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. Author manuscript; available in PMC: 2024 Mar 14.
Published in final edited form as: J Health Soc Behav. 2023 Sep 7;65(1):2–19. doi: 10.1177/00221465231194043

Structural Sexism and Preventive Health Care Use in the United States

Emily C Dore 1, Surbhi Shrivastava 1, Patricia Homan 2
PMCID: PMC10918039  NIHMSID: NIHMS1930656  PMID: 37675877

Preventive health care use can reduce the risk of disease, disability, and death (USDHHS 2022). For example, sigmoidoscopy and colonoscopy screenings are important tools in reducing deaths from colorectal cancer (Brenner, Stock, and Hoffmeister 2014), flu shots decrease the risk of serious illness if infected (Ferdinands et al. 2021), and COVID-19 vaccinations prevented an estimated 14.4 to 19.8 million deaths worldwide during the first year of use (Watson et al. 2022). “Preventive care” generally includes spending on programs for public health information, education, and counseling; immunization, early disease detection, healthy condition monitoring programs (e.g., for monitoring pregnancy, child growth and development, general health checkups); epidemiological surveillance; and emergency preparedness and disaster response (Kamal and Hudman 2020). Despite the clear health benefits preventive care offers, the majority of US adults do not receive all recommended clinical preventive services (Borsky et al. 2018). Thus, understanding and addressing the determinants of preventive care use is critical to reducing preventable deaths — which are higher in the US than all other OECD countries — and vital for improving population health (Kamal and Hudman 2020).

A growing literature points to structural sexism as an important upstream driver of population health in the United States (Homan 2019; Homan 2021; Homan 2021; Kavanagh and Graham 2019; Krieger 2020). Structural sexism refers to systematic gender inequality in power and resources in a given social context (Homan 2019). Structural sexism directs attention beyond individual sexist beliefs and behaviors to highlight the institutional arrangements in a society that privilege men and subordinate women (Homan 2019; Ridgeway and Correll 2004). Studies have shown that structural sexism in US state-level institutions is linked to a variety of negative health outcomes among women, men, and children, including: worse self-rated health, more chronic conditions, worse physical functioning, and higher infant and adult mortality rates (Homan 2019; Kavanagh, Shelley, and Stevenson 2017; Kawachi et al. 1999; Koenen, Lincoln, and Appleton 2006).

One pathway through which structural sexism may harm health is via its impact on health care. There is very little research on the topic, but one study by Rapp and colleagues (2022) found that higher state-level structural sexism is associated with decreased health care access and more barriers to affordability among women. No studies to date have examined the relationship between structural sexism and preventive care use, and how it might differ among women and men. This represents a major gap in knowledge that can hinder efforts to improve health and health equity in the US.

To address this gap, the present study uses the Behavioral Risk Factor Surveillance System (BRFSS) combined with state-level data to explore the relationship between structural sexism and nine different types of preventive health care use in gender-stratified, multilevel models. Gender performance theories and gendered power and resource allocation perspectives generate the study’s two hypotheses about the relationship between structural sexism and preventive care. In high sexism environments, gender performance theory suggests men would be less likely, and women more likely, to access needed preventive care, and gendered power and resource allocation perspective posits both men and women would be less likely to access care. Findings suggest that both theoretical perspectives contribute to our understanding of health care use among men, whereas only a gendered power and resource allocation perspective is relevant for women. Results demonstrate a pattern of universal harm, suggesting that reducing structural sexism is a promising approach to increasing preventive care use for both women and men in the US. This study provides novel empirical evidence and important theoretical insights with policy implications for preventive health care use.

BACKGROUND

The overall picture of preventive health care use in the US is concerning. Preventive care spending in the US has declined as a share of total health expenditure from 3.7% in 2000 to 2.9% in 2018 (Kamal and Hudman 2020). This declining investment in preventing and controlling risk exposure is problematic because more than a quarter of all personal health care spending in the US in 2016 was due to modifiable risk factors and preventable illnesses (Bolnick et al. 2020). Qualitative research finds that while providers know preventive services can reduce the burden of disease, most providers, including hospitals and physicians, are paid to treat rather than to prevent disease (Levine et al. 2019). Studies find that most Americans do not access the benefits of preventive health care. For example, only 8% of US adults aged 35 or older received all recommended, high-priority, appropriate clinical preventive services in 2015, and nearly 5% received none of the routine preventive screenings (Borsky et al. 2018). The present study focuses on preventive health care use because of concerns around the patterns of preventable deaths and illnesses in the US, low rates of preventive health care use, and the promise of preventive health care for improving population health.

There are a number of factors that can affect the use of preventive health care services. Preventive health care use among US adults is consistently and significantly associated with the level of health insurance coverage and having a usual place and provider (Blewett et al. 2008; DeVoe et al. 2003; Faulkner and Schauffler 1997). Other studies have found that a higher purpose in life (Kim, Strecher, and Ryff 2014) and higher life satisfaction (Kim, Kubzansky, and Smith 2015) are also associated with a greater likelihood of preventive health care use. Among adolescents, a greater proportion of those aged 16-17 years compared to younger age groups did not have a usual place for preventive care and did not receive a well-child checkup or visit a dentist in the past 12 months (Black, Nugent, and Vahratian 2016). There are racial and ethnic differences in preventive health care use as well, such as low rates of breast cancer screening among African American women and cancer screening among Asian Americans (Hou, Sealy, and Kabiru 2011; Jones, Katapodi, and Lockhart 2015).

Furthermore, preventive care use is also highly gendered. Research on US adults shows that women score higher than men on a composite measure of preventive care use (Asch et al. 2006), and men are less likely to have their blood pressure and cholesterol checked, less likely to visit the dentist, and less likely to get flu shots compared to women (Vaidya, Partha, and Karmakar 2012). Yet at the same time, research shows that when accessing health care women are less likely than men to receive the most advanced or effective treatments and more likely to experience physician bias and discrimination that can undermine their health (Borkhoff et al. 2008; Chapman, Kaatz, and Carnes 2013; Greenwood, Carnahan, and Huang 2018). Examining these patterns of gender differences is crucial, but this approach alone provides an incomplete picture because it does not capture the impact of structural factors on these outcomes. More specifically, this approach does not consider how gender inequality varies across social contexts in ways that may influence the uptake and receipt of health care services, as well as the effect on health of both men and women (Homan 2019; Schofield 2015). A structural sexism approach shifts the focus from comparisons between men and women, to within-gender comparisons across social contexts characterized by varying levels of systematic gender inequality (i.e., structural sexism). This approach illuminates how the unequal gendered distribution of valued resources and opportunities in a society may shape the health of all its members.

Structural Sexism, Health, and Health Care

A nascent body of research has linked structural sexism in US state-level institutions to a variety of negative health outcomes including: worse self-rated health, more chronic conditions, and worse physical functioning among both men and women; higher mortality risk among men; and more depressive symptoms, higher risk of disordered eating, and more unnecessary c-sections among women; and higher infant mortality rates (Beccia et al. 2022; Chen et al. 2005; Homan 2019; Kavanagh et al. 2017; Kavanagh, Shelley, and Stevenson 2018; Kawachi et al. 1999; Nagle and Samari 2021). While this growing evidence sheds light on the associations between structural sexism and population health outcomes, the role of health care remains poorly understood. Only one study has examined US state-level structural sexism and health care access among men and women, and it found that higher structural sexism is associated with more barriers to health care access for women but not men (Rapp et al. 2022). To our knowledge, no study has focused on understanding structural sexism as a determinant of preventive health care use in the United States.

Nevertheless, Rapp and colleagues’ (2022) study as well as prominent theories of gender and health point to a number of ways for structural sexism to impact preventive health care use, perhaps differentially for women and men. Particularly relevant are gender performance and gendered power and resource allocation perspectives (Courtenay 2000; Kavanagh and Graham 2019), which generate hypotheses regarding the patterns of association between structural sexism and preventive health care use among women and men. A gender performance perspective suggests that structural sexism fosters hegemonic gender norms which men and women enact through the performance of health-related behaviors including preventive health care use. A gendered power and resource allocation perspective argues that structural sexism disempowers women which sets in motion social, political, and economic processes that limit the health promoting resources available to everyone (such as health care and social program spending). Figure 1 illustrates the patterns of association predicted by the two theoretical perspectives, which we discuss in detail below.

