Abstract
Background
Preventive care, like cancer screenings, is an effective tool to reduce the risk of morbidity and mortality. Despite these benefits, preventive care is underutilized in the United States and disparities are well-documented. However, preventive care research has largely been limited to Black, Latino, and White populations, and a better understanding of preventive care use across all racial and/or ethnic populations is needed.
Methods
This study used data from the PREVentive Care AdheREnce in Diverse Populations (PREV-CARE) study, a nationally representative, online survey of American Indian or Alaska Native (AIAN), Asian, Black, Latino (English- and Spanish-speaking), Middle Eastern or North African (MENA), Native Hawaiian or Pacific Islander (NHPI), White, and multiracial adults aged ≥35 years (6/2023-8/2023, N=4,111). Preventive care measures included blood pressure screening; cholesterol screening; breast, cervical, and colorectal cancer screening; and influenza, shingles vaccination, and pneumococcal vaccination. Multivariable Poisson regression was used to estimate the association between race and/or ethnicity and adherence to preventive services guidelines. Ethnicity-stratified analyses among Asian, Latino, and NHPI participants were also performed.
Results
Several disparities in vaccination adherence were observed when compared to White adults, among AIAN (influenza PR: 0.82 [95% CI:0.69-0.98]; shingles PR: 0.76, [0.54-1.08]), Black (shingles PR: 0.75, [0.54-1.06]), Spanish-speaking Latino (pneumococcal PR: 0.78 [0.51-1.19]), MENA (influenza PR: 0.75 [0.62-0.92]), and multiracial adults (influenza PR: 0.84 [0.71-1.00]; shingles PR: 0.53 [0.34-0.81]). MENA females were also less likely to undergo cervical cancer screening (PR: 0.85 [0.62-0.92]). Almost all racial and/or ethnic populations were below Healthy People 2030 targets.
Conclusions
Future research should assess how health beliefs and barriers to care contribute to racial and/or ethnic disparities in preventive care.
Keywords: Preventive care, Cancer screening, Vaccinations, Health care utilization
Classification code: 62P25
Introduction
Preventive care measures, such as cancer screenings, cardiovascular disease screenings, and vaccinations, are effective (and cost effective) ways for individuals to reduce their risk of morbidity and mortality. For example, between 1996 (when breast, cervical, and colorectal cancer screenings were initially recommended by the United States Preventive Services Task Force [USPSTF]) and 2020, breast, cervical, and colorectal cancer screenings have averted 4.75 million cancer deaths [1] and saved $6.5-$8.6 trillion in healthcare spending [2]. Vaccinations have provided similar benefits, with adult vaccinations projected to prevent an estimated 65 million infections and $185 billion in subsequent healthcare costs over the next 30 years [3].
Despite these benefits, preventive care is underutilized in the United States (US) – approximately 1 in 4 adults were not up to date on recommended cancer screenings in 2021 [4] and use of all recommended preventive services has been estimated to be as low as 5.3% in 2020 [5]. The COVID-19 pandemic led to further declines in cancer screening rates [6, 7] and influenza vaccination [8]. Nationally representative estimates of adherence to preventive care guidelines after the official expiration of the COVID-19 Public Health Emergency on May 11, 2023 [9] are still needed to determine whether rates have rebounded to pre-pandemic levels.
Additionally, racial and/or ethnic disparities in adherence to preventive care guidelines are well-documented [8, 10-13], and put certain communities at increased risk of poor health outcomes. These disparities are often due to systemic issues such as access to healthcare, insurance status, access to transportation, and other barriers to care [14-20]. Misinformation may also play an important role [21]. However, most research on adherence has been limited to Black or African American (Black), Hispanic or Latino (Latino), and White populations, with most studies grouping Asian and Pacific Islander populations together and collapsing all other populations into an overly broad “Other” category [22-24]. Studies which provide further disaggregated estimates of preventive care utilization by race and/or ethnicity are needed to better identify gaps in adherence and where culturally appropriate, targeted interventions to increase uptake are needed.
Thus, the objective of this study was to describe race and/or ethnicity specific adherence to preventive care guidelines post-pandemic among a nationally representative sample of US adults that includes Asian, American Indian or Alaska Native (AIAN), Native Hawaiian or Pacific Islander (NHPI), Middle Eastern or North African (MENA), and multiracial adults, as well as Black, Latino and White adults.
Methods
The PREVentive Care AdheREnce in Diverse Populations (PREV-CARE) survey was administered by YouGov, a consumer research firm in Palo Alto, California. All male and female adults ≥ 35 years old were eligible to participate. YouGov uses a proprietary, opt-in survey panel composed of over 2 million US adults (active) to conduct nationally representative, online surveys. Panel members are recruited via Web advertising, permission-based email campaigns, partner-sponsored solicitations, telephone-to-Web recruitment, mail-to-Web recruitment, and other methods of outreach. Panel members receive incentives through a loyalty program to complete surveys. YouGov, and these methods, have been used previously to conduct nationally representative, online surveys for research in the US [25-27]. The PREV-CARE survey was administered in English and Spanish (Latino sample only); for most questions, previously validated, Spanish translations were available and reviewed by a bilingual research team member. All surveys were completed between June 7, 2023 and August 24, 2023.
To obtain a nationally representative sample within each racial and/or ethnic population, YouGov randomly matched eligible panel members to a target sample using race and/or ethnicity, sex, age, and education. The target sample was drawn from a representative “modeled frame” of US adults, based upon the American Community Survey public use microdata file, public voter file records, the 2020 Current Population Survey Voting and Registration supplements, the 2020 National Election Pool exit poll, and the 2020 Current Employment Statistics surveys. The target sample for the PREV-CARE survey included 500 adults each from AIAN, Asian, Black, Latino, MENA, NHPI, White, and multiracial adults (overall n = 4,000). During recruitment, the target quota was increased to 775 for Latino adults to ensure that at least 250 would be Spanish-speaking. The sampling quota for MENA was also unable to be met (final n = 336 out of a target 500).
After sample quotas were met for all other racial and/or ethnic populations, survey weights were generated. The matched cases and modeled frame for each racial and/or ethnic population were combined and multivariable logistic regression, adjusting for age, sex, years of education, region, and language preference (Latino only), was used to estimate propensity scores for inclusion in the study. The propensity scores were then grouped into deciles and post-stratified using a three-way stratification of sex, age, education, and language preference (Latino only) to produce the final weight. This combination of matching to a target sample and weighting means that weighted results are national representative within racial and/or ethnic populations (e.g., AIAN participants represent all AIAN males and females 35 years or older living in the US).
Adherence to Preventive Care Guidelines
Questions from the Medical Expenditure Panel Survey Preventive Care Self-Administered Questionnaire were used to capture information about adherence to preventive care services recommended by the USPSTF (A or B grade) and the Advisory Committee on Immunization Practices. A and B grade preventive care services are the highest levels of recommendation by the USPSTF and are required to be covered without cost-sharing by insurance providers under the Affordable Care Act [20]. For this study, eight preventive services were included: blood pressure screening (performed by a doctor, nurse, or other healthcare professional), cholesterol screening, breast cancer screening (female only), cervical cancer screening (female only), colorectal cancer screening, influenza vaccination, pneumococcal vaccination, and shingles vaccination. Survey participants were only asked questions about preventive services that were recommended for their age group (and sex) based on published guidelines, Supplemental Table 1. Participants also skipped questions if they reported having a contraindication for breast, cervical, or colorectal cancer guidelines; for example, female participants who reported having a hysterectomy or a history of cervical cancer skipped questions about cervical cancer screening. Adherence to colorectal cancer screening was captured using three questions, as multiple screening tools are available (colonoscopy, sigmoidoscopy, fecal occult blood test [FOTB]) and each has a different recommended timeframe. Participants were classified as being adherent if they met the guidelines for at least one colorectal screening tool. The proportion of each racial and/or ethnic population that was eligible for each preventive care guideline (unweighted) and number of contraindications for each cancer screening is reported in Supplemental Table 2.
