Published online Mar 20, 2025. doi: 10.5662/wjm.v15.i1.97512
Revised: July 25, 2024
Accepted: July 29, 2024
Published online: March 20, 2025
Processing time: 120 Days and 8.2 Hours
Barrett's esophagus (BE) is a known premalignant precursor to esophageal adenocarcinoma (EAC). The prevalence rates continue to rise in the United States, but many patients who are at risk of EAC are not screened. Current practice guidelines include male gender as a predisposing factor for BE and EAC. The population-based clinical evidence regarding female gender remains limited.
To study comparative trends of gender disparities in patients with BE in the United States.
A nationwide retrospective study was conducted using the 2009-2019 National Inpatient Sample (NIS) database. Patients with a primary or secondary diagnosis code of BE were identified. The major outcome of interest was determining the gender disparities in patients with BE. Trend analysis for respective outcomes for females was also reported to ascertain any time-based shifts.
We identified 1204190 patients with BE for the study period. Among the included patients, 717439 (59.6%) were men and 486751 (40.4%) were women. The mean age was higher in women than in men (67.1 ± 0.4 vs 66.6 ± 0.3 years, P < 0.001). The rate of BE per 100000 total NIS hospitalizations for males increased from 144.6 in 2009 to 213.4 in 2019 (P < 0.001). The rate for females increased from 96.8 in 2009 to 148.7 in 2019 (P < 0.001). There was a higher frequency of obesity among women compared to men (17.4% vs 12.6%, P < 0.001). Obesity prevalence among females increased from 12.3% in 2009 to 21.9% in 2019 (P < 0.001). A lower prevalence of smoking was noted in women than in men (20.8% vs 35.7%, P < 0.001). However, trend analysis showed an increasing prevalence of smoking among women, from 12.9% in 2009 to 30.7% in 2019 (P < 0.001). Additionally, there was a lower prevalence of alcohol abuse, Helicobacter pylori (H. pylori), and diabetes mellitus among females than males (P < 0.001). Trend analysis showed an increasing prevalence of alcohol use disorder and a decreasing prevalence of H. pylori and diabetes mellitus among women (P < 0.001).
The prevalence of BE among women has steadily increased from 2009 to 2019. The existing knowledge concerning BE development has historically focused on men, but our findings show that the risk in women is not insignificant.
Core Tip: Currently, large epidemiologic data evaluating the gender-based differences in Barrett’s esophagus (BE) remain limited. Using the 2009-2019 National Inpatient Sample database, we conducted this retrospective study to assess the gender disparities and trends in patients with BE. According to our data, the prevalence of BE in women increased steadily during the course of the study. Trend analysis among females revealed an increasing prevalence of obesity, smoking, and alcohol abuse and a declining prevalence of Helicobacter pylori and diabetes. These observations indicate that the risk of BE in women is not insignificant. Further research is required to carefully screen for risk factors and determine the underlying mechanisms of BE and esophageal adenocarcinoma in female patients.
- Citation: Fatakhova K, Inayat F, Ali H, Patel P, Rehman AU, Afzal A, Sarfraz M, Sarfraz S, Nawaz G, Chaudhry A, Dhillon R, Dilibe A, Glazebnik B, Jones L, Glazer E. Gender disparities and woman-specific trends in Barrett’s esophagus in the United States: An 11-year nationwide population-based study. World J Methodol 2025; 15(1): 97512
- URL: https://www.wjgnet.com/2222-0682/full/v15/i1/97512.htm
- DOI: https://dx.doi.org/10.5662/wjm.v15.i1.97512
Barrett’s esophagus (BE), a complication of chronic gastroesophageal reflux disease (GERD), is a precursor lesion of esophageal adenocarcinoma (EAC)[1,2]. The pathological diagnosis of BE is based on the presence of distinctive metaplastic changes in the esophageal squamous epithelium, which transforms into a specialized columnar epithelium[3,4]. In 1950, the Australian-born surgeon Norman Rupert Barrett first used the term "chronic peptic ulcer of the eso
A number of organizations and expert consensus panels have developed clinical practice guidelines for BE[12-15]. These recommendations designate the male gender as a risk factor for developing BE and EAC. However, it is still an ongoing debate whether women should be entitled to screening endoscopies based on specific risk factors. Previous research shows that few studies have evaluated the prevalence of BE in females[16-18]. A meta-analysis based on pooled data from 19337 patients showed that females had a remarkably lower prevalence of BE and a reduced probability of developing high-grade dysplasia and EAC[19]. However, the majority of the gender estimations were from studies with limited sample sizes based on patient groups with institutional or geographical restrictions[19]. This highlights the dearth of large population-based data to adequately address the gender-based differences. The gender gap translates into a lack of robust evidence to tailor the guidelines for surveillance intervals for at-risk women. Therefore, more data are required to provide current trends in the gender distribution of BE. The global increase in incidence rates of EAC may be attributed to the rising prevalence rates of BE risk factors such as GERD, obesity, and smoking[20]. The American Gastroenterological Association Clinical Practice Update has now recommended endoscopic screening for BE and EAC in patients with three or more known risk factors for these conditions[21]. Therefore, gender variations in risk factors also require an epidemiological evaluation.
