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Cancer Medicine. 2024;13:e70097.
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https://doi.org/10.1002/cam4.70097
wileyonlinelibrary.com/journal/cam4
Received: 21 March 2024
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Revised: 10 July 2024
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Accepted: 16 July 2024
DOI: 10.1002/cam4.70097
RESEARCH ARTICLE
State LGBTQ policy environments and the cancer burden
insexual and gender minoritized communities in the
United States
Ben C. D.Weideman
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DonnaMcAlpine
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.
Division of Health Policy and
Management, School of Public Health,
University of Minnesota, Minneapolis,
Minnesota, USA
Correspondence
Ben C. D. Weideman, Division of
Health Policy and Management,
School of Public Health, University
of Minnesota, 420 Delaware St. SE,
Minneapolis, MN 55455, USA.
Email: weide144@umn.edu
Abstract
Purpose: Our objective was to assess the association between state policies re-
lated to sexual orientation and gender identity (SOGI) and cancer prevalence and
survivorship indicators in a sexual and gender minoritized (SGM) population in
the United States.
Methods: Data from the 2017–2021 Behavioral Risk Factor Surveillance System
were used to measure cancer diagnosis, physical and mental health, and sub-
stance use for SGM adult cancer survivors. A state policy Z- score, ranging from
most restrictive to most protective state policies related to SOGI, was computed
from data available from the Movement Advancement Project. Survey- weighted
logistic regression was used to test the relationship between state policies and
cancer- related outcomes for SGM people.
Results: More protective state policies were associated with lower odds of a
cancer diagnosis (adjusted odds ratio [AOR]: 0.92; 95% confidence interval [CI]:
0.87–0.97). Among SGM cancer survivors, increasing protective state policies
were associated with lower odds of poor physical health (AOR: 0.83; 95% CI:
0.74–0.94), lower odds of difficulty walking or climbing stairs (AOR: 0.90; 95%
CI: 0.80–1.00), and lower odds of difficulty concentrating or remembering (AOR:
0.87; 95% CI: 0.78–0.98). No significant associations were found between state
policies and mental health, depression, substance use, diabetes, or cardiovascular
disease among SGM cancer survivors.
Conclusion: SGM people diagnosed with cancer are more likely to live in re-
strictive policy states, and survivors in those states have worse physical health
and cognitive disability. Additional research should investigate potential causal
relationships between state policies and SGM cancer outcomes.
KEYWORDS
cancer, cancer survivorship, LGBTQ policy, sexual and gender minority
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1
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INTRODUCTION
Sexual and gender minoritized (SGM) populations,
which include people who identify as lesbian, gay, bi-
sexual, transgender, and/or queer (LGBTQ) and people
who do not identify as cisgender and/or heterosexual,1
have a significant cancer burden.2 The size of the SGM
population is growing3 and may now exceed 20 million
in the United States.4 Given population growth and the
fact that nearly all sexual orientation and gender iden-
tity (SOGI) data are under- reports due to social stigma,5
a previous approximation of 0.5–1 million SGM cancer
survivors in the United States6 is likely a substantial
underestimate. There is a critical gap in SGM cancer
research exacerbated by the absence of comprehensive
SOGI surveillance data.2,7–14 Several prominent US re-
search institutions have called for more efforts to ad-
dress these gaps.2,9,15–18
1.1
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SGM cancer disparities
SGM people face inequities across the cancer contin-
uum.19 SGM people are at a disproportionate risk for
cancer. High rates of human papillomavirus (HPV)
and human immunodeficiency virus (HIV) infections
among SGM people,20–23 associated with 15.4% of world-
wide cancers,24 lead to higher rates of anal, cervical, and
oropharyngeal, and other infection- related cancers.25–28
Higher rates of alcohol consumption, smoking, tanning,
and stress, and fewer cancer screenings additionally
contribute to the SGM cancer burden.19,29–35 SGM peo-
ple face additional challenges in cancer care, including
negative experiences with providers, a lack of cultur-
ally tailored care, and discrimination.19,26,36–40 Cancer
risks and inequities vary across subgroups. For exam-
ple, transgender people have especially high HIV infec-
tion rates, increasing risk for certain cancers (e.g., anal
cancer),22,26,28,41 and cancer specialists may be particu-
larly uncomfortable or incompetent in providing care
to transgender cancer patients due in part to a lack of
protocols.42
Studies of SGM cancer survivorship demonstrate sev-
eral disparities in mental and physical health, substance
use, and access to health care.43,44 Relative to cisgender
and heterosexual cancer survivors, SGM cancer survivors
report worse mental health (anxiety, depression, and dis-
tress) and quality of life, and higher rates of smoking and
alcohol use.44–51 They also exhibit poorer physical health,
less physical activity, and more comorbidities such as car-
diovascular disease and diabetes.44,50–52 SGM cancer survi-
vorship trends differ in scope and magnitude across SGM
subgroups,50 as well as by race/ethnicity, age, geography,
and cancer type.47,50,51 Certain SGM subgroups (e.g., non-
binary people) and cancer types remain relatively under-
studied.14,44 This evidence highlights the need for more
research to address and eliminate SGM cancer disparities.
