Using sexual orientation and gender identity data in electronic
health records to assess for disparities in preventive health
screening services
Chris Grasso
a
, Hilary Goldhammer
a
, Russell J. Brown
b
, B.W. Furness
c,*
a
The Fenway Institute, Fenway Health, 1340 Boylston St., Boston, MA 02215, United States
b
National Association of Community Health Centers, 7501 Wisconsin Ave., Suite 1100W,
Bethesda, MD 20814, United States
c
Centers for Disease Control and Prevention, Division of STD Prevention, 899 North Capitol
Street, NE, Fourth Floor, Washington, DC 20002, United States
Abstract
Background: Lesbian, gay, bisexual, transgender, and queer (LGBTQ) populations have an
increased risk of multiple adverse health outcomes. Capturing patient data on sexual orientation
and gender identity (SOGI) in the electronic health record (EHR) can enable healthcare
organizations to identify inequities in the provision of preventive health screenings and other
quality of care services to their LGBTQ patients. However, organizations may not be familiar with
methods for analyzing and interpreting SOGI data to detect health disparities.
Purpose: To assess an approach for using SOGI EHR data to identify potential screening
disparities of LGBTQ patients within distinct healthcare organizations.
Methods: Five US federally qualified health centers (FQHCs) retrospectively extracted three
consecutive months of EHR patient data on SOGI and routine screening for cervical cancer,
tobacco use, and clinical depression. The screening data were stratified across SOGI categories.
Chi-Square and Fisher’s Exact test were used to identify statistically significant differences in
screening compliance across SOGI categories within each FQHC.
Results: In all FQHCs, cervical cancer screening percentages were lower among lesbian/gay
patients than among bisexual and straight/heterosexual patients. In three FQHCs, cervical cancer
screening percentages were lower for transgender men than for cisgender (i.e., not transgender)
women. Within each FQHC, we observed statistically significant associations (P < 0.05) between
*
Corresponding author at: HIV/AIDS, Hepatitis, STD & TB Administration, Strategic Information Division, 899 North Capitol Street,
NE, Fourth Floor, Washington, DC 20002 United States, [email protected]v (B.W. Furness).
Authors' contributions
CG and BWF conceived of the study design. CG conducted the analyses; HG and RJB helped to interpret the data analysis; HG wrote
the manuscript with input and revisions by CG, RJB, and BWF.
Declaration of Competing Interest
The authors report no declarations of interest.
We would like to thank Ashley Barrington and Kathleen McNamara of the National Association of Community Health Centers, and
Rodney VanDerWarker of Fenway Health for their guidance and support. We also wish to thank Corey Covell, Bridget Noe, and Dana
King of The Fenway Institute for their work on the data analysis.
HHS Public Access
Author manuscript
Int J Med Inform
. Author manuscript; available in PMC 2022 December 04.
Published in final edited form as:
Int J Med Inform
. 2020 October ; 142: 104245. doi:10.1016/j.ijmedinf.2020.104245.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
SOGI categories and at least one screening measure. The small number of transgender patients,
and limitations in EHR functionality, created challenges in interpretation of SOGI data.
Conclusions: To our knowledge, this is the first published report of using SOGI data from
EHRs to detect potential disparities in healthcare services to LGBTQ patients. Our finding that
lesbian/gay and transgender male patients had lower cervical cancer screening rates compared
to heterosexual, bisexual, and cisgender women, is consistent with the research literature and
suggests that using SOGI EHR data to detect preventive screening disparities has value. EHR
functionality should allow for cross-checking gender identity with sex assigned at birth to reduce
errors in data interpretation. Additional functionality, like clinical decision support based on
anatomical inventories rather than gender identity, is needed to more accurately identify services
that transgender patients need.
Keywords
sexual orientation; gender identity; SOGI data; electronic health record; LGBT; health disparities;
federally qualified health center
1. Introduction
i
Discrimination and stigma create conditions that increase health risks for people who
are lesbian, gay, bisexual, transgender, queer, or other gender and sexual minorities
(LGBTQ) [1-4]. In addition to having a higher prevalence of HIV, sexually transmitted
diseases (STDs), and substance use disorders, LGBTQ people have an increased likelihood
of cigarette smoking, depression, and anxiety [5-8]. LGBTQ people also experience
challenges in accessing appropriate health care For example, lesbian women and transgender
men have lower cervical cancer screening rates compared to heterosexual and cisgender
women [9-11], and transgender people report delaying medically necessary care due to
discrimination [12]. The routine collection of structured sexual orientation and gender
identity (SOGI) patient data in electronic health records (EHRs) has been recommended
as a key strategy for detecting, tracking, addressing, and ultimately reducing LGBTQ health
disparities [13-16]. Collection of SOGI data can also de-stigmatize sexual and gender
diversity, enable clinicians to offer more patient-centered care, and contribute to national and
global research on LGBTQ health [17-19].