Our study aims to address the literature gap by providing a comprehensive analysis of the prevalence of BE among nationwide inpatient hospitalizations over a decade in the United States. We have used substantial epidemiological trends to assess risk factors for BE among women.
The National Inpatient Sample (NIS) is the largest inpatient database in the country[22]. The Agency for Healthcare Research and Quality designed it for the Healthcare Cost and Utilization Project[22]. This database is structured to approximate a stratified 20% sample of medical centers; sampling weights are applied to generate national estimates[22]. The data in NIS are provided using the International Classification of Diseases (ICD), Ninth Revision (ICD-9) (before September 2015), and Tenth Revision (ICD-10) (after October 2015) codes. Our research used the NIS registry to study adult patients (≥ 18 years of age) with a primary or secondary diagnosis code of BE between January 01, 2009 and December 31, 2019. The ICD-9 code "53085" and the ICD-10 codes "K2270", "K22710", "K22711", and "K22719" were used for identification of these patients.
The major outcome of interest was determining the gender disparities in patients with BE. A number of variables were compared, including age, race, Charlson Comorbidity Index (CCI) score, hospital region, teaching status, primary insurance, and socioeconomic status. In addition, we also compared the prevalence of obesity, smoking, alcohol abuse, Helicobacter pylori (H. pylori) infection, diabetes mellitus, and EAC. Trend analysis for respective outcomes for females was also reported to ascertain any time-based shifts.
The Statistical Software for Data Science, version 16.0 (StataCorp LLC, College Station, TX, United States), was used in this work. The analysis used two-sided P values, with a value of 0.05 as the statistically significant threshold. Bivariate analysis was carried out using the independent-samples t-test for continuous variables and the chi-square (χ2) test for categorical data. Frequency (N) and percentage (%) were used to present categorical variables. The mean and standard deviation represented continuous data, as appropriate. A similar analysis has been widely used in NIS-based studies[23]. The adjusted Wald test was utilized to compare the slopes of time-based linear regression outcomes and margin plots to generate figures[24,25].
The NIS comprises de-identified data. It has effective protocols designed to protect the privacy of patients, healthcare providers, and hospitals. Therefore, the institutional review board approval was exempted for this research. Patient consent was not required because the study only used anonymized data from hospitalization records. According to the Healthcare Cost and Utilization Project Data Use Agreement, any individual table cell counts of less than or equal to 10 have been suppressed to protect privacy and compliance.
Of the 1204190 patients with BE, 717439 (59.6%) were men and 486751 (40.4%) were women. Trend analysis revealed that the number of women with a diagnosis of BE increased from 36518 in 2009 to 52665 in 2019 (P < 0.001). The rate of BE per 100000 total NIS hospitalizations for males increased from 144.6 in 2009 to 213.4 in 2019, while the rate for females increased from 96.8 in 2009 to 148.7 in 2019 (P < 0.001) (Figure 1).