1.2
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LGBTQ policy and health disparities
The relationship between policy and cancer in SGM
communities has received little attention in cancer-
related research. Government policies directed at the
SGM community can be protective (e.g., anti- bullying
laws) or restrictive (e.g., gender- affirming care bans).
Record numbers of restrictive anti- LGBTQ policies have
been introduced and passed in US state legislatures in
recent years,53 curtailing the prior trend of increasing
LGBTQ protections since 2010.54 US states now vary
widely in terms of protective or restrictive LGBTQ pol-
icy environments, reflecting an increasingly polarized
political climate.55
Multiple studies have linked state LGBTQ policies to
SGM disparities in mental health, substance use, and
access to care.56–62 For example, White and colleagues
found poorer physical health, higher prevalence of de-
pression, a greater number of mental disability days,
and higher rates of smoking and alcohol use among
sexually minoritized adults living in US states with re-
strictive policies compared to their counterparts living
in states with more protective policies.56 Overall, these
studies provide initial evidence that LGBTQ state policy
environments matter for the health of SGM people and
the fact that LGBTQ policies are associated with out-
comes such as smoking and alcohol use suggests that
they may also matter for cancer outcomes. However, to
our knowledge there has not been a study that explores
the relationship between state LGBTQ policies and SGM
cancer prevalence or health outcomes among SGM can-
cer survivors.
Structural discrimination, which is reflected by and
enacted through social policy, is central to the major
theoretical models that guide SGM health disparities
research. The prevailing framework, minority stress the-
ory, posits that stigma and discrimination (both struc-
tural and interpersonal) elevate chronic and acute levels
of stress in marginalized groups, which negatively af-
fects health.63,64 Minority stress has been shown to have
biological effects in SGM individuals that may impact
the incidence and experience of cancer.65 In concert,
fundamental cause theory argues that structural stigma-
tizing mechanisms regulate access to resources (social,
psychological, economic, health system, etc.) that dis-
advantage SGM individuals and cause health inequities
to emerge and persist.66,67 While the consensus in SGM
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WEIDEMAN and McALPINE
health disparities research is that structural discrimi-
nation matters, most SGM cancer studies have focused
on individual- level variables (e.g., health behaviors)11,44
when those variables alone may be insufficient for ex-
plaining the full scope of SGM cancer inequities. Socio-
structural approaches to the problem of cancer in SGM
communities may also yield information about new
strategies for intervention.
State policy environments are complex composi-
tions of LGBTQ laws across social domains (health care,
family, workplace, etc.) that vary in enforcement and
change over time. The mechanisms by which LGBTQ
policies may shape SGM cancer outcomes are also com-
plex and varied: increasing minority stress, limiting so-
cial support, and financial toxicity to name a few. This
study aims to further this nascent area of research by
quantifying the relationship between state- level policy
environments on average and SGM cancer burden in
terms of (1) cancer prevalence and (2) cancer survivor-
ship outcomes. We hypothesize that states with more
protective policy environments will produce better SGM
cancer outcomes.
