BACKGROUND:Despite human papillomavirus (HPV) vaccines' availability for over a decade, coverage across the United States varies. Although some states have tried to increase HPV vaccination coverage, most model-based analyses focus on national impacts. We evaluated hypothetical changes in HPV vaccination coverage at the national and state levels for California, New York, and Texas using a mathematical model. METHODS:We developed a new mathematical model of HPV transmission and cervical cancer, creating national- and state-level models, incorporating country- and state-specific vaccination coverage and cervical cancer incidence and mortality. We quantified the national- and state-level impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 on cervical cancer outcomes and the time to elimination defined as less than 4 per 100 000 women. RESULTS:Increasing vaccination coverage to 80% in Texas over 10 years could reduce cervical cancer incidence by 50.9% (95% credible interval [CrI] = 46.6%-56.1%) by 2100, from 1.58 (CrI = 1.19-2.09) to 0.78 (CrI = 0.57-1.02) per 100 000 women. Similarly, New York could see a 27.3% (CrI = 23.9%-31.5%) reduction from 1.43 (CrI = 0.93-2.07) to 1.04 (CrI = 0.66-1.53) per 100 000 women, and California a 24.4% (CrI = 20.0%-30.0%) reduction from 1.01 (CrI = 0.66-1.44) to 0.76 (CrI = 0.50-1.09) per 100 000 women. Achieving 80% coverage in 5 years will provide slightly larger and sooner reductions. If the vaccination coverage levels in 2019 continue, cervical cancer elimination could occur nationally by 2051 (CrI = 2034-2064), but state timelines may vary by decades. CONCLUSION:Targeting an HPV vaccination coverage of 80% by 2030 will disproportionately benefit states with low coverage and higher cervical cancer incidence. Geographically focused analyses can better inform priorities.

Proteogenomics is a growing "multi-omics" research area that combines mass spectrometry-based proteomics and high-throughput nucleotide sequencing technologies. Proteogenomics has helped in genomic annotation for organisms whose complete genome sequences became available by using high-throughput DNA sequencing technologies. Apart from genome annotation, this multi-omics approach has also helped researchers confirm expression of variant proteins belonging to unique proteoforms that could have resulted from single-nucleotide polymorphism (SNP), insertion and deletions (Indels), splice isoforms, or other genome or transcriptome variations.A proteogenomic study depends on a multistep informatics workflow, requiring different software at each step. These integrated steps include creating an appropriate protein sequence database, matching spectral data against these sequences, and finally identifying peptide sequences corresponding to novel proteoforms followed by variant classification and functional analysis. The disparate software required for a proteogenomic study is difficult for most researchers to access and use, especially those lacking computational expertise. Furthermore, using them disjointedly can be error-prone as it requires setting up individual parameters for each software. Consequently, reproducibility suffers. Managing output files from each software is an additional challenge. One solution for these challenges in proteogenomics is the open-source Web-based computational platform Galaxy. Its capability to create and manage workflows comprised of disparate software while recording and saving all important parameters promotes both usability and reproducibility. Here, we describe a workflow that can perform proteogenomic analysis on a Galaxy-based platform. This Galaxy workflow facilitates matching of spectral data with a customized protein sequence database, identifying novel protein variants, assessing quality of results, and classifying variants along with visualization against the genome.

