Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465211058153
Journal of Health and Social Behavior
2022, Vol. 63(1) 2–18
© American Sociological Association 2021
DOI: 10.1177/00221465211058153
jhsb.sagepub.com
Article
Health disparities between women and men in the
United States are well documented. Women experi-
ence a higher incidence of chronic conditions,
functional limitations, depression, and disability
compared to men (Case and Paxson 2005; Leveille
et  al. 2000; Nolen-Hoeksema 2001), and rates of
maternal morbidity and mortality in the United
States have increased over the past several decades
(Creanga et al. 2014). Women also experience more
barriers to health care access (Ng et al. 2010; Rustgi,
Doty, and Collins 2009), which may exacerbate
gender gaps in health. Women are more likely to be
adversely affected by barriers to health care access,
in part, because women have greater need for health
care services to manage chronic conditions and
reproductive care (Case and Paxson 2005; Doyal
2000; Owens 2008). Unmet health care need is
associated with higher rates of morbidity and mor-
tality for women and higher infant mortality (Atrash
et al. 2006; Kent, Patel, and Varela 2012).
Availability of care describes the volume and
selection of existing services, including ability to
get care when needed, consistency and timeliness
of care, and choice of providers (Penchansky and
Thomas 1981). Women are more likely than men to
report unmet need for medical care (Long, Stockley,
and Shulman 2011) and delays in receiving medical
care (Ng et  al. 2010). Affordability barriers,
described as patient difficulty or inability to pay for
health insurance, medications, medical tests, and
other out-of-pocket health care costs (Penchansky
1058153 HSBXXX10.1177/00221465211058153Journal of Health and Social BehaviorRapp et al.
research-article2021
1Roanoke College, Salem, VA, USA
2North Carolina State University, Raleigh, NC, USA
3George Mason University, Fairfax, VA, USA
Corresponding Author:
Kristen Schorpp Rapp, Department of Sociology and
Public Health, Roanoke College, 221 College Lane,
Salem, VA 24153, USA.
Email: schorpp@roanoke.edu
State–Level Sexism and
Gender Disparities in Health
Care Access and Quality in the
United States
Kristen Schorpp Rapp1
, Vanessa V. Volpe2,
Tabitha L. Hale3, and Dominique F. Quartararo1
Abstract
In this investigation, we examined the associations between state-level structural sexism—a multi
dimensional index of gender inequities across economic, political, and cultural domains of the gender
system—and health care access and quality among women and men in the United States. We linked
administrative data gauging state-level gender gaps in pay, employment, poverty, political representation,
and policy protections to individual-level data on health care availability, affordability, and quality from the
national Consumer Survey of Health Care Access (2014–2019; N = 24,250). Results show that higher
state-level sexism is associated with greater inability to access needed health care and more barriers to
affording care for women but not for men. Furthermore, contrary to our hypothesis, women residing in
states with higher state-level sexism report better quality of care than women in states with lower levels
of sexism. These findings implicate state-level sexism in perpetuating gender disparities in health care.
Keywords
gender inequity, health care access, state policy, structural sexism, United States

Rapp et al.
3
and Thomas 1981), also disproportionately affect
women. Women are more vulnerable to experienc-
ing gaps or inadequacies in health insurance cover-
age and more often forgo medical care due to cost
(Lavelle and Smock 2012; Rustgi et al. 2009). One
important aspect of quality of medical care is
patient–provider communication, characterized by
attentive listening, answering patient questions, and
involving patients in their own care (Jesus and Silva
2016; Vowles and Thompson 2012). Some studies
find that women report higher satisfaction with pro-
vider communication than men (e.g., Elliott et al.
2012), and others show no gender difference in
patient satisfaction with provider communication
(e.g., Hall and Roter 1995). The extent to which
women face structural inequities may explain some
of the inconsistencies in prior research.
Sexism, defined as gender inequity in power and
resources that systematically privileges men and
disadvantages women (Ridgeway and Correll
2004), is a significant determinant of gender gaps in
access to health care. The majority of past research
on sexism and women’s health care implicates pro-
vider gender bias and discrimination as important
barriers to quality care (Chapman, Kaatz, and
Carnes 2013; Hamberg 2008; Marcum 2017).
However, provider bias and discrimination do not
fully explain gender gaps in the availability and
affordability of health care, which may also be
rooted in sociopolitical inequities that intersect with
the health care system, such as gender inequity in
political representation, pay, and employment. In
addition, the prevalence of provider bias and dis-
crimination within the U.S. health care system may
be influenced by sexism at the institutional level,
such as the underrepresentation of women in policy-
making and health care leadership. Literature exam-
ining racial disparities in health implicates structural
racism as a determinant of poor quality of medical
care among patients of color (Feagin and Bennefield
2014; Yearby 2018), but this rationale has not been
applied to research exploring sexism as a determi-
nant of women’s health care access and quality.
To examine the role of sexism in U.S. health care
inequities, the current investigation tested associa-
tions between state-level structural sexism and dis-
parities in health care access and quality among
women and men. Homan (2019) defines structural
sexism as gender inequity in power and resources
across institutional, interpersonal, and internalized
levels of the gender system that collectively shapes
gender disparities in health. Research has largely
examined interpersonal and internalized levels of
sexism rather than inequities institutionalized in the
systems of our society (e.g., employment, govern-
mental, legal). Although research on the health
effects of gender discrimination and other interper-
sonal interactions (e.g., intimate partner violence) is
important, a paucity of research at other levels may
prevent us from understanding structural sexism.
Because theoretical developments in health dispari-
ties research articulate the role of institutional power
structures in shaping and perpetuating health inequi-
ties (Feagin and Bennefield 2014; Krieger 2001,
2020), we examine state-level sexism as one macro-
level dimension of the larger structural sexism
system.
Based on existing studies examining state-level
gender inequities and health (e.g., Homan 2019;
Kawachi et  al. 1999; Montez, Zajacova, and
Hayward 2016), we constructed a multidimensional
index of state-level sexism that measures gender
inequities in wages and employment, policies that
discriminate against women, underrepresentation of
women in government, and restrictions on women’s
reproductive rights to investigate the role of institu-
tional power structures in shaping access to quality
health care among women and men. We examined
associations between state-level structural sexism
and (1) inability to get needed health care, (2) barri-
ers in health care availability and affordability, and
(3) quality of medical care received. We also tested
whether associations between state-level sexism and
health care access and quality differ by gender. This
investigation provides novel insights about the con-
nections between sexism at the institutional level
and individual health care experiences, which have
important applications in policymaking and health
care reform.
Background
Sexism as a Social Determinant of
Health Care Access and Quality
Sexism is a significant determinant of women’s
health (Homan 2019; Molix 2014; Moss 2002).
Most studies examining the impacts of sexism on
health focus on the impacts of interpersonal dis-
crimination and sexual harassment (Harnois and
Bastos 2018; Krieger 2001; Molix 2014). Although
these investigations provide insight into the effects
of sexism on health, studies have reframed sexism
as a multilevel construct that is expressed and rein-
forced within economic, political, and cultural insti-
tutions (Homan 2019; Krieger 2020). For example,
Risman (2004) conceptualizes gender as a social
structure, embedded in individual, interactional, and