2
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METHODS
2.1
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Analytic sample
The study was exempt from approval by the University
of Minnesota's Institutional Review Board due to being
a secondary analysis of publicly available data. We com-
bined 2017–2021 Behavioral Risk Factor Surveillance
System (BRFSS) data from states that elected to include
the optional SOGI module to collect data regarding SOGI.
This resulted in data from 42 US states.
BRFSS partners with state health departments to ad-
minister over 400,000 telephone surveys annually, collect-
ing data on health- related risk factors, preventative service
use, and chronic conditions in a non- institutionalized
adult population. We selected respondents who indicated
an SGM status in BRFSS SOGI measures, including those
with non- heterosexual sexual orientation (lesbian or gay,
bisexual, or something else), and/or a non- cisgender gen-
der modality (transgender, male- to- female; transgender,
female- to- male; or transgender, gender nonconforming).
Listwise deletion was used to handle missing data for can-
cer diagnosis and control variables, each of which had
less than 1.6% missing data and low potential for bias.
The exception to this approach was income, which had a
substantially higher rate of missing responses (14.9%) that
was included as a category in analyses following previous
studies.50,52 The final analytic sample included 56,309
SGM respondents.
2.2
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Dependent variables
To estimate cancer prevalence, we used yes versus no
responses to two questions: (1) “Have you ever been
told that you have skin cancer?” and (2) “Have you ever
been told that you have cancer, other than skin cancer?”
Respondents answering “yes” to either question were con-
sidered cancer survivors.
Outcomes for cancer survivors consisted of BRFSS
measures of physical and mental health and substance
use. Poor mental health and poor physical health were
measured by asking respondents to report the number of
days in the last 30 days where, respectively, their mental
and physical health was not good. BRFSS computes binary
variables, which this study used, such that ≥14 days was
considered to be poor mental or physical health. To cap-
ture physical and mental conditions, we used yes versus
no responses to the question “have you ever been told you
have (condition)?” Depression, diabetes, and cardiovascu-
lar disease were included because those conditions affect
cancer survival and treatment and have been evaluated in
previous studies of SGM cancer survivorship.51,52 Three
questions about heart attack, coronary heart disease, and
stroke were combined for the measure of cardiovascular
disease following a previous study.52
Since cancer survivors are at increased risk for long
term functional limitations,68 we included a BRFSS item
assessing physical disability as having serious difficulty
walking or climbing up stairs (yes or no) and an item
assessing cognitive disability as having serious difficulty
concentrating, remembering, or making decisions because
of a physical, mental, or emotional condition (yes or no).
Lastly, since avoiding substance use is strongly recom-
mended for cancer survivors,69 we included measures for
current smoking and heavy episodic alcohol use. Current
smoking was defined as smoking cigarettes every day or
some days during the week. Heavy episodic alcohol drink-
ing was defined as ≥4 drinks for people assigned female at
birth and ≥5 drinks for people assigned male at birth on at
least one occasion within the past 30 days.
2.3
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Independent Variable
The main independent variable in this study, the state pol-
icy Z- score, was derived from policy tallies recorded by the
Movement Advancement Project (MAP) LGBTQ advo-
cacy group.55 MAP tracks over 40 different LGBTQ related
laws and policies by state in several major categories (e.g.,
health care and criminal justice). Each anti- LGBTQ law
detracts one point from the tally, and each pro- LGBTQ
law increases the tally by one point. When a policy does
not impact the entire state, fractions of a point are applied.
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Since MAP keeps a real- time tally of these policies,
web archives were used to determine the first policy tally
update in January of the corresponding BRFSS year from
2017 to 2021. These tallies were averaged across all 5 years
and standardized by converting to a Z- score to address
differing denominators by year and to help with inter-
pretability (TableS1). A state policy Z- score of 1 means a
state's averaged MAP policy tally is one standard deviation
above the mean for all states, and the positive direction
indicates a more protective SGM policy environment than
the US average. In bivariate analysis states were character-
ized as restrictive if they scored below the national mean
on the policy tally and protective if they scored at or above
the national mean. Figure1 shows the national distribu-
tion of state policy Z- scores.
