Abstract
Purpose:
Delays in receiving medical care are an urgent problem. This study aims to determine whether the odds of, and reasons for, experiencing care delays differ by gender, race-ethnicity, and survey completion before vs. during the COVID-19 pandemic.
Methods:
We conducted a cross-sectional analysis of survey data from participants age ≥18 in the National Institutes of Health’s All of Us Research Program collected from May 6, 2018, to January 1, 2022. Logistic regressions were performed to assess the association of gender, race-ethnicity, and survey completion date with any of nine reasons for delaying care in the past 12 months.
Results:
Of 119,983 participants, 37.8% reported delaying care in the past 12 months. After adjusting for employment status, education, income, marital status, health insurance, and age, women of every race-ethnicity and Black and other race-ethnicity men were more likely than white men to report delays in care: Asian women (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.13–1.34), Black men (OR 1.15, 95% CI 1.05–1.25) and women (OR 1.46, 95% CI 1.38–1.54), Hispanic women (OR 1.36, 95% CI 1.28–1.44), white women (OR 1.55, 95% CI 1.50–1.60), and other race-ethnicity men (OR 1.15, 95% CI 1.05–1.27) and women (OR 1.79, 95% CI 1.67–1.91). A small but statistically significant difference was seen in reports of care delays for non-pandemic-related reasons during vs. before the COVID-19 pandemic (OR 0.88, 95% CI 0.83–0.93).
Conclusions:
In this study of diverse United States participants, women and Black and other race-ethnicity men were more likely than white men to report delays in care, both before and during COVID-19. Addressing care delays may be necessary to ameliorate health disparities by race-ethnicity and gender.
Keywords: delays in care, gender, race, ethnicity, health disparities
Delays in care occur when a person is unable to access necessary healthcare in a timely manner (Diamant et al., 2004). Care delays may occur for many reasons, including financial barriers, lack of access to transportation, and competing priorities such as childcare (Diamant et al., 2004).
The literature on reasons why people experience care delays is limited. A survey of Los Angeles residents found that the most commonly cited reason for delaying care was inability to take time off of work, followed closely by having to care for someone else and lack of access to transportation (Diamant et al., 2004). A separate study of hospitalized patients in Massachusettes found that by far the most frequently reported reason for delaying care was that the participant “thought the problem would go away or was not serious enough” (Weissman et al., 1991). Further research is needed to better understand why delays in care occur.
We also have a poor understanding of how many people in the United States are impacted by delays in care. However, the number is likely substantial. One study found that 33% of Los Angeles County residents surveyed had experienced a delay in care in the past 12 months (Diamant et al., 2004). Additionally, it is likely that the SARS-CoV-2 (COVID-19) pandemic greatly increased the number of people experiencing delays in care, with one study performed in June 2020 estimating that 40.9% of adults in the United States had avoided medical care because of the pandemic (Czeisler et al., 2020). A systematic review of disruptions in cancer care found that there were multiple reasons for delays in care during the pandemic, including reduced availability of healthcare professionals and disruptions in the operation of healthcare facilities and the supply chain (Riera et al., 2021).
Delays in care are not only common, but also deleterious. Care delays have been linked to numerous adverse outcomes, including higher costs (Chan et al., 2017), increased mortality (Bugiardini et al., 2017; Hanna et al., 2020), and worse patient outcomes (Chan et al., 2017). In order to prevent these adverse outcomes, ameliorating delays in care should be an urgent priority.
The adverse outcomes associated with care delays could be particularly damaging in patients who are already marginalized and discriminated against on the basis of race and gender. People of color experience many well-documented health disparities, including worse patient outcomes (Curry Jr et al., 2010) and higher mortality (Curry Jr et al., 2010; Montez et al., 2011). Although women have greater life expectancy than their male counterparts (Seifarth et al., 2012), they too experience health disparities, including worse quality of life after medical and surgical interventions (Kozlov & Benzon, 2020), higher mortality following ST-elevation myocardial infarction (Bugiardini et al., 2017), and higher morbidity throughout the lifespan (Gorman & Read, 2006).
In addition to these well-docomented health disparities, people of color and women may be more likely than their peers to experience care delays. Some studies suggest that Black race (Krawczyk et al., 2006; Naghavi et al., 2016; Reeder‐Hayes et al., 2019; Weissman et al., 1991) and female gender (Bugiardini et al., 2017; Diamant et al., 2004) are associated with increased risk of experiencing delays in care. However, existing evidence is mixed and other studies have shown no statistically significant differences in delays in care by race-ethnicity (Diamant et al., 2004) and gender (Krawczyk et al., 2006). If women do experience more delays in care, it is despite the fact that they utilize more care than men do. (Juvrud & Rennels, 2017; Pinkhasov et al., 2010; Shalev et al., 2005).
Evidence is also mixed as to how the COVID-19 pandemic has impacted delays in care by race and gender. Some studies have found no significant differences in COVID-19-related care delays by race (Kranz et al., 2021; Papautsky & Hamlish, 2020) or gender (Kranz et al., 2021). However, other studies found that Black and Hispanic (Czeisler et al., 2020; Patel et al., 2022) and female (Czeisler et al., 2020) adults were more likely to experience delayed care due to COVID-19.
In addition to having mixed findings, existing studies on delays in care by demographic group have multiple limitations. These limitations include small sample size (Diamant et al., 2004; Naghavi et al., 2016; Reeder‐Hayes et al., 2019), focus on a specific medical condition or population (Bugiardini et al., 2017; Curry Jr et al., 2010; Diamant et al., 2004; Kranz et al., 2021; Krawczyk et al., 2006; Montez et al., 2011; Papautsky & Hamlish, 2020; Patel et al., 2022; Weissman et al., 1991), and lack of information on races other than white or Black (Naghavi et al., 2016; Reeder‐Hayes et al., 2019; Weissman et al., 1991). To our knowledge, no studies have examined whether the intersection of race-ethnicity and gender is associated with delays in care. It is also unknown whether the reasons that patients experience delays in care differ by race-ethnicity, gender, or the intersection of both identities. Finally, no studies have compared the number and demographic makeup of people experiencing delays in care before vs. during the COVID-19 pandemic.
Understanding how delays in care are distributed throughout the population is critical for determining resource allocation when combating care delays. In order to design effective, evidence-based interventions, it is also necessary to understand the reasons that care delays occur and whether those reasons differ between groups. To address knowledge gaps on the distribution of, and reasons for, care delays, we examined delays in care by gender and race-ethnicity. We hypothesized that minoritized men and women and white women would report more delays in care, both before and during the COVID-19 pandemic. We further hypothesized that there would be an interaction between race-ethnicity, gender, and the COVID-19 pandemic on delays in care. Our study used data from the National Institutes of Health (NIH) All of Us Research Program (AoURP), which is a diverse, ongoing national cohort (Investigators, 2019).
