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Objectives: Cervical cancer screening rates have stagnated, but self-sampling modalities have the potential to increase uptake. This study compares the test characteristics of self-sampled high-risk human papillomavirus (hrHPV) tests with clinician-collected hrHPV tests in average-risk (i.e., undergoing routine screening) and high-risk patients (i.e., receiving follow-up after abnormal screening results). Methods: In this cross-sectional study, a relatively small cohort of average-risk (n?=?35) and high-risk (n?=?12) participants completed both clinician-collected and self-sampled hrHPV testing, along with a brief phone survey. We assessed hrHPV positivity, concordance, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity across both methods (for types 16, 18, or other hrHPV). We also explored the relationship between test concordance and sociodemographic/behavioral factors. Results: Among average-risk participants, hrHPV positivity was 6% for both test methods (i.e., hrHPV-positive cases: n?=?2), resulting in reported concordance, PPV, NPV, sensitivity, and specificity of 100%. Among high-risk participants, hrHPV positivity was 100% for clinician-collected tests but only 67% for self-sampled tests, showing varied concordance and sensitivity. Concordance was not associated with sociodemographic or behavioral factors. Conclusions: Self-sampled hrHPV testing demonstrated high accuracy for average-risk patients in this exploratory study. However, its performance was less consistent in high-risk patients who had already received an abnormal screening result, which could be attributed to spontaneous viral clearance over time. The limited number of participants, particularly HPV-positive cases, suggests caution in interpreting these results. Further research with larger cohorts is necessary to validate these findings and to explore the integration of self-sampled hrHPV testing into routine clinical care, particularly for patients with a history of cervical abnormalities. Clinical Trial Registration: NCT04591977, NCT04585243.

Introduction/Objectives: Screening guidelines for breast, cervical, and colorectal cancer (CRC) are less clear for older adults due to the potential harms that may result from screening. Understanding older adults’ attitudes and perceptions, especially racial/ethnic minority and underserved adults, of cancer screening can help health care providers determine how best to communicate with older adults about cancer screening and screening cessation. The objective of this study was to determine how older adults primarily from minority/underserved backgrounds perceive cancer screening and overscreening. Methods: Four focus groups (n = 39) were conducted with adults (>=65 years of age) in 3 community settings in south-central Pennsylvania. Two focus groups were conducted in Spanish and translated to English upon transcription. Focus group data was managed and analyzed using QSR NVivo 12. Inductive thematic analysis was used to analyze the data where themes emerged following the coding process. Results: The focus group participants had an average age of 74 years and were primarily female (74%) and Hispanic (69%), with 69% reporting having less than a high school degree. Four key themes were identified from the focus groups: (1) importance of tailored and targeted education/information; (2) impact of physician/patient communication; (3) impact of barriers and facilitators to screening on cancer screening cessation; and (4) awareness of importance of screening. Participants were more likely to be agreeable to screening cessation if they received specific information regarding their health status and previous medical history from their physician as to why screening should be stopped and told by their physician that the screening decision is up to them. Conclusions: Older adults prefer individualized information from their physician in order to justify screening cessation but are against incorporating life expectancy into the discussion. Future research should focus on developing interventions to test the effectiveness of culturally tailored screening cessation messages for older adults.

Stay-at-home orders have been an essential component of coronavirus 2019 (COVID-19) management in the United States. As states start lifting these mandates to reopen the economy, voluntary public compliance with public health recommendations may significantly influence the extent of resurgence in COVID-19 infection rates. Population-level risk from reopening may therefore be predicted from public intent to comply with public health recommendations. We are conducting a global, convergent design mixed-methods survey on public knowledge, perceptions, preferred health information sources, and understanding of and intent to comply with public health recommendations. With over 9,000 completed surveys from every US state and over 70 countries worldwide, to our knowledge this is the largest pandemic messaging study to date. Although the study is still ongoing, we have conducted an analysis of 5,005 US surveys completed from April 9-15, 2020 on public intent to comply with public health recommendations and offer insights on the COVID-19 pandemic-related risk of reopening. We found marked regional differences in intent to follow key public health recommendations. Regional efforts are urgently needed to influence public behavior changes to decrease the risk of reopening, particularly in higher-risk areas with low public intent to comply with preventive health recommendations.