2.4
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Control variables
We controlled for sociodemographic characteristics
known to be associated with cancer and standardly in-
cluded in SGM cancer research (race/ethnicity, age, sex,
educational attainment, household income, employment
status, sexual orientation, and gender modality).70 Race/
ethnicity are social constructs, not biological facts, and
the BRFSS categories used in these analyses (American
Indian/Alaskan Native, non- Hispanic; Asian, non-
Hispanic; Black, non- Hispanic; Hispanic; white, non-
Hispanic) were considered measurable proxies for the
impact of systemic and interpersonal racism on health
outcomes.71 We also controlled for poor access to health
care because it is associated with cancer diagnosis and
quality of life.72,73 We used three dichotomous (yes/no)
measures: (1) not having health insurance, (2) avoiding
medical care because of costs, and (3) not having a rou-
tine checkup in the past year. Lastly, for analyses of can-
cer survivors, we additionally controlled for cancer types
using the two available dichotomous (yes/no) measures:
(1) ever told that you have skin cancer? and (2) ever told
you had any other types of cancer?
2.5
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Statistical analyses
Analyses were conducted using R statistical software ver-
sion 4.2.2. We included weights that BRFSS provides to
FIGURE National distribution of state policy Z- Scores for years 2017–2021. AK, Alaska; HI, Hawaii.
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adjust for the probability of selection and non- response.
Standard errors were adjusted according to CDC guid-
ance.74 Because the data were pooled over years, we
followed CDC guidance and divided the weights by the
proportion of respondents in each year to arrive at an
annualized weight.74 We utilized the survey statistical
package to handle complex survey design and perform
weighted analysis for our pooled data.75 The study sam-
ple was characterized using descriptive statistics and
bivariate differences in policy were assessed through
chi- square tests. Multivariate analyses were performed
through logistic regression for each of the dichotomous
dependent variables. We computed variance inflation
factors for all model predictors, which were all 1.5 or
less, indicating minimal risk of multicollinearity.76 An
alpha of 0.05 was set for all statistical tests to determine
significance.
3
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RESULTS
Table1 displays the sample characteristics stratified by re-
strictive versus protective state policy Z- scores. The sam-
ple was predominantly white, and individuals assigned
female at birth comprised a majority. There was an even
distribution of ages in the sample. About 9 percent re-
ported being transgender, and almost 50 percent reported
being bisexual.
SGM people in protective states had higher levels of
income (p < 0.001), higher levels of education (p < 0.001),
higher rates of employment (p < 0.05), and a higher per-
centage of respondents in those states were Asian or
Hispanic (p < 0.001). Restrictive states had a higher pro-
portion of transgender respondents (p < 0.05) and a higher
proportion of Black, and American Indian or Alaska
Native respondents (p < 0.05). Restrictive states also had
a higher proportion of SGM people who were uninsured
(p < 0.001), people who avoided seeing a doctor due to cost
(p < 0.001), and people who did not get a routine checkup
in the past year (p < 0.05). A greater proportion of SGM
individuals living in restrictive states had been previously
diagnosed with cancer at the time of the survey (8.9% vs.
7.7%, p < 0.01).
Logistic regressions were computed to determine
whether the state policy environment was associated
with likelihood of a cancer diagnosis in the total analytic
sample and outcomes among a subset of cancer survivors
(Table2). After controlling for all sociodemographic vari-
ables, SGM individuals diagnosed with cancer had signifi-
cantly lower odds of living in states with more protective
policies (adjusted odds ratio [AOR]: 0.92; 95% confidence
interval [CI]: 0.87–0.97). In these analyses, the continu-
ous variable for policy climate was a Z- score ranging from
−1.28 to 1.79 (with higher scores indicating more protec-
tive policy). Therefore, for example, a one- unit increase
in the state policy Z- score was associated with 8 percent
lower odds of an SGM individual being diagnosed with
cancer. An increase in state policy Z- score from the most
restrictive state (−1.28) to most protective state (1.79)
was associated with 25.5 percent lower odds of a cancer
diagnosis.