Figure 1.

Figure 1.

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Potential Relationships between Structural Sexism and Preventive Care Use Based on Gender Theories

Structural sexism and gender performance.

A gender performance perspective links structural sexism to preventive health care use through its connection to gender relations and norms that shape men’s and women’s behavior. From this perspective, gender is an interactive accomplishment, something people do in everyday life rather than who or what people are (West and Zimmerman 1987). Individuals “do gender” when they orient their behavior toward widely accepted norms of masculinity and femininity (West and Zimmerman 1987). Moreover, gender relations manifest at macro levels such as in the economy and the state (Connell 2012). For instance, institutions such as transnational corporations can maintain gendered division of labor in the workplace. Therefore, a sexist environment at macro levels can shape processes, even if indirectly, that reinforce “doing gender” at meso and micro levels. Additionally, research and theory suggest that the subordination of women in patriarchal societies is linked to stronger dominance hierarchies among men, resulting in increased competition and amplifying the importance of conforming to hegemonic masculine norms (Connell 1987, 2012; Wilkinson 2005).

Masculinities and health theory argues that men demonstrate their conformity to hegemonic masculine ideals of strength, bravery, stoicism, self-reliance, control, dominance and sexual prowess/virility through risk-taking and unhealthy behaviors in order to preserve their status and patriarchal privilege (Cheng 1999; Connell 1987, 2005, 2012; Connell and Messerschmidt 2005; Gray et al. 2002; Kavanagh and Graham 2019; Mahalik, Burns, and Syzdek 2007). Indeed, studies have shown that adherence to these masculine norms is linked to greater substance use, violence, sexual risk behaviors, health care avoidance, and a variety of other negative health related beliefs and behaviors (Courtenay 2000; Fleming and Agnew-Brune 2015; Mahalik et al. 2007; Seidler et al. 2016). In terms of preventive care, there is evidence that masculinity norms (particularly avoidance of femininity, risk-taking, and self-reliance) are inversely associated with colorectal cancer screening (Christy, Mosher, and Rawl 2014).

Norms of femininity are largely framed in opposition to masculinity. Emphasized femininity is the complement to hegemonic masculinity and involves orienting one’s behavior toward the interests and desires of men (Connell 1987). Such femininity typically entails gentleness, nurturing, passivity, beauty, youth, fragility, and a domestic/family orientation. While there is much less research on femininity and health, positive health beliefs and behaviors including health care use, are typically considered feminine-typed behavior (Courtenay 2000; Fleming and Agnew-Brune 2015). Additionally, the feminine ideals of youth, beauty, and nurturing may lead to increased contact with health care providers to obtain services for women themselves and their children (Daly and Groes 2017). In sum, to the extent that structural sexism increases pressure to conform to the hegemonic ideals of masculinity and emphasized femininity (Connell 1987; 2012), we would expect greater structural sexism to be associated with higher levels of preventive health care use among women, and lower levels among men (see Figure 1, Panel A).

Structural sexism and gendered power and resource allocation.

A gendered power perspective links structural sexism to preventive health care use through the impact of women’s (dis)empowerment on social, political, and economic processes that allocate resources relevant for population health. At the state-level, more liberal policies expand economic regulations, protect marginalized groups, and are associated with longer life expectancies (Montez et al. 2020). Such policies are more likely to support women and be supported by women in power (Kavanagh and Graham 2019). Evidence from around the world also show that when women are empowered socially and politically, there are greater investments in education, health care, public health, and other social programs that tend to improve health for the entire population (Boehmer and Williamson 1996; Bolzendahl and Brooks 2007; Little, Dunn, and Deen 2001; Miller 2008; Young 2001). Differences in state-level policies can also affect health care use through barriers to accessing services such as lack of insurance options like expansion of Medicaid, unavailability of flexible appointments, inadequate transportation, poor social support for childcare, among other factors (National Academies of Sciences Engineering and Medicine 2018). This leads to the hypothesis that states with higher levels of structural sexism may offer less generous safety-net policies and allocate fewer resources to health care, leading to lower levels of preventive health care use among both men and women, as illustrated in Figure 1, Panel B. This pattern would be consistent with the findings of Homan (2019) that state-level structural sexism exposure was universally harmful for health, negatively impacting outcomes for both men and women.

Because structural sexism involves material and social advantages for men, it is also theoretically possible that men could leverage their greater personal resources to achieve good health even in the absence of health promoting collective conditions, and we might thereby observe a positive association between greater sexism and preventive care among men. However, this is unlikely based on masculinity and health research as well as previous findings on structural sexism and men’s health which have found harmful effects of state-level structural sexism on men’s health (Homan 2019; Kavanagh et al. 2018). Thus far, no empirical studies have identified a health benefit of state-level sexism exposure for men. For this reason, we do not picture or further explicate this additional possible, but unlikely, scenario.

The Present Study

In this study, we examine the association between state-level structural sexism and preventive health care use among men and women in the US. We construct a measure of structural sexism based on Homan (2019) and estimate gender-stratified multi-level models that capture state-level context and individual-level demographics. We ask: (1) Is structural sexism associated with preventive health care use among women and men? (2) If so, are the patterns more consistent with theories of gender norms and health behaviors, or gendered power and resource allocation? It is important to note that both theoretical perspectives predict a negative relationship between structural sexism and preventive care use for men. Therefore, we can only confirm/deny that one or both of these theories applies to this case. But the theoretical perspectives generate conflicting hypotheses regarding the direction of the relationship among women and we can therefore determine which is most supported by the evidence.

DATA AND METHODS

We build on previous work on structural sexism (Homan 2019; Homan and Burdette 2021) and examine the relationship between individual-level health data with a measure of structural sexism at the state-level, as well as other relevant state-level and individual-level covariates. For individual-level data, we used the Behavioral Risk Factor Surveillance System (BRFSS) from 2018. For state-level environments, we included controls from the US Census Bureau, as well as a measure of structural sexism constructed from various administrative sources including the US Census Bureau, the Bureau of Labor Statistics, the Center for American Women and Politics, and Guttmacher Institute. We linked these state-level measures and individual-level data from BRFSS to examine the association between structural sexism and gendered use of preventive health care.

Sample

The sample is composed of men and women from the BRFSS national survey in 2018. BRFSS is the largest continuously conducted health survey in the world and collects annual, cross-sectional data from respondents in all 50 states about health behaviors and conditions, as well as demographics. Some questions vary each year, thus we used 2018 data because it was the most recent year that had the most applicable and complete data on preventive health care use. It was also important to avoid 2020 data due to the impact of COVID-19 on health care use. Our analytic sample consisted of 425,454 individuals, of which 192,854 were men and 232,600 were women. The sample sizes vary for each preventive health care service outcome, depending on how many individuals responded to each of the questions.

Dependent Variables

Our analysis involves several dependent variables that measure the use of preventive health care from BRFSS. Several of these BRFSS variables have different versions that factor age of respondent and frequency of use of the service. For these measures, we analyzed the versions that included the largest sample sizes to provide the most complete picture of gendered patterns of health care use. For example, there were two versions of the question about mammograms: 1) women who have ever had a mammogram and 2) women aged 40+ who have had a mammogram in the past 2 years. We used the former measure since the 2018 mammogram recommendations vary for women 40 years and older, and therefore the latter measure may miss important variation by limiting the time period. Most health care services apply to both men and women, however there were three that were only asked for women and one that was only asked of men (described below).

All respondents were asked about a variety of health care services. These included dichotomous measures of whether respondents had a person they thought of as their personal doctor, if they had ever had a colonoscopy/sigmoidoscopy or been tested for HIV, and whether within the past year they had visited a doctor for a routine checkup, gotten a flu shot, or visited a dentist, dental hygienist, or dental clinic. For women, they were also asked if they had ever had a mammogram, pap test, or HPV test. For men, they asked if they had ever had a PSA test. All variables were coded 0/1 so that 1 indicated the participant had used the health care service within the specified timeframe.