The mean age was higher in women compared to men (67.1 ± 0.4 vs 66.6 ± 0.3 years, P < 0.001) (Table 1). The mean age for females significantly increased from 66.4 ± 0.7 years in 2009 to 68.0 ± 0.2 years in 2019 (P < 0.001). The age group that was most affected was 65-79 years for both genders. This accounted for 46.8% of females and 46.9% of males. Trend analysis among females in this age group revealed an increasing prevalence, from 15629 (42.8%) in 2009 to 27072 (51.4%) in 2019 (P < 0.001) (Table 2). There was a downward trend in the prevalence of BE in females younger than 65 years (P < 0.001). Male and female patients with BE had a White race predominance (P < 0.001). There was no significant racial trend for females during the course of the study (P = 0.61).
| Males | Females | P value | |
| Total patients | 717439 (59.6) | 486751 (40.4) | |
| Mean age (years) | 66.6 ± 0.3 | 67.1 ± 0.4 | < 0.001 |
| Age groups (years) | < 0.001 | ||
| 18-34 | 14504 (2.0) | 12191 (2.5) | |
| 35-49 | 77463 (10.8) | 56421 (11.6) | |
| 50-64 | 262752 (36.6) | 167052 (34.3) | |
| 65-79 | 336496 (46.9) | 227662 (46.8) | |
| ≥ 80 | 26524 (3.7) | 23425 (4.8) | |
| Race | < 0.001 | ||
| White | 649806 (90.6) | 440110 (90.4) | |
| Black | 24510 (3.4) | 18551 (3.8) | |
| Hispanic | 35231 (4.9) | 22276 (4.6) | |
| Asian/Native American | 7892 (1.1) | 5815 (1.2) | |
| Charlson comorbidity index score | < 0.001 | ||
| 0 | 169348 (23.6) | 129794 (26.7) | |
| 1 | 151256 (21.1) | 128760 (26.4) | |
| 2 | 132933 (18.5) | 85975 (17.7) | |
| ≥ 3 | 263902 (36.8) | 142222 (29.2) | |
| Hospital region | < 0.001 | ||
| Northeast | 159271 (22.2) | 119644 (24.6) | |
| Midwest | 207339 (28.9) | 140049 (28.8) | |
| South | 224560 (31.3) | 151918 (31.2) | |
| West | 126269 (17.6) | 75140 (15.4) | |
| Hospital location and teaching status | < 0.001 | ||
| Rural | 67439 (9.4) | 47346 (9.7) | |
| Urban nonteaching | 220254 (30.7) | 157188 (32.3) | |
| Urban teaching | 429746 (59.9) | 282217 (58.0) | |
| Primary payer | 0.27 | ||
| Medicare | 459878 (64.1) | 329593 (67.7) | |
| Medicaid | 60264 (8.4) | 39804 (8.2) | |
| Private | 177210 (24.7) | 106659 (21.9) | |
| Other | 20087 (2.8) | 10695 (2.2) | |
| Median household income (quartile) | 0.075 | ||
| 1st (0-25th) | 158557 (22.1) | 107916 (22.2) | |
| 2nd (26th -50th) | 189403 (26.4) | 130652 (26.8) | |
| 3rd (51st -75th) | 188685 (26.3) | 128664 (26.4) | |
| 4th (76th -100th) | 180794 (25.2) | 119519 (24.6) | |
| Obesity | 90172 (12.6) | 84844 (17.4) | < 0.001 |
| Smoking | 256152 (35.7) | 100630 (20.8) | < 0.001 |
| Alcohol abuse | 81405 (11.3) | 20609 (4.2) | < 0.001 |
| Helicobacter pylori | 3944 (0.5) | 2124 (0.4) | 0.0002 |