4
Journal of Health and Social Behavior 63(1)
institutional dimensions of society. In addition,
Krieger’s (2020) ecosocial theory of health exam-
ines how legacies of systematic oppression across
institutions structure individual exposure to health
risks and limit availability of health-promoting
resources for oppressed groups.
Researchers examining the relationship between
structural sexism and health have proposed that
health care access and quality are important path-
ways through which structural inequities shape indi-
vidual health outcomes (e.g., Homan 2019; Wisdom,
Berlin, and Lapidus 2005). Indeed, the efficacy of
the health care system in providing consistent and
affordable medical care is highly contingent on eco-
nomic and political contexts, such as the extent of
income inequality, social welfare spending, and
health care legislation (Dickman, Himmelstein, and
Woolhandler 2017). However, no studies to date
have examined the relationship between structural
sexism and health care access and how this relation-
ship differs among women and men.
We argue that sexism across economic, labor
force, and political institutions acts as a fundamental
barrier to women’s health care access through multi-
ple direct and indirect pathways. Labor markets that
value the work of men over women systematically
underpay, underinsure, and underemploy women,
increasing the likelihood that women will be unable
to afford medical care (Gijsbers Van Wijk, Van Vliet,
and Kolk 1996). In addition, women’s political rep-
resentation influences health policies that shape
access to care. Women policymakers are more likely
to pursue policies that invest in health care, social
welfare, and other determinants of public health,
and the extent of women’s political representation
predicts the implementation of these policies
(Bolzendahl and Brooks 2007; Park 2017). Finally,
multiple studies document the impact of U.S.
economic and health care legislation on barriers to
women’s health care access, including the lack of
policy mandating paid family leave (Gault et  al.
2014) and policies that restrict reproductive rights
(Stevenson et al. 2016).
State-Level Sexism, Access to Health
Care, and Women’s Health
In the current study, we measure structural sexism at
the state level. State-level sexism encompasses the
gendered distribution of power and resources within
economic, labor force, political, and cultural institu-
tions among U.S. states. We posit that women’s rel-
ative lack of power within these institutions
produces barriers to health care access for women
via multiple direct and indirect mechanisms, includ-
ing cost barriers due to gender gaps in employment
and wages, availability barriers due to women’s
underrepresentation in political decision-making
regarding health care, and overall access barriers
due to cultural norms that restrict women’s repro-
ductive choice. We chose to examine sexism at the
state level because states vary widely in the extent
of gender inequity (Hess et al. 2015), demonstrating
the need to investigate how sexism at the state level
contributes to gender disparities in health care.
State-level sexism is a robust predictor of wom-
en’s morbidity and mortality, implicating gender
inequities across economic, political, and cultural
institutions as pathways leading to poorer health for
women. Recent research documents higher mortal-
ity rates for women and infants residing in U.S.
states with higher levels of economic inequity
(Pabayo et al. 2019), and studies using state-level
composite scores of economic inequity (e.g., gen-
der gaps in wages, employment, and poverty) find
higher mortality rates and poorer health for women
residing in more inequitable states (Homan 2019;
Montez et al. 2016). Within the political domain,
underrepresentation of women in state legislature is
associated with higher rates of female and infant
mortality (Homan 2017; Kawachi et al. 1999), and
the implementation of state policies designed to
promote gender equity (e.g., paid family and mater-
nal leave, gun ownership restrictions for domestic
violence offenders) predicts better health and lower
mortality rates for women (Doran et al. 2020; Lee
et al. 2020; Wisdom et al. 2005). Finally, state-level
policies regarding Medicaid eligibility and access
to reproductive care have significant implications
for the health of women (Hawkins et  al. 2020;
Johnston et al. 2018; Margerison et al. 2020).
Health care access and quality could underlie the
robust associations between state-level sexism and
women’s health; however, existing literature has not
adequately examined the relationship between state-
level sexism and health care. Because the U.S.
health care system relies heavily on private,
employer-based health insurance, systemic state-
level gender gaps in wages and labor force partici-
pation may reduce both women’s insurance
coverage and ability to afford health care. Indeed,
women are more likely to depend on their spouses
for health insurance and spend a higher share of
their income on health care than men (Patchias and
Waxman 2007), supporting the notion that gender
inequalities in pay and employment increase barri-
ers to care for women. In addition, the extent of
women’s political representation in U.S. state

