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* Abbreviations: CI — : confidence interval COVID-19 — : coronavirus disease 2019 PCR — : polymerase chain reaction SARS-CoV-2 — : severe acute respiratory syndrome coronavirus 2 Abbott BinaxNOW (a bedside lateral flow immunoassay intended for the qualitative detection of nucleocapsid protein antigen) is widely available under US Food and Drug Administration emergency use authorization for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in symptomatic patients within 7 days of symptom onset. Its diagnostic performance in symptomatic children has not been evaluated. Cross-sectional study of symptomatic children ≤20 years presenting for care to 1 of 2 primary care practices in December 2020, during which time coronavirus disease 2019 (COVID-19) rates were … Address correspondence to Nader Shaikh, MD, MPH, University of Pittsburgh Medical Center, Children’s Hospital of Pittsburgh, 1 Children’s Hospital Dr, 4401 Penn Ave, Pittsburgh, PA 15224. E-mail: nader.shaikh{at}chp.edu

In a phase III multi-center cancer clinical trial or a large public health study, sample size is predetermined to achieve desired power, and study participants are enrolled from tens or hundreds of participating institutions. As the accrual is closing to the target size, the coordinating data center needs to project the accrual closure date on the basis of the observed accrual pattern and notify the participating sites several weeks in advance. In the past, projections were simply based on some crude assessment, and conservative measures were incorporated in order to achieve the target accrual size. This approach often resulted in excessive accrual size and subsequently unnecessary financial burden on the study sponsors. Here we proposed a discrete-time Poisson process-based method to estimate the accrual rate at time of projection and subsequently the trial closure date. To ensure that target size would be reached with high confidence, we also proposed a conservative method for the closure date projection. The proposed method was illustrated through the analysis of the accrual data of the National Surgical Adjuvant Breast and Bowel Project trial B-38. The results showed that application of the proposed method could help to save considerable amount of expenditure in patient management without compromising the accrual goal in multi-center clinical trials.

The question of whether health care inequities occur before patients with end-stage liver disease (ESLD) are waitlisted for transplantation has not previously been assessed. To determine the impact of gender, race and insurance on access to transplantation, we linked Pennsylvania sources of data regarding adult patients discharged from nongovernmental hospitals from 1994 to 2001. We followed the patients through 2003 and linked information to records from five centers responsible for 95% of liver transplants in Pennsylvania during this period. Using multinomial logistic regressions, we estimated probabilities that patients would undergo transplant evaluation, transplant waitlisting and transplantation itself. Of the 144 507 patients in the study, 4361 (3.0%) underwent transplant evaluation. Of those evaluated, 3071 (70.4%) were waitlisted. Of those waitlisted, 1537 (50.0%) received a transplant. Overall, 57 020 (39.5%) died during the study period. Patients were less likely to undergo evaluation, waitlisting and transplantation if they were women, black and lacked commercial insurance (p < 0.001 each). Differences were more pronounced for early stages (evaluation and listing) than for the transplantation stage (in which national oversight and review occur). For early management and treatment decisions of patients with ESLD to be better understood, more comprehensive data concerning referral and listing practices are needed.

Introduction As women progress through menopause, they experience changes in sexual functioning that are multifactorial, likely encompassing biological, psychological, and social domains. Aim To examine the effects that physical activity, sleep difficulties, and social support have on partnered sexual activity and sexual functioning in women at different stages of the menopausal progression. Methods As part of an ongoing 5-year longitudinal study, we conducted a cross-sectional analysis of sexual functioning data. Main Outcome Measures Participation in partnered sexual activities, reasons for nonparticipation in such activities among sexually inactive women, and, among sexually active women, sexual functioning defined as engagement in and enjoyment of sexually intimate activities. Results Of 677 participants aged 41–68, 68% had participated in any partnered sexual activities (i.e., were sexually active) during the past 6 months. Reasons for sexual inactivity included lack of a partner (70%), lack of interest in sex (12%) or in the current partner (5%), and physical problems (4%). Sexually active participants tended to be younger, married, more educated, have more social support in general, fewer comorbid medical illnesses, a lower body mass index, and a higher prevalence of vaginal dryness. Among the sexually active participants, their scores for engagement in activities ranging from kissing to sexual intercourse were higher if they were physically active, had more social support, and lacked sleeping difficulties. Likewise, scores for sexual enjoyment were higher if they were physically active, had more social support, and lacked vaginal dryness. Engagement and enjoyment scores were not associated with marital status or other factors. Conclusions In midlife women, having social support and being physically active are associated with enhanced sexual engagement and enjoyment. Hess R, Conroy MB, Ness R, Bryce CL, Dillon S, Chang CCH, and Matthews KA. Association of lifestyle and relationship factors with sexual functioning of women during midlife. J Sex Med 2009;6:1358–1368.

To assess the value of ICD-9-coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function. We found that, for a one-year period, the detectors had sensitivity of 100% (1/1 epidemic) and positive predictive values of 50% (1/2) for RS and 25% (1/4) for IS. The timeliness of detection using ICD-9-coded chief complaints was one week earlier than the detection using Pneumonia and Influenza deaths (the gold standard). The inherent timeliness of ICD-9 data measured by the cross-correlation function was two weeks earlier than the gold standard.

