Asthma in Families Facing Out-of-pocket Requirements With Deductibles
| Status: | Active, not recruiting |
|---|---|
| Conditions: | Asthma |
| Therapuetic Areas: | Pulmonary / Respiratory Diseases |
| Healthy: | No |
| Age Range: | 4 - 64 |
| Updated: | 9/26/2018 |
| Start Date: | March 1, 2017 |
| End Date: | April 30, 2021 |
Comparing Patient-Centered Outcomes for Adults and Children With Asthma in High-Deductible Health Plans With and Without Preventive Drug Lists
Asthma is one of the most common chronic diseases in the U.S. Despite guidelines, adherence
to recommended controller medications is low. Cost is an important barrier to adherence.
Employers are increasingly adopting high-deductible health plans (HDHPs) which require
deductibles of > $1,000 per individual/$2,000 per family each year. In HDHPs with Health
Savings Accounts (HSAs), most medications and non-preventive care must be paid out-of-pocket
(OOP) until the deductible is reached. The lower premiums of HSA-HDHPs are appealing, but the
high level of OOP costs can lead patients to forgo needed care. HSA-HDHPs can exempt
preventive care from the deductible, and employers can add Preventive Drug Lists (PDLs) which
exempt certain chronic medications from the deductible (including asthma medications), making
them free. PDLs have the potential to improve controller medication use, which could prevent
negative health outcomes and reduce cost-related trade-offs for families.
The goal of this research is to evaluate the impact of these two developments in the health
insurance market -- HSA-HDHPs and PDLs -- on medication use and clinical outcomes for adults
and children with asthma. To do this, tteh investigators will first conduct in-depth
interviews with patients with asthma and parents of children with asthma who have HDHPs and
traditional plans. Interviews will collect patient-reported data on how patients and their
families navigate their insurance plan and make health care decisions when faced with OOP
costs. Findings from the interviews will inform analyses of data from a large national health
plan from 2004-2017. Investigators will select adults and children with asthma whose employer
switched them from traditional plans or HSA-HDHPs without PDLs to HSA-HDHPs with or without a
PDL. Analyses will examine changes in asthma medication use, emergency department (ED)
visits, hospitalizations, and OOP costs before and after changing plans compared to similar
patients who did not switch to a HSA-HDHP. The study aims to: 1) understand health care
decision making and experiences of families with asthma with HDHPs; 2) examine the impact of
HSA-HDHPs with and without PDLs on use of asthma medications and asthma-related ED visits and
hospitalizations; 3) 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)
examine the impact of HSA-HDHPs with and without PDLs on OOP costs for families.
to recommended controller medications is low. Cost is an important barrier to adherence.
Employers are increasingly adopting high-deductible health plans (HDHPs) which require
deductibles of > $1,000 per individual/$2,000 per family each year. In HDHPs with Health
Savings Accounts (HSAs), most medications and non-preventive care must be paid out-of-pocket
(OOP) until the deductible is reached. The lower premiums of HSA-HDHPs are appealing, but the
high level of OOP costs can lead patients to forgo needed care. HSA-HDHPs can exempt
preventive care from the deductible, and employers can add Preventive Drug Lists (PDLs) which
exempt certain chronic medications from the deductible (including asthma medications), making
them free. PDLs have the potential to improve controller medication use, which could prevent
negative health outcomes and reduce cost-related trade-offs for families.
The goal of this research is to evaluate the impact of these two developments in the health
insurance market -- HSA-HDHPs and PDLs -- on medication use and clinical outcomes for adults
and children with asthma. To do this, tteh investigators will first conduct in-depth
interviews with patients with asthma and parents of children with asthma who have HDHPs and
traditional plans. Interviews will collect patient-reported data on how patients and their
families navigate their insurance plan and make health care decisions when faced with OOP
costs. Findings from the interviews will inform analyses of data from a large national health
plan from 2004-2017. Investigators will select adults and children with asthma whose employer
switched them from traditional plans or HSA-HDHPs without PDLs to HSA-HDHPs with or without a
PDL. Analyses will examine changes in asthma medication use, emergency department (ED)
visits, hospitalizations, and OOP costs before and after changing plans compared to similar
patients who did not switch to a HSA-HDHP. The study aims to: 1) understand health care
decision making and experiences of families with asthma with HDHPs; 2) examine the impact of
HSA-HDHPs with and without PDLs on use of asthma medications and asthma-related ED visits and
hospitalizations; 3) 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)
examine the impact of HSA-HDHPs with and without PDLs on OOP costs for families.
