Key Points
Question
How did cervical cancer screening rates vary by race and ethnicity, age, language, and census region from 2019 to 2024?
Findings
In this cross-sectional study with annual sample sizes of 9 to 16 million patients, cervical cancer screenings increased across all populations from 2019 to 2024. However, American Indian, Alaska Native, Native Hawaiian, and Pacific Islander patients and those from Western and Southern census regions had lower cervical cancer screening rates.
Meaning
Although cervical cancer screening rates have increased over time, persistent racial and ethnic and geographic disparities remained, highlighting the need for targeted efforts to promote equitable screening uptake.
This cross-sectional study examines cervical cancer screening rates by race and ethnicity, age, language, and census region from 2019 through 2024 and evaluates longitudinal trends across groups.
Abstract
Importance
Longitudinal evidence of cervical cancer screening (CCS) trends before and after COVID-19 across demographic cohorts remains limited.
Objective
To examine CCS rates by race and ethnicity, age, language, and census region among patients with at least 1 health care encounter in an electronic medical record (EMR) system from 2019 through 2024, and to evaluate longitudinal trends across these groups.
Design, Setting, and Participants
Using an EMR database, this cross-sectional study evaluated CCS rates among female patients aged 21 to 64 years between 2019 and 2024, stratified by race and ethnicity, age, language, and census region.
Exposures
Race and ethnicity, age, language, and census region.
Main Outcomes and Measures
Descriptive statistics were used to summarize annual CCS rates across demographic groups. The Cochran-Armitage test assessed linear trends over time.
Results
Annual sample sizes ranged from approximately 9 to 16 million patients and increased steadily over time; analytic population sizes varied across subgroups. Most patients were non-Hispanic White, aged 30 to 49 years, English-speaking, and resided in either the South or Northeast. Non-Hispanic White patients had the highest annual CCS rates from 2019 to 2021 but experienced the smallest percentage increase, from 10.98% (95% CI, 10.96%-11.01%) to 13.85% (95% CI, 13.82%-13.87%), over the study period. Hispanic patients experienced the largest percentage increase in CCS rates, from 9.85% (95% CI, 9.80%-9.90%) to 15.51% (95% CI, 15.47%-15.55%). American Indian, Alaska Native, Native Hawaiian, and Pacific Islander patients had the lowest screening rates throughout the study period. Patients aged 30 to 49 years and non–English-speaking patients consistently had the highest CCS rates across all years. Patients in the Western and Southern census regions had lower CCS rates than those in other regions.
Conclusions and Relevance
In this cross-sectional study of patients with at least 1 health care encounter in an EMR system, all CCS rates increased across all racial and ethnic groups from 2019 to 2024, with the greatest gains observed among Hispanic patients. However, persistent disparities remained, particularly among American Indian, Alaska Native, Native Hawaiian, and Pacific Islander patients and those in the Western and Southern regions. These findings suggest that although screening uptake has improved over time, substantial geographic and racial and ethnic disparities in CCS persist, highlighting important areas for future research and intervention efforts.
Introduction
In the US, cervical cancer accounts for approximately 13 000 new diagnoses and 4000 deaths annually.1 Despite this burden, cervical cancer is largely preventable through routine screening and can be successfully treated when identified as precancer or at an early cancer stage.2 However, current national data indicate that a substantial portion of the eligible population remains underscreened. For instance, only 51.6% of women aged 25 to 44 years and 75.8% of all eligible women reported being up-to-date with cervical cancer screening (CCS) in 2023, which remains well below the Healthy People 2030 target of 84.3%.3,4
Racial and ethnic disparities have long been present in CCS rates, which, in turn, impacts and leads to disparities in cervical cancer rates and mortality. Prior literature has shown both non-Hispanic Black and Hispanic patients have lower CCS rates compared with non-Hispanic White patients.5,6 Historically, CCS has also been lower among Asian patients.7 In addition, disparities in cervical cancer survival were particularly pronounced among non-Hispanic Black and Hispanic women, with Hispanic patients exhibiting the second highest cervical cancer mortality rates after non-Hispanic Black patients.8,9
The average age at diagnosis of cervical cancer is approximately 50 years, and more than 20% of cervical cancers are diagnosed among women older than 65 years, predominantly in individuals who have had inadequate prior screenings.10 CCS rates are also not uniform across age ranges. For example, women aged 30 to 49 years have higher CCS rates compared with other age groups, both younger and older.5,6 Therefore, examining longitudinal trends in CCS across different age groups is critical for identifying gaps in preventive care and periods during which targeted interventions may have the greatest impact.