Independent Variables

We constructed a state-level measure of structural sexism based on Homan (2019) from a variety of data sources. The sexism measure is a composite score of political, economic, cultural, and physical/reproductive factors. We used data that were as chronically close to 2018 to be relevant to the individual BRFSS data, depending on data availability for each source. For the political measure, we calculated percent of state legislature seats occupied by men in 2018 using data from the Institute for Women’s Policy Research. For the cultural measure, we included percent of state population composed of religious conservatives (evangelical Protestant or Latter-Day Saints) from Pew Research in 2014 (Pew Research Centre 2014). This is an important indicator of structural sexism because conservative religious institutions endorse gender essentialist beliefs and restrict women to subordinate roles in the church, the family, and society at large (Barr 2021; CBMW 2023; Chaves and Eagle 2015; Homan 2019). The prevalence of religious conservatives in a state is associated with traditional/patriarchal gender norms net of individual attitudes (Moore and Vanneman 2003). For economic measures we calculated a ratio of men’s to women’s labor force participation rates, ages 16 + and ratio of men’s to women’s poverty rate from IPUMS USA (Steven et al. 2022), as well as a ratio of men’s to women’s median usual weekly earnings of full-time wage and salary workers in 2018 (US Bureau of Labor Statistics 2019). We then standardized and summed all variables to create a structural sexism index (α = 0.79) and standardized the final structural sexism index for an easier interpretation of results. A higher value on the structural sexism index indicates more structural sexism.

Additional Covariates

At the individual-level, we controlled for a range of characteristics known to be associated with health care use. These include continuous measures of age and income, parental status (has at least one child under 18 in the house or no children under 18 in the house), marital status (married or a member of an unmarried couple, or not married/in a couple), and categorical measures of education (less than high school, high school graduate, some college, and bachelor degree) and race (white, black, American Indian or Alaskan Native, Asian, Native Hawaiian or Pacific Islander, Other race, Multiracial, and Hispanic). We also controlled for a categorical variable of self-rated health (poor, fair, good, very good, and excellent), given that individuals in worse health may be more likely to seek health care services. Finally, we controlled for insurance status to isolate the effect of structural sexism on preventive health care use, since Rapp et al. (2022) showed that higher state-level sexism is associated with being uninsured for both men and women.

Since we are examining the associations between individual health care behaviors with state-level sexism contexts, we also control for other relevant state-level measures. These include racial composition (% non-white) and poverty rate in 2018 from IPUMS USA (Steven et al. 2022), the state Gini coefficient (Frank 2021), and if the state is in the South based on US Census definitions (US Census Bureau 2017).

Analytic Strategy

First, we calculated descriptive statistics for the state-level measures and individual-level measures. For the individual-level descriptive statistics, we stratified by gender and estimated t-tests for continuous variables, test of proportion for dichotomous variables, and chi-square tests for categorical variables to understand statistical differences between men and women. Then we stratified the sample by gender and ran several multilevel logit models, with individuals nested within states, for each health care service. These models predict the use of each preventive health care service as a function of state-level structural sexism exposure. Each model includes all previously mentioned state-level and individual-level covariates. We retained the largest sample size for each outcome, which means that the sample size varies depending on how many individuals responded to each question. The sample sizes range from 115,012-182,582 for women and 95,317-159,469 for men.

Supplemental analyses.

We ran several supplemental analyses to check the robustness of our findings to different specifications. First, we replicated the main analyses but included a control for if the state had expanded Medicaid by 2018 (Kaiser Family Foundation 2023). Medicaid expansion increased access to health insurance, and health insurance facilitates access to care, thus it is possible this state-level policy would affect use of preventive care. However, a gendered power and resource allocation perspective suggests that Medicaid expansion decisions are a consequence of structural sexism (i.e., lower structural sexism leads to greater investment in social and health policy that improves population health) and indeed some studies have used Medicaid expansion as an indicator of structural sexism (Rapp et al. 2022). Therefore, controlling for Medicaid expansion risks underestimating the impact of structural sexism on preventive care to the extent that it functions as a mechanism. Thus, we chose to include this measure in supplemental models (Appendix Tables 1 and 2) rather than the main analyses. For the second supplemental analysis (Appendix Tables 3 and 4), we removed self-rated health as a covariate from the main model. We used self-rated health as a measure of general health, which is likely to be associated with health care use in general since individuals who are sick are more likely to receive care than individuals who are not sick. We chose to include this control in our main analyses to remove the impact of health status on preventive care use, but we did not include it for a supplemental analysis to understand if it affects the relationship between structural sexism and preventive health care use.

The third supplemental analysis was a modification to the measure of structural sexism. One recent study suggested the removal of religious conservatism as an indicator of structural sexism (McKetta et al. 2022); therefore, we estimated models with this item left out of the sexism index (Appendix Tables 5 and 6). Results were substantively similar and we chose to retain the religious conservatism item in our main analysis because theory and previous research highlight the important role of conservative religious institutions as a foundational element of sexist oppression in the United States (Barr 2021; CBMW 2023; Chaves and Eagle 2015; Homan 2019; Homan and Burdette 2021; Moore and Vanneman 2003). Fourth, we replicated our analyses with samples limited to only ages recommended by the United States Preventive Services Task Force for the applicable preventive care services (Appendix Tables 7 and 8). Since the screening and testing services, such as mammography and HPV tests, are generally recommended only for specific ages, individuals within these age ranges may be more likely to get the services regardless of structural sexism. However, we include all ages in the main analyses since recommendations may vary by characteristics other than age, such as family history. Finally, we test an interaction between gender and structural sexism for the full sample to understand whether the effect of structural sexism differs by gender (Appendix Table 9). Results remain largely consistent across all these supplemental models, indicating that our key findings and conclusions are robust to a variety of alternative specifications. See the Appendix for further details.

RESULTS

Descriptive statistics are shown in Table 1 for state-level measures and Table 2 for individual-level measures. Earnings ratio, labor force ratio, and poverty ratio all have means that are above 1, indicating gender inequality that favors men. Proportion men in state legislature is 74%, signaling that men consistently outnumber women in local government, and proportion women without abortion access is almost 50%, again showing women in a disadvantaged position, this time regarding reproductive health care access.

Table 1.

State-Level Data and Descriptive Statistics (n=50)

Measure Data Source Mean (SD) Range
Structural Sexism Multiple sources 0.00 (1) [−1.71, 2.84]
Earnings ratio (M:W) Bureau of Labor Statistics 1.25 (0.07) [1.13, 1.47]
Labor force ratio (M:W) IPUMS American Community Survey 1.15 (0.03) [1.08, 1.26]
Poverty ratio (W:M) IPUMS American Community Survey 1.05 (0.06) [0.89, 1.22]
Proportion men in state legislature Center for American Women in Politics 0.74 (0.08) [0.60, 0.89]
Proportion women without abortion access Guttmacher Institute 0.47 (0.26) [0.03, 0.96]
Racial composition US Census Bureau 0.21 (0.12) [0.05, 0.74]
Poverty rate US Census Bureau 0.12 (0.03) [0.07, 0.20]
Gini coefficient Frank, 2013 0.61 (0.03) [0.55, 0.70]
Southern region US Census Bureau 16 states
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Table 2.