Rapp et al.
5
legislatures is associated with the proposal and
implementation of state policies investing in health
care and overall welfare spending (Berkman and
O’Connor 1993; Bratton and Haynie 1999). Such
policies have unique implications for women’s
health care because state-level policy changes in
Medicaid eligibility and health care funding affect
the affordability of women’s care and the availabil-
ity of screening for breast and cervical cancers
(Daniel et al. 2018; Johnston et al. 2018).
State social and economic policies may operate
indirectly to shape health care or may signal broader
policymaking contexts that fail to address women’s
health needs. Although no studies have examined
the effect of state-mandated paid family and medical
leave on women’s health care access, the availability
of paid family leave has been shown to increase
continuity in women’s labor force participation
(Baum and Ruhm 2016), which has implications for
women’s wages and access to employment-based
health insurance. State firearm laws may also be
health-related policies that reflect the commitment
of state legislatures to protect women’s health and
safety. States with fewer laws prohibiting possession
of firearms for domestic violence offenders have
higher rates of intimate partner homicide against
women than states that have more of these laws
(Sivaraman et al. 2019). Although gun laws are not
health care policies per se, the lack of legislation
protecting women from gun violence could signal
the failure of state policymaking contexts to priori-
tize women’s health and safety.
Finally, state political contexts shape women’s
eligibility for health care coverage and access to
reproductive health care. Women residing in states
that expanded eligibility for Medicaid in response
to the Affordable Care Act reported lower rates of
uninsurance and better self-rated health compared
to women in nonexpansion states (Johnston et al.
2018; Margerison et al. 2020). Furthermore, imple-
mentation of restrictive state-level abortion policies
in several states has led to the closure of family
planning clinics that once provided a wide array of
health care services to women, including primary
care, preventive screenings, and reproductive care
(Lawrence and Ness 2017; Stevenson et al. 2016).
State-Level Sexism and Quality of
Patient–Provider Communication
State-level sexism might also influence the quality
of patient care. Patient–provider communication is
an important determinant of patient treatment out-
comes and disease management (Owens 2008;
Street et al. 2009), but the ways in which institu-
tional sexism shapes patient care have not been
studied. Existing literature that explicitly connects
institutional sexism to the quality of interpersonal
interactions examines how organizational structures
in the labor force relate to women’s experiences of
interpersonal workplace discrimination (Bobbitt-
Zeher 2011; Ridgeway and Correll 2004). Aligned
with ecosocial perspectives of the gender system,
this body of research frames sexism as embedded in
institutional structures in which systematic gender
bias shapes women’s experiences of mistreatment
and discrimination (Ridgeway and Correll 2004).
Therefore, we examine connections between state-
level sexism and the quality of women’s interac-
tions with health providers, positing that women
residing in states with high levels of institutional
sexism may also report poorer quality of communi-
cation with health care providers.
The Present Study
This study examines the relationship between state-
level structural sexism across economic, political,
and cultural domains and gender gaps in health care
access and quality. We tested the associations
between state-level sexism and multiple dimensions
of health care access and quality, including inability
to access health care, lack of health insurance, barri-
ers in the availability and affordability of care, and
the quality of patient–provider communication
among women and men who sought out health care
within the past year. We also tested for gender dif-
ferences in each of these associations. It is important
to note that race, education, income, and other
aspects of women’s lived experiences play a role in
health care. In the present study, we focus on eluci-
dating differences as a function of gender, account-
ing for these other factors statistically. This study
contributes to existing literature by drawing novel
connections between macro-level institutional pro-
cesses and individual health care experiences
among women and men in the United States.
Data and Methods
Data
State-level data were compiled from administrative
data sources (e.g., Bureau of Labor Statistics,
Current Population Survey, Guttmacher Institute) to
capture gender inequity in economic standing, polit-
ical representation, policy protections, and repro-
ductive rights. Table 1 includes a full list of measures
with corresponding administrative data sources.

6
Journal of Health and Social Behavior 63(1)
Individual-level data came from the December
2014 to January 2019 waves of the Association of
American Medical Colleges (AAMC) Consumer
Survey of Health Care Access, a repeat cross-sec-
tional, online survey of adults age 18 and older in
the United States. Surveys were conducted by an
external firm that maintains an active panel of
potential study participants. Stratified sampling was
used to collect data based on age and health insur-
ance status, with oversamples of various subpopu-
lations of interest (minority, rural, Medicaid
recipients, etc.) in particular survey waves. U.S.
census weights were available to account for non-
probability sampling procedures.
Of the 25,267 eligible participants in the AAMC
survey, 24,250 (96%) participants had complete
data for all variables in Stage 1 of the analysis
(associations of state-level sexism with inability to
access care and uninsured). The latter stages of the
analysis were restricted to the 21,329 participants
who had at least one medical care visit in the past
year and had complete data for additional health
care access and quality variables. Supplemental
analysis revealed that participants who were
excluded from the analysis were more likely to be
female, younger (ages 18–34), nonwhite, and
unmarried. Excluded participants also had lower
household income on average, were less likely to
have a college education, and were more likely to
live in a rural residential setting. We did not use
multiple imputation to impute missing data because
the source of nearly all missing data was dependent
variables, and imputed values for dependent vari-
ables are not typically included in regression analy-
ses (von Hippel 2007).
Measures
Health care access.  Inability to access health care
was measured using the following item: “Thinking
about the times you needed medical care in the last
12 months, how often were you able to get it?” Par-
ticipants who reported always or sometimes being
able to access care were considered “able to get
care,” and participants who reported never being
able to get care were considered “unable to get
care.” Sensitivity analyses were conducted with
alternative coding of inability to access care. Results
were not driven by choice of coding (see Appendix
Table A in the online version of the article).
No health insurance was measured using a sin-
gle item from the AAMC survey: “What type of
health insurance did you have the most recent time
you needed medical care?” Participants who
reported that they did not have health insurance
were considered uninsured.
Six barriers to health care access were examined
among participants who had at least one medical care
Table 1.  State-Level Data and Descriptive Statistics, 2014–2018 (N = 51; Averaged across Years).
Measure
Data Source
Mean (SD)
Range
State-level sexism index
Multiple sources
.00 (1.00)
–1.98–3.51
Earnings ratio (M:W)
Bureau of Labor Statistics
1.24 (.06)
1.13–1.44
Labor force ratio (M:W)
Bureau of Labor Statistics
1.15 (.04)
1.06–1.28
Poverty ratio (W:M)
IPUMS Current Population Survey
1.05 (.05)
.89–1.14
Proportion men in state legislature
Center for American Women in
Politics
.75 (.07)
.59–.87
No paid family/medical leave policy
National Partnership for Women &
Families
.94 (.24)
0–1
No state law restricting gun ownership
for domestic violence offenders
State Firearms Laws Database
.65 (.45)
0–1
Proportion women without abortion
access
Guttmacher Institute
.46 (.26)
0–.96
No Medicaid expansion
Kaiser Family Foundation
.28 (.45)
0–1
Proportion of white residents
U.S. Census Bureau
.77 (.13)
.25–.95
Proportion of people residing in urban
areas
U.S. Census Bureau
.74 (.15)
.39–1.00
Proportion of people in poverty
U.S. Census Bureau
.13 (3.61)
.07–.22
Gini coefficient
U.S Census Bureau
.46 (.02)
.42–.53
Note: M = men; W = women.