In addition, prior studies have also found that CCS rates are significantly lower among rural compared with urban residents.11 Barriers have also been identified among immigrants and patients with limited English proficiency, particularly among Asian American populations.12,13 Nevertheless, studies examining CCS differences holistically between English-speaking and non–English-speaking populations remain limited. Compounding these existing disparities, the COVID-19 pandemic placed a substantial burden on health care access, especially for those who already had limited or difficult access.14 Although it is estimated that approximately 40 000 CCS were missed between March and June 2020,15 and an estimated 4.4 million fewer women underwent CCS in 2021 compared with 2019,16,17 these decreases were not uniform across all populations. Nguyen et al18 reported that Hispanic individuals experienced a more significant decrease in CCS rates from 2019 to 2020 (−39.27%) compared with non-Hispanic White individuals. Additionally, individuals aged 50 to 64 years experienced the largest decrease in CCS rates compared with other age groups from 2019 to 2020 (−27.37%). Although Nguyen et al18 examined temporal trends reflecting the immediate impact of COVID-19 on screening among Hispanic vs non-Hispanic groups, few studies have conducted longitudinal analyses to assess screening trends more broadly before and after COVID-19 and across additional groups. Existing research has characterized CCS trends by race and ethnicity through 2021, but there remains a paucity of research examining disparities in screening across diverse demographic cohorts, particularly regarding the heterogeneity in uptake by race and ethnicity, age, language, and region during this period.19
Therefore, this study aims to address this gap by leveraging the Epic Cosmos database—a vast clinical database comprising patients seen at medical facilities with use of the Epic electronic medical record (EMR) system—to analyze longitudinal disparities in CCS rates across race and ethnicity, age, language, and census region from 2019 through 2024, including through the COVID-19 pandemic. Understanding the trends and patterns may help identify populations for further investigation and inform future research examining factors contributing to disparities in CCS.
Methods
Study Design and Dataset
A retrospective cross-sectional study was conducted using data from an EMR dataset that contains over 300 million patient records from over 1805 hospitals and 413 000 clinics as of November 18, 2025.20 The University of Texas Health Science Center at Houston Committee for the Protection of Human Subjects reviewed this study and determined that the use of deidentified aggregate data did not meet the regulatory definition of human participants research. Therefore, no further ethical review was required, and informed consent was not applicable. This study also followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies.
Our study utilizes preaggregated, deidentified summary data derived from patient-level EMRs for patients from all 50 US states, Washington, DC, Canada, Lebanon, and Saudi Arabia (international contribution is 0.15% of the database).20 For this analysis, the study population included patients assigned female at birth, aged 21 to 64 years old, who had at least 1 clinical encounter during each study year from 2019 to 2024 with no history of cervical cancer. The numerator consisted of eligible patients who underwent CCS at least once during the corresponding study year. CCS receipt was identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), Current Procedural Terminology (CPT), and Healthcare Common Procedure Coding System (HCPCS) codes (eTables 1 and 2 in Supplement 1). ICD-10 codes were used to identify diagnoses, while CPT and HCPCS codes were used to capture screening procedures and preventive services.
Age groups were selected on the basis of their clinical relevance to CCS guidelines.21 Individuals aged 21 to 29 years are recommended to undergo cytology-based screening, whereas those aged 30 to 65 years have additional screening options, including high-risk HPV testing and cotesting with cytology and HPV.21 The latter age range was further divided into 30 to 49 and 50 to 64 years to examine potential differences in screening patterns among screening-eligible adults. Race and ethnicity was self-reported and standardized within the database. Census regions were derived from patients’ states using US Census Bureau classifications, as region data are not available in the database.22 International participants were retained in the overall study population; however, they were excluded from analyses stratified by US Census Region, as Census Region classifications are only applicable to US-based records.