| Diabetes | 132826 (18.5) | 83322 (17.1) | < 0.001 |
| Esophageal adenocarcinoma | 11733 (1.6) | 2544 (0.5) | < 0.001 |
| Variables | Years | P values | ||||||||||
| 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | ||
| Mean age (years) | 66.4 ± 0.7 | 66.5 ± 0.5 | 66.9 ± 0.4 | 67.1 ± 0.2 | 66.9 ± 0.4 | 67.0 ± 0.8 | 66.9 ± 0.9 | 67.3 ± 0.8 | 67.2 ± 0.9 | 67.8 ± 1.9 | 68.0 ± 0.2 | < 0.001 |
| Age groups (years) | < 0.001 | |||||||||||
| 18-34 | 1168 (3.2) | 1114 (2.9) | 1351 (3.1) | 1174 (2.8) | 948 (2.2) | 1462 (3.2) | 709 (1.9) | 865 (1.8) | 1021 (2.1) | 1223 (2.4) | 1156 (2.2) | |
| 35-49 | 5514 (15.1) | 5685 (14.8) | 5360 (12.3) | 5069 (12.1) | 4695 (10.9) | 5301 (11.6) | 4175 (11.2) | 4710 (9.8) | 5297 (10.9) | 5148 (10.1) | 5467 (10.4) | |
| 50-64 | 12745 (34.9) | 13292 (34.6) | 15779 (36.2) | 14956 (35.7) | 15547 (36.1) | 15766 (34.5) | 13382 (35.9) | 16438 (34.2) | 15989 (32.9) | 16463 (32.3) | 16695 (31.7) | |
| 65-79 | 15629 (42.8) | 16288 (42.4) | 18872 (43.3) | 18516 (44.2) | 19726 (45.8) | 20976 (45.9) | 17185 (46.1) | 23599 (49.1) | 23958 (49.3) | 25841 (50.7) | 27072 (51.4) | |
| ≥ 80 | 1462 (4.0) | 2036 (5.3) | 2223 (5.1) | 2178 (5.2) | 2153 (5.0) | 2194 (4.8) | 1827 (4.9) | 2451 (5.1) | 2333 (4.8) | 2293 (4.5) | 2275 (4.3) | |
| Race | 0.61 | |||||||||||
| White | 33194 (90.9) | 34689 (90.3) | 39009 (89.5) | 37787 (90.2) | 39020 (90.6) | 40992 (89.7) | 33699 (90.4) | 43785 (91.1) | 43884 (90.3) | 46126 (90.5) | 47925 (91.0) | |
| Black | 1023 (2.8) | 1613 (4.2) | 1700 (3.9) | 1718 (4.1) | 1852 (4.3) | 1828 (4.0) | 1417 (3.8) | 1730 (3.6) | 1993 (4.1) | 1886 (3.7) | 1791 (3.4) | |
| Hispanic | 1899 (5.2) | 1652 (4.3) | 2397 (5.5) | 1843 (4.4) | 1766 (4.1) | 2376 (5.2) | 1715 (4.6) | 1923 (4.0) | 2041 (4.2) | 2294 (4.5) | 2370 (4.5) | |
| Asian/Native American | 402 (1.1) | 461 (1.2) | 479 (1.1) | 545 (1.3) | 431 (1.0) | 503 (1.1) | 447 (1.2) | 625 (1.3) | 680 (1.4) | 663 (1.3) | 579 (1.1) | |
| Charlson comorbidity index | < 0.001 | |||||||||||
| 0 | 12562 (34.4) | 11947 (31.1) | 12771 (29.3) | 12526 (29.9) | 12146 (28.2) | 13390 (29.3) | 10028 (26.9) | 11103 (23.1) | 10983 (22.6) | 10857 (21.3) | 11481 (21.8) | |
| 1 | 10078 (27.6) | 10833 (28.2) | 11942 (27.4) | 11353 (27.1) | 12016 (27.9) | 11470 (25.1) | 9804 (26.3) | 12689 (26.4) | 12198 (25.1) | 12895 (25.3) | 13482 (25.6) | |
| 2 | 6318 (17.3) | 7107 (18.5) | 7932 (18.2) | 7164 (17.1) | 7795 (18.1) | 7952 (17.4) | 6598 (17.7) | 8603 (17.9) | 8797 (18.1) | 9072 (17.8) | 8637 (16.4) | |
| ≥ 3 | 7560 (20.7) | 8528 (22.2) | 10940 (25.1) | 10850 (25.9) | 11112 (25.8) | 12887 (28.2) | 10848 (29.1) | 15668 (32.6) | 16620 (34.2) | 18144 (35.6) | 19065 (36.2) | |