Race and/or Ethnicity and Sociodemographics
All participants were asked “Which one of the following would you say best represents your race/ethnicity?” Available responses included Latino/a/x or Hispanic, American Indian or Alaska Native, Asian, Black or African American, Middle Eastern or North African, Pacific Islander, White, and multiracial. Individuals who selected multiracial were then prompted to select all that apply from the above list of racial and/or ethnic populations. AIAN, Asian, Latino, and Pacific Islander participants were also prompted to provide additional information on their ethnicity or national origin. Specifically, Asian participants were asked if they primarily identified as Asian Indian; Chinese; Filipino; Japanese; Korean; Vietnamese; or Other Asian. Latino participants response options included Mexican, Mexican American, Chicano; Puerto Rican; Cuban/Cuban American; Dominican; Central American; South American; and Another Hispanic, Latino, or Spanish origin, and NHPI participants were asked to identify as Guamanian or Chamorro; Native Hawaiian; Samoan; or Other Pacific Islanders. Latino participants were also stratified by their survey language preference into English- and Spanish-speaking Latino; self-reported limited English proficiency was significantly correlated with English- versus Spanish-speaking status (5.3% vs. 44.3%, p < 0.0001).
Self-reported age, sex, English language proficiency, highest education, household annual income, access to a private vehicle, urbanicity, Census division, general health, chronic health conditions, height (feet and inches) and weight (pounds) (to calculate body mass index [BMI, kg/m2]), and disability status were also collected. Limited English proficiency was classified as being able to speak English “not at all”, “poorly”, or “fairly well”. Missing data was minimal for all questions (< 1%) except for household annual income (348 participants [unweighted] reported “Prefer not to say”).
Statistical Analyses
Adherence to preventive services guidelines was estimated across race and/or ethnicity (and language for Latino participants) and other sociodemographic characteristics. Race and/or ethnicity (and language) stratified cancer screening and influenza vaccination rates were then compared to 2030 Healthy People targets: breast cancer screening (80.3%) cervical cancer screening (79.2%), colorectal cancer screening (72.8%), and influenza vaccination (70.0%) [5].
Multivariable Poisson regression with robust variance estimates was used to estimate the association between race and/or ethnicity (and language) and adherence to each preventive services guideline after adjusting for age (continuous), sex (excluding breast and cervical cancer screenings), chronic health conditions (any vs. none), and self-reported health (excellent/very good/good vs. fair/poor). Variables related to access to care (e.g., health insurance status) were not included in the models because it was hypothesized these factors would serve as mediators and potentially explain racial and/or ethnic disparities in adherence. For comparison purposes [28], this study also reports associations between race and/or ethnicity and adherence to all recommended guidelines; the same adjustment set was used. White participants were used as the reference population for all analyses because they represent the majority population in the geographic context (United States) and had the highest adherence to all recommended guidelines among the 8 major racial and/or ethnic populations.
As a secondary analysis, ethnicity/national origin stratified estimates of adherence to preventive care guidelines, comparison to Healthy People 2030 targets, and multivariable regression were also performed among Asian, Latino, and NHPI participants. Ethnicity-stratified analyses could not be performed for AIAN due to the low prevalence of Alaska Native (< 5%). Analyses where the eligible population for a specific guideline was < 11 (unweighted) were not reported due to generalizability concerns. Sample sizes for each ethnicity/national origin stratified analysis are reported in Supplemental Table 2.
All analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC). All analyses were weighted to produce nationally representative estimates within each racial and/or ethnic population, and counts were rounded.
Results
There were 4,287 online survey respondents (response rate: 30.9%) who were matched down to a sample of 4,111 individuals to produce the final, weighted PREV-CARE data set. Demographics of PREV-CARE participants, stratified by race and/or ethnicity, are reported in Table 1. Overall, adherence to preventive services guidelines ranged from 51.0% (shingles vaccine) to 85.5% (blood pressure screening), Fig. 1.
Table 1.
Sociodemographic and health-related characteristics of PREV-CARE survey participants, stratified by race and/or ethnicity, weighted to be nationally representative within racial and/or ethnic populations: United States, June – August 2023 (N = 4,111)
| Latino |
|
||||||||
|---|---|---|---|---|---|---|---|---|---|
| AIAN | Asian | Black | English-speaking | Spanish-speaking | MENA | NHPI | White | Multiracial | |
| Total, N | 500 | 500 | 500 | 402 | 373 | 336 | 500 | 500 | 500 |
| Age, years, median (IQR) | 53 (43, 64) | 52 (42, 63) | 54 (43, 63) | 52 (43, 62) | 49 (42, 58) | 52 (43, 63) | 53 (42, 63) | 59 (47, 69) | 49 (41, 60) |
| Male, n (%) | 242 (48.3) | 226 (45.2) | 221 (44.2) | 220 (54.8) | 158 (42.3) | 180 (53.5) | 246 (49.3) | 239 (47.8) | 237 (47.5) |
| Self-reported general health, n (%) | |||||||||
| Excellent\very good\good | 339 (67.7) | 276 (55.2) | 329 (65.7) | 250 (62.2) | 187 (50.2) | 174 (51.7) | 327 (65.5) | 305 (61.1) | 315 (63.0) |
| Poor\fair | 161 (32.3) | 224 (44.8) | 171 (34.3) | 152 (37.8) | 186 (49.8) | 162 (48.3) | 173 (34.5) | 195 (38.9) | 185 (37.0) |
| Chronic condition(s)a, n (%) | 347 (69.4) | 227 (45.4) | 322 (64.5) | 231 (57.5) | 200 (53.7) | 196 (58.4) | 327 (65.3) | 331 (66.2) | 321 (64.1) |
| BMIb, n (%) | |||||||||
| Underweight (< 18.5) | 3 (0.6) | 14 (2.7) | 5 (1.0) | 2 (0.4) | 8 (2.1) | 4 (1.2) | 7 (1.5) | 11 (2.3) | 4 (0.8) |
| Normal weight (18.5–24.9) | 117 (23.4) | 264 (53.0) | 124 (24.8) | 88 (22.1) | 87 (23.3) | 95 (28.5) | 125 (25.0) | 125 (25.3) | 136 (27.6) |
| Overweight (25.0–29.9) | 173 (34.6) | 148 (29.7) | 157 (31.4) | 145 (36.2) | 137 (36.7) | 131 (39.2) | 149 (30.0) | 171 (34.4) | 166 (33.5) |