Statistical Analysis
Descriptive analyses were conducted to assess annual screening rates by race and ethnicity (American Indian, Alaska Native, Native Hawaiian, and Pacific Islander combined, Asian, Hispanic, non-Hispanic Black, and non-Hispanic White), age groups (21-29, 30-49, and 50-64 years), language (English and any language other than English), and census regions (West, Midwest, South, and Northeast). The Cochran-Armitage test was used to assess the linear trend in the screening rates by race and ethnicity and age groups over the study period. Statistical significance was considered as a 2-sided P < .05. For subgroup analyses, records without valid demographic information for a given characteristic were not included in the corresponding analysis. Analyses were conducted with Stata statistical software version 19 (StataCorp).
Results
Analytic population sizes varied across subgroups. The total number of patients included in the study population increased annually, as shown in Table 1, from approximately 9 million in 2019 to 16 million in 2024. English-speaking individuals represented approximately 76% of the study population, and approximately 47% of participants were between the ages of 30 and 49 years old. Hispanic participants represented between 15% and 19% of the sample; non-Hispanic Black participants, approximately 15% in all years; and non-Hispanic White participants, between 53% and 57%.
Table 1. Patients Annually by Race and Ethnicity, Age, Language, and Census Region.
| Characteristic | Patients, No. (%) | |||||
|---|---|---|---|---|---|---|
| 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
| Race and ethnicity | ||||||
| Asian | 408 636 (4.4) | 471 330 (4.6) | 584 719 (4.9) | 617 396 (4.9) | 678 693 (4.9) | 757 711 (5.0) |
| Hispanic | 1 375 183 (15.0) | 1 582 679 (15.3) | 1 939 879 (16.1) | 2 157 403 (17.0) | 2 461 024 (17.9) | 2 799 604 (18.6) |
| Non-Hispanic Black | 1 369 275 (14.9) | 1 528 997 (14.8) | 1 780 106 (14.8) | 1 890 799 (14.9) | 2 057 130 (14.9) | 2 285 298 (15.2) |
| Non-Hispanic White | 5 260 006 (57.2) | 5 895 255 (57.0) | 6 699 593 (55.7) | 6 993 141 (55.0) | 7 460 770 (54.1) | 7 986 528 (53.2) |
| Othera | 793 774 (8.6) | 871 691 (8.4) | 1 013 154 (8.4) | 1 060 357 (8.3) | 1 125 935 (8.2) | 1 191 844 (7.9) |
| Totalb | 9 188 874 | 10 349 952 | 12 017 451 | 12 719 096 | 13 783 552 | 15 020 985 |
| Age group, y | ||||||
| 21-29 | 1 554 882 (17.3) | 1 817 415 (17.9) | 2 222 308 (18.7) | 2 428 902 (19.2) | 2 770 688 (20.0) | 3 149 597 (20.6) |
| 30-49 | 4 221 239 (46.9) | 4 812 087 (47.3) | 5 639 664 (47.5) | 5 990 327 (47.3) | 6 556 444 (47.3) | 7 213 053 (47.2) |
| 50-64 | 3 217 120 (35.8) | 3 539 551 (34.8) | 4 020 976 (33.8) | 4 258 540 (33.6) | 4 546 498 (32.8) | 4 920 829 (32.2) |
| Totalb | 8 993 241 | 10 169 053 | 11 882 948 | 12 677 769 | 13 873 630 | 15 283 479 |
| Language | ||||||
| English | 8 034 001 (81.5) | 8 909 546 (80.9) | 10 575 171 (80.3) | 11 088 312 (79.2) | 11 820 505 (78.0) | 12 764 885 (76.1) |
| Any other than English | 1 826 421 (18.5) | 2 097 929 (19.1) | 2 594 491 (19.7) | 2 907 852 (20.8) | 3 337 746 (22.0) | 4 003 301 (23.9) |
| Totalb | 9 860 422 | 11 007 475 | 13 169 662 | 13 996 164 | 15 158 251 | 16 768 168 |
| Census region | ||||||
| West | 1 075 541 (12.0) | 2 169 848 (19.9) | 2 544 335 (19.5) | 2 623 488 (19.0) | 2 849 052 (19.0) | 3 107 680 (18.8) |
| Midwest | 2 155 712 (24.1) | 2 433 023 (22.3) | 2 698 534 (20.7) | 2 785 625 (20.2) | 2 976 623 (19.9) | 3 180 251 (19.2) |
| South | 2 929 199 (32.8) | 3 211 465 (29.5) | 4 126 569 (31.7) | 4 647 047 (33.6) | 5 088 379 (34.0) | 5 896 028 (35.6) |
| Northeast | 2 773 620 (31.0) | 3 078 253 (28.3) | 3 657 040 (28.1) | 3 757 864 (27.2) | 4 045 058 (27.0) | 4 367 087 (26.4) |
| Totalb | 8 934 072 | 10 892 589 | 13 026 478 | 13 814 024 | 14 959 112 | 16 551 046 |
Refers to American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander.
