PATIENTS AND METHODS
Study design and patients
The CHECK patients used for this
analysis were between ages 1-27y and had a Medicaid diagnosis of SCD. In
some cases, SCD was among multiple chronic disease diagnoses (most
commonly asthma, diabetes mellitus, or epilepsy).
Procedures
The CHECK program served families throughout the Chicago area. Initial
eligibility was based on Medicaid claims data and is described
extensively elsewhere 23. Patients were passively
selected into the CHECK programs and CHECK program data were collected
from January 1, 2015, through January 12, 2018. All patients were sent a
letter stating that they were enrolled. CHECK CHWs attempted to contact
a subset of patients (based on risk and diagnosis) either by mail,
phone, or household visit31. Patients who completed
the CHW-administered interview were considered as ‘engaged’ in
the CHECK program. Those who did not complete the intake assessment were
considered ‘enrolled’ but not ‘engaged’. Engaged patients were
connected to CHWs who provided consultation, care coordination,
education, and social support services as needed33.
Patients were enrolled and participated in the CHECK program on a
rolling basis over time. The CHECK data collection was approved by The
University of Illinois at Chicago Institutional Review Board (protocol
#2017-0604) 34.
Assessment and criteria
Inclusion criteria were Medicaid insurance, CHECK enrollment, and sickle
cell disease diagnostic ICD9 or ICD10 code. Retrospective data for this
study were extracted from Illinois Medicaid paid claims for a three-year
period per participant: one year prior to CHECK enrollment (Baseline
Year) and the following two years during CHECK enrollment. Exclusion
criterion was diagnostic code for sickle cell trait. Based on Baseline
Year Medicaid claims, patients were categorized as High, Medium, or Low
risk for incurring inpatient expenditures during the CHECK enrollment
period. High risk patients were those having more than 3 emergency
department (ED) visits or were hospitalized more than once during the
Baseline year. Medium risk patients were those who had 1 to 3 ED visits
or 1 hospitalization during the Baseline year and Low risk patients were
those who had no ED visits and no hospitalizations during the Baseline
year33. Outliers with inpatient expenditures more than
$100,000 per year in any CHECK year were excluded from analyses because
such patients were expected to have unique medical problems beyond their
SCD 5,33.
Statistical analysis
The analytic plan was developed to handle skewed data with outliers of
high expenditures and individual heterogeneity over time, which are seen
in all studies of SCD. Expenditure data were analyzed across three years
for everyone, based on each individual’s enrollment in CHECK: a Baseline
year preceding enrollment, then one year and two years after CHECK
enrollment. Analyses were conducted using the R program, version 4.0.
Outliers with inpatient expenditures more than $100,000 per year were
already excluded from analyses 5,33. Because the
overall distribution of expenditures was highly skewed, data were
transformed by taking the natural logarithm of each patient’s
expenditures to reduce the distortion caused by the high values. To
account for the zero expenditure cases, the number one was added to
every expenditure value to enable the logarithmic transformation.
11Geometric means were calculated as the nth root of the product
of n logarithmically transformed expenditure values. They were used
instead of arithmetic means because geometric means are appropriate
summary statistics to report log-transformed data. It is the average
of log-transformed value converted to the original expenditure scale.
Geometric means of the log transformed data were calculated.
Baseline distributions of demographics and medical conditions were
compared by enrollment risk using Pearson’s chi-square test or Fisher’s
exact test. Analyses were conducted using the R program Version 0.7.15.
Because many SCD patients had no inpatient expenditures, a two-part
expenditure analysis based on a statistical decomposition of the
distribution of the outcome into a process that generates zeros and a
process that generates non-zero positive values 35 was
conducted using the GLMMadaptive (v0.7.15) R package. The analysis
accommodates the semi-continuous expenditure data; that is, a continuous
model allowing for data with excess zeros was fitted to the data36,37.
Using this approach, excess zeros were accounted for in an analytically
appropriate way, so that better estimates of effects were produced. The
model consisted of a logistic regression for the binary indicator that
inpatient expenditures were zero or not and a standard linear mixed
model for the log transformed non-zero inpatient expenditures.
Interactions between utilization risk group and CHECK year were examined
in both parts of the model. (Table 3). The first part of the analysis
estimated the percent expenditure differences between utilization risk
groups for each CHECK year while the second part estimated ratios of the
odds of having zero expenditures between utilization groups for each
CHECK year. For subgroup analysis, Wilcoxon pairwise tests were
performed to compare mean inpatient expenditures over CHECK years
(Baseline year, first year in CHECK, and second year in CHECK) within
each utilization risk group. Multiple comparisons were accounted for by
using the Bonferroni correction38.