Rapp et al.
7
visit in the past year, including (1) inconsistency in
ability to access care, (2) delay in accessing care, (3)
limited choice in care, (4) inability to fill a prescrip-
tion due to out-of-pocket cost, (5) inability to com-
plete a medical test or treatment due to cost, and (6)
difficulty paying medical bills. For a description of
questionnaire items and coding, see Appendix Table
B in the online version of the article.
Patient–provider communication.  Three items were
used to gauge the quality of patient–provider com-
munication during the participants’ most recent med-
ical care visit. Participants were asked whether
providers (1) “explain[ed] things in a way that was
easy to understand,” (2) “answer[ed] questions” to
the participant’s satisfaction, and (3) “spen[t] enough
time” with the participant during the visit. For ques-
tionnaire items and coding, see Appendix Table B in
the online version of the article.
State-level sexism.  Aligned with other studies of
structural gender discrimination (Chen et al. 2005;
Homan 2019; Kawachi et al. 1999), state-level sex-
ism was measured using publicly available data to
gauge economic inequity, labor force inequity, polit-
ical inequity, and lack of reproductive rights. State-
level economic and labor force measures included
ratios of men’s to women’s earnings, women’s to
men’s poverty, and men’s to women’s labor force
participation. Political inequity was measured using
the proportion of state legislature seats occupied by
men. Policy-based inequity was measured based on
the absence of three state-level policies that dispro-
portionately benefit women: paid family and medi-
cal leave, Medicaid expansion, and gun ownership
restrictions for domestic violence offenders. Finally,
lack of reproductive choice was measured using the
proportion of women residing in counties without an
abortion provider. We selected these measures
because all were available annually from 2014 to
2018, with the exception of abortion access (avail-
able 2015, 2017). After using linear interpolation for
abortion access missing data, we linked state-level
measures to AAMC study participants based on year
of participation. Participants in the final wave of the
AAMC survey (January 2019) were linked to 2018
state-level measures. Consistent with Homan (2019),
a continuous index of state-level sexism was created
by standardizing state-level measures relative to the
full observation period and then summing standard-
ized scores to create a continuous index of state-level
sexism (Cronbach’s α = .70). We then divided the
index by its standard deviation so a one-unit change
in state-level sexism reflects a 1 SD difference.
Confirmatory factor analysis suggested a one-factor
structure for the state-level sexism index (see Appen-
dix C in the online version of the article). Supple-
mentary analyses also ensured that results were not
driven by any single item in the index (see Appendix
Table D in the online version of the article).
Covariates.  We adjusted for four state-level
covariates, including the percentage of white resi-
dents, percentage of people residing in an urban
area, poverty rate, and Gini coefficient (to capture
state income inequity). Individual-level covariates
included age group, race-ethnicity, marital status,
presence of a child younger than18 years old in the
household, urbanicity, income category, educational
attainment, and frequency of needing medical care
over the past year.
Analytic Methods
Descriptive statistics were computed for the state-
level sexism measures and individual-level measures
from the AAMC survey. Weighted individual-level
descriptive statistics were computed separately for
men and women, and Pearson’s χ2 and Mann-
Whitney U tests were conducted to test for gender
differences.
Logistic regression analyses were completed in
three stages. First, we tested associations of state-
level sexism with inability to access health care
and lack of health insurance. Second, we tested
associations between state-level sexism and spe-
cific barriers to health care access among partici-
pants who accessed medical care at least once over
the past year. Third, we tested associations
between state-level sexism and patient–provider
communication.
For all analyses, regressions were first run sep-
arately for women and men, then run using the
entire sample and including an interaction term to
test for gender differences. All models adjusted for
covariates listed previously. All analyses were
conducted using STATA and employed U.S. cen-
sus weights. To account for the nested nature of
the data, multilevel analysis was employed by
clustering by state.
Results
Descriptive Statistics of State-Level
Sexism
Table 1 shows descriptive statistics for the state-
level structural sexism measures averaged across
study years. The three economic measures of