Total row counts differ across race and ethnicity, age, language, and census region categories because category-specific analyses were based on records with available demographic information as defined by the Epic Cosmos query.
As shown in Table 2, screening frequency and rates across all racial and ethnic groups increased from 2019 to 2024. Despite the 5-year overall increase, screening rates decreased across the board in 2020, coinciding with the height of the COVID-19 pandemic and its associated health care and societal impacts. Screening rates subsequently trended upward after 2020. Hispanic populations showed the largest percentage point increase after 2020 (6.34% increase). Non-Hispanic White patients had the highest annual CCS rates from 2019 to 2021 but experienced the smallest percentage increase, from 10.98% (95% CI, 10.96%-11.01%) to 13.85% (95% CI, 13.82%-13.87%), over the study period. Hispanic patients experienced the largest percentage increase in CCS rates, from 9.85% (95% CI, 9.80%-9.90%) to 15.51% (95% CI, 15.47%-15.55%). Across all 6 years studied, American Indian, Alaska Native, Native Hawaiian, and Pacific Islander patients had lower CCS rates than other racial and ethnic groups analyzed.
Table 2. Annual Cervical Cancer Screening Frequency and Rates by Sociodemographic Factors.
| Factor | Participants, No. (%) | |||||
|---|---|---|---|---|---|---|
| 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
| Race and ethnicity | ||||||
| Asian | 43 889 (10.74) | 45 118 (9.57) | 66 322 (11.34) | 73 585 (11.92) | 86 168 (12.70) | 113 570 (14.99) |
| Hispanic | 135 517 (9.85) | 145 127 (9.17) | 212 465 (10.95) | 270 867 (12.56) | 342 158 (13.90) | 434 169 (15.51) |
| Non-Hispanic Black | 142 796 (10.43) | 152 110 (9.95) | 199 163 (11.19) | 224 476 (11.87) | 265 749 (12.92) | 315 672 (13.81) |
| Non-Hispanic White | 577 776 (10.98) | 601 106 (10.20) | 778 992 (11.63) | 843 004 (12.05) | 952 020 (12.76) | 1 105 821 (13.85) |
| Othera | 72 661 (9.15) | 76 199 (8.74) | 100 983 (9.97) | 109 905 (10.36) | 124 663 (11.07) | 148 134 (12.43) |
| Age group, y | ||||||
| 21-29 | 82 099 (5.28) | 115 969 (6.38) | 182 479 (8.21) | 242 011 (9.96) | 327 819 (11.83) | 442 500 (14.05) |
| 30-49 | 546 162 (12.94) | 565 163 (11.74) | 737 076 (13.07) | 814 624 (13.60) | 933 052 (14.00) | 1 100 995 (15.26) |
| 50-64 | 324 474 (10.09) | 316 996 (8.96) | 417 252 (10.38) | 456 508 (10.72) | 516 795 (11.37) | 597 939 (12.00) |
| Language | ||||||
| English | 806 125 (10.03) | 823 756 (9.25) | 1 121 558 (10.61) | 1 257 558 (11.34) | 1 427 594 (12.08) | 1 694 323 (13.27) |
| Any other than English | 211 279 (11.57) | 214 486 (10.22) | 314 310 (12.11) | 381 834 (13.13) | 475 285 (14.24) | 602 751 (15.06) |
| Census region | ||||||
| West | 88 685 (8.25) | 157 219 (7.25) | 226 200 (8.89) | 262 327 (10.00) | 315 252 (11.07) | 418 680 (13.47) |
| Midwest | 286 289 (13.28) | 296 992 (12.21) | 380 488 (14.10) | 407 564 (14.63) | 455 420 (15.30) | 515 326 (16.20) |
| South | 260 741 (8.90) | 267 786 (8.34) | 395 630 (9.59) | 492 573 (10.60) | 580 174 (11.40) | 723 179 (12.27) |
| Northeast | 313 417 (11.30) | 305 258 (9.92) | 418 691 (11.45) | 457 254 (12.17) | 529 411 (13.09) | 614 290 (14.07) |
Refers to American Indian, Alaskan Native, Native Hawaiian, and Pacific Islander.