ABSTRACT Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

Abstract Background Colorectal cancer is a worldwide health problem. Despite growing evidence that members of the gut microbiota can drive tumorigenesis, little is known about what happens to it after treatment for an adenoma or carcinoma. This study tested the hypothesis that treatment for adenoma or carcinoma alters the abundance of bacterial populations associated with disease to those associated with a normal colon. We tested this hypothesis by sequencing the 16S rRNA genes in the feces of 67 individuals before and after treatment for adenoma (N = 22), advanced adenoma (N = 19), and carcinoma (N = 26). Results There were small changes to the bacterial community associated with adenoma or advanced adenoma and large changes associated with carcinoma. The communities from patients with carcinomas changed significantly more than those with adenoma following treatment (P value < 0.001). Although treatment was associated with intrapersonal changes, the change in the abundance of individual OTUs in response to treatment was not consistent within diagnosis groups (P value > 0.05). Because the distribution of OTUs across patients and diagnosis groups was irregular, we used the random forest machine learning algorithm to identify groups of OTUs that could be used to classify pre and post-treatment samples for each of the diagnosis groups. Although the adenoma and carcinoma models could reliably differentiate between the pre- and post-treatment samples (P value < 0.001), the advanced-adenoma model could not (P value = 0.61). Furthermore, there was little overlap between the OTUs that were indicative of each treatment. To determine whether individuals who underwent treatment were more likely to have OTUs associated with normal colons we used a larger cohort that contained individuals with normal colons and those with adenomas, advanced adenomas, and carcinomas. We again built random forest models and measured the change in the positive probability of having one of the three diagnoses to assess whether the post-treatment samples received the same classification as the pre-treatment samples. Samples from patients who had carcinomas changed toward a microbial milieu that resembles the normal colon after treatment (P value < 0.001). Finally, we were unable to detect any significant differences in the microbiota of individuals treated with surgery alone and those treated with chemotherapy or chemotherapy and radiation (P value > 0.05). Conclusions By better understanding the response of the microbiota to treatment for adenomas and carcinomas, it is likely that biomarkers will eventually be validated that can be used to quantify the risk of recurrence and the likelihood of survival. Although it was difficult to identify significant differences between pre- and post-treatment samples from patients with adenoma and advanced adenoma, this was not the case for carcinomas. Not only were there large changes in pre- versus post-treatment samples for those with carcinoma, but also these changes were toward a more normal microbiota.

ABSTRACT Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria that produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with healthy colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power. IMPORTANCE Considering that colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anticarcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentrations of a cohort of individuals with different colonic tumor burdens who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.

ABSTRACT Human viruses (those that infect human cells) have been associated with many cancers, largely due to their mutagenic and functionally manipulative abilities. Despite this, cancer microbiome studies have focused almost exclusively on bacteria instead of viruses. We began evaluating the cancer virome by focusing on colorectal cancer, a primary cause of morbidity and mortality throughout the world and a cancer linked to altered colonic bacterial community compositions but with an unknown association with the gut virome. We used 16S rRNA gene, whole shotgun metagenomic, and purified virus metagenomic sequencing of stool to evaluate the differences in human colorectal cancer virus and bacterial community composition. Through random forest modeling, we identified differences in the healthy and colorectal cancer viromes. The cancer-associated virome consisted primarily of temperate bacteriophages that were also predicted to be bacterium-virus community network hubs. These results provide foundational evidence that bacteriophage communities are associated with colorectal cancer and potentially impact cancer progression by altering the bacterial host communities. IMPORTANCE Colorectal cancer is a leading cause of cancer-related death in the United States and worldwide. Its risk and severity have been linked to colonic bacterial community composition. Although human-specific viruses have been linked to other cancers and diseases, little is known about colorectal cancer virus communities. We addressed this knowledge gap by identifying differences in colonic virus communities in the stool of colorectal cancer patients and how they compared to bacterial community differences. The results suggested an indirect role for the virome in impacting colorectal cancer by modulating the associated bacterial community. These findings both support the idea of a biological role for viruses in colorectal cancer and provide a new understanding of basic colorectal cancer etiology.

Abstract: The design of early-phase studies of putative screening markers in clinical populations is discussed. Biological, epidemiological, statistical and computational issues all affect the design of early-phase studies of these markers, but there are frequently little or no data in hand to facilitate the design. Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion. Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development. Designing such trials requires explicit assumptions about prevalence and false positive and negative costs in the ultimate target population. Early discussion of these issues strengthens the development process, since enthusiasm for developing technologies is balanced by realism about the requirements of a valid population screen. Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.