Descriptive Statistics, Behavioral Risk Factor Surveillance System 2018

Men (n=192,854) Women (n=232,600) Gender Difference p Value
Preventive Care Use
 Visited a doctor in past year 76.3% 83.8% p<0.001
 Have a personal doctor 78.0% 87.6% p<0.001
 Flu shot within past year 36.7% 41.8% p<0.001
 Sigmoidoscopy or colonoscopy within past year 73.0% 75.6% p<0.001
 Ever tested for HIV 33.0% 33.5% p=0.006
 Visited dentist in past year 65.5% 70.6% p<0.001
 Ever had a mammogram - 79.2%       -
 Ever had a pap test - 94.1%       -
 Women who ever had HPV test - 43.2%       -
 Ever had a PSA test 51.7% -       -
Individual-level controls
 Age 53.5 (17.6) 56.2 (17.2) p<0.001
 Race p<0.001
   White 77.1% 77.3%
   Black 7.1% 9.0%
   American Indian or Alaskan Native 1.8% 1.8%
   Asian 2.6% 1.9%
   Native Hawaiian or Pacific Islander 0.4% 0.3%
   Other race 0.9% 0.6%
   Multiracial 2.1% 1.9%
   Hispanic 8.0% 7.2%
 Married/In a couple 59.0% 52.0% p<0.001
 Income 56,439 (28,658) 50,382 (29,123) p<0.001
 Insurance 90.3% 93.1% p<0.001
 Parent 25.3% 36.9% p<0.001
 Education p<0.001
   Less than high school 7.8% 7.0%
   High school degree 28.2% 26.8%
   Some college 26.1% 29.0%
   Bachelor degree 37.9% 37.2%
 Self-rated health p<0.001
   Excellent 16.8% 16.1%
   Very good 32.5% 33.1%
   Good 32.2% 31.3%
   Fair 13.4% 14.0%
   Poor 5.1% 5.5%
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Individual level descriptive statistics show that women are more likely to receive preventive health care services compared to men, and that the differences are statistically significant. For example, 83.8% of women had seen a doctor in the past year, compared to 76.3% of men. Similarly, 87.6% of women have a person they consider a personal doctor, compared to 78.0% of men. These were the two most highly endorsed measures, while the health care services least likely to be used were getting a flu shot in the past year (36.7% of men and 41.8% of women) and ever having tested for HIV (33.0% of men and 33.5% of women). Women were slightly older on average (56.2 years compared to 53.5 years), were less likely to be married (52.0% compared to 59.0%), were more likely to be parents (36.9% compared to 25.3%), and had lower incomes ($50,382 on average compared to $56,439). The remainder of individual descriptive statistics were statistically different though perhaps not meaningfully different, including the racial breakdown, self-rated health, and education.

The results for the gender stratified multilevel models that predict use of preventive health care services conditional on exposure to structural sexism are in Table 3 for women and Table 4 for men. Figure 2 visualizes the results for both men and women in a forest plot with a darker color representing significant results. Overall, we found that both women and men were less likely to use preventive services in states with more structural sexism. Women were less likely to have had a colonoscopy or sigmoidoscopy (OR=.94, p=.018), to have tested for HIV (OR=.82, p<.001), had a mammogram (OR=.95, p=.009), a pap test (OR=.94, p=.011), and an HPV test (OR=.87, p<.001) in states with more structural sexism compared to women in states with less structural sexism. Men were similar in that they were less likely to have a personal doctor (OR=.91, p=.031), to have had a colonoscopy or sigmoidoscopy (OR=.93, p=.006), and to have tested for HIV (OR=.85, p<.001) in states with more structural sexism compared to men in states with less structural sexism. However, men also were more likely to have had a PSA test in states with more structural sexism (OR=1.06, p=.003), which was the only service positively associated with structural sexism.

Table 3.

Associations between Structural Sexism and Preventive Health Care Use among Women, Odds Ratios

Visited a doctor in past year Has a personal doctor Flu shot in past year Ever had a sigmoidoscopy or colonoscopy Ever tested for HIV Visited dentist in past year Ever had a mammogram Ever had a pap test Ever had HPV test
Structural sexism 0.99 0.92 1.03 0.94* 0.82*** 1.02 0.95** 0.94* 0.87***
Age 1.03*** 1.04*** 1.03*** 1.05*** 0.96*** 1.01*** 1.14*** 1.05*** 0.96***
Race (ref: White)
  Black 2.06*** 1.15*** 0.81*** 1.27*** 2.49*** 0.97 1.52*** 0.96 1.38***
  AIAN 1.31*** 0.54*** 0.99 0.76*** 1.75*** 1.08 1.22** 0.72*** 1.45***
  Asian 1.14** 0.78*** 1.37*** 0.66*** 0.43*** 0.98 0.79*** 0.17*** 0.38***
  NHPI 1.17 0.76* 0.94 0.60*** 0.73** 0.80* 1.30* 0.47*** 0.78*
  Other race 0.93 0.77** 0.85* 0.85 1.46*** 0.97 1.07 0.51*** 1.19*
  Multiracial 1.05 0.89* 0.89** 0.97 1.65*** 0.79*** 1.04 1.01 1.30***
  Hispanic 1.40*** 0.80*** 1.13*** 0.93 1.07** 1.32*** 1.33*** 0.73*** 1.00
Education (ref: <HS)
  High school degree 1.03 1.28*** 0.87*** 1.27*** 0.92** 1.37*** 1.18*** 1.28*** 1.03
  Some college 1.01 1.38*** 0.96 1.56*** 1.37*** 1.63*** 1.25*** 1.94*** 1.35***
  College degree 0.99 1.36*** 1.28*** 1.84*** 1.57*** 2.34*** 1.23*** 3.47*** 1.62***
Married/in a couple 0.98 1.11*** 0.95*** 1.20*** 0.81*** 1.02 1.10*** 2.43*** 1.06***
Income 1.00*** 1.00*** 1.00*** 1.00*** 1.00 1.00*** 1.00*** 1.00*** 1.00***
Insurance 3.67*** 4.39*** 2.21*** 2.42*** 0.98 1.94*** 1.36*** 1.11** 1.16***
Parent 0.90*** 1.12*** 1.01 0.67*** 1.73*** 0.93*** 0.85*** 2.42*** 1.69***
Self-rated health 0.84*** 0.85*** 0.95*** 0.89*** 0.82*** 1.28*** 0.92*** 0.94*** 0.97***
Intercept 0.67 1.02 0.24** 0.10*** 1.08 0.16*** 0.004*** 1.07 4.83***
Level 2 variance (SE) 0.04*** (0.01) 0.08*** (0.02) 0.03*** (0.01) 0.02*** (0.01) 0.04*** (0.01) 0.01*** (0.002) 0.01*** (0.003) 0.02*** (0.01) 0.02*** (0.01)
N 181713 182582 178178 115012 168197 182082 176741 176040 131406
Open in a new tab
*

all models control for state-level measures of poverty rate, Gini coefficient, racial composition, and southern region

***

p<0.001

**

p<0.01

*

p<0.05

Table 4.

Associations between Structural Sexism and Preventive Health Care Use among Men, Odds Ratios

Visited a doctor in past year Has a personal doctor Flu shot in past year Ever had a sigmoidoscopy or colonoscopy Ever tested for HIV Visited dentist in past year Ever had a PSA test
Structural sexism 0.98 0.91* 1.02 0.93** 0.85*** 1.00 1.06**
Age 1.04*** 1.05*** 1.03*** 1.06*** 0.98*** 1.01*** 1.09***
Race (ref: White)
  Black 1.79*** 1.07* 0.90*** 1.15*** 3.02*** 0.94** 1.45***
  AIAN 1.26*** 0.79*** 1.00 0.71*** 1.75*** 0.99 0.76***
  Asian 1.32*** 1.05 1.40*** 0.58*** 0.51*** 0.82*** 0.51***
  NHPI 1.33** 1.21 1.08 0.71* 0.84 0.80* 0.59***
  Other race 0.93 0.72*** 0.80** 0.76** 1.67*** 0.89 0.82*
  Multiracial 1.06 0.93 1.02 0.94 1.58*** 0.79*** 0.83**
  Hispanic 1.25*** 0.79*** 1.12*** 0.86*** 1.23*** 1.28*** 0.99
Education (ref: <HS)
  High school degree 1.15*** 1.29*** 0.99 1.36*** 1.07* 1.39*** 1.46***
  Some college 1.22*** 1.46*** 1.16*** 1.78*** 1.38*** 1.67*** 1.92***
  College degree 1.20*** 1.58*** 1.69*** 2.22*** 1.49*** 2.43*** 2.43***
Married/in a couple 1.07*** 1.20*** 1.12*** 1.40*** 0.75*** 1.19*** 1.27***
Income 1.00*** 1.00*** 1.00*** 1.00*** 1.00 1.00*** 1.00***
Insurance 3.66*** 4.27*** 2.58*** 2.75*** 1.07** 2.03*** 1.82***
Parent 0.87*** 0.97* 0.94*** 0.67*** 1.29*** 0.90*** 0.61***
Self-rated health 0.83*** 0.88*** 0.92*** 0.91*** 0.89*** 1.21*** 1.01
Intercept 0.18*** 0.16* 0.09*** 0.02*** 0.44 0.13*** 0.0005***
Level 2 variance (SE) 0.03*** (0.01) 0.08*** (0.02) 0.03*** (0.01) 0.03*** (0.01) 0.03*** (0.01) 0.01*** (0.003) 0.01*** (0.003)
N 158,644 159,469 155,392 95,317 146,983 159,048 110839
Open in a new tab
*

all models control for state-level measures of poverty rate, Gini coefficient, racial composition, and southern region

***

p<0.001

**

p<0.01

*

p<0.05

Figure 2.