Abstract Background Emerging literature suggests that LGBTQ+ cancer survivors are more likely to experience financial burden than non‐LGBTQ+ survivors. However, LGBTQ+ cancer survivors experience with cost‐coping behaviors such as crowdfunding is understudied. Methods We aimed to assess LGBTQ+ inequity in cancer crowdfunding by combining community‐engaged and technology‐based methods. Crowdfunding campaigns were web‐scraped from GoFundMe and classified as cancer‐related and LGBTQ+ or non‐LGBTQ+ using term dictionaries. Bivariate analyses and generalized linear models were used to assess differential effects in total goal amount raised by LGBTQ+ status. Stratified models were run by online reach and LGBTQ+ inclusivity of state policy. Results A total of N = 188,342 active cancer‐related crowdfunding campaigns were web‐scraped from GoFundMe in November 2022, of which N = 535 were LGBTQ+ and ranged from 2014 to 2022. In multivariable models of recent campaigns (2019–2022), LGBTQ+ campaigns raised $1608 (95% CI: −2139, −1077) less than non‐LGBTQ+ campaigns. LGBTQ+ campaigns with low (26–45 donors), moderate (46–87 donors), and high (88–240 donors) online reach raised on average $1152 (95% CI: −$1589, −$716), $1050 (95% CI: −$1737, −$364), and $2655 (95% CI: −$4312, −$998) less than non‐LGBTQ+ campaigns respectively. When stratified by LGBTQ+ inclusivity of state level policy states with anti‐LGBTQ+ policy/lacking equitable policy raised on average $1910 (95% CI: −2640, −1182) less than non‐LGBTQ+ campaigns from the same states. Conclusions and Relevance Our findings revealed LGBTQ+ inequity in cancer‐related crowdfunding, suggesting that LGBTQ+ cancer survivors may be less able to address financial burden via crowdfunding in comparison to non‐LGBTQ+ cancer survivors—potentially widening existing economic inequities.

OBJECTIVE:This study aimed to describe perspectives from stakeholders involved in the Medicaid system in North Carolina regarding substance use disorder (SUD) treatment policy changes during the coronavirus disease 2019 pandemic.METHODS:We conducted semistructured interviews in early 2022 with state agency representatives, Medicaid managed care organizations, and Medicaid providers (n = 22) as well as 3 focus groups of Medicaid beneficiaries with SUD (n = 14). Interviews and focus groups focused on 4 topics: policies, meeting needs during COVID, demand for SUD services, and staffing.RESULTS:Overall, policy changes, such as telehealth and take-home methadone, were considered beneficial, with participants displaying substantial support for both policies. Shifting demand for services, staffing shortages, and technology barriers presented significant challenges. Innovative benefits and services were used to adapt to these challenges, including the provision of digital devices and data plans to improve access to telehealth.CONCLUSIONS:Perspectives from Medicaid stakeholders, including state organizations to beneficiaries, support the continuation of SUD policy changes that occurred. Staffing shortages remain a substantial barrier. Based on the participants' positive responses to the SUD policy changes made during the coronavirus disease 2019 pandemic, such as take-home methadone and telehealth initiation of buprenorphine, these changes should be continued. Additional steps are needed to ensure payment parity for telehealth services.

OBJECTIVE Despite robust evidence for efficacy of measurement-based care (MBC) in behavioral health care, studies suggest that adoption of MBC is limited in practice. A survey from Blue Cross-Blue Shield of North Carolina was sent to behavioral health care providers (BHCPs) about their use of MBC, beliefs about MBC, and perceived barriers to its adoption. METHODS The authors distributed the survey by using professional networks and snowball sampling. Provider and clinical practice characteristics were collected. Numerical indices of barriers to MBC use were created. Ordered logistic regression models were used to identify associations among practice and provider characteristics, barriers to MBC use, and level of MBC use. RESULTS Of the 922 eligible BHCPs who completed the survey, 426 (46%) reported using MBC with at least half of their patients. Providers were more likely to report MBC use if they were part of a large group practice, had MBC training, had more weekly care hours, or practiced in nonmetropolitan settings. Physicians, self-reported generalists, more experienced providers, and those who did not accept insurance were less likely to report MBC use. Low perceived clinical utility was the barrier most strongly associated with less frequent use of MBC. CONCLUSIONS Although evidence exists for efficacy of MBC in behavioral health care, less than half of BHCPs reported using MBC with at least half of their patients, and low perceived clinical utility of MBC was strongly associated with lower MBC use. Implementation strategies that attempt to change negative attitudes toward MBC may effectively target this barrier to use.

Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC; MD/PhD Program, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC Department of Psychiatry, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC [email protected] The authors have no conflicts of interest or financial disclosures to report.