Regarding age groups, patients aged 30 to 49 years consistently exhibited the highest screening rates. Screening rates for the 30 to 49 and 50 to 64 years age groups declined in 2020, mirroring the trend observed across all racial and ethnic groups with the COVID-19 pandemic. However, there was no observed decrease in CCS in the 21 to 29 years age group, as CCS rates increased steadily across all 6 years evaluated. However, patients aged 21 to 29 years had the lowest screening rates from 2019 to 2022 but experienced significant increases and became higher than those among patients aged 50 to 64 years in 2023.
When comparing CCS rates based on patient language, non–English-speaking patients consistently had higher rates from 2019 to 2024. In addition, patients in the Midwest had the highest rates throughout the study period. In contrast, patients in the West and South had relatively lower rates, with both remaining below 10% until 2022.
Table 3 displays percentage changes in annual CCS. There was an upward trend over time across all groups. However, CCS rates declined between 2019 and 2020, coinciding with the COVID-19 pandemic. Asian patients had the greatest decline (−10.28%), followed by non-Hispanic White patients (−7.27%), and Hispanic patients (−7.07%). All groups demonstrated a rebound starting in 2021, with the largest increase occurring among Hispanic patients (19.57% increase). By 2024, all racial and ethnic groups showed statistically significant increases. Between 2019 and 2020, only the youngest age group, 21 to 29 years, showed an increase in CCS rates (20.83% change). Ages 30 to 49 and 50 to 64 years saw CCS rates decline by about 10%, then rebound with steady increases thereafter. Overall, the youngest group, 21 to 29 years old, demonstrated the largest and most consistent increase in CCS rates throughout the years (18.77% to 21.32%). Both English-speaking patients and non–English-speaking patients shared similar CCS trends. As for CCS trends by census region, the largest increase occurred in the Western region between 2023 and 2024 (21.68% increase). The visualization of CCS trends is also available in eFigures 1 to 4 in Supplement 1.
Table 3. Annual Cervical Cancer Screening Rate Changes Over Time During 2019-2024 by Race and Ethnicity, Age, Language, and Region.
| Variable | Screening rate change, % | P value for trend | ||||
|---|---|---|---|---|---|---|
| 2020 vs 2019 | 2021 vs 2020 | 2022 vs 2021 | 2023 vs 2022 | 2024 vs 2023 | ||
| Race and ethnicity | ||||||
| Asian | −10.28 | 17.71 | 5.31 | 6.72 | 18.11 | <.001 |
| Hispanic | −7.07 | 19.57 | 14.55 | 10.32 | 11.51 | <.001 |
| Non-Hispanic Black | −4.81 | 13.13 | 6.25 | 8.40 | 6.98 | <.001 |
| Non-Hispanic White | −7.27 | 13.73 | 4.31 | 5.79 | 17.81 | <.001 |
| Othera | −5.43 | 14.94 | 4.00 | 6.73 | 11.71 | <.001 |
| Age group, y | ||||||
| 21-29 | 20.83 | 20.17 | 21.32 | 18.78 | 18.77 | <.001 |
| 30-49 | −9.27 | 11.33 | 4.06 | 2.94 | 9.00 | <.001 |
| 50-64 | −11.20 | 15.85 | 3.28 | 6.06 | 5.54 | <.001 |
| Language | ||||||
| English | −7.78 | 14.70 | 6.88 | 6.53 | 9.85 | <.001 |
| Any other than English | −11.67 | 18.49 | 8.42 | 8.45 | 5.76 | <.001 |
| Census region | ||||||
| West | −12.12 | 16.91 | 12.49 | 10.70 | 21.68 | <.001 |
| Midwest | −8.06 | 15.48 | 3.76 | 4.58 | 5.88 | <.001 |
| South | −6.29 | 14.99 | 10.53 | 7.55 | 7.63 | <.001 |
| Northeast | −12.21 | 15.42 | 6.29 | 7.56 | 7.49 | <.001 |
Refers to American Indian, Alaska Native, Native Hawaiian, and Pacific Islander.