8
Journal of Health and Social Behavior 63(1)
state-level sexism are ratios, with values greater
than 1 indicating gender inequity that favors men.
The mean values for all three measures are greater
than 1, meaning that at the state level, men have
higher earnings, higher labor force participation,
and lower poverty rates than women on average.
Among the political measures of state-level sexism,
representation of men in state legislature was calcu-
lated as a proportion, with .5 indicating gender
equality and higher proportions indicating more
representation of men in government relative to
women. All states had higher representation of men
in state government (range = .59–.87). In addition,
the majority of states had no paid family/medical
leave policy across the observation period (94%)
and no policy prohibiting gun ownership for people
charged with domestic violence (65%). Although
the majority of states expanded Medicaid eligibil-
ity, 28% did not expand eligibility during the study
time frame. Finally, the number of women residing
in counties without an abortion provider was mea-
sured as a proportion, with higher proportions indi-
cating lower abortion access. The percentage of
women residing in counties without an abortion
provider varied from 0% to 96%.
Descriptive Statistics of Health Care
Access and Quality among Women
and Men
Table 2 shows weighted descriptive statistics for
the AAMC survey. Women were slightly more
likely to report inability to access care compared to
men (p = .034 for gender difference). There was no
significant gender difference in being uninsured (p
= .351) or inconsistent access to care (p = .208).
However, women were more likely to report lim-
ited choice in care (p < .001), and men were more
likely to report delay in accessing care (p < .001)
and affordability barriers to care (p = .005 for
unable to pay medical bills; p < .001 for medical
tests and prescriptions too expensive). Women
were also less likely to report that providers spent
enough time with them during a recent visit (p =
.045). Women and men in the sample also differed
in most sociodemographic characteristics, includ-
ing age, race, and ethnicity. Men were also more
likely to have higher household income (p < .001),
more years of education (p < .001), be married (p <
.001), have children (p = .002), and live in an urban
residential setting (p < .001). Finally, women
reported significantly more frequent need for health
care than men (p < .001).
Relationship between State-Level Sexism
and Barriers in Access to Health Care
Table 3 shows results from logistic regression mod-
els of the association between state-level sexism,
inability to access health care, and lack of health
insurance. State-level sexism was associated with
significantly higher odds of inability to access
health care for women (odds ratio [OR] = 1.86, 95%
CI, 1.51–2.30) and marginally higher odds for men
(OR = 1.32, 95% CI, 0.98 –1.78), meaning that a 1
SD increase in state-level sexism was associated
with 86% higher odds of inability to access care
among women and 32% higher odds of inability to
access care among men. Results from pooled-sam-
ple models that included an interaction term for
state-level sexism and gender show no significant
gender differences in the associations between
state-level sexism and inability to access care.
Higher state-level sexism was also associated with
higher odds of being uninsured among both women
and men (OR = 1.58, 95% CI, 1.25–2.00 for women;
OR = 1.56, 95% CI, 1.14–2.12 for men). Pooled-
sample models with the interaction term reveal no
significant gender difference in this association.
Table 4 shows logistic regression results for the
associations between state-level sexism and avail-
ability barriers to care (inconsistent access to health
care, limited choice in health care, and delay in
accessing care) among men and women who
accessed medical care at least once during the past
year. Overall, there was no gender difference in the
associations between state-level sexism and barri-
ers in availability to care. State-level sexism was
positively associated with inconsistent access to
health care among women (OR = 1.15, 95% CI
1.02–1.29) but not among men (OR = 1.08, 95% CI
0.91–1.29). However, pooled-sample models that
include an interaction term for state-level sexism
and gender show no significant gender difference in
these associations. State-level sexism was not asso-
ciated with limited choice in care or delay in access-
ing care for women or men.
Table 5 shows logistic regression results for the
associations between state-level sexism and afford-
ability barriers to care (unable to pay medical bills,
unable to complete medical test due to cost, and
unable to full prescription due to cost) among men
and women who accessed medical care at least
once during the past year. For all three affordability
barriers, an increase in state-level sexism predicted
a significant increase in the odds of experiencing a
barrier for women but not for men. Women residing
in states higher in sexism reported 17% higher odds

Rapp et al.
9
Table 2.  Descriptive Statistics, Consumer Survey of Health Care Access (2014–2019).
Women
(n = 14,304)
Men
(n = 9,946)
Gender Difference
p Valuea
Barriers to health care access
  Unable to access care
2.12%
1.57%
.034
  No health insurance
8.14%
7.68%
.351
Availability barriersb
    Inconsistent access to care
10.54%
9.81%
.208
    Limited choice in care
10.85%
5.44%
< .001
    Delay in accessing care
23.13%
33.54%
< .001
  Affordability barriersb
    Unable to pay medical bills
36.08%
38.61%
.005
    Unable to complete medical test due to cost
29.69%
34.46%
< .001
    Unable to fill prescription due to cost
28.91%
35.39%
< .001
Patient–provider communicationb
  Provider spent time
91.04%
92.11%
.045
  Provider explained
96.45%
95.85%
.093
  Provider answered questions
93.02%
93.21%
.691
Controls
  Age group
    18–24
13.80%
7.63%
< .001
    25–34
16.66%
20.87%
    35–44
15.11%
19.81%
    45–54
18.80%
16.79%
    55–64
16.69%
14.52%
    65 +
18.94%
20.38%
  Race-ethnicity
    Non-Hispanic white
65.71%
67.54%
.017
    Non-Hispanic black
10.78%
10.53%
    Hispanic
16.07%
14.53%
    Asian
3.94%
4.54%
    Other
3.50%
2.86%
  Marital status
    Single, never married
25.69%
27.67%
< .001
    Married/cohabiting
50.20%
59.15%
    Widowed
7.32%
2.98%
    Divorced
14.84%
8.80%
    Separated
1.95%
1.40%
  Child
39.96%
42.60%
.002
  Urbanicity
    Suburban
45.46%
39.46%
< .001
    Urban
31.25%
44.32%
    Rural
23.29%
16.22%
  Educational attainment
    Less than high school
4.94%
4.45%
< .001
    High school degree or equivalent
32.59%
24.70%
    Some college
35.81%
31.15%
    College or more
26.65%
39.70%
  Income
    Under $25,000
25.71%
14.42%
< .001
    $25,000–$49,000
26.10%
18.89%
    $50,000–$74,999
19.15%
20.00%
    $75,000–99,999
10.39%
17.63%
    $100,000 +
18.64%
29.05%
  Needed care 2+ times
49.51%
41.30%
< .001
aPearson’s χ2 tests were conducted for nominal variables. Mann-Whitney U tests were conducted for ordinal variables.
bWomen’s n = 12,448; men’s n = 8,881.

10
Journal of Health and Social Behavior 63(1)
Table 3.  Associations between State-Level Sexism, Inability to Access Care, and No Health Insurance
among Women and Men; Odds Ratios, 95% Confidence Intervals (Consumer Survey of Health Care
Access, 2014–2019).
Women
(n = 14,304)
Men
(n = 9,946)
Full Sample
(N = 24,250)
Unable to access health care
State-level sexism
1.86***
(1.51–2.30)
1.32
(.98–1.78)
1.43**
(1.14–1.79)
Female
.81
(.61–1.09)
State-level sexism × female
1.26
(.94–1.69)

No health insurance
State-level sexism
1.58***
(1.25–2.00)
1.56**
(1.14–2.12)
1.50**
(1.15–1.97)
Female
.85*
(.74–.99)
State-level sexism × female
1.07
(.95–1.20)
Note: All models are logistic regressions and control for age, race-ethnicity, state Gini coefficient, state racial
composition, state population density, state poverty rate, household income, education, marital status, children in the
household, urbanicity, and frequency of needing care.
*p < .05, **p < .01, ***p < .001 (two-tailed tests).
Table 4.  Associations between State-Level Sexism and Barriers in Availability of Health Care Access
among Women and Men; Odds Ratios, 95% Confidence Intervals (Consumer Survey of Health Care
Access, 2014–2019).
Women
(n = 12,448)
Men
(n = 8,881)
Full Sample
(N = 21,329)
Inconsistent access to health care
State-level sexism
1.15*
(1.02–1.29)
1.08
(.91–1.29)
1.08
(.94–1.24)
Female
.89
(.78–1.02)
State-level sexism × female

