| Literature DB >> 28050339 |
Monica L Kasting1, Shannon Wilson2, Terrell W Zollinger1, Brian E Dixon3, Nathan W Stupiansky2, Gregory D Zimet2.
Abstract
Among the identified barriers to HPV vaccination is the concern that women may compensate for their reduced susceptibility to cervical cancers by reducing cervical cancer screening. This exploratory study examined the relationship between cervical cancer screening rates and HPV vaccination. We conducted a cross-sectional survey using a convenience sample of women aged 21-35 attending a local minority health fair in July 2015. Data were analyzed in 2015-2016. Outcomes assessed were: receiving a Pap test within the last three years, awareness and comfort with current Pap test recommendations, and knowledge regarding the purpose of a Pap test. A total of 291 women were included in the analyses. Mean age was 28.5 years and 62% were non-Hispanic black. 84% had received a Pap test in the last three years and 33% had received at least one HPV vaccine. Logistic regression results showed that women who had been vaccinated did not have lower odds of having a Pap test in the past three years (OR = 1.32; 95% CI = 0.66-2.65). In an adjusted regression model controlling for age and race, vaccinated women were significantly more likely to have had a Pap test (AOR = 3.06; 95% CI = 1.37-6.83). Yet only 26% of women knew the purpose of a Pap test and the proportion who answered correctly was higher among non-Hispanic white women. Women who have been vaccinated for HPV are more likely to have been screened for cervical cancer. These results suggest areas for more robust studies examining pro-health attitudes, behaviors, and communication regarding vaccination and preventive screening.Entities:
Keywords: Health behaviors; Health disparities; Papanicolaou test; Papillomavirus vaccines; Risk compensation
Year: 2016 PMID: 28050339 PMCID: PMC5200875 DOI: 10.1016/j.pmedr.2016.12.013
Source DB: PubMed Journal: Prev Med Rep ISSN: 2211-3355
Sample description by cervical cancer screening status (current vs. not current).
| Total sample | Current | Not current ( | ||
|---|---|---|---|---|
| Mean age (continuous) | 28.5 | 29.1 | 25.3 | |
| Age (categorical) | ||||
| 21–25 years old | 99 (34.0) | 69 (28.2) | 30 (65.2) | |
| 26–30 years old | 82 (28.2) | 73 (29.8) | 9 (19.6) | |
| 31–35 years old | 110 (37.8) | 103 (42.0) | 7 (15.2) | |
| Race | ||||
| Non-Hispanic white | 66 (22.7) | 56 (22.9) | 10 (21.7) | |
| Non-Hispanic black | 182 (62.5) | 160 (65.3) | 22 (47.8) | |
| Other | 43 (14.8) | 29 (11.8) | 14 (30.4) | |
| Education | 0.112 | |||
| Some high school/high school graduate/GED | 35 (12.0) | 27 (11.0) | 8 (17.4) | |
| Some college/trade school/4-year degree | 154 (52.9) | 136 (55.5) | 18 (39.1) | |
| Some post-grad/graduate degree | 102 (35.1) | 82 (33.5) | 20 (43.5) | |
| HPV vaccine status | 0.497 | |||
| Received > = 1 dose | 97 (33.3) | 84 (34.3) | 13 (28.3) | |
| Never received HPV vaccine or unsure | 194 (66.7) | 161 (65.7) | 33 (71.7) | |
| Purpose of a Pap test | 0.317 | |||
| Incorrect | 104 (35.7) | 84 (34.3) | 20 (43.5) | |
| Partially correct | 110 (37.8) | 97 (39.6) | 13 (28.3) | |
| Correct | 77 (26.5) | 64 (26.1) | 13 (28.3) | |
| Pap recommendation awareness | ||||
| Aware | 132 (45.5) | 122 (50.0) | 10 (21.7) | |
| Unaware | 158 (54.5) | 122 (50.0) | 36 (78.3) | |
| Guideline comfort | ||||
| Very uncomfortable | 63 (21.8) | 59 (24.2) | 4 (8.9) | |
| Somewhat uncomfortable | 61 (21.1) | 52 (21.3) | 9 (20.0) | |
| Neither comfortable nor uncomfortable | 51 (17.6) | 39 (16.0) | 12 (26.7) | |
| Somewhat comfortable | 39 (13.5) | 27 (11.1) | 12 (26.7) | |
| Very comfortable | 75 (26.0) | 67 (27.5) | 8 (17.8) |
Boldface indicates statistical significance (p < 0.05) between those who were current and those who were not current for a Pap test.
Abbreviations: GED, General Educational Development; HPV, Human Papillomavirus; Pap, Papanicolaou.
“Other” category includes people who indicated “other” for their race, people who indicated multiple races, and Hispanics.
Regression analyses assessing the receipt of a pap test in the last three years.
| Bivariate odds ratio (95% CI) | Multivariable odds ratio (95% CI) | |
|---|---|---|
| Ever received HPV vaccine | ||
| No | Ref | Ref |
| Yes | 1.32(0.66–2.65) | |
| Race | ||
| Non-Hispanic white | Ref | Ref |
| Non-Hispanic black | 1.30(0.58–2.91) | 0.94 (0.39–2.27) |
| Other | ||
| Age (continuous) | ||
| Education | ||
| Some high school/high school graduate/GED (ref) | Ref | |
| Some college/trade school/4-year degree | 2.24(0.88–5.67) | |
| Some post-grad/graduate degree | 1.22(0.48–3.07) |
Boldface indicates statistical significance (p < 0.05).
Abbreviations: GED, General Educational Development; HPV, Human Papillomavirus; Pap, Papanicolaou.
These variables were not significant in the bivariate analysis and were subsequently excluded from multivariable regression model.
Answers to “What is the purpose of a Pap smear or Pap test?” by race/ethnicity.
| Purpose of a Pap | Overall ( | Non-Hispanic white ( | Non-Hispanic black ( | Example quotes |
|---|---|---|---|---|
| Correct | 64 (25.8) | 29 (43.9) | 35 (19.2) | “For early detection of cancerous cells in the cervix.” |
| Partially correct | 93 (37.5) | 22 (33.3) | 71 (39.0) | “The check for any abnormalities and to screen for cervical cancer as well as STDs.” |
| Incorrect | 91 (36.7) | 15 (22.7) | 76 (41.8) | “To determine if you possibly have breast cancer.” |
Abbreviations: Pap, Papanicolaou; STD, Sexually Transmitted Disease.
| Author | Contributions |
|---|---|
| Monica L. Kasting, PhD, corresponding author | Conception and design, development of methodology, acquisition of data, analysis and interpretation of data, writing of manuscript |
| Shannon Wilson, BA | Conception and design, acquisition of data, analysis and interpretation of data, review and revision of manuscript |
| Terrell W. Zollinger, DrPH | Conception and design, development of methodology, analysis and interpretation of data, review and revision of manuscript |
| Brian E. Dixon, MPA, PhD | Analysis and interpretation of data, review and revision of manuscript |
| Nathan W Stupiansky, PhD | Analysis and interpretation of data, review and revision of manuscript |
| Gregory D. Zimet, PhD | Conception and design, development of methodology, analysis and interpretation of data, review and revision of manuscript |