| Obese (30.0–39.9) | 151 (30.2) | 63 (12.6) | 156 (31.2) | 142 (35.6) | 123 (33.0) | 84 (25.1) | 155 (31.1) | 150 (30.1) | 141 (28.4) |
| Severe obesity (40.0+) | 55 (11.1) | 10 (1.9) | 58 (11.6) | 23 (5.7) | 18 (4.9) | 20 (6.0) | 62 (12.4) | 40 (8.0) | 48 (9.7) |
| Any disabilityc, n (%) | 210 (42.0) | 104 (20.9) | 149 (29.7) | 102 (25.5) | 78 (20.8) | 111 (33.0) | 171 (34.2) | 177 (35.4) | 187 (37.4) |
| Health insurance, n (%) | |||||||||
| Any private | 175 (35.2) | 324 (64.9) | 212 (42.4) | 180 (44.8) | 131 (35.6) | 173 (51.8) | 230 (46.2) | 259 (52.2) | 228 (45.5) |
| Public insurance only | 269 (53.9) | 149 (29.9) | 226 (45.1) | 171 (42.5) | 138 (37.7) | 112 (33.4) | 224 (45.0) | 212 (42.8) | 224 (44.7) |
| Uninsured | 54 (10.9) | 26 (5.3) | 63 (12.5) | 51 (12.6) | 98 (26.7) | 50 (14.8) | 44 (8.9) | 25 (5.0) | 49 (9.8) |
| Limited English proficiencyd, n (%) | 11 (2.2) | 46 (9.2) | 7 (1.3) | 21 (5.3) | 165 (44.3) | 21 (6.1) | 15 (3.0) | 3 (0.5) | 8 (1.5) |
| Education, n (%) | |||||||||
| Less than high school graduate | 52 (10.3) | 10 (2.1) | 31 (6.2) | 29 (7.2) | 42 (11.3) | 21 (6.3) | 33 (6.7) | 11 (2.1) | 33 (6.7) |
| High school diploma/GED | 174 (34.7) | 116 (23.2) | 181 (36.1) | 203 (50.6) | 132 (35.3) | 77 (23.1) | 170 (34.1) | 151 (30.1) | 122 (24.3) |
| Some college/vocational school | 174 (34.7) | 89 (17.9) | 153 (30.6) | 114 (28.4) | 98 (26.1) | 63 (18.7) | 159 (31.8) | 133 (26.5) | 166 (33.2) |
| College graduate | 65 (13.1) | 158 (31.6) | 82 (16.5) | 35 (8.7) | 71 (18.9) | 99 (29.5) | 93 (18.7) | 121 (24.2) | 107 (21.4) |
| Graduate or advanced degree | 36 (7.2) | 126 (25.2) | 53 (10.6) | 21 (5.2) | 31 (8.4) | 75 (22.5) | 44 (8.8) | 85 (17.0) | 73 (14.5) |
| Employment status, n (%) | |||||||||
| Employed (full or part-time) | 211 (42.2) | 301 (60.2) | 219 (43.8) | 190 (47.2) | 210 (56.7) | 185 (55.0) | 255 (51.0) | 210 (41.9) | 249 (49.8) |
| Retired | 121 (24.2) | 105 (21.0) | 127 (25.3) | 95 (23.5) | 43 (11.5) | 70 (20.7) | 101 (20.2) | 177 (35.5) | 84 (16.8) |
| Not employede | 289 (57.8) | 199 (39.8) | 281 (56.2) | 212 (52.8) | 160 (43.3) | 151 (45.0) | 245 (49.0) | 290 (58.1) | 251 (50.2) |
| Marital status, n (%) | |||||||||
| Married/domestic partnership | 202 (40.4) | 307 (61.5) | 189 (37.9) | 221 (54.8) | 226 (60.5) | 206 (61.2) | 244 (48.7) | 281 (56.2) | 235 (47.0) |
| Separated/divorced/widowed | 170 (34.1) | 77 (15.4) | 120 (24.1) | 88 (22.0) | 90 (24.0) | 64 (19.2) | 148 (29.6) | 112 (22.5) | 108 (21.6) |
| Never married | 128 (25.5) | 116 (23.1) | 190 (38.0) | 93 (23.2) | 58 (15.4) | 66 (19.6) | 109 (21.7) | 107 (21.3) | 157 (31.5) |
| Family annual income, n (%) | |||||||||
| <$20,000 | 133 (18.4) | 53 (7.3) | 103 (14.1) | 62 (8.6) | 107 (14.8) | 42 (5.7) | 83 (11.4) | 59 (8.2) | 84 (11.6) |
| $20,000-$59,999 | 203 (13.5) | 124 (8.2) | 194 (12.8) | 171 (11.3) | 150 (10.0) | 137 (9.1) | 185 (12.3) | 170 (11.3) | 175 (11.6) |
| $60,000-$99,999 | 80 (9.7) | 119 (14.4) | 87 (10.5) | 84 (10.2) | 74 (9.0) | 59 (7.1) | 110 (13.3) | 115 (13.9) | 99 (12.0) |
| ≥$100,000 | 51 (7.2) | 150 (20.9) | 71 (10.0) | 57 (8.0) | 29 (4.1) | 81 (11.3) | 77 (10.8) | 107 (14.9) | 92 (12.8) |
| Access to private vehicle, n (%) | |||||||||
| All of the time | 365 (73.1) | 398 (79.5) | 347 (69.3) | 310 (77.1) | 240 (64.3) | 274 (81.6) | 384 (76.8) | 448 (89.8) | 373 (74.5) |
| Some of the time | 84 (16.9) | 70 (14.0) | 85 (17.0) | 57 (14.3) | 79 (21.2) | 37 (10.9) | 86 (17.2) | 26 (5.3) | 69 (13.8) |
| None of the time | 50 (10.0) | 32 (6.5) | 68 (13.7) | 35 (8.7) | 54 (14.5) | 25 (7.5) | 30 (6.0) | 25 (5.0) | 58 (11.7) |
| Census division, n (%) | |||||||||
| New England | 14 (2.9) | 21 (4.3) | 11 (2.1) | 11 (2.7) | 8 (2.2) | 16 (4.8) | 3 (0.5) | 22 (4.3) | 23 (4.6) |
| Middle Atlantic | 19 (3.8) | 73 (14.6) | 70 (14.1) | 39 (9.8) | 61 (16.3) | 71 (21.0) | 12 (2.4) | 72 (14.4) | 65 (13.0) |
| East North Central | 40 (8.1) | 38 (7.5) | 58 (11.6) | 29 (7.2) | 21 (5.6) | 46 (13.8) | 21 (4.1) | 88 (17.5) | 55 (11.0) |
| West North Central | 27 (5.4) | 17 (3.3) | 27 (5.4) | 10 (2.5) | 3 (0.8) | 17 (5.1) | 9 (1.8) | 50 (9.9) | 19 (3.8) |
| South Atlantic | 61 (12.2) | 71 (14.2) | 187 (37.4) | 55 (13.6) | 93 (24.8) | 44 (13.0) | 26 (5.2) | 97 (19.5) | 101 (20.3) |
| East South Central | 17 (3.4) | 8 (1.7) | 31 (6.1) | 2 (0.6) | 6 (1.5) | 3 (1.0) | 3 (0.7) | 24 (4.8) | 15 (3.1) |
| West South Central | 87 (17.4) | 37 (7.5) | 75 (15.0) | 88 (21.8) | 75 (20.0) | 38 (11.2) | 25 (5.0) | 49 (9.8) | 42 (8.5) |
| Mountain | 106 (21.2) | 26 (5.2) | 15 (3.1) | 59 (14.7) | 27 (7.1) | 33 (9.9) | 93 (18.6) | 30 (5.9) | 57 (11.4) |
| Pacific | 128 (25.6) | 208 (41.6) | 26 (5.2) | 110 (27.3) | 81 (21.6) | 68 (20.2) | 308 (61.7) | 69 (13.7) | 122 (24.4) |
| Residence urbanicity, n (%) | |||||||||
| Big city | 88 (17.6) | 108 (21.5) | 156 (31.2) | 114 (28.4) | 155 (41.5) | 96 (28.7) | 98 (19.7) | 53 (10.6) | 110 (21.9) |
| Smaller city | 72 (14.5) | 73 (14.7) | 97 (19.4) | 86 (21.3) | 102 (27.5) | 35 (10.3) | 88 (17.5) | 83 (16.6) | 99 (19.7) |
| Suburban area | 135 (27.1) | 257 (51.3) | 155 (31.1) | 129 (32.0) | 65 (17.5) | 147 (43.7) | 142 (28.4) | 174 (34.8) | 163 (32.6) |
| Small town | 59 (11.8) | 32 (6.3) | 37 (7.4) | 39 (9.8) | 36 (9.7) | 23 (7.0) | 84 (16.7) | 98 (19.6) | 57 (11.4) |
| Rural | 145 (29.0) | 31 (6.2) | 55 (11.0) | 34 (8.5) | 15 (3.9) | 35 (10.3) | 88 (17.7) | 92 (18.4) | 72 (14.4) |
AIAN American Indian or Alaska Native, MENA Middle Eastern or North African, NHPI Native Hawaiian or Pacific Islander, IQR interquartile range, BMI body mass index, GED general education development
Chronic conditions include: asthma; autoimmune/immunocompromised; history of cancer (excluding skin cancer); chronic kidney disease or on dialysis; chronic liver disease; chronic lung disease; diabetes (type 1 or type 2); heart conditions; high blood pressure or hypertension; immune deficiency, HIV, or AIDS; obesity; sickle cell disease; solid organ transplant recipient; and stroke
Calculated from self-reported height (feet and inches) and weight (pounds)
Disabilities include: deafness or difficulty hearing; blindness or difficulty seeing; difficulty concentrating, remembering, or making decisions; difficulty walking or climbing stairs; difficulty dressing or bathing; and difficulty doing errands alone
Responded to the question ‘How well do you speak English’ with ‘fairly well’, ‘not at all’, or ‘poorly’
Unemployed includes individuals who reported their employment status as: temporarily laid off, unemployed, permanently disabled, homemaker, student, and other
Fig. 1.