Background and Significance Asthma is one of the most common serious chronic diseases of
adults and children in the United States. Despite guidelines and evidence, adherence to
recommended asthma controller medications is low. Cost is an important barrier to
non-adherence to asthma medications. Employers are increasingly adopting high-deductible
health plans (HDHPs) particularly those that qualify for Health Savings Accounts (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
(VBID) have been proposed to promote high-value care by reducing or eliminating cost-sharing
for these services. One common example is a Preventive Drug List (PDL) that can accompany
HSA-HDHPs, which exempts certain chronic medications from the deductible to promote
adherence. Many PDLs include asthma controller and rescue medications. With the increasing
prevalence of HSA-HDHPs, PDLs have the potential to improve controller medication adherence
for both adults and children.
Study Aims: The goal of this research is to provide needed evidence on the impact of two
important developments in the health insurance landscape, HSA-HDHPs and PDLs, and whether
PDLs can mitigate cost barriers associated with HSA-HDHPs and improve patient-centered
outcomes for adults and children with asthma. The Aims of this research are:
1. To understand health care decision making and experiences of families with asthma with
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 OOP costs for patients and
families with asthma.
Sources of Data
Aim 1. This study will collect patient-reported data through in-depth qualitative phone
interviews
Aims 2-4 will use 14 years (2004-2017) of enrollment and claims data from a large U.S.
commercial health plan
Study Population
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
health insurance plans (high-deductible plans with and without a PDL, or traditional plans).
The health plan population will be identified through enrollment and claims data from Harvard
Pilgrim Health Care (HPHC). Eligible patients will include adults aged 18-64 and children
aged 4-17 years with a diagnosis of asthma. Patients will be selected if they have been
enrolled in an employer-sponsored HSA-HDHP with or without a PDL, or a traditional plan
without a high deductible, for the prior year. Among those eligible in each sub-group, the
investigators will randomly select 9 from each cell to send a recruitment mailing, for a
total of 81 patients or parents invited to participate.
A population from the Asthma and Allergy Foundation of America (AAFA) will be recruited
through postings to AAFA's Asthma Online Community, Educational Support Group, email list
serve, Facebook page, and newsletters.
For Aims 2-4, the study population will consist of adults aged 18-64 years and children aged
4-17 years with asthma. 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 in a HSA-HDHP
without a PDL prior to 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 HDHP
without PDL then remain in that plan for at least another 12 months.
Enrollees over age 64 who are eligible for Medicare will be excluded. The investigators will
identify members with asthma during the baseline year using the same claims-based algorithm
in which an asthma diagnosis will be defined as having at least one inpatient or two
outpatient claims in the prior year with a diagnosis of asthma based on International
Classification of Diseases (ICD) 9 and 10 codes for asthma.
Eligible employers are those that offer only one plan type in a given benefit year: 1)
traditional Health Maintenance Organization (HMO)/Preferred Provider Organization (PPO)/POS
(Point of Service) plans (deductibles <$1000, copayments of <$50 for most services, tiered
copayments for medicines); 2) HSA-HDHPs without PDLs; or 3) HSA-HDHPs with PDLs. The
investigators will select "full-replacement" employers that replace a traditional plan or
HSA-HDHP without PDL with an HSA-HDHP without PDL or HSA-HDHP with PDL, respectively, for all
employees at a given point in time; matched comparison employers will include those that keep
all employees in their prior plan.
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.
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. See section on
primary outcomes for details,
Predictors and Covariates
The 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 baseline asthma severity, co-morbidity, presence of other chronic
conditions, self-reported and geocoded race/ethnicity data, neighborhood education and
poverty levels, sex, age, state, individual versus family plan, baseline number of outpatient
visits, presence of an inpatient hospitalization, total expenditures, employer size, average
employer baseline expenditures per capita, number of family members, mean age of children in
the family, mean age of adults, baseline mean family morbidity score, number of family
members with asthma, number of asthma and other medications used by the family, and number of
family ED visits and hospitalizations in the baseline year.
Analysis plan
Aim 1
The study team will analyze qualitative data in iterative cycles of content analysis in the
manner described by Patton. In the second, deductive phase of analysis, investigators will
consider data code-by-code to identify areas of convergence and divergence by insurance type.