A growing number of US healthcare organizations have begun to integrate SOGI data
collection into their EHR systems due in part to the enactment of new federal policies,
including a 2018 requirement that all EHR systems certified under the US Meaningful Use
Stage III incentive program have the capacity to record SOGI demographic data, and a 2016
mandate that all US federally qualified health centers (FQHCs) collect and report SOGI data
[20,21]. Organizations that capture SOGI data have an opportunity to stratify clinical health
indicators across SOGI categories in order to identify disparities in patient health outcomes
or in provision of clinical services within their own patient population [22].
i
LGBTQ: lesbian, gay, bisexual, transgender, queer, or other gender and sexual minorities; SOGI: sexual orientation and gender
identity
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In 2017, we collaborated with five FQHCs to retrospectively extract and analyze patient
EHR data on SOGI and screening for cervical cancer, tobacco use, and clinical depression.
The purpose was to assess for disparities in provision of preventive health screening services
to LGBTQ patients within each FQHC in order to better understand the promise and
challenges of analyzing and interpreting SOGI data collected through EHRs in healthcare
practices.
2. Materials and methods
2.1. Project background
This retrospective study was conducted subsequent to a quality improvement project called
“Transforming Primary Care for LGBT
ii
People” (
Transforming LGBT Care
), which
focused on enhancing provision of comprehensive, culturally-responsive primary care to
LGBTQ people seeking care at ten FQHCs [23]. These ten FQHCS, which were identified
through a competitive application process, were located in rural and urban areas of nine
geographically dispersed US states, and served 441,387 unique patients at 123 clinical sites
in 2016. More detail on the quality improvement project has been published previously [23].
Designed and organized by the National Association of Community Health Centers,
Washington, D.C., the Weitzman Institute, Middletown, CT, and Fenway Health, Boston,
MA, with direct assistance and funding by the Centers for Disease Control and Prevention,
Transforming LGBT Care
included a year-long intervention from March 2016 to March
2017 focused on: making care environments more culturally affirming, implementing SOGI
data collection and reporting, and increasing targeted STD and HIV screening of LGBTQ
patients. As part of the
Transforming LGBT Care
intervention, FQHCs received training
and technical assistance to improve SOGI data collection and documentation. FQHCs also
worked directly with their EHR vendors to make necessary modifications to accommodate
SOGI data fields. In addition, FQHC clinicians received didactic and case-based training
in primary care health topics, including cancer prevention, smoking prevention, and mental
health care for LGBTQ patients.
2.2. FQHC selection
At the completion of the
Transforming LGBT Care
quality improvement project, five of
the ten FQHCs that had participated in the project were selected to join an ancillary study
to detect potential LGBTQ disparities in preventive health services using SOGI EHR data.
To be selected for this ancillary study, an FQHC needed to have met the following criteria:
(a) participated in the
Transforming LGBT Care
intervention; (b) reported from their EHR
to the 2016 Uniform Data System (UDS), which is an annual reporting system of the
Health Resources and Services Administration (HRSA) for tracking patient demographics,
diagnoses, and services [20], and (c) received an EHR Reporter Quality Improvement Award
for Fiscal Year 2016, which is given by HRSA to FQHCs that employed EHRs to report on
all clinical quality measure data for all of their patients.
ii
LGBT was used for the project title because the federal government mostly uses LGBT in its communications and initiatives. For the
purposes of this manuscript, we use LGBTQ when referring to the population, and LGBT when referring to the project title.
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2.3. Study design
In August 2017, the five selected FQHCs retrospectively extracted three months (January
1, 2017 to March 31, 2017) of data on SOGI and routine screening for cervical cancer,
tobacco use, and clinical depression. Data were de-identified and submitted in aggregate
from each FQHC for analysis. We estimated the prevalence of screening for cervical cancer,
tobacco use, and clinical depression stratified by SOGI category, and tested for significant
differences in screening across SOGI categories within each FQHC.