| Hospital region | 0.99 | |||||||||||
| Northeast | 8691 (23.8) | 8490 (22.1) | 10591 (24.3) | 10415 (24.9) | 10810 (25.1) | 11289 (24.7) | 9394 (25.2) | 12256 (25.5) | 12101 (24.9) | 12283 (24.1) | 13324 (25.3) | |
| Midwest | 11174 (30.6) | 11448 (29.8) | 12727 (29.2) | 11968 (28.6) | 11931 (27.7) | 12841 (28.1) | 10587 (28.4) | 13362 (27.8) | 13802 (28.4) | 14883 (29.2) | 15326 (29.1) | |
| South | 11466 (31.4) | 12407 (32.3) | 14339 (32.9) | 13060 (31.2) | 13265 (30.8) | 14349 (31.4) | 11370 (30.5) | 14995 (31.2) | 15114 (31.1) | 15596 (30.6) | 15957 (30.3) | |
| West | 5187 (14.2) | 6070 (15.8) | 5928 (13.6) | 6450 (15.4) | 7063 (16.4) | 7220 (15.8) | 5927 (15.9) | 7450 (15.5) | 7581 (15.6) | 8206 (16.1) | 8058 (15.3) | |
| Hospital location and teaching status | < 0.001 | |||||||||||
| Rural | 3944 (10.8) | 4302 (11.2) | 5187 (11.9) | 5069 (12.1) | 4479 (10.4) | 4250 (9.3) | 3020 (8.1) | 4614 (9.6) | 3985 (8.2) | 4230 (8.3) | 4266 (8.1) | |
| Urban nonteaching | 17090 (46.8) | 18324 (47.7) | 19308 (44.3) | 16925 (40.4) | 17874 (41.5) | 12476 (27.3) | 10885 (29.2) | 12593 (26.2) | 11518 (23.7) | 10347 (20.3) | 9848 (18.7) | |
| Urban teaching | 15484 (42.4) | 15789 (41.1) | 19090 (43.8) | 19899 (47.5) | 20716 (48.1) | 28973 (63.4) | 23373 (62.7) | 30856 (64.2) | 33095 (68.1) | 36391 (71.4) | 38551 (73.2) | |
| Primary payer | < 0.001 | |||||||||||
| Medicare | 22897 (62.7) | 24278 (63.2) | 28809 (66.1) | 28026 (66.9) | 29416 (68.3) | 30846 (67.5) | 25424 (68.2) | 33356 (69.4) | 33581 (69.1) | 35779 (70.2) | 37181 (70.6) | |
| Medicaid | 2301 (6.3) | 3112 (8.1) | 3574 (8.2) | 3142 (7.5) | 3187 (7.4) | 3747 (8.2) | 3131 (8.4) | 4566 (9.5) | 4179 (8.6) | 4230 (8.3) | 4635 (8.8) | |
| Private | 10444 (28.6) | 10180 (26.5) | 10068 (23.1) | 9677 (23.1) | 9303 (21.6) | 10054 (22.0) | 7940 (21.3) | 9516 (19.8) | 10108 (20.8) | 9837 (19.3) | 9532 (18.1) | |
| Other | 876 (2.4) | 845 (2.2) | 1134 (2.6) | 1048 (2.5) | 1163 (2.7) | 1052 (2.3) | 782 (2.1) | 625 (1.3) | 730 (1.5) | 1121 (2.2) | 1317 (2.5) | |
| Median household income | 0.51 | |||||||||||
| 1st (0-25th) | 7486 (20.5) | 8605 (22.4) | 10068 (23.1) | 9300 (22.2) | 9475 (22.0) | 10374 (22.7) | 8648 (23.2) | 10718 (22.3) | 10740 (22.1) | 10652 (20.9) | 11850 (22.5) | |
| 2nd (26th -50th) | 9130 (25.0) | 9911 (25.8) | 11288 (25.9) | 11227 (26.8) | 11672 (27.1) | 12933 (28.3) | 9879 (26.5) | 12593 (26.2) | 13267 (27.3) | 14322 (28.1) | 14430 (27.4) | |
| 3rd (51st -75th) | 9969 (27.3) | 10410 (27.1) | 11681 (26.8) | 10851 (25.9) | 11801 (27.4) | 11379 (24.9) | 9320 (25.0) | 13025 (27.1) | 12684 (26.1) | 14220 (27.9) | 13324 (25.3) | |