Figure 2.

Open in a new tab

Associations between Structural Sexism and Odds of Using Preventive Health Care Services Among Women and Men

The coefficients can be interpreted as the odds ratios for using a particular service associated with a one standard deviation increase in the structural sexism index. For example, a one standard deviation increase in the structural sexism index is associated with a .87 odds ratio of women getting tested for HPV, which translates to 13% lower odds. To further aid in the interpretation of these results we used our model to calculate the predicted probabilities of HPV testing among women across the range of sexism exposures observed in our sample: a woman exposed to the lowest observed level of sexism has a .51 predicted probability of testing for HPV, while a woman exposed to the highest level of sexism has only a .38 predicted probability of testing for HPV.

DISCUSSION

This study measures the association between state-level exposure to structural sexism on gendered use of preventive health care. We build on the growing literature examining the relationship between structural sexism and health (Homan 2019; McKetta et al. 2022; Rapp et al. 2022; Rapp, Volpe, and Neukrug 2021). Preventive health care use is an important outcome to study in this context due to its relevance to population health outcomes, as well as the gendered patterns of use (USDHHS 2022). Most studies focus on individual-level factors in determining health care use, despite the growing emphasis on upstream structural determinants in the health disparities literature (Braveman and Gottlieb 2014; Homan and Brown 2022; Montez 2017; Montez, Hayward, and Zajacova 2021). Our research questions were: (1) Is structural sexism associated with preventive health care use among women and men? (2) If so, are the patterns more consistent with theories of gender norms and health behaviors or gendered power and resource allocation? We had two hypotheses based on two different perspectives. First, based on the gender performance perspective, we hypothesized that in states with more structural sexism men would be less likely to use preventive health care and women would be more likely to use preventive health care compared to their same-gender counterparts in states with less sexism. Second, based on the gendered power and resource allocation perspective, we argued that in states with more structural sexism there would be less funding, education, and infrastructure for health care such that both men and women would be less likely to use preventive health care compared to their counterparts in states with less sexism.

Overall, our results indicate a strong negative relationship between exposure to structural sexism at the state-level and the use of preventive health care services among both men and women. These results partially supported our hypothesis based on a gender performance perspective since men were less likely to use preventive health care in states with more structural sexism, consistent with the idea that performing masculinity entails health care avoidance and negative health beliefs and behaviors (Courtenay 2000). However, a gender performance perspective was not generally supported for women, who were also less likely to use preventive health care in states with more structural sexism, but who were hypothesized to increase health care utilization in conjunction with performing femininity. Our results fully supported a gendered power and resource allocation perspective since both men and women were overall less likely to use preventive health care services in states with more sexism. This pattern would be expected if the disempowerment of women in sexist environments leads to the contraction of health promoting resources for everyone to access.

These findings align with much of the literature on structural sexism and health but contribute a key piece by examining the gendered use of preventive health care. Most relevant to the current study, Rapp et al. (2022) found that women in states with more sexism face more barriers to accessing and affording health care, while men’s access is unaffected by structural sexism. Although they did not examine preventive health care explicitly, it is possible these findings apply to preventive health care and can help to explain our findings that women in states with more sexism were less likely to access preventive health care. However, they do not help to explain our finding that men were generally also less likely to access preventive health care in states with more sexism. Nagle and Samari (2021) found that birthing people living in states with more sexism were more likely to have had a cesarean section. These findings align with the results of this study since women in states with more sexism were not receiving care in the recommended way or frequency. Finally, Homan (2019) found that structural sexism was associated with more chronic conditions, worse self-rated health, and worse physical functioning for women, as well as worse physical functioning for men. Our study in conjunction with Rapp et al. (2022) and Nagle and Samari (2021) provide evidence on negative interactions with the health care system in sexist states, suggesting possible explanations for Homan’s (2019) findings that women and men in sexist states have worse health.

There was one particularly interesting finding in our study that did not fit the same pattern as the other results. In states with more sexism, men were more likely to get PSA tests compared to men in states with less sexism, which was the opposite relationship for all other forms of preventive care among men in this study. While initially somewhat surprising, this can also be understood from a modified gender performance perspective. From a hegemonic masculinity viewpoint, prostate cancer (which can be detected by PSA tests allowing for timely treatment) presents a unique threat to masculinity because it often causes incontinence and erectile dysfunction, which can be perceived as weakness, lack of control over bodily function, and failure to fulfill masculine ideals of sexual prowess (Gray et al. 2002). Thus, while it may be generally considered un-masculine to seek out health care (especially preventive care that is not an urgent necessity), men may make exceptions for care that sustains sexual function. Indeed, a qualitative study of men’s constructions of masculinity and their help-seeking behavior in Scotland found that while there was widespread agreement with the hegemonic view that real men should not need help or consult with physicians for “minor” symptoms or pain, men were much more in favor of help-seeking when it was used as a means to preserve other enactments of masculinity that they valued, especially their physical strength and sexual performance (O’Brien, Hunt, and Hart 2005). Thus, to the extent that exposure to high levels of structural sexism exerts increased pressure to conform to hegemonic norms of masculinity, men in these environments must navigate competing demands of masculine invincibility and self-reliance which encourage health care avoidance versus the masculine imperative to preserve the functioning of the penis—itself the very symbol of manhood, power and sexuality (Cheng 1999; Gray et al. 2002; Potts 2000). In sum, our findings suggest that while higher structural sexism exposure decreases men’s use of preventive care services in general (either through performing masculinity, decreased health care resources and accessibility, or both), it may increase the demand for preventive health care directly affecting men’s sexual performance which functions as a central element of their identity as men. Future research should investigate the associations between structural sexism and other sexual and urological services and outcomes to further explore this issue.

Limitations and Future Research

This research makes an important contribution to the literature on structural factors that shape health care use, and therefore, health outcomes. However, there are limitations to this research worth noting. First, we use cross-sectional data from a single year, and can therefore not claim a causal relationship. Second, we do not explicitly test mechanisms, so the pathways through which structural sexism may shape preventive care remain unclear. Rapp et al. (2022) provides evidence that states with more sexism bar women from accessing health care generally, but determining the specific factors and barriers affecting women’s and men’s preventive health care use in particular requires further research. We suggest future researchers further investigate gender performance perspectives by combining structural sexism measures with individual-level data on gender norms/beliefs and health care use, as well as examining the role of cultural sexism (i.e., contextual level gender norms/ideology) (see Price et al. 2021). Additionally, researchers can further investigate gendered power and resources allocation perspective by examining the relationships between structural sexism and specific social policies (such as welfare spending, unemployment insurance, Medicaid expansion, education spending, health care spending, parental leave, etc.) that redistribute resources in ways that improve population health and may therefore act as mechanisms between structural sexism and preventive health care use. Third, our measures of preventive health care are self-reported and may be subject to recall bias, and we also only include the available measures of preventive health care in BRFSS. The measures are also dichotomous which may miss more nuanced responses. Thus, investigating the associations between structural sexism and a variety of other measures of health care service use and health behaviors is an important area for future research.

Fourth, we captured several important aspects of structural sexism and other state-level and individual-level covariates that may impact preventive health care use, but our measure of structural sexism is not exhaustive. Future research should develop additional measures of structural sexism and examine the strengths and limitations of different measurement approaches. Fifth, there are a variety of other state-level social, economic, and political characteristics not included in our models that may be relevant for preventive care and may or may not be related to structural sexism. While these factors could potentially confound the relationship between sexism and health or health care, it is also possible that they serve as important mechanisms. Thus, careful examination of the complex relationships between structural sexism and various features of state contexts is an important area for future research.