Methods
Data Collection
We performed a cross-sectional study using data from the AoURP. AoURP, part of NIH’s precision medicine initiative, is an ongoing cohort that includes more than 387,000 participants recruited from over 340 sites (Montanez-Valverde et al., 2022; Ramirez et al., 2022). AoURP’s aim is to recruit one million participants from across the United States, with a focus on inclusion of participants from groups historically underrepresented in biomedical research (National Institutes of Health, 2022; Ramirez et al., 2022). As a result, over 75% of current participants are from groups underrepresented in research on the basis of race-ethnicity, gender identity, socioeconomic status, sexual orientation, rural location, disability status, or some combination thereof (Ramirez et al., 2022).
AoURP collects data from multiple sources, including electronic health records, biospecimens, wearable devices, and participant self-report on surveys (National Institutes of Health, 2022; Ramirez et al., 2022). All surveys are optional, with the exception of “the Basics” survey, which is completed by all participants upon enrollment in AoURP and collects demographic information such as race-ethnicity and gender identity (National Institutes of Health, 2022; Ramirez et al., 2022). Participants may also elect to complete the “Healthcare Access and Utilization” survey, which includes nine items on delays in care experienced by participants in the past 12 months (National Institutes of Health, 2022). The initial sample for the current study included all participants who completed the Basics and the Healthcare Access and Utilization surveys.
Data used in the current study were collected from May 6, 2018, to January 1, 2022, and analyzed from July 20, 2022, to May 17, 2023. Data are available via AoURP Researcher Workbench controlled tier data version 6 (National Institutes of Health, 2022).
Ethics Statement
This study was deemed exempt by the University of Puerto Rico Institutional Review Board because it uses pre-collected, de-identified data. Therefore, informed consent was not solicited for this study. All procedures followed were in accordance with the ethical standards of the IRB and the Helsinki Declaration of 1975, as revised in 2000.
Measures
Data for the dependent variable were collected via the following question stem: There are many reasons people delay getting medical care. Have you delayed getting care for any of the following reasons in the PAST 12 MONTHS? Participants were then shown nine potential reasons for delaying care with the option of reporting “Yes,” “No,” or “Don’t Know” for each reason. The nine reasons were: didn’t have transportation; you live in a rural area where distance to the health care provider is too far; you were nervous about seeing a health care provider; couldn’t get time off work; couldn’t get child care; you provide care to an adult and could not leave him/her; couldn’t afford the copay; your deductible was too high/or could not afford the deductible; you had to pay out of pocket for some or all of the procedure. No items queried delayed care because of pandemic measures or fears of infection. Participants were excluded from the final analysis if they did not respond or responded “Don’t Know” to any of the delays in care items.
Independent variables were participant self-reported gender identity (man or woman), race-ethnicity, and completion date of the Healthcare Access and Utilization survey. Participants were excluded from the study if they reported a gender identity other than man or woman, because including other genders would have resulted in too-small cell sizes for the race-ethnicity + gender groups. We also excluded participants who were missing information for race-ethnicity or any other demographic variable used in the study. In order to preserve the anonymity of its participants, AoURP does not allow researchers to report data for categories that contain fewer than 20 individuals. Therefore, to protect participant privacy, several low frequency racial and ethnic groups (American Indian or Alaska Native, Middle Eastern or North African, other, or multiple race-ethnicity) were combined to create a single “other or multiple race-ethnicity” group. Final race-ethnicity groups were: Asian, Black, Hispanic, white, and other or multiple race-ethnicity.
For completion date of the Healthcare Access and Utilization survey, participants were divided into three groups: completion before March 12, 2020 (the date the first COVID-19 activity restriction was issued in the United States); completion from March 12, 2020 through March 11, 2021; and completion on or after March 12, 2021 (Jacobsen & Jacobsen, 2020). March 12, 2020, through March 11, 2021, was considered a washout period and participants who completed the survey during this time were eliminated for analyses by survey completion date. They were retained in the sample for all other analyses.
Statistical Analysis
Descriptive statistics were calculated for sample demographics. Pearson χ2 tests or Fisher exact tests, as appropriate, were used to compare participant demographics by gender identity and race-ethnicity. The number and percentage of participants reporting each individual reason for delaying care and delaying care for any reason enquired about on the survey were calculated for every combination of gender identity and race-ethnicity. The number and percentage of participants delaying care for any reason before and during the COVID-19 pandemic were also calculated.
To model the association between gender, race, and delays in care we performed unadjusted and adjusted logistic regression. Unadjusted and adjusted logistic regression were also used to model the relationship between race-ethnicity + gender, survey completion date (before March 12, 2020 vs on/after March 12, 2021), the interaction between race-ethnicity + gender and survey completion date, and delays in care. The reference group was white men. White men were chosen as the reference group because of previous research suggesting that people who identify as white and/or male are less likely than their peers to experience delays in care (Bugiardini et al., 2017; Diamant et al., 2004; Krawczyk et al., 2006; Naghavi et al., 2016; Reeder‐Hayes et al., 2019; Weissman et al., 1991). Adjustment variables were employment status, education, income, marital status, health insurance status, and age. These variables were chosen based on pre-existing literature (Bugiardini et al., 2017; Montez et al., 2011). Significance was set at p<.05. Data were accessed and analyses were performed using AoURP Researcher Workbench (National Institutes of Health, 2022) and R environment with R version 4.2.2 Patched (2022–11-10 r83330).
Results
From May 6, 2018, to January 1, 2022, 369,483 participants age ≥18 years were enrolled in AoURP. Of these, 160,880 (42.5%) completed the Healthcare Access and Utilization survey (Figure 1). Of participants who completed the Healthcare Access and Utilization survey, 40,897 (25.4%) were excluded from the final analysis because they were missing demographic information used in the study, self-reported a gender identity other than “man” or “woman,” and/or did not answer all delays in care questions on the “Healthcare Access and Utilization” survey.
Figure 1.
Consort diagram of inclusion/exclusion criterion and number of participants who met each criteria.
The final sample of 119,983 participants was 66.1% female, 3.5% Asian, 3.5% Black, 8.5% Hispanic, 73.8% white, and 5.6% other race-ethnicity (Table 1). The median age of the sample was 39 (interquartile range 29). Of all respondents, 12.3% had a high school education or less, 21.1% had low income, and 3.0% did not have health insurance. For employment status, 50.9% of the sample were employed, 2.6% were students, 36.5% were unemployed, and 9.9% selected multiple employment options. For marital status, 60.6% of the sample were married or living with a partner, 20.8% were never married, and 18.6% were divorced, widowed, or separated. Of all respondents, 37.8% reported experiencing delayed care in the past 12 months for one or more reasons.
Table 1.
