Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg’s and Storey’s q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey’s q-value procedure has higher power and lower stability than Benjamini-Hochberg’s procedure. To improve upon the stability of Storey’s q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni’s approach. Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey’s q-value procedure and also result in better FDR control and higher stability than Storey’s q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey’s q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey’s q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg’s procedure. For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey’s q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

Objectives Essential healthcare workers (HCW) uniquely serve as both COVID-19 healers and, potentially, as carriers of SARS-CoV-2. We assessed COVID-19-related stigma and bullying against HCW controlling for social, psychological, medical and community variables. Design We nested an analytical cross-sectional study of COVID-19-related stigma and bullying among HCW within a larger mixed-methods effort assessing COVID-19-related lived experience and impact. Adjusted OR (aOR) and 95% CIs evaluated the association between working in healthcare settings and experience of COVID-19-related bullying and stigma, controlling for confounders. Thematic qualitative analysis provided insight into lived experience of COVID-19-related bullying. Setting We recruited potential participants in four languages (English, Spanish, French, Italian) through Amazon Mechanical Turk’s online workforce and Facebook. Participants Our sample included 7411 people from 173 countries who were aged 18 years or over. Findings HCW significantly experienced more COVID-19-related bullying after controlling for the confounding effects of job-related, personal, geographic and sociocultural variables (aOR: 1.5; 95% CI 1.2 to 2.0). HCW more frequently believed that people gossip about others with COVID-19 (OR: 2.2; 95% CI 1.9 to 2.6) and that people with COVID-19 lose respect in the community (OR: 2.3; 95% CI 2.0 to 2.7), both which elevate bullying risk (OR: 2.7; 95% CI 2.3 to 3.2, and OR: 3.5; 95% CI 2.9 to 4.2, respectively). The lived experience of COVID-19-related bullying relates frequently to public identities as HCW traverse through the community, intersecting with other domains (eg, police, racism, violence). Interpretation After controlling for a range of confounding factors, HCW are significantly more likely to experience COVID-19-related stigma and bullying, often in the intersectional context of racism, violence and police involvement in community settings.

DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. β values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov–Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set.

Background: Early childhood caries (ECC) is the most common chronic childhood disease, with nearly 1.8 billion new cases per year worldwide. ECC afflicts approximately 55% of low-income and minority US preschool children, resulting in harmful short- and long-term effects on health and quality of life. Clinical evidence shows that caries is reversible if detected and addressed in its early stages. However, many low-income US children often have poor access to pediatric dental services. In this underserved group, dental caries is often diagnosed at a late stage when extensive restorative treatment is needed. With more than 85% of lower-income Americans owning a smartphone, mobile health tools such as smartphone apps hold promise in achieving patient-driven early detection and risk control of ECC. Objective: This study aims to use a community-based participatory research strategy to refine and test the usability of an artificial intelligence–powered smartphone app, AICaries, to be used by children’s parents/caregivers for dental caries detection in their children. Methods: Our previous work has led to the prototype of AICaries, which offers artificial intelligence–powered caries detection using photos of children’s teeth taken by the parents’ smartphones, interactive caries risk assessment, and personalized education on reducing children’s ECC risk. This AICaries study will use a two-step qualitative study design to assess the feedback and usability of the app component and app flow, and whether parents can take photos of children’s teeth on their own. Specifically, in step 1, we will conduct individual usability tests among 10 pairs of end users (parents with young children) to facilitate app module modification and fine-tuning using think aloud and instant data analysis strategies. In step 2, we will conduct unmoderated field testing for app feasibility and acceptability among 32 pairs of parents with their young children to assess the usability and acceptability of AICaries, including assessing the number/quality of teeth images taken by the parents for their children and parents’ satisfaction. Results: The study is funded by the National Institute of Dental and Craniofacial Research, United States. This study received institutional review board approval and launched in August 2021. Data collection and analysis are expected to conclude by March 2022 and June 2022, respectively. Conclusions: Using AICaries, parents can use their regular smartphones to take photos of their children’s teeth and detect ECC aided by AICaries so that they can actively seek treatment for their children at an early and reversible stage of ECC. Using AICaries, parents can also obtain essential knowledge on reducing their children’s caries risk. Data from this study will support a future clinical trial that evaluates the real-world impact of using this smartphone app on early detection and prevention of ECC among low-income children. International Registered Report Identifier (IRRID): PRR1-10.2196/32921

Early Childhood Caries (ECC) is the most common childhood disease worldwide and a health disparity among underserved children. ECC is preventable and reversible if detected early. However, many children from low-income families encounter barriers to dental care. An at-home caries detection technology could potentially improve access to dental care regardless of patients’ economic status and address the overwhelming prevalence of ECC. Our team has developed a smartphone application (app), AICaries, that uses artificial intelligence (AI)-powered technology to detect caries using children’s teeth photos. We used mixed methods to assess the acceptance, usability, and feasibility of the AICaries app among underserved parent-child dyads. We conducted moderated usability testing (Step 1) with ten parent-child dyads using "Think-aloud" methods to assess the flow and functionality of the app and analyze the data to refine the app and procedures. Next, we conducted unmoderated field testing (Step 2) with 32 parent-child dyads to test the app within their natural environment (home) over two weeks. We administered the System Usability Scale (SUS) and conducted semi-structured individual interviews with parents and conducted thematic analyses. AICaries app received a 78.4 SUS score from the participants, indicating an excellent acceptance. Notably, the majority (78.5%) of parent-taken photos of children’s teeth were satisfactory in quality for detection of caries using the AI app. Parents suggested using community health workers to provide training to parents needing assistance in taking high quality photos of their young child’s teeth. Perceived benefits from using the AICaries app include convenient at-home caries screening, informative on caries risk and education, and engaging family members. Data from this study support future clinical trial that evaluates the real-world impact of using this innovative smartphone app on early detection and prevention of ECC among low-income children.

Breast and cervical cancers have emerged as major global health challenges and disproportionately lead to excess morbidity and mortality in low- and middle-income countries (LMICs) when compared to high-income countries. The objective of this paper was to highlight key findings, recommendations, and gaps in research and practice identified through a scoping study of recent reviews in breast and cervical cancer in LMICs. We conducted a scoping study based on the six-stage framework of Arskey and O’Malley. We searched PubMed, Cochrane Reviews, and CINAHL with the following inclusion criteria: 1) published between 2005-February 2015, 2) focused on breast or cervical cancer 3) focused on LMIC, 4) review article, and 5) published in English. Through our systematic search, 63 out of the 94 identified cervical cancer reviews met our selection criteria and 36 of the 54 in breast cancer. Cervical cancer reviews were more likely to focus upon prevention and screening, while breast cancer reviews were more likely to focus upon treatment and survivorship. Few of the breast cancer reviews referenced research and data from LMICs themselves; cervical cancer reviews were more likely to do so. Most reviews did not include elements of the PRISMA checklist. Overall, a limited evidence base supports breast and cervical cancer control in LMICs. Further breast and cervical cancer prevention and control studies are necessary in LMICs.