Additionally, when combining all racial and ethnic and age categories, we found that the 21 to 29 years age group across all racial and ethnic groups consistently demonstrated increasing CCS rates from 2019 to 2024 (eTable 3 in Supplement 1). The American Indian, Alaska Native, Native Hawaiian, and Pacific Islander group also had the lowest CCS rates across all age groups (eTable 3 in Supplement 1).
Discussion
This retrospective cross-sectional study demonstrates trends in annual CCS rates by race and ethnicity, age, region, and language. Although CCS rates declined in most populations in 2020, screening rebounded beginning in 2021. Notably, Hispanic patients had higher observed CCS rates than non-Hispanic White patients from 2023 onward, while the American Indian, Alaska Native, Native Hawaiian, and Pacific Islander group consistently had the lowest annual screening rates. To our best knowledge, our study addresses an important gap by providing updated analyses of CCS trends, extending beyond existing national reports.7
The observed decline in CCS rates from 2019 to 2020 was expected and consistent with earlier studies examining the impact of the COVID-19 pandemic. However, the magnitude of the decline differs and may be attributable to differences in the study populations. The magnitude of annual declines from 2019 to 2020 was lower in our study compared with the estimates reported by previous studies (−7.27% for non-Hispanic White patients and −7.07%for Hispanic patients in our study vs −21.15% for non-Hispanic White patients and −39.27% for Hispanic patients in prior literature).18 The annual rates were also lower, which may be explained by differences in the baseline study population of the datasets.
Across racial and ethnic and age groups, we observed an overall upward trend in annual CCS rates from 2019 to 2024. Most groups experienced a decline in screening during the COVID-19 pandemic in 2020, followed by a rebound in 2021, except for the youngest age group, which experienced no decline. While patients aged 30 to 49 years consistently had the highest annual CCS rates over the years, the 21 to 29 year age group demonstrated the largest year-to-year increase (18.77% to 21.32%), highlighting substantial improvements in screening uptake among younger patients.
The American Indian, Alaska Native, Native Hawaiian, and Pacific Islander population had consistently the lowest CCS rates across the study period, which aligns with prior literature.23,24,25 Although non-Hispanic White patients had the highest screening rates from 2019 to 2021, the observed screening rates among Hispanic patients were higher beginning in 2022. These findings were consistent with earlier studies. Nguyen et al18 reported lower annual CCS rates among Hispanic patients compared with non-Hispanic White patients from 2019 to 2021. Ulmer et al26 later found that Hispanic patients had the highest up-to-date CCS from 2022 to 2023.
Across all census regions, patients in the West and South consistently had lower CCS rates. Since 2023, although CCS rates increased in the South, they were the lowest among all regions. Although prior studies have not consistently reported lower CCS rates in the US South than in other census regions, rural residence has been associated with lower screening uptake. Notably, there are more states in the South that have a higher proportion of rural population compared with the national average (20.4%), including West Virginia (55.2%), Mississippi (54.4%), Arkansas (43.9%), Alabama (42.0%), and Kentucky (41.6%).27 This geographic distribution may help explain the persistently lower CCS rates observed in the South. Unfortunately, current incidence rates of cervical cancer are higher in the South than in other parts of the US, with more than 45% of cervical cancer cases coming from the Southeast and Southwest regions.28 Our findings indicate that the South is likely to continue to see high cervical cancer rates if screening rates remain low.
Several factors may help explain the observed higher annual CCS rates among Hispanic patients compared with non-Hispanic White patients beginning in 2023. First is the growing implementation of interventions designed to improve screening uptake among Hispanic populations, and multiple studies have published significant findings highlighting the benefits of multilevel culturally tailored CCS interventions in this population.29,30,31,32,33,34 Programs such as Ayudando a las Mujeres con Información, Guía y Amor para su Salud (AMIGAS), a bilingual educational outreach program supported by the Centers for Disease Control and Prevention, have demonstrated effectiveness in promoting CCS among Hispanic populations.34 The observed increase in screening among Hispanic populations by 2023 may reflect multiple factors, including changes in awareness, engagement, access to care, or ongoing community-based efforts; however, the current descriptive analysis cannot determine the drivers of these trends. These findings highlight the importance of further research to better understand factors associated with improvements in screening uptake and persistent disparities among other groups, including American Indian, Alaska Native, Native Hawaiian, and Pacific Islander populations. Similarly, the regional and rural-urban differences observed in this study may help identify areas for future investigation and inform the development and evaluation of strategies to improve screening access and uptake.
