Sex and gender related health disparities in oral health remain an underappreciated and often over looked aspect of well-being. The goal of this narrative review is to identify sex and gender related oral health disparities by summarizing the current literature related to differences in oral health between men and women. The review identified that men are more likely to: ignore their oral health, have poorer oral hygiene habits, and experience higher rates of periodontal disease, oral cancer, and dental trauma. Men also visit dentists less frequently and compared to women seek oral treatment more often for an acute problem and less often for disease prevention. Women exhibit more positive attitudes about dental visits, greater oral health literacy, and demonstrate better oral health behaviors than men. Men disproportionately develop periodontal diseases due to a combination of biological and gender related reasons including immune system factors, hormone differences, poorer oral hygiene behaviors, and greater tobacco use. There is a male to female ratio of 2:1 for oral cancer, largely attributable to more tobacco use, heavier use of alcohol, and longer sun exposure. Minority men experience a disproportionate burden of oral health disparities because of both their gender and race/ethnic identities. In conclusion, this review identifies several differences between men and women related to oral health and highlights the need for further research to better understand these disparities and how to incorporate them into developing prevention, education and treatment strategies to improve oral health in men.

Background: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.

In 2012, the American Orthopaedic Foot & Ankle Society^® established a national network for collecting and sharing data on treatment outcomes and improving patient care. One of the network’s initiatives is to explore the use of computerized adaptive tests (CATs) for patient-level outcome reporting. We determined whether the CAT from the NIH Patient Reported Outcome Measurement Information System^® (PROMIS^®) Physical Function (PF) item bank provides efficient, reliable, valid, precise, and adequately covered point estimates of patients’ physical function. After informed consent, 288 patients with a mean age of 51 years (range, 18–81 years) undergoing surgery for common foot and ankle problems completed a web-based questionnaire. Efficiency was determined by time for test administration. Reliability was assessed with person and item reliability estimates. Validity evaluation included content validity from expert review and construct validity measured against the PROMIS^® Pain CAT and patient responses based on tradeoff perceptions. Precision was assessed by standard error of measurement (SEM) across patients’ physical function levels. Instrument coverage was based on a person-item map. Average time of test administration was 47 seconds. Reliability was 0.96 for person and 0.99 for item. Construct validity against the Pain CAT had an r value of −0.657 (p < 0.001). Precision had an SEM of less than 3.3 (equivalent to a Cronbach’s alpha of ≥ 0.90) across a broad range of function. Concerning coverage, the ceiling effect was 0.32% and there was no floor effect. The PROMIS^® PF CAT appears to be an excellent method for measuring outcomes for patients with foot and ankle surgery. Further validation of the PROMIS^® item banks may ultimately provide a valid and reliable tool for measuring patient-reported outcomes after injuries and treatment. Level III, diagnostic study. See Instructions for Authors for a complete description of levels of evidence.

Background: As new Patient-Reported Outcomes Measurement Information System (PROMIS) instruments are incorporated into clinical practice, determining how large a change on these instruments represents a clinically relevant difference is important; the metric that describes this is the minimum clinically important difference (MCID). Prior research on MCIDs of the Neck Disability Index (NDI) and Oswestry Disability Index (ODI) has produced values ranging from 5 to 10 points, but these measures have not been presented in relation to MCID values of PROMIS instruments. Questions/purposes: To establish a comprehensive repository of MCID values calculated both with distribution-based and anchor-based methods for four outcomes instruments in spine care, we asked: (1) What are the MCIDs of the PROMIS Physical Function (PF); (2) the PROMIS Pain Interference (PI); (3) the NDI; and (4) the ODI among spine patients? Methods: We conducted a prospective study of previously tested diagnostic measures on 1945 consecutive patients with a reference standard applied. All patients aged 18 years and older visiting an orthopaedic spine clinic between October 2013 and January 2017 completed the PROMIS PF and PI, NDI, and ODI on tablet computers before their clinic visits. Patients were grouped by change level (self-report of meaningful change versus slight or no change) using an anchor question in comparison to baseline. Descriptive statistics, two anchor-based MCID values (mean change and receiver operating characteristic curve), and five distribution-based values (SD at 1/2 and 1/3 values and minimum detectable change [MDC] at 90%, 95%, and 99%) were analyzed four different times between 3 months and > 6 months of followup. A total of 1945 included patients with a wide range of spine conditions and varying treatments had a mean age of 58 years (SD = 15.5), were 51% (988 of 1945) male, 90% (1754 of 1945) self-identified as white, and 5% (94 of 1945) as Hispanic with 1% to 2% of patients refusing participation. Results: The PROMIS PF mean change scores in the changed group (much worse, worse, improved, or much improved) ranged between 7 and 8 points. MCID values ranged from 3 to 23 points depending on the method of calculation with a median of 8. For the PROMIS PI, mean change scores ranged from 8 to 9 points and MCID values from 1 to 24 points with a median of 8. For the NDI, mean change scores ranged from 13 to 18 points and MCID values ranged from 6 to 43 points with a median of 18. For the ODI, mean change ranged from 17 to 19 points and MCID values ranged from 7 to 51 points with a median of 24. For each instrument, distribution-based SD yielded the smallest values, followed by anchor-based methods, with MDC yielding the largest MCID values. Conclusions: This study uses a range of methods for determining MCIDs of the PROMIS PF and PI, NDI, and ODI from anchor-based to distribution-based methods. MCIDs do not have a static value for a given outcome measure, but have a range of values and are dependent on the method calculated. The lowest MCIDs identified for the NDI and ODI are consistent with prior studies, but those at the upper range are much higher. Anchor-based methods are thought to be most relevant in the clinical setting and are more easily understood by clinicians, whereas the distribution-based MCIDs are useful in understanding population breadth. Lower MCID values may be most appropriate for screening purposes or low-risk effects, and the median or higher MCID values should be used for high-risk effects or outcomes. Level of Evidence: Level I, diagnostic study.

Objective: In light of COVID-19, leaders issued stay-at-home orders, including closure of higher-education schools. Most students left campus, likely impacting their employment and social network. Leaders are making decisions about opening universities and modality of instruction. Understanding students' psychological, physiological, academic, and financial responses to the shut-down and reopening of campuses can help leaders make informed decisions. Participants: 654 students from a large western university enrolled during the pandemic shutdown. Methods: Students were invited via email to complete an online survey. Results: Students reported stress, depression, loneliness, lack of motivation, difficulty focusing on schoolwork, restless sleep, appetite changes, job loss concerns, and difficulties coping. Most wanted to return to campus and felt social/physical distancing was effective but were mixed in terms of testing or masks. Conclusions: Moving to remote learning created physical and psychological stress. Students want to return to campus but do not want to take risk-reducing measures.

Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis outcome score for joint reconstruction in orthopaedics