, the AAFA team will 
send out a description of the study to potential subjects through its Asthma Online Community, Educational Support 
Group, email listserv, Facebook page, newsletters, and/or other outreach channels; patients with high-deductible plans 
and traditional plans will be invited to participate. 
Those interested will complete a pre-screening questionnaire, either online or by phone with a member of the research 
team.  The research team will use pre-screening information to confirm eligibility and representation of 
sociodemographic and clinical sub-groups, and will schedule and conduct the phone interviews.  Interviews will take 
approximately 45 minutes and will include open-ended questions and a few demographic questions.  Interviews will be 
audiotaped with permission of the subject, and transcribed for analysis without identifying information.  Children are 
included in the proposed research insofar as their experiences may be reported by their parent, but children themselves 
will not be contacted directly in the proposed research.   
Aims 2-4 will be retrospective studies using existing health plan data.  Subjects will include adults aged 18-64 years and 
children aged 4-17 years who have employer-sponsored insurance coverage from a large national insurance carrier and 
have a diagnosis of asthma based on claims data.  For analyses of intra-familial effects in Aim 3, we will include data for 
other family members sharing the subject’s insurance plan in order to create family-level variables, such as the number 
of family members, family-level morbidity, and presence of other family members with asthma or other chronic 
conditions. The research will be performed at the Department of Population Medicine at the Harvard Pilgrim Health 
Care Institute/Harvard Medical School and the Harvard School of Public Health; datasets will remain solely at the 
Department of Population Medicine. 
 
2. Sources of Materials 
For Aim 1, we will use existing computerized HPHC enrollment data to identify and select subscribers and their families 
for the study samples.  Analysts from the Research Support Data Center (RSDC) in the Department of Population 
Medicine at the Harvard Pilgrim Health Care Institute will access HPHC claims data for eligible patients.  Data on plan 

benefits policies will be provided by HPHC from existing data files, and will be linked by plan to study families. We will 
collect patient-reported data from eligible subjects through in-depth qualitative interviews with patients with asthma or 
parents of children with asthma.  Interviews will be conducted by phone using an interview guide with open-ended 
questions designed to take approximately 45 minutes.  Interviews will be audiotaped and transcribed.   
For Aims 2-4, we will use existing computerized data on insurance enrollment, claims, and linked consumer credit 
bureau data from a large health insurance plan. These data were collected by the health plan for administrative 
purposes and not specifically for the proposed research project. The administrative data are anonymized, linked to 
credit bureau data, archived, and made available for purchase to health services researchers. The health plan will also 
provide linked data on members’ access to a preventive drug list.  All data, including credit report data on household 
income, net worth, and race/ethnicity, will be obtained in de-identified form from Optum.  The dataset provided by 
Optum will not contain names, dates of birth, social security numbers, or exact addresses beyond zip code. The subjects 
themselves will only be identified by an encrypted study ID. Please see the section below on Protections Against Risk for 
further details. 
3. Potential Risks 
The risks to participating subjects are minimal. The greatest potential risks to subjects in our study is loss of 
confidentiality of their medical data or potential psychological distress to the subscribers from being interviewed. 
However, all potential identifying information except service dates and zip code have been removed from claims and 
substituted with encrypted study identifiers.  Please see the section below on Protections Against Risk for further 
details. 
B. Adequacy of Protection Against Risks 
1. Recruitment and Informed Consent 
For the Aim 1 qualitative interviews, study subjects will be identified from existing HPHC computerized enrollment data.  
We will request a waiver of consent and HIPAA authorization to obtain these initial data to establish the sampling frame.  
We will approach up to 660 HPHC members by mail and/or email to invite them to participate.  This message will include 
an information sheet that describes the study and includes elements relevant to informed consent (e.g. the voluntary 
nature of participation, confidentiality, etc.).  We will recruit patients through AAFA through postings to Asthma Online 
Community, Educational Support Group, email listserv, Facebook page, newsletters, flyers, and/or other communication 
channels; subjects recruited through AAFA who are eligible to participate will receive the information sheet following 
completion of the pre-screening questionnaire.  Copies of the information sheet will be made available to all participants 
by their choice of email, mail, or as an immediate download from REDCap (if the pre-screening was completed online).   
We estimate completing approximately 27 phone interviews with each group (HPHC and AAFA).  Participants will be 
offered a $50 gift card incentive for completion of a phone interview.  Interviews will be conducted by the study 
investigators and will be audiotaped and the tapes transcribed.  We will request approval for a waiver of written consent 
for the interviews, and verbal consent will be obtained. 
Because our Aims 2-4 studies involve retrospective analysis of insurance claims data, there will be no recruitment and 
we will request a waiver of consent for these studies. 
2. Protections against Risk 
To mitigate the potential risk of psychological distress from being interviewed, the study protocols will specify that 
respondents be allowed to decline any question or to end the interview at any time.  The risk of loss of confidentiality 
will be minimized by storing computerized data in password-protected files accessible only to research staff. 
Interviewers will take steps to avoid the inclusion of identifiers in interview audio recordings. For transcription of audio 
files, we will use only vendors that have established a Business Associate Agreement with Harvard Pilgrim, detailing their 
compliance with HIPAA data security requirements. 
  Computerized claims and enrollment data will be available to study investigators only as a limited data set.  All data 
used encrypted study identifiers and no identifying information will be included except dates of services and zip code. 
The subjects in the Optum dataset will only be identified by an encrypted study ID.  Optum maintains a link of encrypted 
enrollee ID to other identifiers, but the study team will never have access to this. Through Drs. Wharam and Ross-
Degnan, the Department of Population Medicine (DPM) has licensed these Optum data and has an existing Data Use 