Aim 2
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 HSA-HDHPs without PDLs vs. matched controls remaining in HSA-HDHPs
without PDLs. The investigators 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.
The analyses will use both Interrupted Time Series (ITS) and Difference-in-Differences (DiD)
frameworks. Controlling for potential confounders, analyses 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. Extended GLMs - generalized estimating
equations (GEE) and generalized linear mixed models (GLMM) - are appropriate methods to
adjust for correlation within families and to examine changes in outcomes between baseline
and follow-up.
This study will examine controller medication adherence using ITS. Investigators 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.
For analyses of rescue medication use, investigators will focus on albuterol and levalbuterol
inhaler users. 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 previous studies. Analyses
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. The investigators 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. Negative binomial regression will be used to model the effect for count
outcomes such as ED visits. Investigators will select the conditional mean and variance
functions based on the actual data, using a log link with a Gamma error distribution.
To determine the impact of HSA-HDHP with and without PDLs on vulnerable populations and test
for heterogeneity of treatment effects among vulnerable populations, the investigators 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, high
morbidity based on the Johns Hopkins Adjusted Clinical Groups (ACG) system). Analyses 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. In Aim 2 analyses, the investigators will include both
adults and children together, with age included as a co-variate.
Aim 3
Analyses will use the same population, outcomes, study group comparisons, and modeling
strategies as Aims 1 and 2 except that stratified analyses will be performed, comparing
outcomes between intervention and control groups stratified by adult/child status. To
statistically test for heterogeneity of treatment effect for adults vs. children, analyses
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 a HSA-HDHP without PDL. Analyses will be done at the
individual level, but will include family-level variables as predictors of interest.
Separately for adults and children, the investigators 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.
Aim 4
Analyses will be similar to those of Aim 2, but for OOP cost outcomes. The primary analyses
of changes in OOP costs will use a DiD analytic framework. The investigators 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. GEE or GLMM models
will be used 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).
adults and children in the United States. Despite guidelines and evidence, adherence to
recommended asthma controller medications is low. Cost is an important barrier to
non-adherence to asthma medications. Employers are increasingly adopting high-deductible
health plans (HDHPs) particularly those that qualify for Health Savings Accounts (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
(VBID) have been proposed to promote high-value care by reducing or eliminating cost-sharing
for these services. One common example is a Preventive Drug List (PDL) that can accompany
HSA-HDHPs, which exempts certain chronic medications from the deductible to promote
adherence. Many PDLs include asthma controller and rescue medications. With the increasing
prevalence of HSA-HDHPs, PDLs have the potential to improve controller medication adherence
for both adults and children.
Study Aims: The goal of this research is to provide needed evidence on the impact of two
important developments in the health insurance landscape, HSA-HDHPs and PDLs, and whether
PDLs can mitigate cost barriers associated with HSA-HDHPs and improve patient-centered
outcomes for adults and children with asthma. The Aims of this research are:
1. To understand health care decision making and experiences of families with asthma with
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 OOP costs for patients and
families with asthma.
Sources of Data
Aim 1. This study will collect patient-reported data through in-depth qualitative phone
interviews
Aims 2-4 will use 14 years (2004-2017) of enrollment and claims data from a large U.S.
commercial health plan
Study Population
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
health insurance plans (high-deductible plans with and without a PDL, or traditional plans).
The health plan population will be identified through enrollment and claims data from Harvard
Pilgrim Health Care (HPHC). Eligible patients will include adults aged 18-64 and children
aged 4-17 years with a diagnosis of asthma. Patients will be selected if they have been
enrolled in an employer-sponsored HSA-HDHP with or without a PDL, or a traditional plan
without a high deductible, for the prior year. Among those eligible in each sub-group, the
investigators will randomly select 9 from each cell to send a recruitment mailing, for a
total of 81 patients or parents invited to participate.
A population from the Asthma and Allergy Foundation of America (AAFA) will be recruited
through postings to AAFA's Asthma Online Community, Educational Support Group, email list
serve, Facebook page, and newsletters.
For Aims 2-4, the study population will consist of adults aged 18-64 years and children aged
4-17 years with asthma. 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 in a HSA-HDHP
without a PDL prior to 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 HDHP
without PDL then remain in that plan for at least another 12 months.