Adherence to preventive services guidelines, weighted to be nationally representative within racial and/or ethnic populations: United States, June – August 2023 (N=4,111)
Adherence to Cardiovascular Disease Screenings
Self-reported adherence to blood pressure screening guidelines was ≥ 80% among all racial and/or ethnic populations (Table 2) and Asian, Latino, and NHPI ethnicity/national origin subpopulations (Supplemental Tables 3-5). Prevalence of blood pressure screening increased with age (e.g., 78.1% among those 35–39 years old compared to 96.1% among those 75+) and income (<$20,000: 79.8% vs. ≥$100,000: 88.8%), Supplemental Table 6. Adherence was also substantially higher among those with health insurance (any private insurance: 88.6%; public insurance only: 88.4%), compared to uninsured adults (61.7%), and among those with chronic health conditions (91.7% vs. 75.8%) and disabilities (90.1% vs. 83.3%). After adjusting for age, sex, chronic conditions, and self-reported health, no significant differences in blood pressure screening were observed, Table 3.
Table 2.
Adherence to preventive care guidelines by race and/or ethnicity, weighted to be nationally representative within racial and/or ethnic populations: United States, June – August 2023 (N = 4,111)
| AIAN | Asian | Black | English-speaking Latino |
Spanish-speaking Latino |
MENA | NHPI | White | Multiracial | |
|---|---|---|---|---|---|---|---|---|---|
| Blood pressure screening | 428 (85.6) | 436 (87.2) | 430 (86.0) | 332 (82.6) | 333 (89.4) | 273 (81.2) | 418 (83.7) | 443 (88.6) | 420 (84.0) |
| Cholesterol screening | 370 (77.3) | 398 (85.2) | 392 (82.5) | 314 (81.6) | 325 (88.5) | 266 (83.0) | 388 (81.9) | 381 (86.5) | 385 (80.4) |
| Breast cancer screening | 94 (67.1) | 88 (70.9) | 108 (75.2) | 68 (70.6) | 76 (84.4) | 37 (53.4) | 87 (72.1) | 100 (69.8) | 74 (60.9) |
| Cervical cancer screening | 103 (66.9) | 128 (66.0) | 131 (77.4) | 75 (70.6) | 119 (76.3) | 60 (55.7) | 110 (69.7) | 88 (66.0) | 124 (69.4) |
| Colorectal cancer screening | 183 (69.4) | 177 (74.6) | 205 (76.4) | 130 (65.7) | 127 (79.0) | 102 (64.8) | 175 (69.3) | 208 (74.5) | 144 (64.2) |
| Influenza vaccine | 232 (46.5) | 304 (60.8) | 273 (54.6) | 206 (51.3) | 198 (53.0) | 139 (41.4) | 268 (53.6) | 301 (60.1) | 229 (45.8) |
| Pneumococcal vaccine | 65 (56.3) | 75 (64.5) | 86 (75.6) | 64 (72.8) | 27 (52.9) | 56 (68.0) | 81 (72.7) | 133 (78.0) | 63 (69.9) |
| Shingles vaccine | 49 (42.6) | 68 (58.7) | 49 (43.6) | 44 (49.9) | 25 (50.1) | 43 (53.0) | 67 (61.0) | 104 (61.0) | 27 (29.6) |
AIAN American Indian or Alaska Native, MENA Middle Eastern or North African, NHPI Native Hawaiian or Pacific Islander
Table 3.
Adjusted prevalence ratios (and 95% confidence interval)a between race and/or ethnicity and adherence to preventive care, weighted to be nationally representative within racial and/or ethnic populations: United States, June – August 2023 (N = 4,111)
| Latino |
|
||||||||
|---|---|---|---|---|---|---|---|---|---|
| AIAN | Asian | Black | English-speaking | Spanish-speaking | MENA | NHPI | White | Multiracial | |
| Blood pressure screening | 0.90 (0.78, 1.04) | 1.05 (0.91, 1.20) | 0.97 (0.84, 1.12) | 0.98 (0.84, 1.13) | 1.07 (0.92, 1.25) | 0.99 (0.85, 1.16) | 0.97 (0.84, 1.12) | 1.0 (ref) | 0.96 (0.83, 1.11) |
| Cholesterol screening | 0.98 (0.85, 1.12) | 1.04 (0.91, 1.18) | 0.99 (0.86, 1.13) | 0.96 (0.84, 1.11) | 1.05 (0.91, 1.21) | 0.94 (0.81, 1.10) | 0.96 (0.84, 1.10) | 1.0 (ref) | 0.97 (0.85, 1.11) |
| Breast cancer screening | 1.00 (0.75, 1.32) | 1.08 (0.81, 1.44) | 1.10 (0.84, 1.45) | 1.05 (0.77, 1.43) | 1.26 (0.93, 1.70) | 0.79 (0.54, 1.16) | 1.07 (0.80, 1.43) | 1.0 (ref) | 0.92 (0.68, 1.24) |
| Cervical cancer screening | 1.02 (0.76, 1.35) | 1.02 (0.77, 1.34) | 1.17 (0.89, 1.54) | 1.08 (0.80, 1.48) | 1.16 (0.88, 1.52) | 0.85 (0.61, 1.18) | 1.06 (0.80, 1.40) | 1.0 (ref) | 1.05 (0.80, 1.38) |
| Colorectal cancer screening | 0.96 (0.79, 1.18) | 1.07 (0.88, 1.31) | 1.07 (0.88, 1.30) | 0.93 (0.75, 1.16) | 1.13 (0.90, 1.41) | 0.91 (0.71, 1.15) | 0.97 (0.79, 1.19) | 1.0 (ref) | 0.91 (0.73, 1.12) |
| Influenza vaccine | 0.82 (0.69, 0.98) | 1.15 (0.98, 1.35) | 0.97 (0.83, 1.15) | 0.94 (0.79, 1.12) | 1.03 (0.86, 1.24) | 0.75 (0.62, 0.92) | 0.96 (0.81, 1.13) | 1.0 (ref) | 0.84 (0.71, 1.00) |
| Pneumococcal vaccine | 0.80 (0.59, 1.08) | 0.91 (0.69, 1.22) | 1.02 (0.77, 1.33) | 1.03 (0.76, 1.39) | 0.78 (0.51, 1.19) | 0.97 (0.71, 1.34) | 0.95 (0.72, 1.26) | 1.0 (ref) | 0.97 (0.72, 1.31) |
| Shingles vaccine | 0.76 (0.54, 1.08) | 1.04 0.77, 1.42) | 0.75 (0.54, 1.06) | 0.89 (0.62, 1.26) | 0.92 (0.59, 1.42) | 0.93 (0.65, 1.33) | 1.03 (0.76, 1.40) | 1.0 (ref) | 0.53 (0.34, 0.81) |
AIAN American Indian or Alaska Native, MENA Middle Eastern or North African, NHPI Native Hawaiian or Pacific Islander
Adjusted for age, sex (excluding breast and cervical cancer screenings), chronic health conditions, and self-reported health
Self-reported adherence to cholesterol screening was also ≥ 80% for all racial and/or ethnic populations besides AIAN adults (77.3%, Table 2) and Asian, Latino, and NHPI ethnicity/national origin subpopulations (Supplemental Tables 3-5). After adjustment, no significant differences were observed across race and/or ethnicity, Table 3. Similar to blood pressure screening, the prevalence of cholesterol screening also increased with age (72.8% among those 35–39 year olds to 96.3% among those 70–74); a smaller increase was also observed across income (<$20,000: 80.9% vs. ≥$100,000: 85.0%), Supplemental Table 6. Adults with health insurance were also more likely to undergo cholesterol screening (any private: 86.0%; public insurance only: 85.0%; uninsured: 62.3%).
