Methods
We analyzed hospitalization records from January 1, 2003 through
December 31, 2017 that were contained in the Nationwide Inpatient Sample
(NIS). The NIS datasets constitute the largest all-payer, publicly
available inpatient database in the US and are made available by the
Healthcare Cost and Utilization Project (HCUP). Although the systematic
sampling process used to select the hospitalizations to be included in
the NIS has changed over time, the result is an approximate 20% sample
of hospital discharges from participating states. The sampling strategy
ensures that hospitalizations in the NIS are representative of the
population on important factors including month of admission, primary
reason for hospitalization, and hospital size, location, ownership, and
teaching status. The NIS contained approximately seven million inpatient
hospitalizations each year (35 million when weighted) from around 47
participating states.
Our study sample included hospitalizations among transition-aged
patients within the age range of 16 to 24 years. Diagnoses and
procedures are coded using International Classification of Diseases,
Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes till
the 3rd quarter of 2015, after which HCUP transitioned to ICD-10-CM
format. To assess the study’s primary exposure, we first scanned the
diagnosis codes (the principal diagnosis and up to 29 secondary
diagnoses) in each patient’s discharge record for an indication of SCD
(ICD-9-CM - 282.41, 282.42, 282.61, 282.62, 282.63, 282.64, 282.60,
282.68, 282.69; ICD-10-CM - D57.4x, D57.0x, D57.2x, D57.1x, D57.8x).
These sickle cell-related encounters were then sub-divided into four
mutually exclusive categories based on ICD-9-CM and ICD-10-CM codes:
HbSS (ICD-9-CM - 282.61, 282.62; ICD-10-CM - D57.0x), HbSC (ICD-9-CM -
282.62, 282.63; ICD-10-CM - D57.2x), HbS/β-thalassemia (ICD-9-CM -
282.41, 282.42; ICD-10-CM - D57.4x), and ‘Other SCD’ (ICD-9-CM - 282.60,
282.68, 282.69; ICD-10-CM - D57.1x, D57.8x)
The covariates in this study included individual‐level sociodemographic
and hospital characteristics. We categorized patients’ ages in groups of
16 to 18 years, 19 to 21 years and 22 to 24 years. Ethnicity was first
stratified on the basis of reported ethnicity (Hispanic, non‐Hispanic);
and the non‐Hispanic group was subdivided (White, Black, or other). The
primary payer for the hospitalization was classified into Medicare,
Medicaid, private, self‐pay and other (including under/uninsured). As a
proxy for socioeconomic status, the Healthcare Cost and Utilization
Project provides zip‐code‐level estimates of median household income,
grouped into quartiles based on the patient’s residence. Hospital
factors included census region (Northeast, Midwest, South, West), bed
size (small, medium, large), and type (rural, urban‐nonteaching,
urban‐teaching).
Descriptive statistics were utilized to represent the prevalence of SCD
among all transition-aged hospitalizations and to demonstrate the
prevalence of sub-types of SCD among all SCD hospitalizations for each
year of the study period. We conducted bivariate analyses to assess the
association between socio-demographic characteristics of the patients
and SCD and its sub-types. Furthermore, to describe the trends in rates
of in-hospital mortality among patients with SCD over the study period,
we conducted joinpoint regression analysis, which is a statistical
modeling approach specifically designed to evaluate and describe the
extent to which the rate of a condition changes over time. The model
first fits the annual rates of outcome of interest (i.e. in-hospital
mortality) to a model with the minimum number of joinpoints (zero),
suggesting that a straight line and single trend best fits the annual
prevalence data. Then, more joinpoints are added iteratively to test the
statistical significance of the various models using the Monte Carlo
permutation method. Once the final (best-fitting) model with the optimal
number of joinpoints has been selected, each joinpoint represents a
statistically significant change in the trend, and each distinct trend
is characterized using an annual percent change (APC) measure and its
95% confidence interval (CI). Lastly, we conducted adjusted survey
logistic regression model to assess the association between various
patient characteristics and in-hospital mortality among patients with
and without a diagnosis of SCD. Analyses were performed using R (version
3∙6∙1) and RStudio (Version 1∙2∙5001) and the Joinpoint Regression
Program, version 4.7.0.0 (National Cancer Institute); we assumed a 5%
type I error rate for all hypothesis tests (two-sided). This study was
deemed exempt by the IRB of Baylor College of Medicine as the study was
performed on secondary de-identified data.