The Community Health Center, Inc. Institutional Review Board approved a retrospective
data analysis of the
Transforming LGBT Care
project. The FQHCs did not receive any
financial compensation for participating.
2.4. Data Measures
2.4.1. Sexual orientation and gender identity (SOGI) measures—FQHCs
collected SOGI data and entered the data into EHRs as part of routine workflow processes.
Patients provided SOGI information during registration, during check-in with a nurse or
medical assistant, and/or during the medical visit with a primary care provider. SOGI
questions were answered by patients verbally, or were entered on paper forms, electronic
tablets, or through computer portals.
FQHCs were guided to use SOGI questions based on HRSA UDS 2017 instructions [24], as
follows:
Do you think of yourself as:
•
Lesbian, gay, or homosexual
•
Straight or heterosexual
•
Bisexual
•
Something else
•
Don’t know
•
Choose not to disclose
What is your gender identity?
•
Male
•
Female
•
Transgender male, female-to-male (FTM), trans man
•
Transgender female, male-to-female (MTF), trans woman
•
Other (genderqueer)
•
Choose not to disclose
What sex were you assigned at birth?
•
Male
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•
Female
The gender identity question has two steps: the first assesses a person’s self-reported gender
identity. The second assesses a person's assigned sex at birth. Cross-checking both data
points enables healthcare organizations to identify transgender people who currently identify
as simply male or female (rather than transgender male or transgender female). This method
has been recommended by authorities on transgender health, and is considered a best
practice [16]. For this study, the participating FQHCs were encouraged to use the two-step
method to identify and count transgender people. However, the FQHCs reported that their
EHRs did not have the functionality to use the two-step method to identify transgender
people while also reporting on screening measures. Therefore, the FQHCs submitted gender
identity data based only on the first step of the question (i.e.,
What is your current gender
identity?
).
FQHCs with large numbers of Spanish-speaking populations translated the SOGI questions
into Spanish. Patients who did not answer the SO or GI questions, or who checked “choose
not to disclose,” were grouped together in the analysis as “not disclosed/unknown.”
2.4.2. Preventive health screening measures—Preventive health screening data
were collected by clinicians as part of routine care. EHR functionality indicated the
screenings that patients were due for at their next visit. During or after patient visits,
clinicians recorded in the EHR the screenings given to the patients. The three screening
measures included in this analysis (Table 1) were defined and reported according to HRSA
UDS 2017 instructions (except as noted in Table 1) [
24].
We chose to use measures on screening for cervical cancer, tobacco use, and clinical
depression because: (a) FQHCs were already required by HRSA to report these as quality
of care performance data; (b) preventive screenings are good proxies for engagement in care
and positive health outcomes; and (c) there is consistent evidence of disparities of cervical
cancer screening, tobacco use, and depression in LGBTQ populations [6-8,10,11].
2.5. Data analysis
We calculated screening prevalence stratified by SOGI using SAS version 9.4. Chi-Square
and Fisher’s Exact tests were used to identify differences in compliance among SOGI
categories in each FQHC; statistical significance was determined at the
p
< 0.05 level.
3. Results
3.1. Health center characteristics
The participating FQHCs were located in Arizona, California, Colorado, Connecticut, and
Pennsylvania. While the race/ethnicity proportions at each of the FQHCs differed, all
FQHCs had over 50% racial/ethnic minority patients, and all but one FQHC had over 50%
of unique patients who identified their ethnicity as Hispanic/Latino (Table 2). The majority
of patients at each FQHC were on Medicaid and/or Medicare and had a household income at
or below 100% of the federal poverty level.
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Table 3 presents the SOGI distribution of all unduplicated FQHC patients with at least one
visit during the study period. The FQHCs, on average, saw 1.4% lesbian/gay patients (range:
0.8 to 1.8%), 1.0% bisexual (range: 0.7 to 1.5%) patients, 0.2% transgender male patients
(range: 0.0% to 0.4%) and .2% transgender female (range: 0.0% to 0.5%). About 32% of
patients (range: 17.6 to 46.8%) had not disclosed/unknown SO, and 27.8% (range: 5.1 to
46.7%) had not disclosed/unknown GI. The SOGI data include patients of all ages, including
the approximately 34% of patients who were under 18 years old. Because FQHCs are not
required by HRSA to ask the SOGI of patients younger than 18 years, the not disclosed/
unknown category is likely comprised primarily of patients under 18 years old.