Background: Lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) individuals are twice as likely to live in poverty and carry a greater cancer burden than non-LGBTQ+ individuals. Crowdfunding, a type of online fundraising, is increasingly used for cancer-related financial support, but the extent to which LGBTQ+ inequities exist in crowdfunding success has not previously been studied. Methods: In December 2022, we extracted 494,242 publicly available crowdfunding campaigns from GoFundMe using web-scraping. We then applied two textual dictionaries to categorize the sample for analysis. The first dictionary identified health campaigns that were in English and contained cancer terms (n=196,038). The second dictionary used a list of terms from prior research that were supplemented by an LGBTQ+ study advisory board to stratify the cancer campaigns by LGBTQ+ identity (yes vs. no) of the campaign creator and/or beneficiary. Outliers in the fifth and ninety-fifth percentile for fundraising goal amount were dropped resulting in a final sample of N=179,793 campaigns for analysis. Summary statistics and regression models were calculated using Stata 17 to describe differences in funding goals, amount raised, and number of donors by LGBTQ+ identity, adjusting for year of the campaign and geographic location. Results: In total, the average campaign goal was $15,891 (Standard Deviation (SD): $13,816), average amount raised was $6,281 (SD: $5,409), and average number of donors was 61 (SD: 47). There were n=1,280 LGBTQ+ cancer campaigns (0.65%). In multivariable models, LGBTQ+ cancer campaigns had goals that were on average $2,009 lower than non-LGBTQ+ cancer campaigns (95% Confidence Interval (CI): -$2,798 - -$1,220, p<0.001), when controlling for year of campaign and geographic location. Similarly, LGBTQ+ cancer campaigns raised $500 less than non-LGBTQ+ cancer campaigns (95% CI -$821 - -$178; p=0.002) when controlling for year of campaign and geographic location. LGBTQ+ campaigns had on average 2.81 more donors than non-LGBTQ+ campaigns (95% CI: 0.05 - 5.56, p=0.046), adjusting for year of campaign and geographic location. Conclusions: When controlling for year of campaign and geographic location we observed significant disparities in goal amount and funds raised between LGBTQ+ and non-LGBTQ+ cancer campaigns. However, LGBTQ+ campaigns on average had more donors. Our findings suggest that while there may be stronger community among LGBTQ+ populations (i.e., higher number of donors), LGBTQ+ cancer survivors may face substantial financial burden inequities. LGBTQ+ specific supportive services and interventions may help improve economic equity among cancer patients. Citation Format: Echo L. Warner, Austin R. Waters, Caleb Easterly, Cindy Turner. Inequity in cancer crowdfunding among LGTBQ+ cancer survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB140.

BackgroundCancer survivors frequently experience cancer-related financial burdens. The extent to which Lesbian, Gay, Bisexual, Transgender, Queer, Plus (LGBTQ+) populations experience cancer-related cost-coping behaviors such as crowdfunding is largely unknown, owing to a lack of sexual orientation and gender identity data collection and social stigma. Web-scraping has previously been used to evaluate inequities in online crowdfunding, but these methods alone do not adequately engage populations facing inequities. ObjectiveWe describe the methodological process of integrating technology-based and community-engaged methods to explore the financial burden of cancer among LGBTQ+ individuals via online crowdfunding. MethodsTo center the LGBTQ+ community, we followed community engagement guidelines by forming a study advisory board (SAB) of LGBTQ+ cancer survivors, caregivers, and professionals who were involved in every step of the research. SAB member engagement was tracked through quarterly SAB meeting attendance and an engagement survey. We then used web-scraping methods to extract a data set of online crowdfunding campaigns. The study team followed an integrated technology-based and community-engaged process to develop and refine term dictionaries for analyses. Term dictionaries were developed and refined in order to identify crowdfunding campaigns that were cancer- and LGBTQ+-related. ResultsAdvisory board engagement was high according to metrics of meeting attendance, meeting participation, and anonymous board feedback. In collaboration with the SAB, the term dictionaries were iteratively edited and refined. The LGBTQ+ term dictionary was developed by the study team, while the cancer term dictionary was refined from an existing dictionary. The advisory board and analytic team members manually coded against the term dictionary and performed quality checks until high confidence in correct classification was achieved using pairwise agreement. Through each phase of manual coding and quality checks, the advisory board identified more misclassified campaigns than the analytic team alone. When refining the LGBTQ+ term dictionary, the analytic team identified 11.8% misclassification while the SAB identified 20.7% misclassification. Once each term dictionary was finalized, the LGBTQ+ term dictionary resulted in a 95% pairwise agreement, while the cancer term dictionary resulted in an 89.2% pairwise agreement. ConclusionsThe classification tools developed by integrating community-engaged and technology-based methods were more accurate because of the equity-based approach of centering LGBTQ+ voices and their lived experiences. This exemplar suggests integrating community-engaged and technology-based methods to study inequities is highly feasible and has applications beyond LGBTQ+ financial burden research.