After adjusting for previously described confounders,
SGM cancer survivors had significantly lower odds of
living in states with protective policies if they had poor
physical health (AOR: 0.83; 95% CI: 0.74–0.94), diffi-
culty concentrating or remembering (AOR: 0.87; 95% CI:
0.78–0.98), and/or serious difficulty walking or climbing
stairs (AOR: 0.90; 95% CI: 0.80–1.00). An increase in state
policy Z- score from most restrictive state (−1.28) to most
protective state (1.79) thus reflected 43.6 percent lower
odds of poor physical health, 34.8 percent lower odds of
difficulty concentrating or remembering, and 27.6 per-
cent lower odds of serious difficulty walking, or climbing
stairs. SGM cancer survivors with cardiovascular disease
showed a trend (p = 0.071) toward lower odds of living in
a state with protective policies (AOR: 0.89; 95% CI: 0.78–
1.01). We did not find sufficient evidence of an association
between state policy and poor mental health, depression,
smoking, heavy episodic alcohol use, diabetes, or cardio-
vascular disease.
4
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DISCUSSION
Our findings demonstrate that SGM people with a cancer
diagnosis are significantly more likely to live in states with
restrictive LGBTQ policies after adjusting for sociodemo-
graphic characteristics and health access. SGM cancer sur-
vivors with poor physical health, and physical and cognitive
disabilities are also significantly more likely to live in restric-
tive policy states. We estimated sizeable decreases in the
odds of these outcomes given a one- unit increase in protec-
tive state LGBTQ policy: cancer diagnosis (8%), poor physical
health (17%), difficulty concentrating or remembering (13%),
and serious difficulty walking or climbing stairs (10%). A pol-
icy change from the most restrictive state (Z- score-1.28) to
most protective state (Z- score 1.79) corresponded to substan-
tial decreases in the odds of cancer diagnosis (25.5%), poor
physical health (43.6%), cognitive difficulty (34.8%), and dif-
ficulty with walking or stairs (27.6%). These results are the
first evidence of an association between LGBTQ state polices
and SGM cancer prevalence and survivorship outcomes and
thus have significant implications for SGM cancer control
and future SGM cancer research.
Little is known about how SGM cancer prevalence varies
across the United States. Most cancer registries, including
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TABLE Characteristics of sexual and gender and minoritized population by state policies.
Sample characteristics Total sample (N = 56,309), n (%)
Restrictive states (Z- score <0)
(N = 26,305), n (%)
Protective states (Z- Score ≥0)
(N = 30,004), n (%) p- value
Gender modality 0.015*
Transgender, male- to- female 1652 (3.0) 846 (3.1) 806 (2.8)
Transgender, female- to- male 1794 (3.4) 1064 (3.9) 730 (2.8)
Transgender, gender nonconforming 1206 (2.4) 571 (2.2) 635 (2.6)
Cisgender 51,657 (91.2) 23,824 (90.7) 27,833 (91.8)
Sex (assigned at birth) 0.088
Male 24,178 (42.1) 11,117 (41.4) 13,061 (42.9)
Female 32,131 (57.9) 15,188 (58.6) 16,943 (57.1)
Sexual orientation <0.001*
Gay or lesbian 18,106 (29.5) 7855 (28.5) 10,251 (30.7)