1.05
(0.96–1.15)
Limited choice in health care
State-level sexism
1.07
(.98–1.17)
1.03
(.85–1.24)
1.11
(.96–1.28)
Female
1.72***
(1.53–1.93)
State-level sexism × female
.94
(.83–1.06)
Delay in accessing care
State-level sexism
.95
(.85–1.07)
.96
(.88–1.05)
.93
(.84–1.02)
Female
.68***
(.63–.73)
State-level sexism × female
1.05
(.98–1.12)
Note: All models are logistic regressions and control for age, race-ethnicity, state Gini coefficient, state racial
composition, state population density, state poverty rate, household income, education, marital status, children in the
household, urbanicity, and frequency of needing care.
*p < .05, ***p < .001 (two-tailed tests).

Rapp et al.
11
of being unable to pay medical bills compared to
women in states with lower sexism scores (OR =
1.17, 95% CI 1.08–1.27). In addition, women
exposed to high state-level sexism had 12% higher
odds of reporting that medical tests and prescrip-
tions were too expensive (OR = 1.12, 95% CI 1.03–
1.22). State-level sexism was not associated with
affordability barriers for men. Pooled-sample mod-
els including interaction terms between state-level
sexism and gender reveal that gender differences in
the associations between state-level sexism and
affordability barriers were significant.
Based on results from the pooled-sample interac-
tion models, we plotted gender differences in the
predicted probability of reporting each affordability
barrier by state-level sexism scores (Figure 1). The
predicted probability of inability to pay medical
bills was 49% among women who resided in states
with high sexism scores (3.5 SD above the mean),
compared to 30% among women in states with low
sexism scores (1.5 SD below the mean). In addition,
the predicted probability of women reporting expen-
sive medical tests was 40% in states high in sexism,
compared to 25% in states with low sexism scores.
A similar pattern was observed for women’s report-
ing of expensive prescriptions (38% in high-sexism
states compared to 23% in low-sexism states).
Among men, the predicted probability of reporting
affordability barriers showed little change as state-
level sexism increased, remaining within 3 percent-
age points from the lowest state-level sexism score
to the highest for each barrier. Supplemental analy-
sis of average marginal effects of the interaction
between gender and state-level sexism (see
Appendix Table E in the online version of the arti-
cle) shows that in states with low state-level sexism,
the predicted probability of reporting an affordabil-
ity barrier was significantly lower for women com-
pared to men. Furthermore, in states with above
average state-level sexism scores, the predicted
probability of reporting an affordability barrier was
significantly higher for women compared to men.
Table 5.  Associations between State-Level Sexism and Barriers in Affordability of Health Care Access
among Women and Men; Odds Ratios, 95% Confidence Intervals (Consumer Survey of Health Care
Access, 2014–2019).
Women
(n = 12,448)
Men
(n = 8,881)
Full Sample
(N = 21,329)
Unable to pay medical bills
State-level sexism
1.17***
(1.08–1.27)
1.00
(.91–1.10)
1.03
(.96–1.11)
Female
.90*
(.83–.98)
State-level sexism × female
1.14***
(1.07–1.21)

Unable to complete medical test due to cost
State-level sexism
1.12**
(1.03–1.22)
1.03
(.94–1.12)
1.03
(.95–1.11)
Female
.91
(.82–1.01)
State-level sexism × female
1.11*
(1.02–1.21)

Unable to fill prescription due to cost
State-level sexism
1.12**
(1.03–1.22)
1.02
(.95–1.09)
.98
(.92–1.05)
Female
.82***
(.74–.89)
State-level sexism × female
1.17***
(1.08–1.26)
Note: All models are logistic regressions and control for age, race-ethnicity, state Gini coefficient, state racial
composition, state population density, state poverty rate, household income, education, marital status, children in the
household, urbanicity, and frequency of needing care.
*p < .05, **p < .01, ***p < .001 (two-tailed tests).

12
Journal of Health and Social Behavior 63(1)
Relationship between State-Level
Sexism and Patient–Provider
Communication
We tested associations between state-level sexism
and patient–provider communication during the
respondent’s most recent health care visit (Table 6).
State-level sexism was not associated with quality
of patient–provider communication for men.
Among women, the odds of reporting that providers
explained things and answered questions increased
as state-level sexism increased. Among women, a 1
SD increase in state-level sexism was associated
with 19% higher odds that providers explained
things well (OR = 1.19, 95% CI 1.04–1.36) and
14% higher odds that providers answered questions
(OR = 1.14, 95% CI 1.05–1.24). However, pooled
models showed no significant gender difference in
these associations.
DISCUSSION
Summary of Findings and Connections
to Existing Research
The current study examined the associations
between state-level sexism, barriers to health care
access, and health care quality among women and
men who reported needing medical care within the
past year. This study is innovative in examining the
consequences of sexism at the structural level on
health care experiences. Aligned with recent work
investigating structural sexism as a determinant of
health (Homan 2019), we used a measure of state-
level sexism that encompassed economic, political,
and cultural domains of the gender system. We also
incorporated several measures of health care access
and quality to assess ability to access health care,
specific barriers in the availability and affordability
of care, and the quality of patient–provider
communication.
Consistent with our hypotheses, as state-level
sexism increased, the odds of being unable to get
health care increased for women. This association
was robust after adjusting for covariates, suggesting
that state-level sexism may shape ability to access
care above and beyond other individual- and state-
level determinants of access to care. Higher state-
level sexism was associated with higher odds of
being uninsured for both women and men. Higher
levels of sexism may be equally detrimental for
women’s and men’s access to health insurance
because health care policies in states with higher
state-level sexism (e.g., Medicaid eligibility) uni-
formly restrict access to health insurance. Indeed,
supplemental analysis that tested the associations
between Medicaid expansion and insurance cover-
age showed that both women and men residing in
states that did not expand Medicaid during the
study period were more likely to be uninsured.
However, restrictions in Medicaid eligibility have
been shown to disproportionately affect women’s
barriers to health care access and subsequent health
(Margerison et  al. 2020; Stimpson, Pintor, and
Wilson 2019). We may not have observed such dif-
ference because men and women reported similar
rates of being insured in the current sample.
More state-level sexism was associated with
more affordability barriers to care (unable to pay for
medical bills, medical tests, and prescriptions) for
women but not for men. Although the mechanisms
Test Due to Cost
Figure 1.  Gender Differences in the Association between State-Level Sexism and Barriers in
Affordability of Health Care Access (Consumer Survey of Health Care Access, 2014–2019).