Results | The 2 NDDs that had the largest association with bullying exposure were ASD and ADHD (Figure 1).Special health care needs (SHCN) (OR, 1.9; 95% CI, 1.6-2.2),adverse childhood experience (OR, 1.6; 95% CI, 1.4-1.9),and disadvantaged neighborhoods (OR, 1.3; 95% CI, 1.1-1.5)were associated with a higher risk of being bullied, while older age was associated with a lower risk for only some children (OR, 0.8; 95% CI, 0.7-0.9).Notably, the interaction analyses showed that older children with ASD (age 12-17 years) were more likely to be bullied than younger children with ASD (6-11 years).In addition, disadvantaged neighborhoods and lack of SHCN further increased the association of ASD with the risk of bullying exposure.Figure 2 illustrates how age or SHCN interacted with the association between risk of being bullied and ASD.The association between bullying exposure and ADHD was not modified by any sociodemographic factors, neighborhood-related features, or SHCN.Discussion | Some individual attributes, such as age or SHCN, could exert different effects between children with ASD and their non-ASD peers.Individuals with ASD who do not have SHCN may be those with less severe symptoms.Compared with children with low-functioning ASD, they may be more likely to attend public schools, where bullying is more prevalent, especially in areas with low socioeconomic status. 5 Note that the causal relationship between SHCN and bullying exposure cannot be clarified in our cross-sectional study.However, these findings are consistent with previous findings that children with high-functioning ASD who attended public schools are more likely to be bullied than other children with ASD. 6 Older age could be a protective factor for children who do not have ASD, but it could be a risk factor for children with ASD.Additional research is indicated about bullying prevention programs targeting identified risk factors for children with ASD.

Multi-omics approaches focused on mass-spectrometry (MS)-based data, such as metaproteomics, utilize genomic and/or transcriptomic sequencing data to generate a comprehensive protein sequence database. These databases can be very large, containing millions of sequences, which reduces the sensitivity of matching tandem mass spectrometry (MS/MS) data to sequences to generate peptide spectrum matches (PSMs). Here, we describe a sectioning method for generating an enriched database for those protein sequences that are most likely present in the sample. Our evaluation demonstrates how this method helps to increase the sensitivity of PSMs while maintaining acceptable false discovery rate statistics. We demonstrate increased true positive PSM identifications using the sectioning method when compared to the traditional large database searching method, whereas it helped in reducing the false PSM identifications when compared to a previously described two-step method for reducing database size. The sectioning method for large sequence databases enables generation of an enriched protein sequence database and promotes increased sensitivity in identifying PSMs, while maintaining acceptable and manageable FDR. Furthermore, implementation in the Galaxy platform provides access to a usable and automated workflow for carrying out the method. Our results show the utility of this methodology for a wide-range of applications where genome-guided, large sequence databases are required for MS-based proteomics data analysis.

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.

The dataset contains files used as input for running functional tools, and it also contains the output files generated by them. These output files were then used for data analysis to comparing them.

Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide spectrum matches (PSMs). To improve robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a LC-MS run, decoy PSMs, and length of peptide. We created a post-analysis Bayesian confidence measure based on score and PMD, called PMD-FDR. We tested PMD-FDR on four datasets across three types of MS-based proteomics projects: standard (single organism; reference database), proteogenomics (single organism; customized genomic-based database plus reference), and metaproteomics (microorganism community; customized conglomerate database). On a ground truth dataset and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits). PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. Going forward, PMD-FDR should provide detection of high-scoring but likely false-hits, aiding applications which rely heavily on accurate PSMs, such as proteogenomics and metaproteomics.

Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.