Agreement to use these data (described in the application’s letter of support from Michael Sanky from Optum).  Dr. 
Galbraith has completed Optum’s required privacy and compliance training for users licensing these data.  License 
requirements stipulate that re-identification of the data is prohibited by law.  We will not attempt to link Optum data 
with other data sources other than through geographic linkages provided by Optum as part of the deidentified data. 
To protect the privacy and confidentiality of participant data and to minimize the risk of a breach of confidentiality, we 
have strong confidentiality and data security measures in place for these electronic records.  The Optum datasets will be 
stored at DPM on password-protected, virus-protected DPM hard drives subject to rigorous Harvard Pilgrim Health Care 
security.  Data will be stored only on a secure network and not on any laptops, portable drives, or other unsecured 
devices.  Data will remain on DPM’s local servers/computers at all times, as per our data-use agreement with 
Optum.  Investigators may access these servers/computers remotely within the United States using DPM's approved 
Citrix software, which is approved under the DUA that governs the dataset.   
These data are only available to the study teams whose research projects are approved for their use and may be used 
only for the purposes of completing the approved analyses.  Only investigators Dr. Galbraith, Dr. Wharam, and Dr. 
Zhang, programmer, data manager, and project manager will have access to the data on their computers at DPM for the 
proposed research.  All investigators and study staff who access the data must have completed required CITI training 
and privacy and security training. These data will not be accessed by DPM or Harvard Pilgrim employees not directly 
involved in the work required to complete the project.  Each member of the study team will undergo structured HIPAA 
training in procedures to maintain confidentiality and must sign an oath of confidentiality; breach of confidentiality is 
grounds for immediate termination. Study investigators outside of the DPM will not be allowed to access these data, and 
only summarized data will be shared outside the DPM.   
3. Potential Benefits of the Proposed Research to Human Subjects and Others 
The proposed research will provide no direct benefit to study subjects.  The proposed research may benefit other similar 
populations by providing important information about health plan policies that may lead to improvements in the design 
of health insurance benefits.  The future potential benefits on insurance plan design that might emerge from the 
findings of this study outweigh the minimal risk of loss of confidentiality. 
4. Importance of the Knowledge to be Gained 
The analyses in this study have potential benefits for individuals and families with asthma with commercial insurance. 
Results could help health plans and employers refine insurance benefit design to target the needs of patients with 
asthma while containing costs.  Findings may be especially useful to vulnerable populations who may suffer worse 
health under insurance with high levels of cost sharing. This project will increase awareness of particular sub-groups 
who are at risk for adverse outcomes, and for whom strategies such as preventive drug lists may be beneficial. Risks to 
subjects are small and the benefits from the knowledge to be gained far outweigh these minimal risks. 
INCLUSION OF WOMEN AND MINORITIES 
This research focuses on adults and children enrolled in commercial health insurance plans.  The proposed study will 
include all women and minorities who qualify for the study based on age, presence of asthma, and having commercial 
insurance from an employer or (in Aim 1) through the non-group market. The proposed project does not use 
race/ethnicity or sex/gender as part of its inclusion criteria.   We expect the distribution of study subjects by sex/gender 
and race/ethnicity to be similar to that of commercially-insured populations with asthma nationally, in which asthma is 
more prevalent among certain racial and ethnic minorities and among women overall (although more prevalent among 
boys for children). Study aims will specifically examine potential disparities for racial/ethnic minority populations in 
response to high levels of cost sharing in high deductible plans, based on prior evidence that these groups may be 
adversely affected.  We expect that we will have sufficient sample size to make comparisons based on race/ethnicity and 
on sex/gender.  Thus, we do not propose specific outreach programs for recruiting subjects of a particular race/ethnicity 
or sex/gender. 
 INCLUSION OF CHILDREN  
Subjects in the Aim 1 interviews will be adults, including parents of children with asthma, but children themselves will 
not be contacted directly.  Children aged 4-17 years with asthma will be included as subjects in the proposed claims-
based research in Aim s 2-4.  Children under 4 years of age will be excluded because their diagnosis of asthma may not 