Sample demographics by race-ethinicity and gender identity.*
| Demographic | Asian Men N = 1,693 (1.4%) | Black Men N = 2,667 (2.2%) | Hispanic Men N = 2,827 (2.4%) | White Men N = 31,340 (26.1%) | Other or Multiple Race Men N = 2,176 (1.8%) | Asian Women N = 2,555 (2.1%) | Black Women N = 7,635 (6.4%) | Hispanic Women N = 7,317 (6.1%) | White Women N = 57,208 (47.7%) | Other or Multiple Race Women N = 4,565 (3.8%) | P value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Employment Status | <0.001 | ||||||||||
| Employed | 1,129 (66.7%) | 1,037 (38.9%) | 1,626 (57.5%) | 14,726 (47.0%) | 1,175 (54.0%) | 1,551 (60.7%) | 3,715 (48.7%) | 3,917 (53.5%) | 29,694 (51.9%) | 2,534 (55.5%) | |
| Not employed | 247 (14.6%) | 1,344 (50.4%) | 869 (30.7%) | 13,200 (42.1%) | 575 (26.4%) | 423 (16.6%) | 3,107 (40.7%) | 2,565 (35.1%) | 20,439 (35.7%) | 1,083 (23.7%) | |
| Student | 174 (10.3%) | 50 (1.9%) | 106 (3.7%) | 521 (1.7%) | 145 (6.7%) | 283 (11.1%) | 153 (2.0%) | 218 (3.0%) | 1,235 (2.2%) | 258 (5.7%) | |
| Multiple forms of employment | 143 (8.4%) | 236 (8.8%) | 226 (8.0%) | 2,893 (9.2%) | 281 (12.9%) | 298 (11.7%) | 660 (8.6%) | 617 (8.4%) | 5,840 (10.2%) | 690 (15.1%) | |
| Education | <0.001 | ||||||||||
| Some college or more | 1,647 (97.3%) | 1,705 (63.9%) | 1,940 (68.6%) | 28,979 (92.5%) | 1,946 (89.4%) | 2,462 (96.4%) | 5,643 (73.9%) | 4,618 (63.1%) | 52,201 (91.2%) | 4,083 (89.4%) | |
| High school or less | 46 (2.7%) | 962 (36.1%) | 887 (31.4%) | 2,361 (7.5%) | 230 (10.6%) | 93 (3.6%) | 1,992 (26.1%) | 2,699 (36.9%) | 5,007 (8.8%) | 482 (10.6%) | |
| Income | <0.001 | ||||||||||
| Not low income | 1,443 (85.2%) | 1,342 (50.3%) | 1,863 (65.9%) | 27,078 (86.4%) | 1,678 (77.1%) | 2,163 (84.7%) | 4,030 (52.8%) | 4,413 (60.3%) | 47,306 (82.7%) | 3,348 (73.3%) | |
| Low income | 250 (14.8%) | 1,325 (49.7%) | 964 (34.1%) | 4,262 (13.6%) | 498 (22.9%) | 392 (15.3%) | 3,605 (47.2%) | 2,904 (39.7%) | 9,902 (17.3%) | 1,217 (26.7%) | |
| Marital Status | <0.001 | ||||||||||
| Married or living with partner | 1,003 (59.2%) | 1,019 (38.2%) | 1,586 (56.1%) | 22,678 (72.4%) | 1,224 (56.2%) | 1,351 (52.9%) | 1,019 (38.2%) | 3,853 (52.7%) | 35,317 (61.7%) | 2,318 (50.8%) | |
| Divorced, widowed, or separated | 97 (5.7%) | 672 (25.2%) | 409 (14.5%) | 3,914 (12.5%) | 254 (11.7%) | 224 (8.8%) | 672 (25.2%) | 1,694 (23.2%) | 11,934 (20.9%) | 761 (16.7%) | |
| Never married | 593 (35.0%) | 976 (36.6%) | 832 (29.4%) | 4,748 (15.1%) | 698 (32.1%) | 980 (38.4%) | 976 (36.6%) | 1,770 (24.2%) | 9,957 (17.4%) | 1,486 (32.6%) | |
| Health insurance status | <0.001 | ||||||||||
| Insured | 1,652 (97.6%) | 2,398 (89.9%) | 2,512 (88.9%) | 30,791 (98.2%) | 2,107 (96.8%) | 2,503 (98.0%) | 2,398 (89.9%) | 6,545 (89.4%) | 56,334 (98.5%) | 4,440 (97.3%) | |
| Not insured | 41 (2.4%) | 269 (10.1%) | 315 (11.1%) | 549 (1.8%) | 69 (3.2%) | 52 (2.0%) | 269 (10.1%) | 772 (10.6%) | 874 (1.5%) | 125 (2.7%) | |
| Age | <0.001 | ||||||||||
| Median (Interquartile Range) | 24 (25) | 40 (21) | 31 (27) | 47 (24) | 28 (29) | 21 (25) | 36 (23) | 28 (24) | 39 (28) | 23 (25) |
Pearson χ2 or one-way ANOVA, as appropriate, were used to compare each demographic factor by race-ethnicity + gender. Significance set at p<.05.
Delays in care by race and gender
Women in the other race-ethnicity category were most likely to report delaying care for any reason enquired about on the survey (54.0%), followed by Hispanic (48.7%), Black (45.8%), Asian (44.4%), and white women (40.1%, Table 2). Among men, other race-ethnicity participants had the highest prevalence of delays in care (39.6%), followed by Hispanic (39.5%), Black (38.5%), Asian (34.3%), and white participants (26.1%, Table 3).
Table 2.