Adherence to Cancer Screenings
Adherence to breast, cervical, and colorectal cancer screenings were consistently the lowest among MENA populations (breast: 53.4%; cervical: 55.7%; colorectal: 64.8%), Table 2. Less consistency was observed across Asian, Latino, and NHPI ethnicity (Supplemental Tables 3-5). For example, breast cancer screening was lowest among Japanese females (53.2% vs. 70.0–90.6%), whereas cervical cancer screening was lowest among Chinese (54.4%) and Korean (55.1%) females (other ethnicities ranged from 61.8 to 79.7%). Both breast cancer and colorectal cancer screening adherence increased with age, Supplemental Table 6. Adults with chronic conditions, those with public or private health insurance, and those with higher annual household incomes were also more likely to be adherent to cancer screening guidelines.
Only Spanish-speaking Latina females met the 2030 Healthy People breast cancer screening target, and no racial and/or ethnic populations met the target for cervical cancer screening, Table 4. Asian, Black, Spanish-speaking Latino, and White adults met the 2030 Healthy People target for colorectal cancer screening. When results were further stratified by ethnicity, Asian Indian, Chinese, Filipino, Puerto Rican, Guamanian or Chamorro, and other Pacific Islander participants met breast cancer screening guidelines; Puerto Rican, South American and Other Latino participants met cervical cancer screening guidelines; and Asian Indian, Chinese, Filipino, Cuban, and Puerto Rican participants met colorectal cancer screening guidelines, Supplemental Table 7.
Table 4.
Difference between adherence to cancer screenings and influenza vaccination guidelines in PREV-CARE survey participants and Healthy People 2030 targetsa, stratified by race and/or ethnicity
| N | Breast cancer screening |
Cervical cancer screening |
Colorectal cancer screening |
Influenza vaccine |
|
|---|---|---|---|---|---|
| AIAN | 500 | −13.2 | −12.3 | −3.4 | −23.5 |
| Asian | 500 | −9.4 | −13.2 | 1.8 | −9.2 |
| Black | 500 | −4.8 | −1.8 | 3.6 | −15.4 |
| Latino, English-speaking | 402 | −9.7 | −8.6 | −7.1 | −18.7 |
| Latino, Spanish-speaking | 373 | 4.1 | −2.9 | 6.2 | −17.0 |
| MENA | 336 | −26.9 | −23.5 | −8.0 | −28.6 |
| NHPI | 500 | −8.2 | −9.5 | −3.5 | −16.4 |
| White | 500 | −10.5 | −13.2 | 1.7 | −9.9 |
| Multiracial | 500 | −19.4 | −9.8 | −8.6 | −24.2 |
Adherence estimates were weighted to be nationally representative within racial and/or ethnic populations
AIAN American Indian or Alaska Native, MENA Middle Eastern or North African, NHPI Native Hawaiian or Pacific Islander
Healthy People 2030 targets: breast cancer screening (80.3%), cervical cancer screening (79.2%), colorectal cancer screening (72.8%), and influenza vaccination (70.0%)[5]
After adjustment, Spanish-speaking Latina females still appeared marginally more likely to undergo breast cancer screening (PR 1.26, 95% CI 0.93–1.70), and Black females more likely to undergo both breast cancer (PR 1.10, 95% CI 0.84–1.45) and cervical cancer (PR 1.17, 95% CI 0.89–1.54) screening, compared to White females, although the confidence intervals were wide, Supplemental Table 3.
Adherence to Vaccinations
Influenza, pneumococcal, and shingles vaccinations were consistently the lowest reported preventive care by all racial and/or ethnic populations, Table 2. Less than half of AIAN (46.5%), MENA (41.4%), and multiracial (45.8%) adults reported getting the influenza vaccine in the past year, and no racial and/or ethnic population met the 2030 Healthy People target for influenza vaccination, Supplemental Table 5. When stratified by ethnicity, only Filipino participants met the 2030 Healthy People target, Supplemental Table 7. Low adherence to shingles vaccine guidelines was also observed in several populations (AIAN: 42.6%, Black: 43.6%, multiracial: 29.6%). Adherence to pneumococcal vaccination guidelines, which had the highest adherence of the vaccines (overall: 69.2%), was lowest among AIAN (56.3%) and Spanish-speaking Latino (52.9%) adults.
After adjustment, AIAN (PR 0.82, 95% CI 0.69–0.98), MENA (PR 0.75, 95% CI 0.62–0.92), and multiracial (PR 0.84, 95% CI 0.71–1.00) adults were still significantly less likely to receive the influenza vaccine compared to White adults, Table 3. After adjustment, lower vaccination rates also persisted among Black (PR 0.75, 95% CI 0.54–1.06) and multiracial (PR 0.53, 95% CI 0.34–0.81) adults for the shingles vaccine compared to White adults.
When looking at other sociodemographic characteristics, influenza vaccination increased with age (42.2% among adults 35–39 years old increased to 80.7% among adults 75+) and family annual income (44.5% among those making <$20,000 up to 57.3% among those making ≥$100,000), Supplemental Table 2. Influenza vaccination was also higher among those with chronic conditions (58.9% vs. 42.1%), insurance (any private: 55.7%, public insurance only: 54.8%, uninsured: 28.4%), not limited English proficiency (52.9% vs. 44.1%), and who graduated from college or attended graduate school (56.7% and 65.2%, respectively, compared to 47.3–51.0% some college/vocational school or less than high school graduate). Pneumococcal and shingles vaccinations were also higher with increasing age, those with chronic conditions, those with insurance, higher education, and higher family annual income. Pneumococcal and shingles vaccinations were also higher among adults living in New England (pneumococcal: 89.4%; shingles: 72.5%), compared to other Census divisions (pneumococcal range: 51.7–74.1%; shingles range: 37.0–58.6%).