3.2. Screening compliance across SOGI categories
3.2.1. Cervical cancer screening—In each of the five FQHCs, a lower percentage
of lesbian/gay and bisexual patients compared to straight/heterosexual patients received
cervical cancer screening (Table 4). Additionally, cervical cancer screening percentages
were lower for transgender men than for cisgender women in three FQHCs (FQHC2,
FQHC3, and FQHC4). FQHC5, however, had higher cervical cancer screening percentages
for transgender men than for cisgender women. The number of transgender men due for
cervical cancer screening in each FQHC was small, however (range: 3 to 70).
Unexpectedly, four FQHCs reported cervical cancer screening in cisgender men. Because
the FQHCs were unable to cross-check sex assigned at birth with gender identity, we could
not determine if these patients were assigned female at birth who identified as men (rather
than as transgender men) and were due for screening (assuming they retained a cervix); or,
if patients were misclassified as cisgender men, when in fact they were cisgender women or
transgender. It is also possible that these were simply data entry errors.
Three FQHCs reported cervical cancer screening in transgender women. There are no
current guidelines for cytology screening of transgender women who have undergone
genital surgeries. Although these FQHCs reported some transgender women as due for and
receiving cytology screening, we do not know if these data were a result of data entry error,
misclassification, or the clinicians’ decision to screen transgender women.
Among four of the five FQHCs, we observed a statistically significant relationship between
SO and cervical cancer screening. In all five FQHCs, we observed a statistically significant
relationship between GI and cervical cancer screening.
3.2.2. Tobacco and depression screening—We did not detect clear trends in
disparities for tobacco and depression screening for LGBTQ patients in any of the FQHCs
(Tables 4 and 5). However, in each of the five FQHCs, patients whose SOGI was not
disclosed/unknown had lower clinical depression screening percentages than patients in
all other SOGI categories. In addition, we observed statistically significant relationships
(P < 0.05) between depression screening and both SO and GI in four FQHCs; a
statistically significant relationship between SO and tobacco screening in three FQHCs, and
a statistically significant relationship between GI and tobacco screening in all five FQHCs.
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4. Discussion
This study provides an example of how healthcare organizations can extract and analyze
SOGI data from the EHR to detect possible disparities in preventive services for LGBTQ
patients. We believe this is the first publication that demonstrates such an approach.
Consistent with the research literature [9-11], our data showed that lesbian/gay patients
had lower cervical cancer screening percentages than straight/heterosexual and bisexual
patients, and that transgender men had lower screening percentages than cisgender women,
except in one of the FQHCs. Stratification of SOGI data across screening measures also
revealed statistically significant differences in distribution on at least one screening measure
for each of the FQHCs, although it does not tell us which categories accounted for those
differences. These findings suggest that using SOGI EHR data to detect screening disparities
in healthcare organizations is feasible and has value, although we did encounter some
challenges in analyzing and interpreting the data.
4.1. Challenges and limitations with EHRs and SOGI data
This study was completed as ancillary to a quality improvement project among a small
number of FQHCs; its findings, therefore, cannot be generalized to other FQHCs.
Nevertheless, the point of the study was not to perform generalizable research, but to present
an example of using SOGI data in the EHR in order to manage the population health of
LGBTQ patients within a healthcare organization. The limitations, therefore, are related to
making conclusions based on the data within each FQHC. Because the data for this study
were collected as part of standard of care, rather than as part of a research protocol, the
data reflect the current limitations and realities of capturing and analyzing data in healthcare
settings.
For example, EHR reporting functionality prevented FQHCs from using the recommended
two-step method of cross-checking gender identity with sex assigned at birth [16]. As a
result, we could not verify that all people who identified as men or as women were cisgender
and not transgender. Notably, the inability to cross-check gender identity with sex assigned
at birth made it impossible to know whether the cisgender men reported as receiving cervical
cancer screening were assigned female at birth (and had a cervix), or were misclassified.
Another limitation of the data was the relatively high percentage of patients with “not
disclosed/unknown” or otherwise missing data in several FQHCs. In order to improve
data completeness and accuracy, healthcare organizations need to improve workflows and
prioritize staff training in asking SOGI questions. Patients may also need to be educated
in why these questions are being asked and how the data can benefit public and personal
health. Further, those responsible for data cleaning will want to run monthly reports of SOGI
data to identify problem areas and look for unexpected patterns and statistical outliers. They
can also select patient charts at random and cross-check forms with data entered in the EHR.