Unadjusted and adjusted logistic regression for delays in care reported by male participants.*
| In the past 12 months, delayed care because… | Asian N = 1693 (1.4%) | Black N = 2667 (2.2%) | Hispanic N = 2827 (2.4%) | White N = 31340 (26.1%) | Other or Multiple Race N = 2176 (1.8%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | |
| Didn’t have transportation | 66 (3.9%) | 0.97 (0.75–1.24), 0.83 | 0.78 (0.60–1.00), 0.06 | 397 (14.9%) | 4.20 (3.72–4.73), <0.001 | 1.74 (1.53–1.98), <0.001 | 247 (8.7%) | 2.30 (1.99–2.64), <0.001 | 1.19 (1.02–1.38), 0.02 | 1,254 (4.0%) | 1 (Reference) | 1 (Reference) | 159 (7.3%) | 1.89 (1.59–2.24), <0.001 | 1.19 (0.99–1.42), 0.06 |
| Live in a rural area | ≤20 (≤1.2%) | 0.49 (0.29–0.78), 0.005 | 0.46 (0.27–0.74), 0.002 | 104 (3.9%) | 2.09 (1.68–2.58), <0.001 | 0.99 (0.79–1.22), 0.91 | 86 (3.0%) | 1.62 (1.28–2.02), <0.001 | 0.92 (0.72–1.15), 0.46 | 596 (1.9%) | 1 (Reference) | 1 (Reference) | 52 (2.4%) | 1.26 (0.94–1.66), 0.11 | 0.90 (0.67–1.20), 0.49 |
| Nervous about seeing a healthcare provider | 147 (8.7%) | 1.03 (0.87–1.23), 0.71 | 0.63 (0.52–0.75), <0.001 | 277 (10.4%) | 1.26 (1.10–1.43), <0.001 | 0.91 (0.80–1.04), 0.19 | 343 (12.1%) | 1.50 (1.33–1.69), <0.001 | 0.95 (0.83–1.07), 0.38 | 2,639 (8.4%) | 1 (Reference) | 1 (Reference) | 275 (12.6%) | 1.57 (1.38–1.79), <0.001 | 0.98 (0.86–1.13), 0.83 |
| Couldn’t get time off work | 265 (15.7%) | 2.73 (2.37–3.13), <0.001 | 1.34 (1.16–1.54), <0.001 | 229 (8.6%) | 1.38 (1.19–1.59), <0.001 | 1.32 (1.13–1.53), <0.001 | 377 (13.3%) | 2.26 (2.01–2.54), <0.001 | 1.32 (1.17–1.50), <0.001 | 1,998 (6.4%) | 1 (Reference) | 1 (Reference) | 326 (15.0%) | 2.59 (2.28–2.93), <0.001 | 1.54 (1.35–1.76), <0.001 |
| Couldn’t get childcare | 32 (1.9%) | 2.07 (1.41–2.94), <0.001 | 1.32 (0.89–1.88), 0.15 | 52 (1.9%) | 2.14 (1.57–2.85), <0.001 | 1.94 (1.41–2.60), <0.001 | 62 (2.2%) | 2.41 (1.81–3.15), <0.001 | 1.35 (1.01–1.77), 0.04 | 289 (0.9%) | 1 (Reference) | 1 (Reference) | 47 (2.2%) | 2.37 (1.72–3.20), <0.001 | 1.42 (1.02–1.93), 0.03 |
| Had to provide care to an adult | 21 (1.2%) | 1.39 (0.86–2.11), 0.15 | 1.68 (1.04–2.57), 0.02 | 53 (2.0%) | 2.24 (1.65–2.99), <0.001 | 1.58 (1.16–2.12), 0.003 | 62 (2.2%) | 2.48 (1.86–3.25), <0.001 | 2.14 (1.60–2.82), <0.001 | 281 (0.9%) | 1 (Reference) | 1 (Reference) | 35 (1.6%) | 1.81 (1.25–2.54), 0.001 | 1.83 (1.26–2.58), <0.001 |
| Couldn’t afford the copay | 90 (5.3%) | 1.15 (0.92–1.43), 0.20 | 0.75 (0.60–0.94), 0.01 | 344 (12.9%) | 3.04 (2.68–3.44), <0.001 | 1.76 (1.54–2.01), <0.001 | 312 (11.0%) | 2.55 (2.24–2.90), <0.001 | 1.31 (1.14–1.50), <0.001 | 1,454 (4.6%) | 1 (Reference) | 1 (Reference) | 209 (9.6%) | 2.18 (1.87–2.54), <0.001 | 1.42 (1.21–1.65), <0.001 |
| Deductible was too high | 140 (8.3%) | 1.26 (1.05–1.51), 0.01 | 0.85 (0.70–1.01), 0.07 | 312 (11.7%) | 1.86 (1.64–2.11), <0.001 | 1.42 (1.24–1.61), <0.001 | 317 (11.2%) | 1.77 (1.56–2.01), <0.001 | 1.09 (0.95–1.24), 0.21 | 2,085 (6.7%) | 1 (Reference) | 1 (Reference) | 252 (11.6%) | 1.84 (1.60–2.11), <0.001 | 1.30 (1.12–1.49), <0.001 |
| Had to pay out of pocket for some or all of the procedure | 283 (16.7%) | 1.37 (1.20–1.56), <0.001 | 0.98 (0.86–1.12), 0.82 | 448 (16.8%) | 1.38 (1.24–1.53), <0.001 | 1.03 (0.92–1.15), 0.59 | 488 (17.3%) | 1.43 (1.29–1.58), <0.001 | 0.92 (0.83–1.03), 0.14 | 3,999 (12.8%) | 1 (Reference), <0.001 | 1 (Reference) | 408 (18.8%) | 1.58 (1.41–1.76), <0.001 | 1.17 (1.05–1.32), 0.006 |
| For any of the above reasons | 581 (34.3%) | 1.48 (1.33–1.64), <0.001 | 0.87 (0.78–0.97), 0.01 | 1,026 (38.5%) | 1.77 (1.63–1.92), <0.001 | 1.15 (1.05–1.25), 0.002 | 1,118 (39.5%) | 1.85 (1.71–2.01), <0.001 | 1.03 (0.94–1.12), 0.51 | 8,177 (26.1%) | 1 (Reference) | 1 (Reference) | 862 (39.6%) | 1.86 (1.70–2.03), <0.001 | 1.15 (1.05–1.27), 0.003 |
Adjusted model controlled for employment status, education, income, marital status, health insurance status, and age. Reference group = white men. Significance set at p<.05.
Table 3.
Unadjusted and adjusted logistic regression for delays in care reported by female participants.*
| In the past 12 months, delayed care because… | Asian N = 2555 (2.1%) | Black N = 7635 (6.4%) | Hispanic N = 7317 (6.1%) | White N = 57208 (47.7%) | Other or Multiple Race N = 4565 (3.8%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | Number (%) | Unadjusted odds ratio (95% confidence interval), p value | Adjusted odds ratio (95% confidence interval), p value | |
| Didn’t have transportation | 149 (5.8%) | 1.49 (1.24–1.76), <0.001 | 1.04 (0.87–1.25), 0.65 | 1,116 (14.6%) | 4.11 (3.77–4.47), <0.001 | 1.80 (1.64–1.97), <0.001 | 861 (11.8%) | 3.20 (2.92–3.50), <0.001 | 1.35 (1.22–1.49), <0.001 | 3,117 (5.4%) | 1.38 (1.29–1.48), <0.001 | 1.10 (1.03–1.18), 0.005 | 479 (10.5%) | 2.81 (2.52–3.14), <0.001 | 1.54 (1.37–1.73), <0.001 |
| Live in a rural area | 38 (1.5%) | 0.78 (0.55–1.07), 0.14 | 0.64 (0.45–0.89), 0.01 | 342 (4.5%) | 2.42 (2.11–2.77), <0.001 | 1.23 (1.06–1.41), 0.005 | 310 (4.2%) | 2.28 (1.98–2.62), <0.001 | 1.03 (0.89–1.20), 0.68 | 1,716 (3.0%) | 1.60 (1.45–1.75), <0.001 | 1.34 (1.21–1.47), <0.001 | 172 (3.8%) | 2.02 (1.70–2.39), <0.001 | 1.26 (1.05–1.50), 0.01 |