Cervical cancer incidence and mortality rates are high among women from Appalachia, yet data do not exist on human papillomavirus (HPV) prevalence among these women. We examined the prevalence of genital HPV among Appalachian women and identified correlates of HPV detection.We report data from a case-control study conducted between January 2006 and December 2008 as part of the Community Awareness, Resources, and Education (CARE) Project. We examined HPV prevalence among 1116 women (278 women with abnormal Pap tests at study entry [cases], 838 women with normal Pap tests [controls]) from Appalachian Ohio. Analyses used multivariable logistic regression to identify correlates of HPV detection.The prevalence of HPV was 43.1% for any HPV type, 33.5% for high-risk HPV types, 23.4% for low-risk HPV types, and 12.5% for vaccine-preventable HPV types. Detection of any HPV type was more common among women who were ages 18-26 (OR = 2.09, 95% CI: 1.26-3.50), current smokers (OR = 1.86, 95% CI: 1.26-2.73), had at least five male sexual partners during their lifetime (OR = 2.28, 95% CI: 1.56-3.33), or had multiple male sexual partners during the last year (OR = 1.98, 95% CI: 1.25-3.14). Similar correlates were identified for detection of a high-risk HPV type.HPV was prevalent among Appalachian women, with many women having a high-risk HPV type detected. Results may help explain the high cervical cancer rates observed among Appalachian women and can help inform future cervical cancer prevention efforts in this geographic region.

Previous observational studies have reported associations between prostate cancer and alpha-linolenic acid (ALA). However, few investigations have been able to study this relationship prospectively and in well-controlled settings. Moreover, no studies have determined whether single nucleotide polymorphisms (SNPs) that influence ALA metabolism are associated with this common cancer. The purpose of this study was to explore associations between prostatic levels of ALA, SNPs and prostate cancer-specific biomarkers in samples collected from a previous randomized clinical trial conducted using a presurgical model and which tested the effects of flaxseed supplementation, a rich source of ALA, prior to prostatectomy (n = 134). Serum prostate-specific antigen (PSA) was determined and immunohistochemistry was used to assess tumor proliferation rate (Ki67). Prostatic ALA was determined with gas chromatography. Seven previously identified SNPs associated with delta-6 desaturase activity (rs99780, rs174537, rs174545, rs174572, rs498793, rs3834458 and rs968567) were tested for associations with prostatic ALA, PSA and Ki67. Despite consuming seven times more ALA per day, men in the flaxseed arm had similar amounts of prostatic ALA relative to men not consuming flaxseed. In unadjusted analysis, there were significant positive associations between prostatic ALA and PSA (ρ = 0.191, p = 0.028) and Ki67 (ρ = 0.186, p = 0.037). After adjusting for covariates (flaxseed, age, race, BMI and statin-use) the association between ALA and PSA remained (p = 0.004) but was slightly attenuated for Ki67 (p = 0.051). We did not observe associations between any of the SNPs studied and prostatic ALA; however, in models for PSA there was a significant interaction between rs498793 and ALA and for Ki67 there were significant interactions with ALA and rs99780 and rs174545. Independent and inverse associations were observed between rs174572 and Ki67. This study provides evidence that prostatic ALA, independent of the amount of ALA consumed, is positively associated with biomarkers of aggressive prostate cancer and that genetic variation may modify this relationship.

Background: Antidepressants are used by primary care providers to treat a variety of conditions, including (but not limited to) depression and anxiety. A trial-and-error approach is typically used to identify effective therapy, as treatment efficacy and safety can vary based on the response, which is affected by certain gene types. Pharmacokinetic pharmacogenomic (PGx) testing provides phenotypic classification of individuals as poor, intermediate, extensive, and ultrarapid CYP450 metabolizers, providing information for optimal drug selection. Objective: The objective of this pilot study is to examine the feasibility, acceptability, and preliminary effectiveness of PGx testing when used after starting a new antidepressant medication. Methods: We are conducting a pilot study with physicians from 6 Department of Family Medicine clinics at the University of Michigan who are willing to use PGx test results to manage antidepressant medication use. From enrolled physicians, patients were recruited to participate in a 6-month randomized, wait-list controlled trial in which patient participants newly prescribed an antidepressant had PGx testing and were randomized equally to have the results released to their primary care physician as soon as results were available or after 3 months. Patients were excluded if they had been taking the antidepressant for more than 4 weeks or if they had undergone PGx testing in the past. Physician participants completed a baseline survey to assess demographics, as well as knowledge, feasibility, and acceptability of PGx testing for this population. At the conclusion of the study, physician participants will complete a survey to assess knowledge, satisfaction, feasibility, acceptability, perceived effectiveness, and barriers to widespread adoption of PGx testing. Patient participants will complete a baseline, 3-month, and 6-month assessment, and control patient participants will have an additional 9-month assessment. Data collected will include the reason for antidepressant use, self-reported medication adherence, side effects, patient health questionnaire 8-item depression scale, generalized anxiety disorder 7-item scale, 12-Item Short-Form Health Survey, work status or changes, and physician and emergency department visits. PGx knowledge and perceptions (including acceptability and feasibility) as well as demographic information will also be obtained. Results: We recruited 23 physician participants between November 2017 and January 2019, and 52 patient participants between January 2018 and April 2019. Currently, all physician and patient participants have been recruited, and we expect data collection to conclude in January 2020. Conclusions: This study will examine the preliminary effectiveness of PGx testing after treatment initiation and determine the feasibility and acceptability of PGx testing for use in primary care. Through this study, we expect to demonstrate the benefit of PGx testing and lay the foundation for translating this approach into use within primary care. Trial Registration: ClinicalTrials.gov NCT03270891; https://clinicaltrials.gov/ct2/show/NCT03270891 International Registered Report Identifier (IRRID): RR1-10.2196/13848 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]