Bisexual 24,306 (46.9) 11,527 (47.3) 12,779 (46.4)
Straight 2531 (4.1) 1505 (5.0) 1026 (3.1)
Something else 11,366 (19.5) 5418 (19.2) 5948 (19.8)
Race <0.001*
American Indian/Alaska Native 1048 (1.2) 658 (1.5) 390 (0.9)
Asian 1602 (4.8) 351 (2.7) 1251 (7.2)
Black 4277 (12.1) 2538 (14.6) 1739 (9.2)
Hispanic 6171 (18.7) 2506 (17.5) 3665 (20.1)
White 40,028 (60.0) 19,072 (60.8) 20,956 (59.0)
Something else 3183 (3.2) 1180 (2.9) 2003 (3.6)
Age 0.010*
18–24 9218 (27.3) 4594 (28.3) 4624 (26.1)
25–34 11,496 (25.5) 5448 (24.9) 6048 (26.3)
35–44 8078 (14.5) 3712 (14.4) 4366 (14.6)
45–54 7146 (11.1) 3143 (11.4) 4003 (10.8)
55–64 8629 (10.8) 3780 (10.3) 4849 (11.4)
65 or older 11,742 (10.7) 5628 (10.7) 6114 (10.7)
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Sample characteristics Total sample (N = 56,309), n (%)
Restrictive states (Z- score <0)
(N = 26,305), n (%)
Protective states (Z- Score ≥0)
(N = 30,004), n (%) p- value
Education level <0.001*
Did not graduate high school 4131 (13.2) 2159 (14.1) 1972 (12.2)
Graduated high school 14,142 (27.5) 7259 (29.5) 6883 (25.2)
Attended college/technical school 16,302 (34.0) 8092 (34.9) 8210 (33.1)
Graduated college/technical school 21,734 (25.3) 8795 (21.6) 12,939 (29.6)
Income <0.001*
Less than $10,000 2796 (5.5) 1444 (5.8) 1352 (5.1)
$10,000–$14,999 2631 (4.8) 1349 (5.2) 1282 (4.3)
$15,000–$19,999 3969 (7.2) 2048 (7.7) 1921 (6.6)
$20,000–$24,999 4706 (8.8) 2417 (9.9) 2289 (7.6)
$25,000–$34,999 5881 (10.3) 2993 (11.1) 2888 (9.5)
$35,000–$49,999 6772 (10.6) 3368 (11.4) 3404 (9.7)
$50,000–$74,999 7268 (11.7) 3363 (11.8) 3905 (11.5)
$75,000 or more 13,921 (23.8) 5348 (19.8) 8573 (28.4)
Don't know/refused/missing 8365 (17.4) 3975 (17.3) 4390 (17.4)
Employment status 0.022*
Unemployed 4226 (9.1) 1852 (9.8) 2374 (8.6)
Healthcare access measures
<0.001*
<0.001*
0.025*
No health insurance 5902 (14.9) 3516 (18.9) 2386 (10.4)
Did not see medical doctor because of cost 9397 (19.9) 5209 (23.4) 4188 (16.0)
No routine checkup within past year 14,165 (28.9) 6818 (29.8) 7347 (27.9)
Cancer prevalence measures
0.002*
0.047*
0.102
Ever diagnosed with cancer 7168 (8.3) 3526 (8.9) 3827 (7.7)
Ever diagnosed with skin cancer 3627 (3.8) 1768 (4.1) 1859 (3.5)
Ever diagnosed with other type of cancer 4351 (5.4) 2041 (5.7) 2310 (5.1)
Note: Sample sizes are unweighted and percentages are weighted.
*p < 0.05.
TABLE (Continued)
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the Surveillance, Epidemiology, and End Results (SEER)
program, do not record SOGI,11,77 and oncology clinics have
been slow to operationalize SOGI collection.78 This is a
major barrier to assessing SGM cancer frequency, distribu-
tion, and survival across SGM subpopulations and by spe-
cific cancer types nationally. Population health surveys like
BRFSS are useful but limited in assessing important clini-
cal factors such as specific cancer diagnosis, stage, and time
since diagnosis, which are necessary information to test if
LGBTQ policies have a causal relationship with the SGM
cancer burden. Future studies should aim to make this con-
tribution, given our evidence that SGM people diagnosed
with cancer are more likely to live in restrictive policy states.