| Nervous about seeing a healthcare provider | 394 (15.4%) | 1.98 (1.77–2.22), <0.001 | 1.08 (0.96–1.22), 0.20 | 1,058 (13.9%) | 1.75 (1.62–1.89), <0.001 | 1.16 (1.07–1.26), <0.001 | 1,049 (14.3%) | 1.82 (1.69–1.96), <0.001 | 1.03 (0.95–1.12), 0.48 | 8,886 (15.5%) | 2.00 (1.91–2.09), <0.001 | 1.63 (1.55–1.70), <0.001 | 974 (21.3%) | 2.95 (2.72–3.20), <0.001 | 1.60 (1.47–1.74), <0.001 |
| Couldn’t get time off work | 495 (19.4%) | 3.53 (3.16–3.93), <0.001 | 1.63 (1.45–1.82), <0.001 | 979 (12.8%) | 2.16 (1.99–2.34), <0.001 | 1.66 (1.52–1.81), <0.001 | 1,236 (16.9%) | 2.98 (2.77–3.22), <0.001 | 1.77 (1.63–1.92), <0.001 | 6,967 (12.2%) | 2.04 (1.93–2.15), <0.001 | 1.61 (1.52–1.70), <0.001 | 913 (20.0%) | 3.67 (3.37–4.00), <0.001 | 1.87 (1.70–2.04), <0.001 |
| Couldn’t get childcare | 95 (3.7%) | 4.15 (3.26–5.23), <0.001 | 2.12 (1.66–2.69), <0.001 | 325 (4.3%) | 4.78 (4.07–5.61), <0.001 | 3.82 (3.23–4.52), <0.001 | 562 (7.7%) | 8.94 (7.75- 10.34), <0.001 | 3.73 (3.20–4.35), <0.001 | 1,894 (3.3%) | 3.68 (3.25–4.17), <0.001 | 2.53 (2.24–2.88), <0.001 | 288 (6.3%) | 7.23 (6.13–8.54), <0.001 | 3.15 (2.65–3.75), <0.001 |
| Had to provide care to an adult | 41 (1.6%) | 1.80 (1.28–2.48), <0.001 | 2.17 (1.53–3.00), <0.001 | 223 (2.9%) | 3.33 (2.78–3.97), <0.001 | 2.54 (2.11–3.05), <0.001 | 275 (3.8%) | 4.32 (3.65–5.11), <0.001 | 3.51 (2.93–4.20), <0.001 | 1,055 (1.8%) | 2.08 (1.82–2.37), <0.001 | 2.08 (1.82–2.38), <0.001 | 122 (2.7%) | 3.04 (2.44–3.75), <0.001 | 3.05 (2.44–3.79), <0.001 |
| Couldn’t afford the copay | 210 (8.2%) | 1.84 (1.58–2.14), <0.001 | 1.12 (0.96–1.30), 0.15 | 1,137 (14.9%) | 3.60 (3.31–3.90), <0.001 | 2.02 (1.85–2.20), <0.001 | 1,045 (14.3%) | 3.42 (3.15–3.72), <0.001 | 1.61 (1.47–1.77), <0.001 | 5,066 (8.9%) | 2.00 (1.88–2.12), <0.001 | 1.62 (1.52–1.72), <0.001 | 645 (14.1%) | 3.38 (3.06–3.73), <0.001 | 1.88 (1.70–2.09), <0.001 |
| Deductible was too high | 274 (10.7%) | 1.69 (1.47–1.92), <0.001 | 1.07 (0.93–1.22), 0.35 | 990 (13.0%) | 2.09 (1.93–2.26), <0.001 | 1.47 (1.35–1.60), <0.001 | 1,002 (13.7%) | 2.23 (2.05–2.41), <0.001 | 1.29 (1.18–1.40), <0.001 | 6,522 (11.4%) | 1.81 (1.72–1.90), <0.001 | 1.50 (1.42–1.58), <0.001 | 664 (14.5%) | 2.34 (2.17–2.62), <0.001 | 1.46 (1.33–1.61), <0.001 |
| Had to pay out of pocket for some or all of the procedure | 546 (21.4%) | 1.86 (1.68–2.05), <0.001 | 1.23 (1.15–1.42), <0.001 | 1,534 (20.1%) | 1.72 (1.61–1.83), <0.001 | 1.24 (1.15–1.32), <0.001 | 1,471 (20.1%) | 1.72 (1.61–1.84), <0.001 | 1.08 (1.00–1.15), 0.04 | 10,879 (19.0%) | 1.61 (1.54–1.67), <0.001 | 1.39 (1.34–1.45), <0.001 | 1,139 (25.0%) | 2.27 (2.11–2.45), <0.001 | 1.53 (1.41–1.65), <0.001 |
| For any of the above reasons | 1,135 (44.4%) | 2.26 (2.09–2.46), <0.001 | 1.23 (1.13–1.34), <0.001 | 3,497 (45.8%) | 2.39 (2.27–2.52), <0.001 | 1.46 (1.38–1.54), <0.001 | 3,563 (48.7%) | 2.69 (2.55–2.83), <0.001 | 1.36 (1.28–1.44), <0.001 | 22,954 (40.1%) | 1.90 (1.84–1.96), <0.001 | 1.55 (1.50–1.60), <0.001 | 2,464 (54.0%) | 3.32 (3.12–3.54), <0.001 | 1.79 (1.67–1.91), <0.001 |
Adjusted model controlled for employment status, education, income, marital status, health insurance status, and age. Reference group = white men. Significance set at p<.05.
After adjusting for demographic variables, women of every race-ethnicity were more likely to report delaying care than were white men: Asian (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.13–1.34), Black (OR 1.46, 95% CI 1.38–1.54), Hispanic (OR 1.36, 95% CI 1.28–1.44), white (OR 1.55, 95% CI 1.50–1.60), and other race-ethnicity (OR 1.79, 95% CI 1.67–1.91).
Black men (OR 1.15, 95% CI 1.05–1.25) and other race-ethnicity men (OR 1.15, 95% CI 1.05–1.27) were also more likely than white men to report delaying care for any reason. Asian men (OR 0.87, 95% CI 0.78–0.97) were less likely than white men to report delaying care. No statistically significant difference in likelihood of delaying care was found between white and Hispanic men (OR 1.03, 95% CI 0.94–1.12).
Reasons for delaying care
The most reported reasons for delays in care were: “Had to pay out of pocket for some or all of the procedure” (17.7%), “nervous about seeing a healthcare provider” (13.4%), and “couldn’t get time off work” (11.5%). For every combination of race-ethnicity + gender, “had to pay out of pocket” was the most cited reason for delaying care. “Nervous about seeing a provider” and “couldn’t get time off work” were the second or third most common reasons for delaying care for seven of ten race-ethnicity + gender groups. After having to pay out of pocket, Black participants most frequently cited not being able to afford the copay (men=12.9%, women=14.9%) and not having transportation (men=14.9%, women=14.6%).
After adjusting for demographic variables, Black and other race-ethnicity women were more likely than white men to report all nine reasons for delaying care. They were followed by Hispanic women and other race-ethnicity men, who were more likely than white men to report seven of nine reasons for delaying care. Black men were more likely than white men to report six of nine reasons for delaying care, and Hispanic men were more likely to report five of nine reasons. Asian women were more likely than white men to report four of nine reasons for delaying care and less likely to report one reason. Finally, Asian men were more likely than white men to report two of nine reasons for delaying care and less likely to report three reasons.