Background: Women with chronic medical conditions, such as diabetes and hypertension, have a higher risk of pregnancy-related complications compared with women without medical conditions and should be offered contraception if desired. Although evidence based guidelines for contraceptive selection in the presence of medical conditions are available via the United States Medical Eligibility Criteria (US MEC), these guidelines are underutilized. Research also supports the use of decision tools to promote shared decision making between patients and providers during contraceptive counseling. Objective: The overall goal of the MiHealth, MiChoice project is to design and implement a theory-driven, Web-based tool that incorporates the US MEC (provider-level intervention) within the vehicle of a contraceptive decision tool for women with chronic medical conditions (patient-level intervention) in community-based primary care settings (practice-level intervention). This will be a 3-phase study that includes a predesign phase, a design phase, and a testing phase in a randomized controlled trial. This study protocol describes phase 1 and aim 1, which is to determine patient-, provider-, and practice-level factors that are relevant to the design and implementation of the contraceptive decision tool. Methods: This is a mixed methods implementation study. To customize the delivery of the US MEC in the decision tool, we selected high-priority constructs from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework to drive data collection and analysis at the practice and provider level, respectively. A conceptual model that incorporates constructs from the transtheoretical model and the health beliefs model undergirds patient-level data collection and analysis and will inform customization of the decision tool for this population. We will recruit 6 community-based primary care practices and conduct quantitative surveys and semistructured qualitative interviews with women who have chronic medical conditions, their primary care providers (PCPs), and clinic staff, as well as field observations of practice activities. Quantitative survey data will be summarized with simple descriptive statistics and relationships between participant characteristics and contraceptive recommendations (for PCPs), and current contraceptive use (for patients) will be examined using Fisher exact test. We will conduct thematic analysis of qualitative data from interviews and field observations. The integration of data will occur by comparing, contrasting, and synthesizing qualitative and quantitative findings to inform the future development and implementation of the intervention. Results: We are currently enrolling practices and anticipate study completion in 15 months. Conclusions: This protocol describes the first phase of a multiphase mixed methods study to develop and implement a Web-based decision tool that is customized to meet the needs of women with chronic medical conditions in primary care settings. Study findings will promote contraceptive counseling via shared decision making and reflect evidence-based guidelines for contraceptive selection. Trial Registration: ClinicalTrials.gov NCT03153644; https://clinicaltrials.gov/ct2/show/NCT03153644 (Archived by WebCite at http://www.webcitation.org/6yUkA5lK8) [ABSTRACT FROM AUTHOR]

Purpose Significant concerns exist regarding the potential for unwarranted behavior changes and the overuse of health care resources in response to direct-to-consumer personal genomic testing (PGT). However, little is known about customers' behaviors after PGT. Methods Longitudinal surveys were given to new customers of 23andMe (Mountain View, CA) and Pathway Genomics (San Diego, CA). Survey data were linked to individual-level PGT results through a secure data transfer process. Results Of the 1,042 customers who completed baseline and 6-month surveys (response rate, 71.2%), 762 had complete cancer-related data and were analyzed. Most customers reported that learning about their genetic risk of cancers was a motivation for testing (colorectal, 88%; prostate, 95%; breast, 94%). No customers tested positive for pathogenic mutations in highly penetrant cancer susceptibility genes. A minority of individuals received elevated single nucleotide polymorphism-based PGT cancer risk estimates (colorectal, 24%; prostate, 24%; breast, 12%). At 6 months, customers who received elevated PGT cancer risk estimates were not significantly more likely to change their diet, exercise, or advanced planning behaviors or engage in cancer screening, compared with individuals at average or reduced risk. Men who received elevated PGT prostate cancer risk estimates changed their vitamin and supplement use more than those at average or reduced risk (22% v 7.6%, respectively; adjusted odds ratio, 3.41; 95% CI, 1.44 to 8.18). Predictors of 6-month behavior include baseline behavior (exercise, vitamin or supplement use, and screening), worse health status (diet and vitamin or supplement use), and older age (advanced planning, screening). Conclusion Most adults receiving elevated direct-to-consumer PGT single nucleotide polymorphism-based cancer risk estimates did not significantly change their diet, exercise, advanced care planning, or cancer screening behaviors.