Another limitation of the data is the small number of patients identifying as transgender.
The average percentage of transgender patients (0.4%) in these FQHCs was similar to
the percentages found in an analysis of all FQHCs in 2016, and to other US population
estimates of transgender populations [25]. Therefore, most organizations will encounter a
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similar issue of having a low number of transgender people in a table cell, which can make
it difficult to interpret the data with confidence. Healthcare organizations with small or
average LGBTQ patient populations will likely need more than three months of data before
definitively characterizing disparities in services or health outcomes. Still, quarterly data can
offer insight on potential disparities and can allow FQHCs to make adjustments in real-time
rather than waiting for annual data.
4.2. Next steps
As increasing numbers of FQHCs and other healthcare organizations implement SOGI data
collection into their EHRs, they will look for ways to apply the data to improve quality
of care and health outcomes for LGBTQ patients. The methods described in this article
could be adapted in various healthcare settings, with some caveats and improvements, such
as determining how to cross-check sex assigned at birth with gender identity categories.
Three months of data provides only a snapshot of one point in time, but can be used as
a baseline. Organizations can continue to track data each quarter to look for trends. If
disparities continue, organizations can form quality improvement teams to begin uncovering
possible reasons for the disparities. For example, they can share the data with clinical teams
to receive feedback, hold focus groups of patients, and review other care and services
measures. Once the issue(s) are identified, the organization can offer targeted training,
develop materials, or make modifications to the workflow or EHR.
The Health Information Technology (HIT) staff of healthcare organizations will also need
to ensure their EHR is capturing SOGI data according to best practices, and that the data
can be extracted in a way that enables comparisons along clinical measures. To support
their customers, EHR vendors can work on improving the flexibility of their products to
accommodate SOGI data since many EHR systems still need modifications. In addition,
EHR vendors can expand clinical decision support tools to incorporate SOGI fields. For
example, the creation of an anatomical inventory form could more accurately identify
people due for cervical cancer screening than gender identity or assigned sex at birth [21].
Finally, it is critical for healthcare organizations to access training for their clinical and
front-line staff to help build understanding and confidence around talking to patients about
routine SOGI data collection and its relationship to health outcomes and equity. Free online
training materials are available from the National LGBTQIA + Health Education Center,
www.lgbthealtheducation.org.
Funding
This work was supported by Grant 6 NU38OT000223-05-03 from the Centers for Disease Control and Prevention.
The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views
of the Centers for Disease Control and Prevention.
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Summary Points
What was already known on the topic:
•
Capturing patient data on sexual orientation and gender identity (SOGI) in the
electronic health record (EHR) can enable healthcare organizations to identify
inequities in the provision of clinical services to LGBTQ patients.
•
An increasing number of healthcare organizations are collecting SOGI data;
however, organizations may not be familiar with methods for analyzing and
interpreting the data to detect disparities.
What this study added to our knowledge:
•
This first example of using SOGI data in the EHR to detect potential
disparities in healthcare services to LGBTQ patients shows potential for
adaptation in other healthcare settings, with some caveats due to EHR
functionality.
•
Expanded EHR functionality is needed to more accurately identify services
for transgender patients.
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Table 1
Definitions of preventive health screening measures
Measure Definition
Cervical Cancer Screening Percentage of unduplicated patients 23–64 years of age with at least one medical visit during the measurement period who were due for cervical cytology based on
anatomy (a cervix)
+
and who received cervical cytology one or more times during the measurement period (or two years prior to the measurement period for patients who
are at least 21 years old at the time of the test)
++
.
Screening for Clinical
Depression and Follow-Up
Plan
Percentage of unduplicated patients age 12 years and older with at least one eligible medical visit during the measurement period who were screened for clinical
depression on the date of the visit using an age-appropriate standardized depression screening tool,
and
, if screened positive, who had a follow-up plan documented on the
date of the positive screen.
Tobacco Use: Screening
and Cessation Intervention
Percentage of unduplicated patients aged 18 years and older with at least one medical visit during the measurement period who were screened for tobacco use one or more
times within 24 months before the end of the measurement period
and
who received cessation counseling intervention if defined as a tobacco user.
Notes :
+
In the Reporting Instructions for 2017 Health Center Data, cervical cancer screening is written as “percentage of women 23–64 years of age.”
++
Measurement period was January 1, 2017 to March 31, 2017.
