Evidence for cross-protection but not type-replacement over the 11 years after human papillomavirus vaccine introduction

Examination of cross-protection and type replacement after human papillomavirus (HPV) vaccine introduction is essential to guide vaccination recommendations and policies. The aims of this study were to examine trends in non-vaccine-type HPV: 1) genetically related to vaccine types (to assess for cross-protection) and 2) genetically unrelated to vaccine types (to assess for type replacement), among young women 13-26 years of age during the 11 years after HPV vaccine introduction. Participants were recruited from a hospital-based teen health center and a community health department for four cross-sectional surveillance studies between 2006 and 2017. Participants completed a survey that assessed sociodemographic characteristics and behaviors, and cervicovaginal swabs were collected and tested for 36 HPV genotypes. We determined changes in proportions of non-vaccine-type HPV prevalence and conducted logistic regression to determine the odds of infection across the surveillance studies, propensity-score adjusted to control for selection bias. Analyses were stratified by vaccination status. Among vaccinated women who received only the 4-valent vaccine (n = 1,540), the adjusted prevalence of HPV types genetically related to HPV16 decreased significantly by 45.8% (adjusted odds ratio [AOR] = 0.48, 95% confidence interval [CI] = 0.31-0.74) from 2006-2017, demonstrating evidence of cross-protection. The adjusted prevalence of HPV types genetically related to HPV18 did not change significantly (14.2% decrease, AOR = 0.83, 95% CI = 0.56-1.21). The adjusted prevalence of HPV types genetically unrelated to vaccine types did not change significantly (4.2% increase, AOR = 1.09, CI = 0.80-1.48), demonstrating no evidence of type replacement. Further studies are needed to monitor for cross-protection and possible type replacement after introduction of the 9-valent HPV vaccine

Chi-Square Test for Goodness of Fit in a Plant Breeding Example

In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance with many genes contributing, etc. The question then is how do plant breeders determine if the data is close enough to what they expected to determine if the hypothesis is supported or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis of single gene, completely dominant inheritance. Overview and Objectives In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance with many genes contributing, etc. Next the breeder sets up some crosses and observes the resulting progeny to test that inheritance hypothesis. However, when the data is collected, oftentimes the breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis. The question then is how do plant breeders determine if the data supports their hypothesis or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis. This lesson is written for undergraduate and graduate students studying plant breeding, as well as agriculture professionals unfamiliar with the use of the chi-square analysis. Objectives After completing this lesson module, you should be able to 1. Calculate expected phenotypic and genotypic ratios and the number of plants expected in each class for a given plant breeding scheme. 2. Calculate chi-square values for plant genetics data sets from both phenotypic and genotypic observations. 3. Calculate degrees of freedom. 4. Accurately interpret results from a chi-square test. 5. Identify appropriate uses and limitations of the chi-square test in plant breeding and genetics research. Modules: Lesson home Overview and Objectives Introduction - Why Chi-Square Is Needed Chi-square Description Step 1 - Calculating What Is Expected Step 2 - Measuring The Observations Step 3 - Determining Deviations Step 4 - Plugging Numbers Into The Formula Step 5 - Interpreting The Results Genotyping Example, Step 1 - Calculating What Is Expected Genotyping Example, Step 2 - Measuring The Observations Genotyping Example, Step 3 - Determining Deviations Genotyping Example, Step 4 - Plugging Numbers Into The Formula Genotyping Example, Step 5 - Interpreting The Results When Chi-square Is Appropriate - Strengths/Weaknesses Summary Glossary Video