Delays in care before vs. during the COVID-19 pandemic
The Healthcare Access and Utilization Survey was completed by 66,493 people before March 12, 2020, and by 34,326 people on or after March 12, 2021. Before March 12, 2020, 37.9% of participants reported experiencing delayed care for any reason enquired about on the Healthcare Access and Utilization survey (a list that did not include pandemic-related reasons), as opposed to 38.0% of participants on or after March 12, 2021 (p = .90). In the fully adjusted model, participants were less likely to report delaying care during the pandemic than before (OR 0.86, 95% CI 0.80–0.92, Table 4).
Table 4.
Unadjusted and adjusted logistic regression for interaction of race-ethnicity + gender and survey completion before vs. during the COVID-19 pandemic.*
| Number (%) reporting delay in care for any reason | Unadjusted logistic regression | Adjusted logistic regression | ||||
|---|---|---|---|---|---|---|
| Before March 12, 2020 | On/After March 12, 2021 | Odds ratio (95% CI) | P value | Odds ratio (95% CI) | P value | |
| Main effect | ||||||
| Asian men | 247 (34.3) | 114 (31.0) | 1.46 (1.24–1.71) | <0.001 | 0.87 (0.73–1.02) | 0.09 |
| Black men | 345 (37.7) | 325 (37.4) | 1.87 (1.62–2.15) | <0.001 | 1.15 (0.99–1.34) | 0.06 |
| Hispanic men | 384 (38.8) | 363 (37.5) | 1.95 (1.70–2.23) | <0.001 | 1.08 (0.93–1.24) | 0.30 |
| White men | 4,036 (26.4) | 1,592 (26.5) | 1 (Reference) | |||
| Other or multiple race men | 383 (39.6) | 189 (40.1) | 1.83 (1.60–2.09) | <0.001 | 1.15 (1.00–1.33) | 0.05 |
| Asian women | 512 (45.8) | 240 (44.5) | 2.36 (2.09–2.67) | <0.001 | 1.29 (1.13–1.47) | <0.001 |
| Black women | 1,294 (47.1) | 878 (43.7) | 2.49 (2.29–2.71) | <0.001 | 1.45 (1.33–1.59) | <0.001 |
| Hispanic women | 1,331 (53.2) | 948 (43.3) | 3.17 (2.91–3.46) | <0.001 | 1.59 (1.45–1.75) | <0.001 |
| White women | 11,577 (40.3) | 4,729 (40.6) | 1.89 (1.81–1.97) | <0.001 | 1.57 (1.50–1.65) | <0.001 |
| Other or multiple race women | 1,045 (54.5) | 584 (55.8) | 3.34 (3.03–3.68) | <0.001 | 1.86 (1.68–2.07) | <0.001 |
| Survey completion date on/after March 12, 2021 | 1.00 (0.94–1.07) | 0.90 | 0.86 (0.80–0.92) | <0.001 | ||
| Interaction between survey completion date on/after March 12, 2020 and: | ||||||
| Asian men | 0.86 (0.65–1.13) | .27 | 0.81 (0.60–1.08) | .14 | ||
| Black men | 0.89 (0.73–1.09) | .27 | 1.01 (0.81–1.25) | .94 | ||
| Hispanic men | 0.86 (0.70–1.04) | .12 | 0.91 (0.74–1.12) | .39 | ||
| White men | 1 (Reference) | 1 (Reference) | ||||
| Other or multiple race men | 1.02 (0.80–1.29) | .88 | 0.99 (0.77–1.27) | .94 | ||
| Asian women | 0.95 (0.76–1.18) | .62 | 0.91 (0.72–1.14) | .41 | ||
| Black women | 0.87 (0.76–0.99) | .04 | 1.00 (0.87–1.15) | 1.00 | ||
| Hispanic women | 0.67 (0.58–0.76) | <.001 | 0.72 (0.62–0.83) | <0.001 | ||
| White women | 1.01 (0.93–1.09) | .88 | 0.94 (0.86–1.02) | .15 | ||
| Other or multiple race women | 1.05 (0.89–1.24) | .56 | 0.94 (0.79–1.12) | .51 | ||
Adjusted model controlled for employment status, education, income, marital status, health insurance status, and age. Reference group = white men. Significance set at p<.05.
In the unadjusted model, Black women were less likely to report delaying care for any of the nine queried reasons during the pandemic than before the pandemic (OR 0.87, 95% CI 0.76–0.99). Hispanic women were less likely to report delaying care during the pandemic than before in both the unadjusted (OR 0.67, 95% CI 0.62–0.83) and adjusted (OR 0.72, 95% CI 0.62–0.83) models. No other significant interactions were noted between race-ethnicity + gender and survey completion date.
Discussion
The results of this study suggest that delays in care for one or more of the nine reasons queried are common, with over one-third of participants reporting delaying care in the past 12 months. Financial difficulty was the most common cause of care delays, with 17.7% of the sample reporting delaying care because they had to pay out of pocket. Our results also suggest delays in care are inequitably distributed, with female, minoritized, and, in particular, minoritized female participants facing a high burden of delays in care.
Women of every race-ethnicity reported more delays in care than their male counterparts. These results at first appear incongruent with research showing women make more preventative, emergency, and overall healthcare visits than do men (Juvrud & Rennels, 2017; Pinkhasov et al., 2010; Shalev et al., 2005). However, the apparent inconsistency may be explained by differences in the base rate of healthcare-seeking behavior between men and women.
Woman may be more likely than men to desire medical attention for multiple reasons, including gender differences in morbidity (Pinkhasov et al., 2010) and/or perceived social norms around help-seeking behavior (Juvrud & Rennels, 2017). When these women engage in healthcare-seeking behavior, some of them will successfully access healthcare, while others may encounter barriers that cause delays in care. In contrast, if fewer men attempt to access healthcare, then men will be less likely than women both to successfully complete healthcare visits and to encounter barriers that prevent them from accessing healthcare.
The most common reasons for delaying care (had to pay out of pocket, nervous to see a provider, couldn’t get time off work) were consistent across gender. However, more women than men reported delaying care for all three reasons. Women may be more susceptible to financial and work-based delays in care because, as compared to men, they have a lower median wage (Shrider et al., 2021), are more likely to live in poverty (Shrider et al., 2021), and are less likely to receive pay when taking time off work (Herr & Klerman, 2020). Additionally, women may be nervous to see providers for fear of bias and discrimination during medical encounters (FitzGerald & Hurst, 2017; Hamberg, 2008).
Asian, Black, Hispanic, and other race-ethnicity participants were more likely than their white peers to report delays in care for one or more of the nine reasons queried. Participants with other race-ethnicity reported more delays in care than any other group. The reasons for this finding are unclear, and this group deserves greater attention in future studies.
Minoritized participants likely experienced more care delays for multiple reasons, including a well-founded fear of discrimination by healthcare providers (FitzGerald & Hurst, 2017) and economic inequality (Shrider et al., 2021). In particular, the high prevalence of economic barriers, such as paying out of pocket and inability to get time off work, highlights the important role that structural racism may play in creating care delays. Previous research has shown that a “racial opportunity gap” exists between white and Black Americans for socioeconomic measures including unemployment, poverty, median income, and college graduation (O’Brien et al., 2020). Furthermore, this research showed that larger racial opportunity gaps are associated with larger mortality differences between white and Black Americans (O’Brien et al., 2020). Socioeconomic reasons for delaying care may partially explain the association between racial differences in socioeconomic opportunity and mortality.