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Table 2
Sociodemographic characteristics of the total unique patient populations served by the five participating federally qualified health centers (FQHCs) in
2017
+
FQHC 1 FQHC 2 FQHC 3 FQHC 4 FQHC 5
Total patient population
15,548 96,204 101,536 25,003 53,842
Race and ethnicity (% of known)
Racial and/or ethnic minority 86.1 74.6 77.4 50.0 92.3
Non-Hispanic White 14.3 48.0 23.3 63.3 7.7
Hispanic/Latino 64.5 56.5 64.7 33.7 50.9
Black/African American
++
15.7 26.6 4.6 20.1 3.1
Asian
++
5.3 5.7 1.7 0.7 1.5
Native Hawaiian/ Other Pacific Islander
++
0.1 0.1 0.2 0.7 0.1
American Indian/Alaska Native
++
1.2 0.7 7.3 0.3 0.2
More than one race
++
2.5 0.6 0.8 0.8 Data unavailable
Insurance Status (% of total patients)
Uninsured 21.6 12.2 13.6 11.0 41.1
Medicaid/CHIP 70.4 67.3 54.3 65.7 49.8
Medicare 3.7 7.1 11.9 9.4 5.5
Dually Eligible (Medicare and Medicaid) 2.9 5.77 4.7 4.6 3.5
Other Third Party Insurance 4.3 13.5 20.2 13.9 3.6
Age (% of total patients)
<18 years old 23.3 41.1 33.9 40.9 32.3
18 - 64 years old 71.0 52.6 55.7 53.6 62.4
Age 65 and older 5.7 6.3 10.4 5.5 5.3
Income Status (% of known)
At or below 200% of federal poverty level 80.0 88.4 91.7 89.6 96.2
At or below 100% of federal poverty level 49.2 59.8 69.0 54.4 72.6
Notes :
+
Data are from the Health Resources and Services Administration (HRSA) Health Center Program 2017 grantee data: https://bphc.hrsa.gov/uds/datacenter.aspx.
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++
Includes Hispanic/Latino and Non-Hispanic/Latino
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Table 3
Sexual orientation and gender identity distribution of unduplicated FQHC patients with at least one visit during the study period (January 1, 2017 to
March 31, 2017)
Lesbian/Gay Bisexual Straight/Heterosexual Something Else Don't Know Not Disclosed/Unknown
% % % % % %
FQHC 1 (n = 7,691)
1.2 0.8 83.4 0.3 1.6 12.6
FQHC 2 (n = 38,462)
1.5 1.3 66.9 0.4 0.2 29.7
FQHC 3 (n = 46,095)
1.7 0.8 44.1 6.6 0.0 46.8
FQHC 4 (n = 8,868)
1.8 1.5 68.8 0.2 0.4 27.4
FQHC 5 (n = 26,326)
0.8 0.7 63.3 0.2 17.4 17.6
All FQHCs (n = 127,442)
1.4 1.0 59.1 2.6 3.8 32.2
Cisgender Women Cisgender Men Transgender Women Transgender Men Other Not Disclosed/Unknown
% % % % % %
FQHC 1 (n = 7,691)
58.1 36.6 0.1 0.1 0.1 5.1
FQHC 2 (n = 38,462)
42.1 27.9 0.5 0.4 0.1 29.0
FQHC 3 (n = 43,591)
34.7 18.3 0.0 0.2 0.1 46.7
FQHC 4 (n = 8,868)
45.6 27.0 0.1 0.1 0.0 27.1
FQHC 5 (n = 26,326)
47.8 30.7 0.1 0.0 15.7 5.7
All FQHCs (n = 124,938)
41.9 25.6 0.2 0.2 3.4 28.7
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Table 4
Preventive health screenings stratified by sexual orientation categories
Cervical cancer screening
Lesbian/Gay n =
502
Bisexual n =
545
Straight/
Heterosexual n
= 30,888
Something Else n
= 679
Don't Know n
=
901
Not Disclosed/
Unknown n =
6,733
X
2
(df)
P-value
FQHC 1 (n =
2,614)