Sixth, we have focused specifically on the role of structural sexism in shaping preventive care given recent evidence of its associations with health, but it does not operate in isolation. Homan et al. (2021) provide a theoretical framework to examine health and health care outcomes through a structural intersectional lens. Since gendered expectations can be both classed and racialized, future research should investigate use of preventive health care at the intersection of multiple oppressive systems. This will involve measuring multiple forms of structural oppression (e.g., structural sexism and structural racism) and examining their individual and joint effects on healthcare use among individuals with varied intersectional identity categories (e.g., black women with a college degree vs. white men without a high school diploma). Research on structural sexism has thus far used data with predominantly white samples, suggesting an intersectional approach is crucial for providing further theoretical insight.

It is also important to consider the role of sexuality in affecting men’s health-seeking behavior. Studies find that gay men are likelier to get PSA testing than their heterosexual counterparts (Ma et al. 2021; Wilcox Vanden Berg et al. 2022). However, whether exposure to higher structural sexism may or may not affect greater PSA testing among gay men warrants further study. Finally, our sample is limited by data constraints in the BRFSS such that we can only study the associations between structural sexism and preventive health care among cisgender men and women. It is essential that future research investigate the impact of structural sexism among transgender and non-binary people, as well as develop new measures of structural sex-gender-sexuality based oppression such as cisheteropartiarchy and gender binarism (Everett et al. 2022; Krieger 2020).

Conclusion

Structural sexism is an important determinant of preventive health care use for both men and women. These findings suggest a few possible explanations related to the gender performance and gendered power and resource allocation perspectives. Specific to men, structural sexism may exacerbate gender norms that disincentivize them from using care to avoid appearing vulnerable. For both men and women, a lack of resources and state-level supports may prevent them from receiving preventive health care services. This work contributes to a growing body of research on the universal harm of structural sexism and the urgency of dismantling oppressive gender systems to improve population health.

Supplementary Material

Appendix

Acknowledgments

Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award No. F31MD017935, and the Network on Life Course Health Dynamics and Disparities in 21st Century America (Award No. 2 R24 AG 045061-06) from the National Institutes on Aging. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