| 4th (76th -100th) | 9933 (27.2) | 9489 (24.7) | 10548 (24.2) | 10515 (25.1) | 10121 (23.5) | 11013 (24.1) | 9431 (25.3) | 11727 (24.4) | 11907 (24.5) | 11774 (23.1) | 13061 (24.8) | |
| Obesity (BMI ≥ 30 kg/m2) | 4477 (12.3) | 4728 (12.3) | 5625 (12.9) | 6030 (14.4) | 7210 (16.7) | 7995 (17.5) | 6874 (18.4) | 9320 (19.4) | 9975 (20.5) | 11055 (21.7) | 11555 (21.9) | < 0.001 |
| Smoking | 4704 (12.9) | 5464 (14.2) | 6308 (14.5) | 6105 (14.6) | 6045 (14.0) | 6625 (14.5) | 5875 (15.8) | 13240 (27.5) | 14645 (30.1) | 15474 (30.4) | 16145 (30.7) | < 0.001 |
| Alcohol abuse | 1063 (2.9) | 1243 (3.2) | 1599 (3.7) | 1410 (3.4) | 1570 (3.6) | 1630 (3.6) | 1590 (4.2) | 2600 (5.4) | 2484 (5.1) | 2560 (5.0) | 2860 (5.4) | < 0.001 |
| Helicobacter pylori | 242 (0.7) | 244 (0.6) | 293 (0.7) | 290 (0.7) | 195 (0.5) | 200 (0.4) | 135 (0.4) | 115 (0.2) | 150 (0.3) | 140 (0.3) | 120 (0.2) | < 0.001 |
| Diabetes | 6541 (17.9) | 7014 (18.3) | 8284 (19.0) | 8195 (19.6) | 8445 (19.6) | 8540 (18.7) | 7315 (19.6) | 8554 (17.8) | 6829 (14.1) | 6715 (13.2) | 6890 (13.1) | < 0.001 |
| Esophageal adenocarcinoma | 341 (0.9) | 250 (0.7) | 348 (0.8) | 240 (0.6) | 330 (0.8) | 320 (0.7) | 300 (0.8) | 115 (0.2) | 115 (0.2) | 110 (0.2) | 75 (0.1) | 0.1 |
Females had a lower CCI score of ≥ 3 than males (29.2% vs 36.8%, P < 0.001). Trend analysis among females revealed an increasing CCI score of ≥ 3, from 20.7% in 2009 to 36.2% in 2019 (P < 0.001). A higher frequency of obesity was noted in females compared to males (17.4% vs 12.6%, P < 0.001). Female BE patients had a rising obesity prevalence from 12.3% in 2009 to 21.9% in 2019 (P < 0.001). A lower prevalence of smoking was noted in females than males (20.8% vs 35.7%, P < 0.001). However, trend analysis among females revealed an increasing prevalence of smoking, from 12.9% in 2009 to 30.7% in 2019 (P < 0.001). Additionally, there was a lower prevalence of alcohol abuse, H. pylori, and diabetes mellitus among females than males during the study period (P < 0.001). Trend analysis showed an increasing prevalence of alcohol use disorder and a decreasing prevalence of H. pylori and diabetes mellitus among women (P < 0.001). The prevalence of EAC also showed a decline from 0.9% in 2009 to 0.1% in 2019 (P = 0.1).
This is the first nationwide study to evaluate gender disparities in patients with BE and associated risk factors using a multicenter database. It shows that the prevalence of BE among women has steadily increased from 2009 to 2019 in the United States. The rate of BE for females in 2019 was comparable to the rate for males in 2009. Our findings also highlight the feasibility and potential of screening a larger population based on gender differences in BE risk factors.