Screened n (%) 10 (34%) 23 (55%) 1,343 (54%) 5 (56%) 5 (63%) 110 (46%)
0.0993
+
Not Screened n 19 19 1154 4 3 127
FQHC 2 (n =
12,966)
Screened n (%) 116 (53%) 109 (55%) 6,647 (64%) 31 (70%) 6 (40%) 1,165 (55%) 71.2441 (5)
< .0001
++
Not Screened n 102 91 3754 13 9 938
FQHC 3 (n =
13,488)
Screened n (%) 76 (62%) 119 (74%) 7,341 (75%) 407 (67%) 0 (0%) 1986 (73%) 44.2673 (4)
< .0001
++
Not Screened n 46 41 2446 200 0 826
FQHC 4 (n =
2,803)
Screened n (%) 17 (39%) 31 (57%) 1,402 (54%) 1 (20%) 6 (43%) 26 (34%) 18.8199 (5)
0.0021
++
Not Screened n 27 23 1207 4 8 51
FQHC 5 (n =
8,154)
Screened n (%) 48 (54%) 60 (67%) 3,349 (60%) 9 (64%) 460 (53%) 963 (64%) 30.1575(5)
< .0001
++
Not Screened n 41 29 2245 5 404 541
Tobacco use: screening and cessation intervention
Lesbian/Gay n =
1,560
Bisexual n =
804
Straight/
Heterosexual n
= 51,762
Something Else n
=
701
Don't Know n
=
2,912
Not Disclosed/
Unknown n =
9,395
P-value
FQHC 1 (n =
2,890)
Screened n (%) 34 (83%) 17 (81%) 2,475 (95%) 7 (88%) 8 (100%) 193 (93%)
0.0026
+
Not Screened n 7 4 130 1 0 14
FQHC 2 (n =
27,254)
Screened n (%) 422 (88%) 301 (84%) 20,287 (92%) 100 (94%) 46 (96%) 3,802 (89%) 102.7376 (5)
< .0001
++
Not Screened n 56 56 1682 6 2 494
FQHC 3 (n =
25,084)
Screened n (%) 561 (77%) 181 (70%) 14,346 (80%) 1 (100%) 1,779 (80%) 2,930 (86%) 3435.0775 (5)
< .0001
++
Not Screened n 164 79 3576 0 452 486
FQHC 4 (n =
4,360)
Screened n (%) 121 (100%) 79 (99%) 4,002 (99%) 9 (100%) 19 (100%) 82 (96%)
0.2623
+
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Not Screened n 0 1 44 0 0 3
FQHC 5 (n =
8,137)
Screened n (%) 91 (88%) 73 (96%) 5,217 (90%) 17 (89%) 604 (90%) 1,324 (89%) 6.589 (5)
0.253
++
Not Screened n 104 13 3 560 2 67
Screening for clinical depression and follow-up plan
Lesbian/Gay n =
1,032
Bisexual n =
720
Straight/
Heterosexual
n=51,283
Something Else n
= 396
Don't Know n
=
314
Not Disclosed/
Unknown n =
4,801
X
2
(df)
P-value
FQHC 1 (n =
5,212)
Screened n (%) 52 (76%) 30 (70%) 3,687 (80%) 15 (88%) 10 (83%) 325 (67%) 54.4408 (5)
< .0001
++
Not Screened
n
16 13 897 2 2 163
FQHC 2 (n =
23,501)
Screened n (%) 296 (87%) 263 (89%) 15,282 (89%) 86 (83%) 54 (87%) 3,188 (62%) 2015.3671 (5)
<. 0001
++
Not Screened
n
44 34 1929 18 8 1959
FQHC 3 (n =
24,394)
Screened n (%) 353 (85%) 174 (82%) 13,697 (86%) 1,142 (83%) 9 (100%) 4,849 (74%) 469.6665 (5)
< .0001
++
Not Screened
n
64 39 2152 242 0 1663
FQHC 4 (n =
4,333)
Screened n (%) 71 (97%) 60 (95%) 3,676 (97%) 9 (100%) 16 (84%) 334 (90%)
< .0001
+
Not Screened
n
2 3 121 0 3 38
FQHC 5 (n =
14,471)
Screened
n (%)
128 (96%) 91 (88%) 8,748 (89%) 22 (92%) 1,433 (87%) 2,403 (88%) 11.3849 (5)
0.0443
++
Not Screened
n
6 13 1094 2 212 319
+
Fisher’s Exact Test
++
Chi-square Test; FQHC = federally qualified health center
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Table 5
Preventive health screenings stratified by gender identity categories
Cervical cancer screening