be well established before that point, and because their asthma medication use may differ from that of older children 
and adults.  We will use existing health plan data on children to measure medication use, health care utilization, and 
out-of-pocket costs.  We will not be approaching or contacting children directly as part of the project. The research team 
includes two practicing pediatricians with experience working with children and conducting health services research on 
pediatric populations.  
 

 
CONSORTIUM CONTRACTUAL ARRANGEMENTS  
 
Describe the proposed research projects that subcontracted organizations will perform. Explain the strengths that 
these partners bring to the overall project. 
Our proposed plan involves two subcontracted organizations, the Harvard T. H. Chan School of Public Health and the 
Asthma and Allergy Foundation of America. 
The Harvard T. H. Chan School of Public Health (HSPH) in Boston, Massachusetts is a premier U.S. educational institution 
dedicated to advancing the health of populations worldwide. Our subcontract to HSPH will be for the services and effort 
of our co-investigators on the application, Anna Sinaiko, PhD and Meredith Rosenthal, PhD as well as a research 
assistant. Dr. Sinaiko is a Research Scientist in the Department of Health Policy and Management at HSPH with extensive 
expertise in health economics and health policy. She has conducted claims-based and qualitative studies of consumer 
evaluation of consumer response to health insurance coverage options and benefit design.  Dr. Rosenthal is a Professor 
of Health Economics and Policy and Associate Dean for Diversity in the Department of Health Policy and Management at 
HSPH. Dr. Rosenthal has broad experience conducting research on the impact of market-based health policy reforms, 
and has advised both federal and state policymakers in healthcare payment policy and implementation The principal 
investigator on this study, Dr. Alison Galbraith, has a history of successful collaboration with Drs. Sinaiko and Rosenthal 
on studies of health insurance innovations such as high-deductible plans and health insurance exchanges plans and their 
impact on patient decision-making strategies, insurance plan selections, and spending. Drs. Sinaiko will collaborate and 
provide economic expertise on all aims of the project.  Her work will include assisting with research strategy design, 
conceptualization and definition of variables, providing input on preliminary results, manuscript writing, and interpreting 
and inseminating results. She will lead a manuscript as part of Aim 4 on the impact of HDHPs and PDLs on out-of-pocket 
costs for families. Dr. Rosenthal will also provide economic expertise and guidance for all aims of the project, particularly 
for Aim 4. She will advise on generation of study hypotheses, the design for the empirical analyses, the interpretation 
and framing of findings in a policy context, and dissemination of findings.  She will take a senior author role in 
manuscript-writing for Aim 4.  
The Asthma and Allergy Foundation of America (AAFA) is a not-for-profit voluntary health organization that has been 
recognized nationally and internationally as the trusted source of information and education about asthma and allergies 
for patients, their families, and caregivers. AAFA has a national network of health professionals, certified asthma 
educators, and volunteers (patients, parents and family caregivers) that delivers its national and community-focused 
programs through five state and regional chapters, with over 45 affiliated educational support groups and 141 
community awareness partners, including clinics and pharmacies, and allied health providers and faculty.  AAFA’s 
websites house hundreds of pages of evidence-based content and resources for disease prevention, management, 
treatment, and support; together, these websites host an average of 700,000 unique visitors each month. AAFA has 
received funding from multiple cooperative agreements from the Centers for Disease Control and Prevention (CDC), 
including a five-year comprehensive school-based initiative to educate school staff, teens, parents, and community 
health professionals with asthma using AAFA’s Power Breathing Program and a five-year project to create easy-to-read 
educational lesson materials in English and Spanish for home visitors and childcare providers to educate families and 
their preschool-age children with asthma.   AAFA has also received cooperative agreements from EPA including a six-
year project to educate child care providers about asthma management and controlling indoor and outdoor 
environmental triggers. In addition to AAFA’s federal projects, AAFA educates and informs the asthma and allergy 
community via print and e-newsletters, regular social media posts (Facebook and Twitter), monthly webinars, numerous 
public awareness campaigns, the product certification program, educational support groups, and through their extensive 
website content.  These capabilities, resources, and relationships are unique strengths that AAFA will bring to the 
proposed project which will enhance the project’s scientific quality, patient and stakeholder engagement, and 
dissemination. 
Meryl Bloomrosen, MBI, MBA, Senior Vice President of Policy, Advocacy, and Research, will be a co-investigator on the 
project. She has over 30 years of experience in healthcare and currently oversees AAFA’s research portfolio, including 
projects supported by the CDC, Genetic Alliance, and PCORI. As Co-Investigator, Ms. Bloomrosen will attend monthly 
study team meetings via conference call, and attend meetings with the project’s Stakeholder Advisory Board three times 