Our results indicate that SGM cancer survivors with
the most complex medical needs are also more likely to
live in restrictive policy states. Regular physical activity is
recommended for cancer survivors,69 but those with poor
physical health and physical disability, more common in
restrictive states, are likely less able to engage in it. The
same is true for survivors experiencing cognitive difficulty
or impairment, which is a common consequence of can-
cer treatments, particularly chemotherapy.79 Poor physical
health and cognitive impairment can also limit treatment
options, decreasing cancer survival. We did not find signif-
icant variation in survivors' mental health or substance use
across policy environments, despite these being key SGM
health disparities linked to LGBTQ policy in non- cancer
studies.56–60 Further studies should clarify this discrep-
ancy. Neither did we detect significant variation by policy
in diabetes or cardiovascular disease, although these are
important conditions to manage for cancer survivors that
disproportionately affect SGM people.52,80–83 It remains un-
clear why SGM survivors in restrictive states have worse
physical health and cognitive disability. Several factors
could be behind it: variations in the distribution of cancer
types, severity, or length of survivorship, differences in the
quality of treatment received, disparities in social support
and caregiving, or some unconsidered reason. The mecha-
nisms by which LGBTQ policy could explain these differ-
ences should be investigated. It is especially troubling that
SGM cancer survivors are more likely to live in restrictive
states, and with worse health, given that clinicians and
providers are often ill- equipped to provide culturally tai-
lored care to SGM people.37,38,40,42 Protective state LGBTQ
policies are important for ensuring access to high- quality
healthcare, training an affirming health care workforce,
and reducing stigma for SGM people with cancer.84
It is the mandate of state and federal policymakers to
improve the collection of SOGI as standard demograph-
ics in cancer registries, oncology clinics, and populations
health surveillance, following current recommenda-
tions,13 to assist researchers in understanding how to al-
leviate the SGM cancer burden. Such data are necessary
to explore the mechanisms by which LGBTQ policies may
Outcome variables
Adjusted odds
ratios (95% CI)
Percent change in odds by
policy score (1.28 to 1.79)
from most restrictive to most
protective state (95% CI)
Total analytic sample (N = 56,309)
Cancer diagnosis 0.92 (0.87, 0.97)*−25.5 (−34.8, −8.9)*
SGM cancer survivors (N = 7168)
Poor mental health for ≥14 days 0.96 (0.85, 1.08) −11.8 (−39.3, 26.7)
Depression 0.98 (0.89, 1.09) −6.0 (−30.1, 30.2)
Current smoking 0.94 (0.83, 1.06) −17.3 (−43.6, 19.6)
Heavy episodic alcohol use 1.03 (0.89, 1.20) 9.5 (−30.1, 75.0)
Difficulty concentrating or
remembering
0.87 (0.78, 0.98)*−34.8 (−53.4, −6.0)*
Poor physical health for ≥14 days 0.83 (0.74, 0.94)*−43.6 (−60.3, −17.3)*
Diabetes 0.94 (0.83, 1.06) −17.3 (−43.6, 19.6)
Cardiovascular disease 0.89 (0.78, 1.01) −30.1 (−53.4, 3.1)
Serious difficulty walking or
climbing stairs
0.90 (0.80, 1.00)*−27.6 (−49.6, 0)*
Note: Odds ratio is the odds of experiencing the outcome associated with a one- unit change in the state
policy Z- score.
Abbreviations: CI, Confidence Interval; LGBTQ, Lesbian, gay, bisexual, transgender, queer; SGM, sexual
and gender minoritized.
*p < 0.05.
TABLE Adjusted logistic regression
of likelihood of cancer diagnosis and
cancer survivorship outcomes predicted
by more protective state LGBTQ policy
environments.
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WEIDEMAN and McALPINE
shape or contribute to the SGM cancer burden, which is
warranted by the results presented herein. Policy inter-
ventions have been effective in reducing SGM health dis-
parities in other domains,85 and may be effective levers to
advance SGM cancer equity.
Taken together, the results of this study compel further
structural analyses in the context of SGM cancer control.
Prior studies have largely focused on individual behaviors
(e.g., smoking) to explain cancer burden in SGM com-
munities. By neglecting structural discrimination against
SGM communities in our research questions and meth-
ods, we may fail to address what could be a fundamental
cause of SGM cancer inequities.66,67 Previous studies have
been limited in assessing LGBTQ policy effects because
MAP is a real- time policy tracker,56,59,62,86 but this study
demonstrated novel use of web archives to assess MAP
policy tallies across years. Future studies should consider
the following:
1. Study designs should explore the associations between
specific policies, or social domains of policy, and the
SGM cancer burden. Testing interactions between state
policies and individual- level variables (e.g., health ac-
cess) could provide deeper insights into the mecha-
nisms driving SGM cancer disparities.