Rapp et al.
13
linking state-level sexism to individual-level afford-
ability of care cannot be determined in our analyses,
we found that associations between state-level sex-
ism and affordability barriers were not explained by
individual sociodemographic and health-related
characteristics. These results suggest that state-level
sexism may shape women’s ability to afford health
care in ways that go beyond individual-level socio-
economic standing. Future studies should investi-
gate the ways in which state-level sexism shapes
community-level contexts that affect health care
affordability for women.
Interestingly, in plots showing gender differ-
ences in the association between state-level sexism
and barriers to health care access, it appears that
women residing in states with higher than average
state-level sexism reported more affordability barri-
ers to care compared to men, whereas women in
states with lower than average state-level sexism
reported fewer barriers than men. The finding that
women residing in states with lower sexism scores
reported fewer barriers to care compared to men
warrants further research. Perhaps states with lower
levels of sexism have more comprehensive health
care legislation that specifically targets the health
care needs of women (e.g., expanded Medicaid eli-
gibility for women of reproductive age or widely
available and affordable reproductive care), and
this legislation substantially reduces affordability
barriers to care for women but not for men. A grow-
ing literature has identified the benefits of health
care legislation for women’s health care access
(Johnston et al. 2018; Stevenson et al. 2016), but
little research has examined the impacts of similar
legislation on men’s access to care. Alternatively,
given the greater health care needs of women on
average, state-level social and economic conditions
might have a more substantial impact on access to
affordable care for women compared to men.
Further research is needed to identify strategies that
both improve women’s access to care and promote
equitable access for women and men.
Table 6.  Associations between State-Level Sexism and Patient–Provider Communication among
Women and Men; Odds Ratios, 95% Confidence Intervals (Consumer Survey of Health Care Access,
2014–2019).
Women
(n = 12,448)
Men
(n = 8,881)
Full Sample
(N = 21,329)
Provider spent time
State-level sexism
1.09
(1.00–1.18)
.91
(.79–1.03)
.95
(.85–1.07)
Female
.95
(.85–1.06)
State-level sexism × female
1.12*
(1.03–1.23)

Provider explained
State-level sexism
1.19**
(1.04–1.36)
1.02
(.84–1.25)
1.06
(.89–1.27)
Female
1.21*
(1.00–1.46)
State-level sexism × female
1.10
(.95–1.27)

Provider answered questions
State-level sexism
1.14**
(1.05–1.24)
.99
(.87–1.14)
1.02
(.90–1.14)
Female
1.09
(.93–1.27)
State-level sexism × female
1.12
(.97–1.30)
Note: All models are logistic regressions and control for age, race-ethnicity, state Gini coefficient, state racial
composition, state population density, state poverty rate, household income, education, marital status, children in the
household, urbanicity, and frequency of needing care.
*p < .05, **p < .01 (two-tailed tests).

14
Journal of Health and Social Behavior 63(1)
We found no association of state-level sexism
with barriers in the availability of care for women
and men. These results were unexpected given that
women are more likely to report unmet health care
needs and delays in receiving care than men (Long
et al. 2011; Ng et al. 2010; Rustgi et al. 2009) and
that state policies can affect the availability of care
for women (Johnston et al. 2018; Stevenson et al.
2016). Because results could be a function of over-
all measurement and/or availability of health care
in the present sample, future research should test
this relationship using various measures of health
care availability in different samples.
Surprisingly, across two of the three measures of
patient–provider communication, women who
resided in states with higher levels of sexism reported
more positive interactions with their providers com-
pared to women in states with lower levels of sex-
ism. We propose several explanations. Because some
gender stereotypes may entail benevolent sexism,
defined as seemingly innocuous and patronizing
behaviors that may appear complimentary to some
individuals (Glick and Fiske 1996), perhaps women
in states high in structural sexism do not interpret
sexist patient–provider communication as negative.
Women may also be more likely to minimize inter-
personal experiences of sexism that are more ambigu-
ous as a coping mechanism (Ruggiero and Taylor
1995, 1997), particularly in contexts where women
feel that they may not be believed or may be retaliated
against (Kaiser and Miller 2001; Sechrist and Delmar
2009). Perhaps women in states where sexism is the
structural norm utilize this coping mechanism more
frequently to preserve their psychosocial health
(Bosson, Pinel, and Vandello 2010) in the otherwise
toxic state-level environment. Conversely, they may
be putting more effort into communication with pro-
viders given the hostile structural-level environment
and, in the process of doing so, getting more positive
outcomes due to their own self-advocacy, in line with
research on confronting sexism (Ayres, Friedman, and
Leaper 2009; Good et  al. 2019). Such propositions
highlight the importance of further research that will
incorporate examination of the social psychological
aspects of gender and the gendered system, which we
were not able to do given data limitations.
Finally, provider communication with patients
may not necessarily reflect institutional and cultural
contexts where they are currently practicing medi-
cine but instead reflect communication skills devel-
oped through medical training from institutions in
other geographic areas.
Suggestions for Future Research
Although this study enhances our understanding of
the relationship between structural sexism and
health care access and quality, several limitations
should be addressed in future research. Because
data utilized in this study were observational and
cross-sectional, our results do not imply causality.
Although sampling was stratified by age and insur-
ance status and all analyses incorporated U.S. cen-
sus weights, study participants were more likely to
be insured and were less likely to be unable to get
care compared to U.S. national averages (Azam and
Moy 2016). Therefore, our estimates are likely to be
conservative and do not necessarily generalize to
the U.S. population. In addition, although we utilize
multiple measures of state-level sexism that mirror
dimensions of structural inequity explored in exist-
ing research (e.g., Homan 2019; Wisdom et  al.
2005), these measures are not exhaustive. State leg-
islation relating to reproductive rights and spending
on public assistance programs were not included in
the present study due to gaps in the availability of
data during the study years. We could not examine
the relative importance of indicators of structural
sexism given multicollinearity. Consequently, we
cannot glean which dimensions are particularly
important.
Although we adjusted for potential state-level
confounders in the relationship between state-level
sexism and barriers to care, other state-level charac-
teristics may contribute to the results we observed.
Future research is needed to identify additional
state-level economic, political, and cultural factors
that may contribute to health care disparities. In
addition, we limited our measurement of structural
sexism to the state level and did not examine
county-, neighborhood-, or household-level indica-
tors of sexism. Policies and inequalities at the state
level impact individual health and health care
(Hawkins et al. 2020; Lee et al. 2020; Montez et al.
2016), but state-level measures do not account for
within-state variation in economic, political, and
cultural contexts.
We did not analyze relationships between state-
level sexism and barriers to health care access
among transgender and nonbinary populations due
to data limitations. Growing literature documents
extensive barriers to health care access for trans and
nonbinary people due to discriminatory policies at
the federal and state levels (Bakko and Kattari
2019; Goldenberg et al. 2020). Future data collec-
tion efforts must adopt inclusive measures of