A male predominance has been historically observed in patients with BE[26]. The results of our study indicate that women also have a considerable risk of developing this condition. The available clinical evidence shows that there are several risk factors associated with the development of BE[27-30]. It is important to note that patients may also exhibit gender-specific variations in these risk factors[31-33]. Our findings show several correlations that may explain the increasing prevalence of BE among females. We found a higher frequency of obesity among women compared to men (17.4% vs 12.6%). Obesity in females increased from 12.3% in 2009 to 21.9% in 2019. Clinical data show that a high body mass index (BMI) has been strongly associated with the development of frequent reflux symptoms[34,35]. A prospective cohort study from the Netherlands based on 120852 participants revealed that increased BMI was a significant risk factor for BE development in females but not in males[36]. A study from the United States surveyed 10545 women to under
Gender-related differences in body fat distribution may potentially have an impact on BE development. Females usually have fat located in subcutaneous tissues compared to males, who have centralized, visceral fat[39]. Adipokines, such as leptin and adiponectin, play an active role in the pathogenesis of BE[40]. Therefore, the higher overall prevalence of BE among men could be attributed to the higher production of these adipokines by visceral fat[40]. In our analysis, the prevalence of BE showed a significant increase from 42.4% in 2009 to 73.2% in 2019 in urban teaching hospitals. Certain urban layouts may impact physical activity and promote a sedentary lifestyle, which can result in weight gain. These factors increase the risk of GERD, leading to a higher probability of BE. Moreover, obese patients with nondysplastic BE have a significantly higher risk of dysplasia development in three to five years compared to nonobese patients[41]. These findings highlight the critical need to curtail the ongoing obesity epidemic in order to reduce the burden of BE and EAC.
In our data, women aged 65-79 years showed a significant increase in BE prevalence from 42.8% in 2009 to 51.4% in 2019. Contrarily, females under the age of 65 years showed a downtrend over the study period. The National Ambulatory Medical Care Survey-based study analyzed the trend in physician visits for GERD from 1995 to 2006[42]. It found that ambulatory visits for GERD were strongly associated with older age, female gender, and use of calcium channel blockers[42]. Therefore, the trend of BE seen in women older than 65 in our data may be explained by worsening acid reflux with age. Our analysis showed that the smoking prevalence among females also increased from 12.9% in 2009 to 30.7% in 2019. The biological pathway of smoking in BE is unclear. However, a study based on patient data from five case-control studies included in the international Barrett's and Esophageal Adenocarcinoma Consortium suggested cigarette smoking as a risk factor for BE[43]. An Australian case-control study reported that people with both acid reflux and smoking had a higher risk of BE than those with either reflux or smoking alone (odds ratio: 51.4)[44]. However, even in the absence of acid reflux, the metabolic syndrome alone may also increase the risk of BE. A population-based study from the United States suggested that metabolic syndrome-related systemic inflammation could increase BE risk in individuals without GERD[45].
We found the highest prevalence of BE among White Americans. The American College of Gastroenterology guidelines included Caucasian race among the risk factors for BE[46]. A retrospective case-control study from the United Kingdom also revealed White Caucasian ethnicity as an independent risk factor for BE[47]. Similar observations have also been made in other population-based studies, predicting that the prevalence of EAC is three to five times higher among Caucasians compared to African Americans[48,49]. An observational study based on the United States Surveillance, Epidemiology, and End Results Program also showed that the incidence of EAC remains higher in non-Hispanic Whites[50]. In our data, trend analysis among females revealed an increasing CCI score of ≥ 3, from 20.7% in 2009 to 36.2% in 2019. Compared to men, women had lower rates of diabetes, H. pylori, and alcohol abuse during the study period. A trend analysis of female patients revealed a rise in the prevalence of alcohol usage and a decrease in the prevalence of H. pylori and diabetes. Therefore, it is plausible that the interaction of the risk factors may contribute to the rise of BE among females. Future research should investigate the causal link between BE and the concurrent occurrence of various risk variables.
Patients with BE may have gender-based differences in presentation patterns. A comparative study from the United States reported that women with reflux have less esophageal acid exposure than men[51]. In addition, the female gender does not provide the same protection against the development of BE as males in patients with advanced GERD[51]. A population-based study conducted in Italy found that 46.2% of BE patients may develop an asymptomatic disease[52]. Therefore, a diagnosis for females with asymptomatic BE might not be made until the disease has progressed. It complicates clinical management, directly impacting healthcare costs. In our study, the prevalence of EAC showed a decline for the study period, but the downtrend was not significant (P = 0.1). A multicenter prospective cohort study from the Netherlands revealed that males had a higher probability and time to cancer progression, but females were proportionally more frequently diagnosed with advanced stages of EAC[53]. Therefore, new prediction models for BE patients should focus on gender-based EAC risk stratification[53].
