The Relationship Between Poverty Income Ratio and HPV Prevalence Among Adults in the United States

To explore the relationship between Human papillomavirus prevalence and Poverty Income Ratio among adults in the United States.

Data was collected through the 2015-2016 NHANES cycles in which a multistage probability design was used to recruit participants from 30 locations.  The final analysis included 2,969 participants. In home interviews were scheduled for initial testing followed by HPV testing using the Roche HPV Linear Array to determine positive or negative HPV status.  Our independent variable, Poverty Income Ratio (PIR), was separated into two groups: PIR ≥ 1, and PIR < 1 which indicated above the poverty line and below the poverty line, respectively.  Confounding variables included age, gender, race/hispanic origin, marital status, citizenship status, vaccination status, and education.  Logistic Regression was performed to analyze the statistical significance of these confounders in regards to our dependent variable, HPV status.

HPV Status and Poverty Income Ratio

The association between HPV Status and Poverty Income Ratio was analyzed using logistic regression models.  In the unadjusted logistic regression model, populations living below the poverty line (PIR < 1) were 37% more likely to be HPV positive (OR 1.37, 95% CI: 1.14-1.64) than those above the poverty line (PIR ≥ 1).  After adjusting for confounders, the association was insignificant (AOR: 1.10, 95% CI: 0.90-1.36), suggesting confounding variables contribute to the overall association between the outcome and predictor variables.

Significant Demographic Variables

In the adjusted model, several demographic variables were significant.  

Non-Hispanic Black populations had a 74% increased chance of contracting HPV (AOR: 1.74) compared to Non-Hispanic White populations.  Other races including multiracial populations conversely, had a 30% decreased risk of HPV (AOR: 0.70).

Compared to married populations, those who were widowed (AOR: 1.75), divorced (AOR: 1.91), separated (AOR: 2.42), never married (AOR: 1.66), or living with a partner (AOR: 1.93), had significantly increased odds of HPV.

In regards to sex, females had slightly lower odds of HPV compared to males (AOR: 0.81).  

Education was also a significant predictor for HPV status as those who were college graduates or above had decreased odds of HPV (AOR: 0.62).

For every year of age, an individual becomes about 1% more likely to be HPV positive (AOR: 1.01).

Vaccination Variable

Those who were not vaccinated against HPV had significantly lower odds of HPV contraction (AOR: 0.65, 95% CI: 0.49-0.87) compared to those who were vaccinated.

Several demographic factors contributed to the association between Poverty Income Ratio and HPV Status.  These variables include race/hispanic origin, marital status, education, sex, age, and vaccination status.  

For future research, focus on the vaccination variable is inherent.  The paradoxical relationship between vaccinated individuals and non-vaccinated individuals in relation to HPV status should be studied.  Timing of vaccination could be a contributor to this significant correlation.  Studies of specific strains of HPV could also be used to explore the relationship further.

We firstly want to thank Dr. Ghimire for giving us the space, support, and opportunity to research such an important topic. Thank you to our MBI461 Capstone class for creating an environment where mistakes could be made and learning opportunities could come from them.  

1. Centers for Disease Control and Prevention. (2017, September). DEMO_I. https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.htm 

2. Centers for Disease Control and Prevention. (2018, November). HPVSWR_I. https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/HPVSWR_I.htm 

3. Centers for Disease Control and Prevention. (2019, November). HPVP_I. https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/HPVP_I.htm  

4. Centers for Disease Control and Prevention. (n.d.). NHANES 2015-2016 overview. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overview.aspx?BeginYear=2015  

5. Centers for Disease Control and Prevention. (2017). IMQ_I. https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/IMQ_I.htm 

6. Centers for Disease Control and Prevention. (2024, December 18). What NHANES Covers and How it Works

https://www.cdc.gov/nchs/nhanes/about/survey-content-operations.html 

Through this research experience we have gained skills in teamwork, technology, and professionalism.  Our team was dedicated to producing quality work representing our individual strengths.  We each brought professionalism and commitment to our research addressing a topic that is of the utmost importance.  In order to effectively communicate accurate results, the technology platform SAS was used to model and perform data analysis.  Having never used SAS before, there was a learning curve that we adapted to and were able to produce quality results.

In order to gather our data for the study we used a Deidentified Secondary Dataset from NHANES.  Research compliance protocols were approved by this source.