Rapp et al.
15
gender identity to further this area of research using
national samples.
Conclusion
State-level sexism appears to increase barriers to
health care access among women in the United
States by affecting women’s ability to afford medi-
cal care. These results were robust after adjusting
for individual-level sociodemographic characteris-
tics, suggesting that state-level sexism acts on envi-
ronmental or organizational contexts to directly
shape women’s access to health care. Our findings
are consistent with existing research that documents
the negative impacts of structural gender inequities
on women’s health and longevity (Homan 2019;
Montez et al. 2016; Wisdom et al. 2005). Furthermore,
in our analysis, state-level sexism appears to have
no effect on men’s access to health care. This coun-
ters some research that finds structural gender
inequity to be universally harmful for both wom-
en’s and men’s health (Homan 2019; Kawachi
et al. 1999) and instead suggests that macro-level
structural sexism is uniquely detrimental for wom-
en’s health care access. Finally, women in states
with higher levels of structural sexism reported
better quality of patient–provider communication
compared to women in states with lower levels of
sexism. Further research is needed to unpack these
associations.
These findings demonstrate the need to use a
multilevel approach to understand and act on the
social determinants of women’s health care access
and quality. The implications of these findings are
vital given that multiple states have enacted legisla-
tion to restrict women’s access to reproductive care
and weakened antipoverty programs that provide
needed assistance to millions of women and moth-
ers (Kogan et  al. 2019; McKernan and Ratcliffe
2018; Reingold and Gostin 2019). Meanwhile, the
gender gap in wages has remained stagnant in the
United States since 2004, and women continue to
be severely underrepresented in federal, state, and
local government (Homan 2017; Patton and
Fording 2020; U.S. Bureau of Labor Statistics
2019). Findings from the current investigation
implicate sexism across economic, labor force, and
political institutions as a key determinant of wom-
en’s barriers to accessing health care. As the future
of U.S. health care reform continues to be debated,
we urge lawmakers to also consider social and eco-
nomic policies as essential forms of health care
legislation.
Acknowledgments
This material is based on data provided by the Association
of American Medical Colleges (AAMC) to the Principal
Investigators (Rapp and Volpe) and their team. The views
expressed herein are those of the authors and do not neces-
sarily reflect the position or policy of the AAMC.
ORCID iD
Kristen Schorpp Rapp
 https://orcid.org/0000-0003-
4829-1612
Supplemental Material
Appendices A through E are available in the online version
of the article.
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Author Biographies
Kristen Schorpp Rapp is an assistant professor in the
Department of Sociology and Public Health at Roanoke
College. Her research examines the social determinants
of health disparities in the United States, with a particu-
lar focus on the political, economic, and cultural condi-
tions that shape health inequalities by gender and
race-ethnicity.
Vanessa V. Volpe is an assistant professor of psychology
at North Carolina State University. Her applied health psy-
chology research examines the health impacts of structural
systems of inequity and interpersonal experiences of dis-
crimination and the ways that minoritized individuals can
preserve their health in the United States. Her program of
research seeks to inform policy and practice focused on the
elimination of racial-ethnic health disparities.
Tabitha L. Hale is a graduate student of public health in
the Department of Global and Community Health at
George Mason University. Her research focuses on the
effects of endocrine disrupting compounds on the repro-
ductive health of women, specifically examining racial
disparities and impacts on vulnerable populations.
Dominique F. Quartararo is a recent graduate from the
Department of Sociology and Public Health at Roanoke
College. She is primarily interested in health policy, wom-
en’s access to reproductive care, and population health
disparities.

    Scientific Research Study Methods:

Answer: We built a multidimensional state-level sexism index—standardizing gaps in earnings, labor-force participation, poverty, women’s legislative representation, and three gender-salient policies (paid-leave, Medicaid expansion, gun restrictions for domestic-violence offenders) plus county abortion-provider access—using 2014-2018 administrative sources and scaled it so 1 unit = 1 SD . We then linked this index to repeat-cross-sectional data from the Association of American Medical Colleges’ Consumer Survey of Health Care Access (2014-2019; N = 24,250), retaining 21,329 adults who had at least one medical visit for detailed access-and-quality analyses . Employing U.S. census weights, we estimated three stages of multilevel logistic models—first for women, then men, then pooled with gender-interaction terms—that regressed (i) inability to obtain needed care or insurance, (ii) six availability/affordability barriers, and (iii) three patient-provider communication indicators on state sexism. All models clustered errors by state and adjusted for state racial composition, urbanization, poverty, and income inequality plus individual sociodemographics, household structure, residence type, and frequency of care need. This design isolates the independent association of structural sexism with health-care access and quality while testing whether those relationships differ for women and men.