The American College of Gastroenterology and the European Society of Gastrointestinal Endoscopy recommend screening for a nondysplastic BE every three to five years, based on the length of the Barrett’s segment[54,55]. However, there is an ongoing debate about how to shorten the surveillance intervals using molecular and individualized risk stratification[56]. It would not be financially feasible to rule out BE based only on heartburn complaints, given the frequency of these symptoms in large communities[57]. Therefore, the initial use of nonendoscopic screening devices may help in this regard[58,59]. A careful evaluation of the risk factor profiles of suspected patients could aid in improving noninvasive screening for BE and EAC[60,61]. In addition, novel artificial intelligence-driven risk assessment models can also be combined with nonendoscopic screening techniques[62-65]. These artificial intelligence tools can also improve the diagnostic yield of advanced endoscopic modalities[66]. Our study indicates that there has been a significant increase in BE diagnoses among females, especially in older age groups. This trend could be linked to long-term metaplasia caused by GERD, obesity, smoking, and other concurrent comorbidities. Therefore, we suggest that healthcare departments should also consider suspected female patient populations while identifying the high-risk groups for BE. The selected patients may then undergo upper endoscopy, with proper follow-up surveillance to monitor the disease progression. Asymptomatic individuals who progress to EAC may have worse clinical outcomes. Therefore, early detection of dysplasia may also help guide effective management to halt cancer progression in BE[67].
Our retrospective study is one of the largest comparative analyses conducted to examine the prevalence of BE among women. It implies that women may have BE at a higher rate than previously believed and that this condition is not exclusive to men. We also assessed the risk of BE among females through variables such as obesity, smoking, alcohol use disorder, H. pylori, and diabetes mellitus. The 11-year study period is one of the major strengths of this research. The nationwide sample population broadens the applicability of our findings by providing greater generalizability and enhancing the understanding of the gender distribution patterns. Clinical outcomes can be improved by a tailored surveillance approach for BE patients who may be at risk for EAC. Our findings carry pertinent clinical implications and underscore the importance of screening and surveillance considering gender-based risk variables. Public health campaigns can be carried out among communities to educate individuals about risk factors. Our data could also prove to be a forerunner for future comprehensive studies to further refine the surveillance recommendations for BE among women.
This study has certain limitations. The use of coding systems may introduce inaccuracies, potentially skewing the analysis when utilizing a large database. The ICD coding system for BE may not be sufficiently reliable for epidemiological studies[68,69]. However, these codes have been previously used to investigate inpatient outcomes of atrial fibrillation among BE patients and the prevalence of BE among Asian Americans[70,71]. Since BE is not typically an inpatient diagnosis and does not usually lead to hospitalization, we used these inpatient diagnosis codes as a surrogate to measure changes in the overall prevalence of BE among females. The NIS database does not provide information on the progression of BE to EAC. In addition, data on the severity of the disease and treatment course are also not reported. Therefore, patients classified as BE in our data might already have progressed to EAC due to a lack of endoscopic screening. BE was not the primary diagnosis in these patients hospitalized for any cause. Therefore, we did not report hospital stays, charges, or procedures. Moreover, the data collected from NIS might only include those hospitals participating in the Healthcare Cost and Utilization Project[72]. Despite these limitations, this is the first national research study to compare gender-based differences in patients with BE. It will add to the current clinical understanding of the prevalence of BE in women and the related risk factors.
This study has thoroughly analyzed the prevalence and trends of BE and its risk factors among women. According to our findings, there was a noticeable increase in the prevalence of BE in females from 2009 to 2019. The prevalence was significantly higher in the older age groups. Women had a higher frequency of obesity compared to men. A trend analysis of female BE patients revealed a rise in the prevalence of obesity, smoking, and alcohol usage and a decline in the prevalence of H. pylori and diabetes. These results underscore the clinical importance of recognizing gender differences in BE risk factors. Endoscopic evaluation tailored to woman-specific risk factors could also help refine surveillance intervals. Such actions may facilitate the detection of EAC at less advanced stages among women. It could potentially improve clinical outcomes by decreasing morbidity and mortality in at-risk patient populations.
The preliminary form of these data was presented as an abstract at the Annual Scientific Meeting of the American College of Gastroenterology, 20-25 October 2023, in Vancouver, BC, Canada.
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