  1. Asch Steven M., Kerr Eve A., Keesey Joan, Adams John L., Setodji Claude M., Malik Shaista, and McGlynn Elizabeth A. 2006. “Who Is at Greatest Risk for Receiving Poor-Quality Health Care?” New England Journal of Medicine 354(11):1147–56. doi: 10.1056/nejmsa044464. [DOI] [PubMed] [Google Scholar]
  2. Barr Beth Allison. 2021. The Making of Biblical Womanhood: How the Subjugation of Women Became Gospel Truth. Grand Rapids, Michigan: Brazos Press. [Google Scholar]
  3. Beccia Ariel L., Austin S. Bryn, Baek Jonggyu, Agénor Madina, Forrester Sarah, Ding Eric Y., Jesdale William M., and Lapane Kate L. 2022. “Cumulative Exposure to State-Level Structural Sexism and Risk of Disordered Eating: Results from a 20-Year Prospective Cohort Study.” Social Science & Medicine 301:114956. doi: 10.1016/j.socscimed.2022.114956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Black Lindsey I., Nugent Colleen N., and Vahratian Anjel. 2016. “Access and Utilization of Selected Preventive Health Services Among Adolescents Aged 10-17.” NCHS Data Brief (246):1–8. [PubMed] [Google Scholar]
  5. Blewett Lynn A., Pamela Jo Johnson Brian Lee, and Scal Peter B. 2008. “When a Usual Source of Care and Usual Provider Matter: Adult Prevention and Screening Services.” Journal of General Internal Medicine 23(9):1354–60. doi: 10.1007/S11606-008-0659-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boehmer Ulrike, and Williamson John B. 1996. “The Impact of Women’s Status on Infant Mortality Rate: A Cross-National Analysis.” Social Indicators Research 37(3):333–60. [Google Scholar]
  7. Bolnick Howard J., Bui Anthony L., Bulchis Anne, Chen Carina, Chapin Abigail, Lomsadze Liya, Mokdad Ali H., Millard Francois, and Dieleman Joseph L.. 2020. “Health-Care Spending Attributable to Modifiable Risk Factors in the USA: An Economic Attribution Analysis.” The Lancet Public Health 5(10):e525–35. doi: 10.1016/S2468-2667(20)30203-6. [DOI] [PubMed] [Google Scholar]
  8. Bolzendahl Catherine, and Brooks Clem. 2007. “Women’s Political Representation and Welfare State Spending in 12 Capitalist Democracies.” Social Forces 85(4):1509–34. doi: 10.1353/sof.2007.0061. [DOI] [Google Scholar]
  9. Borkhoff Cornelia M., Hawker Gillian A., Kreder Hans J., Glazier Richard H., Mahomed Nizar N., and Wright James G. 2008. “The Effect of Patients’ Sex on Physicians’ Recommendations for Total Knee Arthroplasty.” CMAJ : Canadian Medical Association Journal 178(6):681–87. doi: 10.1503/cmaj.071168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Borsky Amanda, Zhan Chunliu, Miller Therese, Quyen Ngo-Metzger Arlene S. Bierman, and Meyers David. 2018. “Few Americans Receive All High-Priority, Appropriate Clinical Preventive Services.” Health Affairs (Milwood) 37(6):925–28. doi: 10.1377/HLTHAFF.2017.1248. [DOI] [PubMed] [Google Scholar]
  11. Braveman Paula, and Gottlieb Laura. 2014. “The Social Determinants of Health: It’s Time to Consider the Causes of the Causes.” Public Health Reports 129(Suppl 2):19–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Brenner Hermann, Stock Christian, and Hoffmeister Michael. 2014. “Effect of Screening Sigmoidoscopy and Screening Colonoscopy on Colorectal Cancer Incidence and Mortality: Systematic Review and Meta-Analysis of Randomised Controlled Trials and Observational Studies.” BMJ (Clinical Research Ed.) 348(g2467):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. CBMW. 2023. “The Council on Biblical Manhood and Womanhood.” CBMW. Retrieved May 3, 2023 (http://cbmw.org/).
  14. Chapman Elizabeth N., Kaatz Anna, and Carnes Molly. 2013. “Physicians and Implicit Bias: How Doctors May Unwittingly Perpetuate Health Care Disparities.” Journal of General Internal Medicine 28(11):1504–10. doi: 10.1007/s11606-013-2441-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chaves Mark, and Eagle Allison. 2015. Religious Congregations in 21st Century America. [Google Scholar]
  16. Chen Ying-Yeh, Subramanian SV, Acevedo-Garcia Doloros, and Kawachi Ichiro. 2005. “Women’s Status and Depressive Symptoms: A Multilevel Analysis.” Social Science & Medicine 60(1):49–60. doi: 10.1016/j.socscimed.2004.04.030. [DOI] [PubMed] [Google Scholar]
  17. Cheng Cliff. 1999. “Marginalized Masculinities and Hegemonic Masculinity: An Introduction.” The Journal of Men’s Studies 7(3):295–315. doi: 10.3149/jms.0703.295. [DOI] [Google Scholar]
  18. Christy Shannon M., Mosher Catherine E., and Rawl Susan M.. 2014. “Integrating Men’s Health and Masculinity Theories to Explain Colorectal Cancer Screening Behavior.” American Journal of Men’s Health 8(1):54–65. doi: 10.1177/1557988313492171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Connell RW 1987. Gender and Power: Society, the Person, and Sexual Politics. 1 edition. Stanford, Calif: Stanford University Press. [Google Scholar]
  20. Connell RW, and Messerschmidt James W. 2005. “Hegemonic Masculinity Rethinking the Concept.” Gender & Society 19(6):829–59. doi: 10.1177/0891243205278639. [DOI] [Google Scholar]
  21. Connell Raewyn. 2012. “Gender, Health and Theory: Conceptualizing the Issue, in Local and World Perspective.” Social Science & Medicine 74(11):1675–83. doi: 10.1016/j.socscimed.2011.06.006. [DOI] [PubMed] [Google Scholar]
  22. Connell Raewyn W. 2005. Masculinities. Revised edition. Cambridge: Polity Press. [Google Scholar]
  23. Courtenay Will H. 2000. “Constructions of Masculinity and Their Influence on Men’s Well-Being: A Theory of Gender and Health.” Social Science and Medicine 50(10):1385–1401. doi: 10.1016/S0277-9536(99)00390-1. [DOI] [PubMed] [Google Scholar]
  24. Daly Moira, and Groes Fane. 2017. “Who Takes the Child to the Doctor? Mom, Pretty Much All of the Time.” Applied Economics Letters 24(17):1267–76. doi: 10.1080/13504851.2016.1270410. [DOI] [Google Scholar]
  25. DeVoe Jennifer E., Fryer George E., Phillips Robert, and Green Larry. 2003. “Receipt of Preventive Care among Adults: Insurance Status and Usual Source of Care.” American Journal of Public Health 93(5):786–91. doi: 10.2105/AJPH.93.5.786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Everett Bethany G., Limburg Aubrey, Homan Patricia, and Philbin Morgan M.. 2022. “Structural Heteropatriarchy and Birth Outcomes in the United States.” Demography 59(1):89–110. doi: 10.1215/00703370-9606030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Faulkner LA, and Schauffler HH. 1997. “The Effect of Health Insurance Coverage on the Appropriate Use of Recommended Clinical Preventive Services - PubMed.” American Journal of Preventive Medicine 13(6):453–58. [PubMed] [Google Scholar]
  28. Ferdinands Jill M., Gaglani Manjusha, Martin Emily T., Monto Arnold S., Middleton Donald, Silveira Fernanda, Talbot H. Keipp, Zimmerman Richard, and Patel Manish. 2021. “Waning Vaccine Effectiveness Against Influenza-Associated Hospitalizations Among Adults, 2015–2016 to 2018–2019, United States Hospitalized Adult Influenza Vaccine Effectiveness Network.” Clinical Infectious Diseases 73(4):726–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Fleming Paul J, and Agnew-Brune Christine. 2015. “Current Trends in the Study of Gender Norms and Health Behaviors.” Current Opinion in Psychology 5:72–77. doi: 10.1016/j.copsyc.2015.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Frank Mark W. 2021. “The U.S. State-Level Income Inequality Data.” Retrieved November 21, 2022 (https://www.shsu.edu/eco_mwf/inequality.html).
  31. Gray Ross E., Fitch Margaret I., Fergus Karen D., Mykhalovskiy Eric, and Church Kathryn. 2002. “Hegemonic Masculinity and the Experience of Prostate Cancer: A Narrative Approach.” 7(1):43–62. [Google Scholar]
  32. Greenwood Brad N., Carnahan Seth, and Huang Laura. 2018. “Patient–Physician Gender Concordance and Increased Mortality among Female Heart Attack Patients.” Proceedings of the National Academy of Sciences 115(34):8569–74. doi: 10.1073/pnas.1800097115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Homan Patricia. 2019. “Structural Sexism and Health in the United States: A New Perspective on Health Inequality and the Gender System.” American Sociological Review 84(3):486–516. doi: 10.1177/0003122419848723. [DOI] [Google Scholar]
  34. Homan Patricia. 2021. “Sexism and Health: Advancing Knowledge Through Structural and Intersectional Approaches.” American Journal of Public Health 111(10):1725–27. doi: 10.2105/AJPH.2021.306480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Homan Patricia A. 2021. “Structural Sexism and Life Course Health: Implications for Public Policy.” in Current Issues in Aging and the Life Course: Public Policy, edited by Wilmoth JM and S A. London. Routledge. [Google Scholar]
  36. Homan Patricia A., and Brown Tyson H.. 2022. “Sick And Tired Of Being Excluded: Structural Racism In Disenfranchisement As A Threat To Population Health Equity.” Health Affairs 41(2):219–27. doi: 10.1377/hlthaff.2021.01414. [DOI] [PubMed] [Google Scholar]
  37. Homan Patricia, Brown Tyson H., and King Brittany. 2021. “Structural Intersectionality as a New Direction for Health Disparities Research.” Journal of Health and Social Behavior 62(3):350–70. doi: 10.1177/00221465211032947. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Homan Patricia, and Burdette Amy. 2021. “When Religion Hurts: Structural Sexism and Health in Religious Congregations.” American Sociological Review 86(2):234–55. [Google Scholar]
  39. Hou Su-I., Sealy Diadrey-Anne, and Kabiru Caroline W.. 2011. “Closing the Disparity Gap: Cancer Screening Interventions among Asians--a Systematic Literature Review.” Asian Pacific Journal of Cancer Prevention: APJCP 12(11):3133–39. [PubMed] [Google Scholar]
  40. Jones Tarsha P., Katapodi Maria C., and Lockhart Joan S.. 2015. “Factors Influencing Breast Cancer Screening and Risk Assessment among Young African American Women: An Integrative Review of the Literature.” Journal of the American Association of Nurse Practitioners 27(9):521–29. doi: 10.1002/2327-6924.12223. [DOI] [PubMed] [Google Scholar]
  41. Kaiser Family Foundation. 2023. “Status of State Medicaid Expansion Decisions: Interactive Map.” KFF. Retrieved March 25, 2023 (https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map/).