Adherence to All Preventive Care Guidelines
Overall, only 34.4% of survey participants were adherent to all their recommended preventive care guidelines and disparities were observed across race and/or ethnicity after adjustment, with AIAN (PR 0.69, 95% CI 0.56–0.86), MENA (PR 0.64, 95% CI 0.50–0.82), and multiracial (PR 0.79, 95% CI 0.64–0.97) participants being significantly less likely to be adherent to all recommended guidelines, compared to White participants. Adherence to all preventive care guidelines by race and/or ethnicity are reported in Supplemental Table 6.
Discussion
In a nationally representative, online survey conducted in summer 2023 that included disaggregated estimates for adults from all eight major US racial and/or ethnic populations (as defined by the US Office of Budget and Management [OMB]), this analysis found notable differences in adherence across race and/or ethnicity, as well as other sociodemographic characteristics. AIAN, Black, MENA, and multiracial adults were less likely to be adherent to at least one vaccination guideline compared to White adults. Conversely, Black and Spanish-speaking Latina females were more likely to undergo breast and cervical (Black females only) cancer screenings. Despite these differences, most racial and/or ethnic populations (and subpopulations) were below Healthy People 2030 targets for cancer screening and influenza vaccination. Having insurance, being older, having higher annual household income, having chronic conditions, and having disabilities were also consistently associated with adherence to preventive care guidelines. To the best of our knowledge, this is one of the most recent assessments on the prevalence of adherence to preventive care in the United States, and the first to include AIAN, MENA, NHPI, and multiracial adults – populations that are typically aggregated with other populations (or included as ‘other race’) in previous preventive care research – and report ethnicity/national origin stratified results for Asian, Latino, and NHPI adults.
Overall, our national estimates of adherence to preventive care guidelines were largely similar to recently reported estimates. For example, Star et al. found a similar estimate for adherence to breast cancer screening (n = 6,829) in their cross-sectional study of the 2023 National Health Interview Survey (NHIS) (64.9% vs. 69.8% in this study) [29]. This study also found similar adherence for the pneumococcal (64.0% vs. 69.2% in this study), shingles (48.0% vs. 51.0% in this study), and influenza (57.8% vs. 52.3% in this study) vaccinations compared to an analysis of the 2022 NHIS and 2022–2023 influenza season [22]. Estimates for blood pressure (84.0% [24] vs. 85.5%) were also similar to estimates obtained using 2021 NHIS data [24]. However, many recent assessments of preventive care have assessed screening in the past year rather than adherence to preventive care guidelines, which gives an incomplete picture of preventive care use, especially for screenings that only need to be performed every five or ten years. For example, screening within the past year for cervical (40.9% vs. 69.1% in this study) and colorectal (24.3% vs. 71.1% in this study) cancer and cholesterol screening (68.8% vs. 82.8% in this study) are much lower than our adherence estimates [24]. Our ability to assess recent/current adherence to preventive care guidelines, rather than screening within the past year, fills a critical gap in our current understanding of preventive care utilization.
The findings from this study and others [22, 24, 29] highlight that there are significant areas of improvement in preventive care utilization in the United States. And while all racial and/or ethnic groups would benefit from increased cancer screening and vaccination, AIAN, MENA, and multiracial populations would likely see an outsized effect on health outcomes given documented disparities. Studies have shown that culturally tailored interventions [30, 31] and health communication [32] can increase healthcare utilization within racial and/or ethnic populations and other communities. However, additional research is needed to understand how to reduce barriers and facilitate action [32, 33], and future research should focus on addressing these gaps in order to improve preventive care utilization and reduce health disparities.
Various individual and systemic barriers to care likely contribute to suboptimal adherence to preventive care guidelines in the US, but there is limited evidence on the effects of specific barriers on preventive care utilization or the effectiveness of interventions [22]. To date, most studies have focused on health insurance coverage and cost as the primary cause for nonadherence, especially for cancer screenings [23]. However, since the 2010 Affordable Care Act was passed, which expanded health insurance coverage and required insurance providers to cover preventive services free of charge, only modest increases in cancer screening rates have been observed [24, 25] – indicating that insurance status/cost is not the only barrier individuals experience.
Perceived benefits (or lack thereof) also likely contribute to suboptimal adherence, especially for vaccination rates. Previous studies have suggested that the dramatic increase in misinformation and distrust (particularly around vaccinations) on social media during the COVID-19 pandemic has also spilled over into other vaccine-preventable diseases, including influenza [26, 27]. Ability to take time off work [16], housing instability [34], food insecurity [18, 34, 35], transportation barriers [16, 34, 35], socioeconomic status [16, 18, 20, 35], and incarceration history [36] have also been linked to lower adherence to cancer screenings and vaccinations. Additional research is needed to understand the multifactorial-multilevel causes of suboptimal adherence to preventive care guidelines across racial and/or ethnic populations, in order to identify modifiable risk factors that can be targeted for intervention and identify high-risk populations that may benefit most from culturally tailored programming.
Both systemic barriers to care [14] and health misinformation [21] have been found to create or exacerbate racial and/or ethnic disparities in healthcare utilization, and may explain the disparities in adherence to preventive care observed in our study and others [14, 16]. Notably, the largest disparities in adherence observed in this study were among AIAN and MENA populations. Future studies should take steps to investigate these communities in healthcare utilization and outcomes research to better understand their specific needs and barriers. For example, while AIAN populations are more likely to be insured than White adults because of the Indian Health Service (IHS), geographical isolation, limited access to IHS facilities, and other systemic issues may explain lower healthcare utilization and preventive care use in AIAN populations [37]. Previous studies also suggest that perceived negative attitudes toward Muslims has been linked to poorer access to healthcare, and MENA individuals have reported higher rates of perceived healthcare discrimination when seeking healthcare [38]. A deeper understanding (and targeted solutions) of the barriers specific communities, like AIAN and MENA, face could lead to the largest improvements in healthcare utilization, including preventive care, among high-risk populations.
Strengths and Limitations
This study has several notable strengths. First, the PREV-CARE survey was conducted to generate nationally representative estimates of adherence to preventive care guidelines within the eight major racial and/or ethnic populations in the U.S., as defined by the updated 2024 OMB standards. The PREV-CARE sampling quotas (initially n = 500 for each racial and/or ethnic population) allowed us to recruit enough AIAN, NHPI, and MENA individuals to estimate adherence to preventive care guidelines within these populations, as well as stratify Latino adults by English- and Spanish-speaking status. For comparison, the Medical Expenditure Panel Survey (MEPS) and National Health Interview Survey (NHIS) include < 500 AIAN adults, NHPI is combined with Asian, and MENA adults are coded as White in currently available data. This study also used previously validated survey measures to capture adherence to preventive care.
This study also has some limitations. First, this survey was administered online, and individuals with limited digital literacy and/or internet access may have been less likely to participate, potentially introducing selection bias. Lower digital literacy has been associated with many health outcomes, including higher prevalence of chronic diseases and poorer long-term disease management [39], and it is possible that those with low digital literacy may also be less likely to adhere to preventive care guidelines. And while participants were matched and weighted on sociodemographics to obtain nationally representative estimates, some selection bias may exist, and our results may overestimate adherence to preventive care in the U.S. The ethnicity-stratified results among Asian, Latino, and NHPI participants may be particularly prone to bias, due to small sample sizes; this survey was also not designed to be national representative within these groups. Second, the survey was administered in English and Spanish (Latino participants only), and non-Latino participants with low English proficiency were also less likely to participate. Low English proficiency is a known barrier to healthcare, and studies have found that non-English speaking adults are less likely to adhere to preventive care guidelines [40]. This may especially impact our findings for Asian adults; in our study only 9.2% self-reported limited English compared to 26.2% in recent national estimates [41]. Ethnicity-stratified analyses among AIAN and multiracial adults were also not performed. Future studies should assess differences in adherence to preventive care guidelines and potential barriers to care among these populations. We also acknowledge the limitations of using White participants as reference; [42] however, Healthy People 2030 targets were not available for all preventive care guidelines included in this analysis, and had to defer to comparisons across racial and/or ethnic groups. Finally, adherence to preventive care was self-reported and may be subject to nondifferential misclassification.