In addition to financial barriers, we found that Black participants face unique transportation-related challenges. This finding is consistent with previous studies showing that, as compared to their white peers, Black people are more likely to face transportation-related barriers such as lacking access to a vehicle and/or driver and living farther from a treatment center (Syed et al., 2013). Many of these transportation barriers are likely due to structural racism. For example, Black participants may be more likely to live farther from treatment centers because treatment centers are more likely to be built and maintained in areas that serve white urban populations (Planey et al., 2023).
The inequity in delays in care faced by women and minoritized participants appeared to be additive, with minoritized female participants reporting more care delays than their white or male peers. Strikingly, over half of other race-ethnicity women reported delaying care for one or more of the nine reasons queried in the past 12 months. Almost half of Black and Hispanic women reported the same.
A small but statistically significant difference was found in the percentage of participants experiencing delays in care before and during the COVID-19 pandemic. With the exception of Hispanic women, no significant interactions were found between race-ethnicity + gender and survey completion before vs. during the pandemic. This is likely because the Healthcare Access and Utilization survey does not specifically enquire if participants delayed care due to COVID-19. The survey does include a free-response option for participants to report care delays for reasons not specifically enquired about on the survey. It is possible that a large percentage of participants used this free response option to report delaying care due to COVID-19. However, neither the percentage of respondents who completed this question nor their responses have been made available by the AoURP.
Despite this limitation, this study makes a critical contribution to our understanding of delays in care. First, it demonstrates that, even before the COVID-19 pandemic, a large proportion of the population experienced delays in care for a variety of causes. Second, many experts in the medical and public health communities have expressed concern over delays in care caused by COVID-19 (Czeisler et al., 2020; Kranz et al., 2021; Papautsky & Hamlish, 2020; Patel et al., 2022). While COVID-19-related care delays are undoubtedly important, this study demonstrates that, throughout the pandemic, a large percentage of people continued to experience care delays for reasons other than COVID-19. If left unaddressed, these causes of care delays will continue to negatively impact public health and contribute to health disparities long after the COVID-19 pandemic has been controlled.
Limitations
In addition to the unavailability of free response answers, this study had several limitations. As with any analysis of survey data, this study was vulnerable to recall bias and social desirability bias. Additionally, a quarter of the initial sample was excluded due to missing data. These participants may have differed meaningfully from those included in the final sample in terms of demographics and experiences with delays in care.
AoURP does not recruit participants using a formal statistical sampling method, which may limit the generalizability of results to the United States population as a whole (Ramirez et al., 2022). However, researchers have demonstrated the external validity of the AoURP sample by replicating the findings of earlier population-based research on depression medications, diabetes, and the relationship between smoking and cancer (Ramirez et al., 2022).
This study was also limited by the use of race-ethnicity categories that combined many disparate groups with different life experiences. As the AoURP sample grows, future researchers could disaggregate the races and ethnicities included in the “other” group used for this study or could use country of origin to provide more granularity. Finally, this study excluded participants with gender identity other than man or woman, despite research suggesting transgender and non-binary participants face a high burden of delays in care (Jaffee et al., 2016).
Despite these limitations, to our knowledge this is the largest-ever study on delays in care and the first to examine the intersection of race and gender. This study was especially critical given the growing minoritized population in the United States (Jones et al., 2021). From 2010–2020 the number of people identifying as Hispanic and multiracial grew 23% and 276%, respectively (Jones et al., 2021).
Implications for Practice and Policy
We found that financial difficulty was the most common reason for delaying care. This finding suggests that state and federal governments should take steps to make care more affordable and accessible, especially for women and minoritized people. Potential steps include expanding insurance access; limiting the use of high deductibles and copays; increasing the availability and transparency of sliding-scale fees for patients paying out of pocket; and mandating employers provide paid time off for medical visits.
We also found that people frequently delayed care because they were nervous to see a healthcare provider. Participants may have been nervous to see a healthcare provider, at least in part, because of the large body of evidence demonstrating that healthcare providers are biased against women and minoritized patients (FitzGerald & Hurst, 2017; Hamberg, 2008). Hospitals, medical schools, and governmental and professional organizations should therefore take steps to create a diverse healthcare workforce capable of providing non-biased care to all patients. Potential steps include expanding trainings on diversity, equity, and bias and increasing the percentage of the healthcare workforce from groups historically excluded from medical education.
Conclusions
In this study involving a diverse group of United States participants, we found that women of every race-ethnicity and Black and other race-ethnicity men were more likely than white men to report delays in care both before and during the COVID-19 pandemic. These inequities may contribute to health disparities and must be addressed to promote the health and wellbeing of people marginalized on the basis of gender, race, or both.
Acknowledgments
This project was supported by CAPAC (Award Grant Number# R25CA240120) from the NCI. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. Katherine Hill had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding Statement
This project was supported by CAPAC (Award Grant Number# R25CA240120) from the NCI. CAPAC was not involved in study design, data collection, data analysis, or data interpretation.
Biographies
Katherine Hill is a neurology resident at Mayo Clinic Minnesota. Their research focuses on health equity and diversity, equity, and inclusion in the biomedical workforce.
Vivian Colón-López is a senior investigator in Cancer Control and Population Sciences at University of Puerto Rico Comprehensive Cancer Center. She performs epidemiologic and community-based outreach research. Her studies focus on HPV infection, HPV-related cancers, and vaccine hesitancy.
Footnotes
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Declaration of Interest
The authors have no relevant financial or non-financial interests to disclose.