a year via. She will assist with study design, selection of target populations, and outcomes to be measured. In Aim 1 of 
the study, she will review and provide input on the interview guide for the qualitative interviews with patients and will 
recruit patient participants for interviews and for the Advisory Board and Patient and Family Research Council.  Ms. 
Bloomrosen will also review findings from the qualitative and quantitative analyses and co-author presentations and 
manuscripts.  Through AAFA’s extensive resources and relationships with the patient, provider, research, and policy 
communities, MS. Bloomrosen and AAFA will be able to implement a robust dissemination strategy for project findings. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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APPENDIX (optional)  
 
 
APPENDIX: DRAFT INTERVIEW GUIDE FOR AIM 1 
 
As part of the prescreening process, we will ask subjects to have their insurance card on hand during the interview.  After 
describing the study and obtaining verbal consent and permission to audiotape the interview, the interview will be 
conducted as follows: 
 
Introduction: 
In this study, we want to understand how people living with asthma obtain health care and manage their illness. We are 
especially interested in how health insurance affects their choices. In answering the following questions, please think of 
your experiences as a [patient with asthma/parent of a child with asthma] in the past year. 
 
 
INTERVIEW QUESTIONS (with bulleted questions as prompts) 
 
1. Tell me about [your/your child’s] asthma. 
a. At what age [were you/your child] diagnosed? 
b. What medications [do you/your child] take for asthma? 
c. How often [do you/does your child] take these medications? 
d. How well do these medications work for [you/your child]?  Have [you/your child] had to change 
medications over time, and if so, why? 
e. In the past year, have [you/your child] had to miss [work/school] because of [your/his/her] asthma? 
 
2. I’d like to talk more about [your/your child’s] asthma medications. For some people, taking these medications is 
challenging. What challenges have [you/your child] faced with taking asthma medications? 
a. How often [do you/ does your child] miss doses? 
b. Why [do you/does your child] miss doses? 
 
3. Health care for asthma can be expensive, so the next questions are about cost. 
a. How affordable are [your/your child’s] asthma medications? 
b. How much of the cost is covered by your insurance?  How much of the cost do you pay for yourself? 
c. What challenges have you faced paying for asthma medications? Paying for other asthma care such as 
doctor visits or emergency room visits? 
d. How often have [you/your child] had to skip doses of asthma medication because the medication is not 
affordable? How did this affect [your/your child’s] asthma? 
e. How much of a challenge is the cost of asthma medications compared to other challenges that 
[you/your child] face in taking asthma medications?  
f. Can you describe a situation when you talked with [your/your child’s] doctor about the costs of asthma 
medications? 
g. What strategies do you use to reduce asthma costs?  Have [you/your child] used medication sharing as a 
way to reduce asthma costs? 
 
 
 
 
4. Tell me about how the affordability of asthma medications affects your family’s finances as a whole. 
a. What other health-related costs affect your family? 
b. To what extent have you faced trouble paying medical bills? 
c. How have the costs of asthma medications affected your family’s use of other medical care?   

d.  C a n y o u t hi n k of a ti m e w h e n t h e c ost of h e alt h c ar e m a d e it a c h all e n g e t o m e et t h e n e e ds of m or e 
t h a n o n e f a mil y m e m b er ? H o w di d y o u r es p o n d ?  
e.  H o w h av e t h e c osts of ast h m a m e dic ati o ns r e q uir e d y o ur f a mil y t o m ak e  tr a d e-offs wit h ot h er n o n -
m e dic al n e e ds ?  
 