2. Longitudinal studies that follow SGM cancer inci-
dence and survivorship outcomes post- diagnosis will
be necessary to test causal relationships between policy
and SGM cancer outcomes. Researchers can examine
trends in state policies, whether they remain stable or
become more or less restrictive, and explore changes in
outcomes before and after the implementation of pro-
tective or restrictive policies.
3. SGM subgroups and specific cancer types may be
uniquely influenced by policy and should be consid-
ered in isolation and with comparative studies.
4. Alternative measures of structural stigma against SGM
people87 and structural intersectional approaches (e.g.,
interactions with structural racism)88–90 should be ap-
plied and developed for SGM cancer research.
4.1
|
Limitations
Our study has limitations. First, there is the potential for
other state- level factors to confound the significant as-
sociations between LGBTQ state policies and SGM can-
cer outcomes. For example, states with more restrictive
policies have higher poverty rates and tend to more rural
geographies, which may shape the allocation of state re-
sources and health services to the general state population.
Second, the optional SOGI module was only delivered by
42 states throughout the study period. Missing states were
more likely to have restrictive policy scores, meaning the
sample was biased toward including SGM people in more
protective policy states. Third, in order to power a state-
level analysis, this study aggregated all SGM people which
likely masked important subgroup differences. A fourth
limitation is that SGM individuals may be reluctant to
disclose their identities in population surveys, leading to
underreporting.91 However, this bias is unlikely to signifi-
cantly affect our outcomes of interest. Lastly, there was a
survivor bias in our interpretations, and the specific diag-
nosis, time since diagnosis, and treatment history for can-
cer survivors were unknown, which placed limitations on
our interpretation of the relationship between state poli-
cies and cancer prevalence and health among survivors.
5
|
CONCLUSION
We found that SGM people diagnosed with cancer are
more likely to live in US states with restrictive LGBTQ
polices and survivors in these states are more likely
to have poor physical health and cognitive disability.
These results are timely because record numbers of anti-
LGBTQ bills in recent years have increasingly polarized
the LGBTQ policy landscape in the United States.53,55 We
know that harmful social policies can erode the health
and well- being of SGM communities, though future stud-
ies will be needed to understand whether and how these
policies shape the SGM cancer burden. Addressing struc-
tural discrimination against SGM should become a central
focus in cancer research to elucidate how policy reforms
may enhance health outcomes. The dismantling of harm-
ful LGBTQ policies and the implementation of protective
measures at the local, state, and federal level may be es-
sential steps toward eliminating cancer disparities within
SGM communities.
AUTHOR CONTRIBUTIONS
Ben C. D. Weideman: Conceptualization (lead); data
curation (lead); formal analysis (lead); investigation
(lead); methodology (lead); project administration (lead);
software (lead); visualization (lead); writing – original
draft (lead). Donna McAlpine: Conceptualization (sup-
porting); data curation (supporting); formal analysis
(supporting); investigation (supporting); methodology
(supporting); project administration (supporting); super-
vision (lead); validation (supporting); visualization (sup-
porting); writing – review and editing (lead).
FUNDING INFORMATION
No funding was obtained to support this research.
10 of 13
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CONFLICT OF INTEREST STATEMENT
No competing financial interests exist.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are made
available by Centers for Disease Control and Prevention,
Behavioral Risk Factor Surveillance (https:// www. cdc.
gov/ brfss/ index. html) and the Movement Advancement
Project (https:// www. lgbtm ap. org/ ). These data are avail-
able in the public domain.
PRIOR PRESENTATIONS
Presented at the Science of Cancer Health Equity in
Sexual and Gender Minority Communities, New York
University, New York, NY, October 2023.
ORCID
Ben C. D. Weideman https://orcid.
org/0009-0000-6843-5578
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SUPPORTING INFORMATION
Additional supporting information can be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Weideman BCD,
McAlpine D. State LGBTQ policy environments and
the cancer burden in sexual and gender minoritized
communities in the United States. Cancer Med.
2024;13:e70097. doi:10.1002/cam4.70097