Public Health Implications
While adherence to some recommended preventive care guidelines appears to be increasing, adherence to cancer screenings remains below the Healthy People 2030 target in almost every racial and/or ethnic population and all racial and/or ethnic populations are below the 2030 Healthy People influenza vaccination target. Substantial disparities in adherence to preventive care also exist, particularly among AIAN, MENA, NHPI, and multiracial adults. Our nuanced assessment of preventive care across all eight major racial and/or ethnic populations provides critical information for identifying high-risk populations and reducing the impact of disease in the U.S., especially among AIAN, MENA, and NHPI populations. Future research should assess how health beliefs and systemic barriers to care influence adherence to preventive care guidelines across race and/or ethnicity and other sociodemographic characteristics so culturally tailored interventions and health communication strategies can be developed. In the interim, there should be a heightened focus on ensuring all individuals have access to preventive care. Physicians and healthcare providers should recognize cultural differences in preventive care access, health behaviors, and health beliefs to better support patients as they seek preventive care and provide information to assuage any concerns.
Supplementary Material
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s40615-026-03159-9.
Funding
This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. This work was also supported by the National Institute on Minority Health and Health Disparities William G. Coleman, Jr., Ph.D., Minority Health and Health Disparities Research Innovation Award.
Footnotes
Ethics Approval This study was performed in line with the principles of the Declaration of Helsinki. Both the National Institutes of Health Office of Institutional Review Board Operations (IRB# 001550) and University of Maryland (IRB# 2239331-1) determined that this study does not qualify as human subjects research because data were de-identified by YouGov before sending to the research team. The YouGov research protocol was approved for Federalwide Assurance by the Department of Health and Human Services (FWA00010960).
Consent to Participate The YouGov panel members consented to being contacted for potential surveys when recruited to the panel. Panel members are recruited through a variety of methods to ensure diversity, including Web advertising, permission-based email campaigns, partner-sponsored solicitations, telephone-to-Web recruitment, and mail-to-Web recruitment. Informed consent for this survey (PREVentive Care AdheREnce in Diverse Populations) from eligible participants was implied if the survey was completed and returned.
Competing interests The authors have no relevant financial or non-financial interests to disclose.
Data Availability
Data and code used to conduct this analysis are available upon reasonable request. Please contact the corresponding author (P Strassle).
References
- 1.Philipson TJ, Durie T, Cong Z, Fendrick AM. The aggregate value of cancer screenings in the United States: full potential value and value considering adherence. BMC Health Serv Res. 2023;23:829. 10.1186/s12913-023-09738-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Goddard KAB, Feuer EJ, Mandelblatt JS, Meza R, Holford TR, Jeon J, et al. Estimation of cancer deaths averted from prevention, screening, and treatment efforts, 1975–2020. JAMA Oncol. 1975. 10.1001/jamaoncol.2024.5381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Carrico J, Talbird SE, La EM, Poston S, Poirrier J-E, DeMartino JK, et al. Cost-benefit analysis of vaccination against four preventable diseases in older adults: impact of an aging population. Vaccine. 2021;39:5187–97. 10.1016/j.vaccine.2021.07.029. [DOI] [PubMed] [Google Scholar]
- 4.Sabatino SA, Thompson TD, White MC, Villarroel MA, Shapiro JA, Croswell JM, et al. Up-to-date breast, cervical, and colorectal cancer screening test use in the United States, 2021. Prev Chronic Dis. 2021;20:E94. 10.5888/pcd20.230071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Increase the proportion of adults who get recommended evidence-based. preventive health care — Data - Healthy People 2030 ∣ odphp.health.gov [Internet]. [cited 2024 Dec 19]. https://odphp.health.gov/healthypeople/objectives-and-data/browse-objectives/health-care-access-and-quality/increase-proportion-adults-who-get-recommended-evidence-based-preventive-health-care-ahs-08/data. Accessed 19 Dec 2024.
- 6.DeGroff A, Miller J, Sharma K, Sun J, Helsel W, Kammerer W, et al. COVID-19 impact on screening test volume through the National Breast and Cervical Cancer early detection program, January–June 2020, in the United States. Prev Med. 2020;151:106559. 10.1016/j.ypmed.2021.106559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bermudez Y, DeGroff A, Miller J, Kenney K, Lockhart J, Joseph D, et al. Changes in screening test volume in the National Breast and Cervical Cancer Early Detection Program during the COVID-19 pandemic, 2020–2022. Int J Environ Res Public Health. 2020;21:816. 10.3390/ijerph21070816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Black CL, O’Halloran A, Hung MC, Srivastav A, Lu PJ, Garg S, et al. Vital Signs: Influenza Hospitalizations and Vaccination Coverage by Race and Ethnicity-United States, 2009-10 Through 2021-22 Influenza Seasons. MMWR Morb Mortal Wkly Rep. 2022;71:1366–73. 10.15585/mmwr.mm7143e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Affairs (ASPA) AS for P. Fact Sheet: End of the COVID-19 Public Health Emergency [Internet]. 2023. [cited 2024 Dec 17]. https://www.hhs.gov/about/news/2023/05/09/fact-sheet-end-of-the-covid-19-public-health-emergency.html. Accessed 17 Dec 2024.
