Statistical Analysis
We first characterized data missingness and calculated descriptive
statistics for the sample. Patients without a valid street addresses and
those with missing clinical data were excluded from the analytical
sample. As less than 7% of the sample had missing clinical data, we
used listwise deletion to account for missingness. A STROBE diagram of
the study population is presented in Figure 1 .
The distribution of neighborhood-level and patient characteristics was
obtained for the overall sample and by recurrent ACS status. Bivariate
associations were estimated using simple logistic regression. Models of
recurrent ACS were estimated with multiple logistic regression. As 52%
of Census block groups included singleton observations with only one
individual per block group, we used robust standard errors to account
for clustering of individuals within block groups.21We estimated generalized linear models to compare recurrent ACS to
single and no ACS combined, with results represented as incidence-rate
ratios (IRR). We estimated multinomial logistic regression models to
compare each category (no ACS/single ACS/recurrent ACS), with results
represented as relative risk ratios (RRR). To better understand the
contribution of residential characteristics to ACS recurrence, we four
separate models were estimated: Model 1 with neighborhood socioeconomic
deprivation alone, Model 2 with neighborhood racial composition alone,
Model 3 with rurality alone, and Model 4 with all three characteristics
together. All four models included the same covariates: age, sex,
insurance type, BMI, chronic transfusions, hydroxyurea, asthma, and
phenotype. Statistical tests were two-sided and were performed using a
5% significance level (α=0.05). Analyses were performed using Stata
software, version 15 (StataCorp, College Station, TX).