References
- Bugiardini R, Ricci B, Cenko E, Vasiljevic Z, Kedev S, Davidovic G, Zdravkovic M, Miličić D, Dilic M, & Manfrini O (2017). Delayed care and mortality among women and men with myocardial infarction. Journal of the American Heart Association, 6(8), e005968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chan CW, Farias VF, & Escobar GJ (2017). The impact of delays on service times in the intensive care unit. Management Science, 63(7), 2049–2072. [Google Scholar]
- Curry WT Jr, Carter BS, & Barker FG (2010). Racial, ethnic, and socioeconomic disparities in patient outcomes after craniotomy for tumor in adult patients in the United States, 1988–2004. Neurosurgery, 66(3), 427–438. 10.1227/01.NEU.0000365265.10141.8E [DOI] [PubMed] [Google Scholar]
- Czeisler MÉ, Marynak K, Clarke KE, Salah Z, Shakya I, Thierry JM, Ali N, McMillan H, Wiley JF, & Weaver MD (2020). Delay or avoidance of medical care because of COVID-19–related concerns—United States, June 2020. Morbidity and Mortality Weekly Report, 69(36), 1250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Diamant AL, Hays RD, Morales LS, Ford W, Calmes D, Asch S, Duan N, Fielder E, Kim S, & Fielding J (2004). Delays and unmet need for health care among adult primary care patients in a restructured urban public health system. American Journal of Public Health, 94(5), 783–789. [DOI] [PMC free article] [PubMed] [Google Scholar]
- FitzGerald C, & Hurst S (2017). Implicit bias in healthcare professionals: a systematic review. BMC medical ethics, 18(1), 1–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gorman BK, & Read J. n. G. (2006). Gender disparities in adult health: an examination of three measures of morbidity. Journal of health and social behavior, 47(2), 95–110. [DOI] [PubMed] [Google Scholar]
- Hamberg K (2008). Gender bias in medicine. Women’s health, 4(3), 237–243. 10.2217/17455057.4.3.237 [DOI] [PubMed] [Google Scholar]
- Hanna TP, King WD, Thibodeau S, Jalink M, Paulin GA, Harvey-Jones E, O’Sullivan DE, Booth CM, Sullivan R, & Aggarwal A (2020). Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ, 371. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Herr J, & Klerman J (2020). Gender Differences in Needing and Taking Leave. Abt Associates. Retrieved May 3 from https://www.dol.gov/sites/dolgov/files/OASP/evaluation/pdf/WHD_FMLAGenderShortPaper_Ja nuary2021.pdf [Google Scholar]
- Investigators, A. o. U. R. P. (2019). The “All of Us” research program. New England Journal of Medicine, 381(7), 668–676. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jacobsen GD, & Jacobsen KH (2020). Statewide COVID‐19 stay‐at‐home orders and population mobility in the United States. World Medical & Health Policy, 12(4), 347–356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jaffee KD, Shires DA, & Stroumsa D (2016). Discrimination and delayed health care among transgender women and men. Medical Care, 54(11), 1010–1016. 10.1097/MLR.0000000000000583 [DOI] [PubMed] [Google Scholar]
- Jones N, Marks R, Ramirez R, & Rios‐Vargas M (2021). 2020 census illuminates racial and ethnic composition of the country. United States Census Bureau. [Google Scholar]
- Juvrud J, & Rennels JL (2017). “I don’t need help”: Gender differences in how gender stereotypes predict help-seeking. Sex Roles, 76(1), 27–39. [Google Scholar]
- Kozlov N, & Benzon HT (2020). Role of gender and race in patient-reported outcomes and satisfaction. Anesthesiology clinics, 38(2), 417–431. 10.1016/j.anclin.2020.01.012 [DOI] [PubMed] [Google Scholar]
- Kranz A, Gahlon G, Dick A, & Stein B (2021). Characteristics of US adults delaying dental care due to the COVID-19 pandemic. JDR Clinical & Translational Research, 6(1), 8–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Krawczyk CS, Funkhouser E, Kilby JM, & Vermund SH (2006). Delayed access to HIV diagnosis and care: Special concerns for the Southern United States. AIDS Care, 18(sup1), 35–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Montanez-Valverde R, McCauley J, Isasi R, Zuchner S, & Carrasquillo O (2022). Revisiting the Latino Epidemiologic Paradox: An analysis of data from the All of Us Research Program. Journal of General Internal Medicine, 37(15), 4013–4014. 10.1007/s11606-022-07625-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- Montez JK, Hummer RA, Hayward MD, Woo H, & Rogers RG (2011). Trends in the educational gradient of US adult mortality from 1986 through 2006 by race, gender, and age group. Research on aging, 33(2), 145–171. 10.1177/0164027510392388 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Naghavi AO, Echevarria MI, Strom TJ, Abuodeh YA, Ahmed KA, Venkat PS, Trotti A, Harrison LB, Green BL, & Yamoah K (2016). Treatment delays, race, and outcomes in head and neck cancer. Cancer epidemiology, 45(1), 18–25. 10.1016/j.canep.2016.09.005 [DOI] [PubMed] [Google Scholar]
- National Institutes of Health. (2022). Researcher Workbench. Retrieved December 13 from https://www.researchallofus.org/data-tools/workbench/ [Google Scholar]
- O’Brien R, Neman T, Seltzer N, Evans L, & Venkataramani A (2020). Structural racism, economic opportunity and racial health disparities: evidence from US counties. SSM-Population health, 11, 100564. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Papautsky EL, & Hamlish T (2020). Patient-reported treatment delays in breast cancer care during the COVID-19 pandemic. Breast cancer research and treatment, 184, 249–254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Patel MI, Ferguson JM, Castro E, Pereira-Estremera CD, Armaiz-Peña GN, Duron Y, Hlubocky F, Infantado A, Nuqui B, & Julian D (2022). Racial and ethnic disparities in cancer care during the COVID-19 pandemic. JAMA Network Open, 5(7), e2222009–e2222009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pinkhasov RM, Wong J, Kashanian J, Lee M, Samadi DB, Pinkhasov MM, & Shabsigh R (2010). Are men shortchanged on health? Perspective on health care utilization and health risk behavior in men and women in the United States. International journal of clinical practice, 64(4), 475–487. [DOI] [PubMed] [Google Scholar]
- Planey AM, Planey DA, Wong S, Mclafferty SL, & Ko MJ (2023). Structural Factors and Racial/Ethnic Inequities in Travel Times to Acute Care Hospitals in the Rural US South, 2007–2018. The Milbank Quarterly. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, & Deflaux N (2022). The All of Us Research Program: Data quality, utility, and diversity. Patterns, 3(8), 100570. 10.1016/j.patter.2022.100570 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Reeder‐Hayes KE, Mayer SE, Olshan AF, Wheeler SB, Carey LA, Tse CK, Bell ME, & Troester MA (2019). Race and delays in breast cancer treatment across the care continuum in the Carolina Breast Cancer Study. Cancer, 125(22), 3985–3992. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riera R, Bagattini ÂM, Pacheco RL, Pachito DV, Roitberg F, & Ilbawi A (2021). Delays and disruptions in cancer health care due to COVID-19 pandemic: systematic review. JCO Global Oncology, 7(1), 311–323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Seifarth JE, McGowan CL, & Milne KJ (2012). Sex and life expectancy. Gender medicine, 9(6), 390–401. 10.1016/j.genm.2012.10.001 [DOI] [PubMed] [Google Scholar]
- Shalev V, Chodick G, Heymann A, & Kokia E (2005). Gender differences in healthcare utilization and medical indicators among patients with diabetes. Public health, 119(1), 45–49. [DOI] [PubMed] [Google Scholar]
- Shrider EA, Kollar M, Chen F, & Semega J (2021). Income and poverty in the United States: 2020. US Census Bureau, Current Population Reports(P60–273). [Google Scholar]
- Syed ST, Gerber BS, & Sharp LK (2013). Traveling towards disease: transportation barriers to health care access. Journal of Community Health, 38(5), 976–993. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Weissman JS, Stern R, Fielding SL, & Epstein AM (1991). Delayed access to health care: risk factors, reasons, and consequences. Annals of Internal Medicine, 114(4), 325–331. 10.7326/0003-4819-114-4-325 [DOI] [PubMed] [Google Scholar]