METHODS
We conducted a 10-year retrospective chart review of all Black American,
Haitian, African, and White American women seeking infertility care at
Boston Medical Center (BMC) between January 1, 2005 and July 15, 2015.
Patients with infertility ICD-9 diagnoses seen by a reproductive
endocrinologist were included in the cohort; these patients were
identified by analyzing the BMC Clinical Data Warehouse database
(Appendix S1). The study was approved by the Boston University School of
Medicine Institutional Review Board (IRB# H-34265). No funding was
received for this study.
Charts were reviewed to determine infertility diagnosis with information
obtained from physician notes, clinical history, and fertility testing.
Data was first stratified by place of birth, and then subdivided by
self-identified race (White or Black) and among Black women ethnicity
(defined as Haitian, African, or Black American) as determined by place
of birth and primary language. Women were included if they had a
confirmed infertility diagnosis, identified as either Black or White,
and were born in either Haiti, Africa, or the United States (US). Women
were excluded if race and place of birth were unavailable, they
identified with an ethnicity different from those of interest regardless
of place of birth, or the infertility diagnosis could not be
corroborated from the medical record. White American women were used as
a comparison group. Demographic and infertility testing results
including day 3 follicle-stimulating hormone (FSH) levels were compared
between groups. Infertility diagnoses were compared between White and
Black women. Subgroup analyses were then performed comparing White women
to Black Haitian, African, and American women seeking infertility
treatment.
Statistical analyses using unpaired t-test or one-way ANOVA were used
for analysis of continuous variables. For ANOVA, a significant omnibus
F-test was followed by Fisher’s PLSD post hoc comparisons. Discrete data
were analyzed by chi squared tests followed by comparison of cell
chi-squared contributions. Multivariate multinomial logistic regression
was then used to evaluate associations of independent variables with the
dichotomous outcome variable, infertility diagnosis. Univariate and
multivariable regression models were used to identify pertinent risk
factors. Medical insurance type was used as a proxy for socioeconomic
status, with uninsured status and Medicaid insurance as an indicator of
low socioeconomic status (SES). SAS (version 9.3) and StatView (version
5.0.1) statistical software were used to perform the analyses.
Statistical significance was defined as a p-value < 0.05.