Chi-Square Test for Goodness of Fit in a Plant Breeding Example

In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance with many genes contributing, etc. The question then is how do plant breeders determine if the data is close enough to what they expected to determine if the hypothesis is supported or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis of single gene, completely dominant inheritance. Overview and Objectives In plant breeding and genetics research, plant breeders establish a hypothesis to explain how they think a particular trait is inherited, such as if it is due to one gene with complete dominance, an interaction of more than one gene, or quantitative inheritance with many genes contributing, etc. Next the breeder sets up some crosses and observes the resulting progeny to test that inheritance hypothesis. However, when the data is collected, oftentimes the breeder discovers the number of plants observed in each class is not exactly what was expected from the hypothesis. The question then is how do plant breeders determine if the data supports their hypothesis or not? Following a tomato disease resistance example in this lesson, you will learn a simple statistical test that breeders can use to conclude if the experimental data supports their hypothesis. This lesson is written for undergraduate and graduate students studying plant breeding, as well as agriculture professionals unfamiliar with the use of the chi-square analysis. Objectives After completing this lesson module, you should be able to 1. Calculate expected phenotypic and genotypic ratios and the number of plants expected in each class for a given plant breeding scheme. 2. Calculate chi-square values for plant genetics data sets from both phenotypic and genotypic observations. 3. Calculate degrees of freedom. 4. Accurately interpret results from a chi-square test. 5. Identify appropriate uses and limitations of the chi-square test in plant breeding and genetics research. Modules: Lesson home Overview and Objectives Introduction - Why Chi-Square Is Needed Chi-square Description Step 1 - Calculating What Is Expected Step 2 - Measuring The Observations Step 3 - Determining Deviations Step 4 - Plugging Numbers Into The Formula Step 5 - Interpreting The Results Genotyping Example, Step 1 - Calculating What Is Expected Genotyping Example, Step 2 - Measuring The Observations Genotyping Example, Step 3 - Determining Deviations Genotyping Example, Step 4 - Plugging Numbers Into The Formula Genotyping Example, Step 5 - Interpreting The Results When Chi-square Is Appropriate - Strengths/Weaknesses Summary Glossary Video

Appendix 3: Comparison of Pharmacists’ Scoring of Fall Risk to Other Fall Risk Assessments

Deidentified participant case IDs linked to the deidentified pharmacists\u27 scoring. Appendix to the article: Panus, P.C., Covert, K.L., Odle, B.L., Karpen, S.C., Walls, Z.F., and Hall, C.D. (2022) Comparison of pharmacists\u27 scoring of fall risk to other fall risk assessments. Journal of the American Pharmacists Association, 62, 505-511. https://doi.org/10.1016/j.japh.2021.11.00

Sexual Network Patterns and Their Association With Genital and Anal Human Papillomavirus Infection in Adolescent and Young Men

PurposeThis study aimed to determine individual- and partner-level factors associated with human papillomavirus (HPV) infection in vaccinated and unvaccinated men.MethodsA total of 747 men, aged 13-26 years, completed a survey of sexual behaviors and were tested for genital and perianal/anal HPV (36 types). Sexual network variables included recent and lifetime concurrency (being in more than one sexual relationship at the same time) and recent sex partner discordance (by race, ethnicity, age, and number of sexual partners). We determined individual-level and sexual network variables associated with ≥1 HPV type and HPV16/18, stratified by vaccination status, using separate multivariable logistic regression models.ResultsParticipants' mean age was 21.2 years; 64% were positive for ≥1 HPV type and 21% for HPV16/18. Factors associated with ≥1 HPV type in unvaccinated men included recruitment site and lifetime concurrency. Factors associated with ≥1 HPV type among vaccinated men included recruitment site, Chlamydia history, main male partner, number of lifetime female partners, and no condom use with female partner. Factors associated with HPV16/18 in unvaccinated men included race and partner concurrency. Factors associated with HPV16/18 in vaccinated men included ethnicity, main male partner, and recent concurrency.ConclusionsSexual network variables associated with HPV infection were different based on vaccination status and HPV type, suggesting risk factors for HPV infection may change as the proportion of vaccinated men increases. In addition, participant report of concurrency and not knowing whether one had practiced concurrency were consistent risk factors; clinicians should consider including concurrency in the sexual history to determine the risk of HPV