Our study offers a unique contribution by leveraging an integrated EMR dataset to examine recent CCS rate trends by race and ethnicity and age groups. Unlike large-scale surveys such as the National Health Interview Survey or the Health Information National Trends Survey, which sample the general US population, our analysis used individual-level EMR data across large hospital and clinical systems.
Limitations
There are several limitations to our study. First, because ICD-10, CPT, and HCPCS codes were used to identify diagnoses and procedures for study inclusion, and because the database aggregates data from more than 1805 hospitals across diverse health systems, there may be substantial variability in coding practices and documentation. Such heterogeneity could lead to misclassification and inconsistencies in screening rate estimates. Second, subgroup analyses were conducted using category-specific analytic populations generated by the database based on available demographic information for the characteristic of interest. Consequently, denominators varied across race and ethnicity, age, language, and census region analyses. Because information on records not included in these analytic populations was not available, the potential impact of incomplete demographic information on the reported CCS rates could not be assessed. Third, the study’s generalizability may be limited. The database is derived exclusively from health systems that utilize a particular EMR system and, therefore, does not capture CCS performed in community health clinics or other settings that do not adopt that EMR. Because the EMR system is more commonly adopted by larger, well-resourced health systems and has a greater penetration in certain geographic regions, patient populations represented in the database may differ systematically from those receiving care in settings that do not use the EMR system. As a result, our patient population may not fully represent individuals seeking care across diverse clinical environments, which limits the external validity of our findings. In addition to limited external validity, the internal validity of the findings may also be affected by selection bias and collider bias introduced by the use of the database. Inclusion in the database is contingent upon receiving care within participating health systems affiliated with the EMR system, and factors such as demographic information, geographic location, and health care access may be associated with both CCS utilization and inclusion in the database. Because only aggregated, deidentified data were available, formal assessment of the magnitude or direction of the biases was not possible. Therefore, future research should examine geographic variation and incorporate demographic data beyond the EMR system, given its regional spread among hospitals and clinics, to better capture the intended target population.
Conclusions
In this cross-sectional study of patients with at least 1 health care encounter in an EMR system, CCS rates increased across all groups from 2019 to 2024. Despite the improvements, disparities persisted, particularly among American Indian, Alaska Native, Native Hawaiian, and Pacific Islander patients and those in the Western and Southern regions. These findings identify persistent inequities in CCS uptake that should be considered in future research and public health efforts to promote screening equity.
eTable 1. Exclusion Codes for Cervical Cancer Screenings
eTable 2. Inclusion Codes for Cervical Cancer Screenings
eTable 3. Cervical Cancer Screening Rates by Race/Ethnicity and Age Group
eFigure 1. Annual Cervical Cancer Screening Rates during 2019-2024 by Race/Ethnicity
eFigure 2. Annual Cervical Cancer Screening Rates during 2019-2024 by Age Group
eFigure 3. Annual Cervical Cancer Screening Rates during 2019-2024 by Language
eFigure 4. Annual Cervical Cancer Screening Rates during 2019-2024 by Region
Data Sharing Statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eTable 1. Exclusion Codes for Cervical Cancer Screenings
eTable 2. Inclusion Codes for Cervical Cancer Screenings
eTable 3. Cervical Cancer Screening Rates by Race/Ethnicity and Age Group
eFigure 1. Annual Cervical Cancer Screening Rates during 2019-2024 by Race/Ethnicity
eFigure 2. Annual Cervical Cancer Screening Rates during 2019-2024 by Age Group
eFigure 3. Annual Cervical Cancer Screening Rates during 2019-2024 by Language
eFigure 4. Annual Cervical Cancer Screening Rates during 2019-2024 by Region
Data Sharing Statement