Enrollees over age 64 who are eligible for Medicare will be excluded. The investigators will
identify members with asthma during the baseline year using the same claims-based algorithm
in which an asthma diagnosis will be defined as having at least one inpatient or two
outpatient claims in the prior year with a diagnosis of asthma based on International
Classification of Diseases (ICD) 9 and 10 codes for asthma.
Eligible employers are those that offer only one plan type in a given benefit year: 1)
traditional Health Maintenance Organization (HMO)/Preferred Provider Organization (PPO)/POS
(Point of Service) plans (deductibles <$1000, copayments of <$50 for most services, tiered
copayments for medicines); 2) HSA-HDHPs without PDLs; or 3) HSA-HDHPs with PDLs. The
investigators will select "full-replacement" employers that replace a traditional plan or
HSA-HDHP without PDL with an HSA-HDHP without PDL or HSA-HDHP with PDL, respectively, for all
employees at a given point in time; matched comparison employers will include those that keep
all employees in their prior plan.
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.
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. See section on
primary outcomes for details,
Predictors and Covariates
The 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 baseline asthma severity, co-morbidity, presence of other chronic
conditions, self-reported and geocoded race/ethnicity data, neighborhood education and
poverty levels, sex, age, state, individual versus family plan, baseline number of outpatient
visits, presence of an inpatient hospitalization, total expenditures, employer size, average
employer baseline expenditures per capita, number of family members, mean age of children in
the family, mean age of adults, baseline mean family morbidity score, number of family
members with asthma, number of asthma and other medications used by the family, and number of
family ED visits and hospitalizations in the baseline year.
Analysis plan
Aim 1
The study team will analyze qualitative data in iterative cycles of content analysis in the
manner described by Patton. In the second, deductive phase of analysis, investigators will
consider data code-by-code to identify areas of convergence and divergence by insurance type.
Aim 2
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 HSA-HDHPs without PDLs vs. matched controls remaining in HSA-HDHPs
without PDLs. The investigators 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.
The analyses will use both Interrupted Time Series (ITS) and Difference-in-Differences (DiD)
frameworks. Controlling for potential confounders, analyses 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. Extended GLMs - generalized estimating
equations (GEE) and generalized linear mixed models (GLMM) - are appropriate methods to
adjust for correlation within families and to examine changes in outcomes between baseline
and follow-up.
This study will examine controller medication adherence using ITS. Investigators 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.
For analyses of rescue medication use, investigators will focus on albuterol and levalbuterol
inhaler users. 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 previous studies. Analyses
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. The investigators 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. Negative binomial regression will be used to model the effect for count
outcomes such as ED visits. Investigators will select the conditional mean and variance
functions based on the actual data, using a log link with a Gamma error distribution.
To determine the impact of HSA-HDHP with and without PDLs on vulnerable populations and test
for heterogeneity of treatment effects among vulnerable populations, the investigators 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, high
morbidity based on the Johns Hopkins Adjusted Clinical Groups (ACG) system). Analyses 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. In Aim 2 analyses, the investigators will include both
adults and children together, with age included as a co-variate.
Aim 3
Analyses will use the same population, outcomes, study group comparisons, and modeling
strategies as Aims 1 and 2 except that stratified analyses will be performed, comparing
outcomes between intervention and control groups stratified by adult/child status. To
statistically test for heterogeneity of treatment effect for adults vs. children, analyses
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 a HSA-HDHP without PDL. Analyses will be done at the
individual level, but will include family-level variables as predictors of interest.
Separately for adults and children, the investigators 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.
Aim 4
Analyses will be similar to those of Aim 2, but for OOP cost outcomes. The primary analyses
of changes in OOP costs will use a DiD analytic framework. The investigators 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. GEE or GLMM models
will be used 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).
Inclusion Criteria:
- adult or child with asthma, defined as having one outpatient claim, one emergency
department claim, or one inpatient claim with an ICD-9/10 diagnosis code for asthma in
the baseline period
- has employer-sponsored insurance from an employer who offers only one plan
- at least 24 months of continuous enrollment with pharmacy benefits between 2004 - 2017
Exclusion Criteria:
- other co-morbid pulmonary conditions identified in claims data (cystic fibrosis,
immunodeficiency, bronchiectasis, congestive heart failure, pulmonary hypertension, or
pulmonary embolism)
- enrolled through an employer who offers a choice of health insurance plans
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