5.  T h er e ar e m a n y diff er e nt ty p es of i ns ur a nc e, a n d it w o ul d b e h el pf ul f or m e t o k n o w m or e a b o ut t h e ki n d of pl a n 
y o u h av e. F e el fr e e t o us e y o ur i ns ur a nc e c ar d t o h el p y o u a ns w er.   
a.  Pl e as e d escri b e y o ur c urr e nt h e a lt h i ns ur a nc e pl a n. 
b.  H o w di d y o u c h o os e y o ur c urr e nt pl a n ?  
c. Di d y o u h av e ot h er pl a ns t o c h o os e fr o m ?  
d.  H o w m uc h d o y o u h av e t o p ay o ut -of -p oc k et b ef or e y o ur i ns ur a nc e c o v ers m ost ty p es of c ar e ?  T his is 
k n o w n as t h e d e d ucti bl e a m o u nt.  
 
6.  [ F or r es p o n d e nts wit h P DL] Y o u h av e a s p eci al ty p e of i ns ur a nc e t h at c o v ers s o m e i m p ort a nt m e dic ati o ns f or 
fr e e. C a n y o u d escri b e t his b e n efit ? 
a.  T ell m e a b o ut h o w y o u h av e us e d t his b e n efit.  
•  
T o w h at ext e nt d o es t his b e n efit c o v er [ y o ur/ y o ur c hil d’s] ast h m a m e dic ati o ns ?  
•  
W h e n it c o m es t o m a n a gi n g [ y o ur/y o ur c hil d’s] ast h m a, h o w h el pf ul is t his b e n efit ?  
•  
W h at ot h er ty p es of m e dic ati o ns d o es t his c o v er ?  H av e y o u us e d a n y of t h es e m e dic ati o ns ?  
•  
H o w d o es t his b e n efit aff ect h o w y o u a n d y o ur f a mil y us e ot h er m e dic ati o ns ?  
 
 
 
S O CI O D E M O G R A P HI C Q U E S TI O NS   
 
7.  W h at is y o ur a g e ?  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  
 
8.  W h at is y o ur c hil d’s a g e ?  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  
 
9.  I ncl u di n g y o u, h o w m a n y f a mil y m e m b ers li v e i n y o ur h o us e h ol d ?  _ _ _ _ _ _ _ _ _ _ _ 
 
1 0.  I n w h at st at e d o y o u li v e ? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  
 
1 1.  Ar e [y o u /y o ur c hil d] of His p a nic or L ati n o ori gi n or d esc e nt ?  
 Y es , His p a nic or L ati n o 
 N o , n ot His p a nic or L ati n o 
 D o n’t k n o w  
 R ef us e d  
 
 
 
 
1 2.  W h at is [ y o ur/y o ur c hil d’s] r ac e ? Pl e as e s el ect 1 or m or e of t h e f oll o wi n g r ac es:   
 W hit e  
 Bl ack or Afric a n A m eric a n  
 A m eric a n I n di a n or Al as k a N ati v e  
 Asi a n  
 N ati v e H a w aii a n or ot h er P acific Isl a n d er  
 Ot h er  →  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  
 D o n’t K n o w  
 R ef us e d  

 
13. This study looks at the costs of health care.  While we don’t need to know the exact amount, we would like to 
know roughly your annual household income to be able to interpret your answers accurately.  These data are 
completely confidential and will only be used to come up with averages for the purpose of this study.   
 
a. Could you tell me whether your total annual household income in 2016 was under $30,000 or over $30,000 
before taxes?   
□  Under $30,000 (go to 13b) 
□  Don’t know (skip to CLOSE) 
□  Over $30,000 (go to 13c) 
□  Refused (skip to CLOSE) 
 
 
  
b.   Was it under $10,000, between $10,000 and $20,000, or over $20,000? 
□  Under $10,000 (skip to CLOSE) 
□  Don’t know (skip to CLOSE) 
□  Between $10,000 and $20,000 (skip to 
CLOSE) 
□  Refused (skip to CLOSE) 
□  Over $20,000 (skip to CLOSE) 
 
 
c. Was it under $40,000, between $40,000 and $50,000, between $50,000 and $80,000, or over $80,000? 
□  Under $40,000 
□  Over $80,000 
□  Between $40,000 and $50,000 
□  Don’t know  
□  Between $50,000 and $80,000 
□  Refused  
 
Close: 
 
We are nearing the end of the interview.  Those are all the questions I have.  Do you have any comments or questions?  
Pause, record comments below.   
 
 
 
 
Thank you very much for your time.  Your knowledge and insights will be very helpful to this study.