- 10.McDaniel CC, Hallam HH, Cadwallader T, Lee HY, Chou C. Persistent racial disparities in cervical cancer screening with Pap test. Prev Med Rep. 2021;24:101652. 10.1016/j.pmedr.2021.101652. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Granade CJ, Lindley MC, Jataoui T, Asif AF, Jones-Jack N. Racial and Ethnic Disparities in Adult Vaccination: A Review of the State of Evidence. Health Equity. 2022;6:206–23. 10.1089/heq.2021.0177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sokale IO, Rosales O, Montealegre JR, Oluyomi AO, Thrift AP. Trends in up-to-date colorectal cancer screening among U.S. adults aged 50–75 years and variations by race/ethnicity and U.S. census bureau divisions. AJPM Focus. 2023;2:100055. 10.1016/j.focus.2022.100055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Carey TS, Bekemeier B, Campos-Outcalt D, Koch-Weser S, Millon-Underwood S, Teutsch S. National institutes of health pathways to prevention workshop: achieving health equity in preventive services. Ann Intern Med. 2020;172:272–8. 10.7326/M19-3171. [DOI] [PubMed] [Google Scholar]
- 14.Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389:1453–63. 10.1016/s0140-6736(17)30569-x. [DOI] [PubMed] [Google Scholar]
- 15.Caston NE, Spees LP, Waters AR, Wheeler SB. Associations between insurance status, financial hardship, and rurality on receipt of guideline-concordant mammography. Cancer Epidemiol Biomarkers Prev. 2025;34:2150–7. 10.1158/1055-9965.EPI-25-0896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gurayah AA, An A, Kuchakulla M, Morley F, Markowitz DM, Mao J, et al. Barriers to Health Care and Cancer Screening. JAMA Netw Open. 2026;9:e267024. 10.1001/jamanetworkopen.2026.7024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wolfe MK, McDonald NC, Holmes GM. Transportation Barriers to Health Care in the United States: Findings From the National Health Interview Survey, 1997–2017. Am J Public Health. 2020;110:815–22. 10.2105/AJPH.2020.305579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Schroeder T, Ozieh MN, Thorgerson A, Williams JS, Walker RJ, Egede LE. Social Risk Factor Domains and Preventive Care Services in US Adults. JAMA Netw Open. 2024;7:e2437492. 10.1001/jamanetworkopen.2024.37492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Granade CJ, Lindley MC, Jataoui T, Asif AF, Jones-Jack N. Racial and Ethnic Disparities in Adult Vaccination: A Review of the State of Evidence. Health Equity. 2022;6:206–23. 10.1089/heq.2021.0177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Nguyen T, Barefield A, Nguyen G-T. Social determinants of health associated with the use of screenings for hypertension, hypercholesterolemia, and hyperglycemia among American adults. Med Sci. 2021;9:19. 10.3390/medsci9010019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Southwell BG, Machuca JO, Cherry ST, Burnside M, Barrett NJ. Health misinformation exposure and health disparities: observations and opportunities. Annu Rev Public Health. 2023;44:113–30. 10.1146/annurev-publhealth-071321-031118. [DOI] [PubMed] [Google Scholar]
- 22.Hung M-C, Srivastav A, Lu P-J, Black C, Lindley M, Singleton J. Vaccination Coverage among Adults in the United States, National Health Interview Survey, 2022 [Internet]. AdultVaxView. 2024. [cited 2025 Mar 31]. https://www.cdc.gov/adultvaxview/publications-resources/adult-vaccination-coverage-2022.html. Accessed 31 Mar 2025. [Google Scholar]
- 23.Terlizzi E, Black L. Products - Data Briefs - Number 370- July 2020 [Internet]. 2022. [cited 2024 Dec 10]. https://www.cdc.gov/nchs/products/databriefs/db370.htm. Accessed 10 Dec 2024. [Google Scholar]
- 24.Alba C, Zheng Z, Wadhera RK. Changes in Health Care Access and Preventive Health Screenings by Race and Ethnicity. JAMA Health Forum. 2024;5:e235058. 10.1001/jamahealthforum.2023.5058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Strassle PD, Stewart AL, Quintero SM, Bonilla J, Alhomsi A, Santana-Ufret V, et al. COVID-19–related discrimination among racial/ethnic minorities and other marginalized communities in the United States. Am J Public Health. 2022;112:453–66. 10.2105/AJPH.2021.306594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Dixon G, Garrett K, Susmann M, Bushman BJ. Public opinion perceptions, private support, and public actions of US adults regarding gun safety policy. JAMA Netw Open. 2020;3:e2029571. 10.1001/jamanetworkopen.2020.29571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sherchan JS, Fernandez JR, Njoku A, Brown TH, Forde AT. Perceptions of racial-ethnic inequities in COVID-19 healthcare and willingness to receive the COVID-19 vaccine. Epidemiology. 2024;35:377–88. 10.1097/EDE.0000000000001722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Search Healthy People - Healthy. People 2030 ∣ odphp.health.gov [Internet]. [cited 2025 Mar 27]. https://odphp.health.gov/healthypeople/search?query=&f%5B0%5D=content_type%3Ahealthy_people_objective&f%5B1%5D=aggregated_topic%3A90. Accessed 27 Mar 2025.
- 29.Star J, Han X, Smith RA, Schafer EJ, Jemal A, Bandi P. Cancer screening 3 years after the onset of the COVID-19 pandemic. JAMA. 2025. 10.1001/jama.2025.0902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rubin L, Okitondo C, Haines L, Ebell M. Interventions to increase colorectal cancer screening adherence in low-income settings within the United States: a systematic review and meta-analysis. Prev Med. 2023;172:107522. 10.1016/j.ypmed.2023.107522. [DOI] [PubMed] [Google Scholar]
- 31.Fritzell K, Wangmar J, Hedberg B, Woudstra A, Forsberg A, Kottorp A, et al. Making the BEST decision-the BESTa project: description of the design and alpha phases as part of the development of a digital decision aid for cancer screening in Sweden. J Cancer Educ. 2026;41:123–36. 10.1007/s13187-025-02633-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Griffith DM, Efird CR, Baskin ML, Webb Hooper M, Davis RE, Resnicow K. Cultural Sensitivity and Cultural Tailoring: Lessons Learned and Refinements After Two Decades of Incorporating Culture in Health Communication Research. Annu Rev Public Health. 2024;45:195–212. 10.1146/annurev-publhealth-060722-031158. [DOI] [PubMed] [Google Scholar]
- 33.Brewer NT, Chapman GB, Rothman AJ, Leask J, Kempe A. Increasing Vaccination: Putting Psychological Science Into Action. Psychol Sci Public Interest J Am Psychol Soc. 2017;18:149–207. 10.1177/1529100618760521. [DOI] [PubMed] [Google Scholar]
- 34.Ewing AP, Tounkara F, Lawrence WR, Teteh-Brooks DK, Nelson N, Chakraborty S, et al. Colorectal Cancer Screening and Health-Related Social Needs in a National Sample of US Adults. JAMA Netw Open. 2026;9:e266000. 10.1001/jamanetworkopen.2026.6000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Sedani AE, Gomez SL, Lawrence WR, Moore JX, Brandt HM, Rogers CR. Social Risks and Nonadherence to Recommended Cancer Screening Among US Adults. JAMA Netw Open. 2025;8:e2449556. 10.1001/jamanetworkopen.2024.49556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Zhao J, Star J, Han X, Zheng Z, Fan Q, Shi SK, et al. Incarceration history and access to and receipt of health care in the US. JAMA Health Forum. 2024;5:e235318–e235318. 10.1001/jamahealthforum.2023.5318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Adakai M, Sandoval-Rosario M, Xu F, Aseret-Manygoats T, Allison M, Greenlund KJ, et al. Health disparities among American Indians/Alaska Natives — Arizona, 2017. MMWR Morb Mortal Wkly Rep. 2017;67:1314–8. 10.15585/mmwr.mm6747a4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Samari G, Alcalá HE, Sharif MZ. Islamophobia, health, and public health: a systematic literature review. Am J Public Health. 2018;108:e1–9. 10.2105/AJPH.2018.304402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Arias López M, Ong BA, Borrat Frigola X, Fernández AL, Hicklent RS, Obeles AJT, et al. Digital literacy as a new determinant of health: a scoping review. PLoS Digit Health. 2023;2:e0000279. 10.1371/journal.pdig.0000279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Ponce-Chazarri L, Ponce-Blandón JA, Immordino P, Giordano A, Morales F. Barriers to Breast Cancer-Screening Adherence in Vulnerable Populations. Cancers Basel. 2023;15:604. 10.3390/cancers15030604. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.AANHPI Community Data Explorer [Internet]. [cited 2026 June 8]. https://explore.aapidata.com/_gl=1*ed7h3p*_ga*ODQxMTk2NjcwLjE3NTI1OTc3MTg.*_ga_N7QSZMRZ4M*czE3NTI1OTc3MTckbzEkZzEkdDE3NTI1OTc3NTMkajI0JGwwJGgw#dashboard. Accessed 8 Jun 2026.
- 42.Malinowska JK. White by default: conceptual and methodological limits of binary white logic in global health equity research. Med Health Care Philos. 2026;29:261–81. 10.1007/s11019-026-10330-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data and code used to conduct this analysis are available upon reasonable request. Please contact the corresponding author (P Strassle).