  42. Kamal Rabah, and Hudman Julie. 2020. “What Do We Know about Spending Related to Public Health in the U.S. and Comparable Countries?” Peterson-KFF Health System Tracker. Retrieved November 19, 2022 (https://www.healthsystemtracker.org/chart-collection/what-do-we-know-about-spending-related-to-public-health-in-the-u-s-and-comparable-countries/#Preventive care spending by government/compulsory schemes as a share of total national health expenditures, 2).
  43. Kavanagh Shane A., and Graham Melissa. 2019. “How Gender Inequity Impacts on Men’s Health. An Exploration of Theoretical Pathways.” International Journal of Men’s Social and Community Health 2(1):e11–21. doi: 10.22374/ijmsch.v2i1.5. [DOI] [Google Scholar]
  44. Kavanagh Shane A., Shelley Julia M., and Stevenson Christopher. 2017. “Does Gender Inequity Increase Men’s Mortality Risk in the United States? A Multilevel Analysis of Data from the National Longitudinal Mortality Study.” SSM - Population Health 3:358–65. doi: 10.1016/j.ssmph.2017.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kavanagh Shane A., Shelley Julia M., and Stevenson Christopher. 2018. “Is Gender Inequity a Risk Factor for Men Reporting Poorer Self-Rated Health in the United States?” PLOS ONE 13(7):e0200332. doi: 10.1371/journal.pone.0200332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Kawachi Ichiro, Kennedy Bruce P., Gupta Vanita, and Prothrow-Stith Deborah. 1999. “Women’s Status and the Health of Women and Men: A View from the States.” Social Science & Medicine 48(1):21–32. doi: 10.1016/S0277-9536(98)00286-X. [DOI] [PubMed] [Google Scholar]
  47. Kim Eric S., Kubzansky Laura D., and Smith Jacqui. 2015. “Life Satisfaction and Use of Preventive Health Care Services.” Health Psychology 34(7):779–82. doi: 10.1037/HEA0000174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kim Eric S., Strecher Victor J., and Ryff Carol D. 2014. “Purpose in Life and Use of Preventive Health Care Services.” Proceedings of the National Academy of Sciences 111(46):16331–36. doi: 10.1073/PNAS.1414826111/SUPPL_FILE/PNAS.201414826SI.PDF. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Koenen Karestan C., Lincoln Alisa, and Appleton Allison. 2006. “Women’s Status and Child Well-Being: A State-Level Analysis.” Social Science & Medicine 63(12):2999–3012. doi: 10.1016/j.socscimed.2006.07.013. [DOI] [PubMed] [Google Scholar]
  50. Krieger Nancy. 2020. “Measures of Racism, Sexism, Heterosexism, and Gender Binarism for Health Equity Research: From Structural Injustice to Embodied Harm—an Ecosocial Analysis.” Annual Review of Public Health 41(1):annurev-publhealth-040119-094017. doi: 10.1146/annurev-publhealth-040119-094017. [DOI] [PubMed] [Google Scholar]
  51. Levine Susan, Malone Erin, Lekiachvili Akaki, and Briss Peter. 2019. “Health Care Industry Insights: Why the Use of Preventive Services Is Still Low.” Preventing Chronic Disease 16(180625):1–6. doi: 10.5888/PCD16.180625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Little Thomas H., Dunn Dana, and Deen Rebecca E.. 2001. “A View from the Top: Gender Differences in Legislative Priorities Among State Legislative Leaders.” Women & Politics 22(4):29–50. doi: 10.1300/J014v22n04_02. [DOI] [Google Scholar]
  53. Ma Sung Jun, Oladeru Oluwadamilola T., Wang Katy, Attwood Kristopher, Singh Anurag K., Haas-Kogan Daphne A., and Neira Paula M.. 2021. “Prostate Cancer Screening Patterns Among Sexual and Gender Minority Individuals.” European Urology 79(5):588–92. doi: 10.1016/j.eururo.2020.11.009. [DOI] [PubMed] [Google Scholar]
  54. Mahalik James R., Burns Shaun M., and Syzdek Matthew. 2007. “Masculinity and Perceived Normative Health Behaviors as Predictors of Men’s Health Behaviors.” Social Science & Medicine 64(11):2201–9. doi: 10.1016/j.socscimed.2007.02.035. [DOI] [PubMed] [Google Scholar]
  55. McKetta Sarah, Prins Seth J., Hasin Deborah, Patrick Megan E., and Keyes Katherine M.. 2022. “Structural Sexism and Women’s Alcohol Use in the United States, 1988–2016.” Social Science and Medicine 301(114976):1–10. doi: 10.1016/j.socscimed.2022.114976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Miller Grant. 2008. “Women’s Suffrage, Political Responsiveness, and Child Survival in American History.” The Quarterly Journal of Economics 123(3):1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Montez JK, Beckfield J, Cooney JK, Grumbach JM, Hayward MD, Koytak HZ, Woolf SH, and Zajacova A. 2020. “US State Policies, Politics, and Life Expectancy.” Milbank Quarterly 1–32. doi: 10.1111/1468-0009.12469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Montez Jennifer Karas. 2017. “Deregulation, Devolution, and State Preemption Laws’ Impact on US Mortality Trends.” American Journal of Public Health 107(11):1749–50. doi: 10.2105/AJPH.2017.304080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Montez Jennifer Karas, Hayward Mark D., and Zajacova Anna. 2021. “Trends in U.S. Population Health: The Central Role of Policies, Politics, and Profits.” Journal of Health and Social Behavior 62(3):286–301. doi: 10.1177/00221465211015411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Moore Laura M., and Vanneman Reeve. 2003. “Context Matters: Effects of the Proportion of Fundamentalists on Gender Attitudes.” Social Forces 82(1):115–39. doi: 10.1353/sof.2003.0099. [DOI] [Google Scholar]
  61. Nagle Amanda, and Samari Goleen. 2021. “State-Level Structural Sexism and Cesarean Sections in the United States.” Social Science & Medicine 289:114406. doi: 10.1016/j.socscimed.2021.114406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. National Academies of Sciences Engineering and Medicine. 2018. Health-Care Utilization as a Proxy in Disability Determination. Washington, DC: The National Academies Press. [PubMed] [Google Scholar]
  63. O’Brien Rosaleen, Hunt Kate, and Hart Graham. 2005. “‘It’s Caveman Stuff, but That Is to a Certain Extent How Guys Still Operate’: Men’s Accounts of Masculinity and Help Seeking.” Social Science & Medicine (1982) 61(3):503–16. doi: 10.1016/j.socscimed.2004.12.008. [DOI] [PubMed] [Google Scholar]
  64. Pew Research Centre. 2014. “Evangelical Protestants.” Retrieved November 21, 2022 (https://www.pewresearch.org/religion/religious-landscape-study/religious-tradition/evangelical-protestant/).
  65. Potts Annie. 2000. “‘The Essence of the Hard On’: Hegemonic Masculinity and the Cultural Construction of ‘Erectile Dysfunction.’” Men and Masculinities 3(1):85–103. doi: 10.1177/1097184X00003001004. [DOI] [Google Scholar]
  66. Price Maggi A., Sarah McKetta John R. Weisz, Ford Jessie V., Lattanner Micah R., Skov Hilary, Wolock Elizabeth, and Hatzenbuehler Mark L.. 2021. “Cultural Sexism Moderates Efficacy of Psychotherapy: Results from a Spatial Meta-Analysis.” Clinical Psychology: Science and Practice 28:299–312. doi: 10.1037/cps0000031. [DOI] [Google Scholar]
  67. Rapp Kristen Schorpp, Volpe Vanessa V., Hale Tabitha L., and Quartararo Dominique F.. 2022. “State–Level Sexism and Gender Disparities in Health Care Access and Quality in the United States.” Journal of Health and Social Behavior 63(1):2–18. doi: 10.1177/00221465211058153. [DOI] [PubMed] [Google Scholar]
  68. Rapp Kristen Schorpp, Volpe Vanessa V., and Neukrug Hannah. 2021. “State-Level Sexism and Women’s Health Care Access in the United States: Differences by Race/Ethnicity, 2014-2019.” American Journal of Public Health 111(10):1796–1805. doi: 10.2105/AJPH.2021.306455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Ridgeway Cecilia L., and Correll Shelley J.. 2004. “Unpacking the Gender System a Theoretical Perspective on Gender Beliefs and Social Relations.” Gender and Society 18(4):510–31. doi: 10.1177/0891243204265269. [DOI] [Google Scholar]
  70. Schofield Dr Toni. 2015. A Sociological Approach to Health Determinants. 1 edition. Port Melbourne, VIC, Australia: Cambridge University Press. [Google Scholar]
  71. Seidler Zac E., Dawes Alexei J., Rice Simon M., Oliffe John L., and Dhillon Haryana M.. 2016. “The Role of Masculinity in Men’s Help-Seeking for Depression: A Systematic Review.” Clinical Psychology Review 49:106–18. doi: 10.1016/j.cpr.2016.09.002. [DOI] [PubMed] [Google Scholar]
  72. Steven Ruggles, Flood Sarah, Goeken Ronald, Schouweiler Megan, and Sobek Matthew. 2022. IPUMS USA: Version 12.0 [Dataset]. Minneapolis, MN: IPUMS, 2022. [Google Scholar]
  73. U.S. Bureau of Labor Statistics. 2019. “Highlights of Women’s Earnings in 2018.” Retrieved November 21, 2022 (https://www.bls.gov/opub/reports/womens-earnings/2018/home.htm).
  74. U.S. Census Bureau. 2017. “Census Bureau Region and Division Codes and Federal Information Processing System (FIPS) Codes for States.” Retrieved July 24, 2022 (https://www2.census.gov/programs-surveys/popest/geographies/2016/state-geocodes-v2016.xls).
  75. USDHHS. 2022. “Preventive Care.” Healthy People 2030. Retrieved September 28, 2022 (https://health.gov/healthypeople/objectives-and-data/browse-objectives/preventive-care#cit1).
  76. Vaidya Varun, Partha Gautam, and Karmakar Monita. 2012. “Gender Differences in Utilization of Preventive Care Services in the United States.” Journal of Women’s Health 21(2):140–45. [DOI] [PubMed] [Google Scholar]
  77. Watson Oliver J., Barnsley Gregory, Toor Jaspreet, Hogan Alexandra B., Winskill Peter, and Ghani Azra C.. 2022. “Global Impact of the First Year of COVID-19 Vaccination: A Mathematical Modelling Study.” The Lancet Infectious Diseases 22(9):1293–1302. doi: 10.1016/S1473-3099(22)00320-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. West Candace, and Zimmerman Don H.. 1987. “Doing Gender.” Gender & Society 1(2):125–51. doi: 10.1177/0891243287001002002. [DOI] [Google Scholar]
  79. Wilcox Vanden Berg Rand N., Basourakos Spyridon P., Shoag Jonathan, Scherr Douglas, and Al Awamlh Bashir Al Hussein. 2022. “Prostate Cancer Screening for Gay Men in the United States.” Urology 163:119–25. doi: 10.1016/j.urology.2021.07.027. [DOI] [PubMed] [Google Scholar]
  80. Wilkinson Richard G. 2005. The Impact Of Inequality: How To Make Sick Societies Healthier. 1st edition. New York: New Pr. [Google Scholar]
  81. Young Frank W. 2001. “Structural Pluralism and Life Expectancy in Less-Developed Countries: The Role of Women’s Status.” Social Indicators Research 55(2):223–40. doi: 10.1023/A:1010982822560. [DOI] [Google Scholar]

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Appendix
NIHMS1930656-supplement-Appendix.pdf (303.4KB, pdf)

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