Cisgender
Women n =
34,027
Cisgender Men n
=
114
Transgender
Women n =
13
Transgender Men n
= 99
Other n = 763 Not Disclosed/
Unknown n =
4,959
X
2
(df)
P-value
FQHC 1 (n =
2,822)
Screened n (%) 1,461 (54%) 1 (33%) 1 (25%) – 1 (100%) 32 (35%)
0.0009
+
Not Screened n 1262 2 3 – 0 59
FQHC 2 (n =
12,977)
Screened n (%) 6,933 (64%) – 0 (0%) 41 (59%) 7 (47%) 1,094 (55%) 57.0991 (4)
< .0001
++
Not Screened n 3979 – 4 29 8 885
FQHC 3 (n =
13,488)
Screened n (%) 8,112 (75%) 22 (54%) – 5 (28%) 4 (44%) 1,786 (70%) 50.1116 (4)
< .0001
++
Not Screened n 2771 19 – 13 5 751
FQHC 4 (n =
2,803)
Screened n (%) 1,446 (53%) 8 (62%) – 1 (33%) 0 (0%) 28 (35%)
0.005
+
Not Screened n 1260 5 – 2 1 52
FQHC 5 (n =
8,154)
Screened n (%) 4,164 (61%) 26 (46%) 2 (40%) 7 (88%) 376 (51%) 157 (58%)
< .0001
+
Not Screened n 2639 31 3 1 361 115
Tobacco use: screening and cessation intervention
Cisgender
Women n =
34,548
Cisgender Men n
=
23,000
Transgender
Women n =
173
Transgender Men n
=
176
Other n =
677
Not Disclosed/
Unknown n =
9,871
X
2
(df)
P-value
FQHC 1 (n =
2,890)
Screened n (%) 1,847 (97%) 833 (90%) 0 (0%) 1 (100%) – 53 (88%)
< .0001
+
Not Screened n 54 94 1 0 – 7
FQHC 2 (n =
27,254)
Screened n (%) 13,050 (94%) 8,022 (90%) 138 (91%) 105 (95%) 32 (97%) 3,611 (88%) 161.0507 (5)
< .0001
++
Not Screened n 903 890 14 5 1 483
FQHC 3 (n =
25,084)
Screened n (%) 11,509 (84%) 4,965 (72%) – 38 (73%) 16 (80%) 4,257 (84%) 498.8324 (4)
< .0001
++
Not Screened n 2184 1977 – 14 4 840
FQHC 4 (n =
4,360)
Screened n (%) 2,753 (99%) 1,462 (98%) 6 (100%) 7 (100%) 1 (100%) 83 (97%)
0.0245
+
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Not Screened n 21 24 0 0 0 3
FQHC 5 (n =
8,137)
Screened n (%) 1,955 (88%) 4,329 (91%) 12 (86%) 5 (83%) 552 (89%) 473 (89%) 26.6029 (5)
< .0001
++
Not Screened n 272 404 2 1 71 61
Screening for clinical depression and follow-up plan
Cisgender
Women n =
31,662
Cisgender Men n
=
25,027
Transgender
Women n =
164
Transgender Men n
=
155
Other n =
1,486
Not Disclosed/
Unknown n =
13,077
X
2
(df)
P-value
FQHC 1 (n =
5,212)
Screened n (%) 2,536 (81%) 1,451 (79%) 6 (75%) 4 (100%) 1 (50%) 121 (57%)
< .0001
+
Not Screened n 602 397 2 0 1 91
FQHC 2 (n =
23,161)
Screened n (%) 8,887 (88%) 7,009 (89%) 125 (93%) 78 (87%) 33 (89%) 3,037 (61%) 2146.8855 (5)
< .0001
++
Not Screened n 1177 835 10 12 4 1954
FQHC 3 (n =
24,394)
Screened n (%) 9,999 (87%) 5,329 (86%) 3 (100%) 37 (73%) 9 (64%) 4,847 (73%)
< .0001
+
Not Screened n 1529 863 0 14 5 1759
FQHC 4 (n =
4,333)
Screened n (%) 2,273 (97%) 1,570 (96%) 3 (100%) 1 (50%) 1 (100%) 318 (89%)
< .0001
+
Not Screened n 67 59 0 1 0 40
FQHC 5 (14,471)
Screened n (%) 4,048 (88%) 6,680 (89%) 14 (93%) 8 (100%) 1,260 (88%) 816 (90%) 4.4998 (5)
0.4799
++
Not Screened n 544 834 1 0 172 94
+
Fisher’s Exact Test
++
Chi-square Test; FQHC = federally qualified health center; – = No patients reported
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