It has been reported that the age-specific prevalence and incidence of dementia and cognitive impairment in the United States have either remained stable or even declined during the 1980s–1990s (1,2). A recent Dutch study also showed age-adjusted dementia incidence rates to be consistently, although nonsignificantly, lower in the subcohort assessed in 2000 than that assessed a decade earlier (3). In behavioral sciences, one of the persistent predictors of disease prevalence, incidence, and mortality is years of educational attainment. Although there is still a debate regarding potential mechanisms underlying the association between education and overall health (eg, income, accessibility to health care, lifestyle and environment, mother’s nutrition during prenatal period, nutrition during infancy, etc.), we expect that cognitive functions can be very much affected by educational attainment. Besides the above potential effects of education on overall health, test-taking skill (which influences performance on neuropsychological tests) can be also associated with educational attainment. Furthermore, where educational opportunities are uniform, higher educasstional level may reflect higher intelligence and cognitive reserve (4), and more highly educated individuals may undertake occupations, which are more cognitively stimulating and require “continuing education” throughout life. The cognitive stimulation may itself further stimulate synaptic density and dendritic branching (4). Yet, there is a paucity of studies, which examine the extent to which educational attainment explains observed cohort effects in cognitive aging, specifically age-associated cognitive decline measured longitudinally over time. Facing a rapid rate of population aging, there is growing interest in projecting future trends in dementia prevalence and incidence. These projections would be benefit from information on whether educational attainment explains cohort effects on cognitive trajectories. In a population-based cohort of older adults, the present study assessed whether (i) cohort effects could be observed in age-associated trajectories of cognitive functions and (ii) the observed cohort effects could be explained by changes in educational attainment among cohorts. Practice or learning effects, which refer to the improvement in cognitive test scores over repeated administrations of cognitive tests, could possibly mask or distort the age-associated cognitive trajectories (5,6). Therefore, we also examined cohort differences in practice effects in the above assessments. Trajectories of neuropsychological tests tapping three cognitive domains (psychomotor speed, executive function, and language) were compared among cohorts born between 1902 and 1911, 1912 and 1921, 1922 and 1931, and 1932 and 1943. Data Data come from two large epidemiological studies of dementia: the Monongahela Valley Independent Elders Study (aka, MoVIES) and the Monongahela-Youghiogheny Healthy Aging Team study (aka, MYHAT). The two studies were conducted in geographically contiguous areas of southwestern Pennsylvania between 1987 and 2012. Both studies recruited age-stratified random samples of individuals aged 65 and older from the Voter Registration lists for targeted communities. Brief descriptions of each study are given below. MoVIES. This project recruited and assessed 1,681 individuals during the years 1987–1989 from a group of largely rural communities and followed them biennially until 2001 to investigate incidence, risk factors for cognitive impairment, and dementia. Details of sampling, recruitment, assessments, and follow-up have been reported previously (7,8). At baseline, the study response rate was approximately 60%. MYHAT. This project recruited and assessed 1,982 individuals during the years 2005–2007 from a group of small-town communities and followed them annually to investigate outcomes and predictors of outcomes in mild cognitive impairment. Details of sampling, recruitment, assessments, and follow-up have been reported previously (9,10). The study is currently in the sixth wave of data collection. At baseline, the study response rate was approximately 63% (11). Pooling data from the two studies, we categorized participants into the following four 10-year birth cohorts: those born between 1902 and 1911, between 1912 and 1921, between 1922 and 1931, and between 1932 and 1943. We excluded 46 participants born before 1901 from longitudinal analyses due to small sample size. Educational attainment. We used three education categories: less than high school education, completed high school education but less than college education, and completed college or more education. Neuropsychological tests. The following four neuropsychological tests were administered in the identical manner across two studies and were, therefore, examined in this study: Trail Making Test A (attention/psychomotor speed) (12); Trail Making Test B (executive function) (12); verbal fluency for initial letters P and S, aka letter fluency (executive function); and verbal fluency for the category of animals, aka category fluency (language) (13). The Trail Making Tests are conventionally scored in the time (in seconds) to complete the test, but in our population-based cohort, this measure showed skewed distributions and ceiling effects. Hence, we calculated the number of correct connections per second (connections/s) to use in the current study. Although both studies assessed memory in detail, they used different memory tests as MoVIES addressed dementia and MYHAT was focused on mild cognitive impairment; we, therefore, did not include memory measures in the present study. For fair comparisons of magnitudes of coefficients across four cognitive tests, all tests were standardized using mean and standard deviation of each test score at baseline of the combined data set. Statistical Analysis Differences in educational attainment by birth cohorts were compared using Pearson chi-square statistics. We used mixed-effects models with the outcomes being standardized scores on each cognitive test to examine age-associated cognitive trajectories. We fit two models (Models 1 and 2) for each outcome. In both models, we controlled for sex, practice effects, which identify second and third assessments (two dummy variables, each indicating second or third assessment) and its interaction with a variable indicating MYHAT cohort. The interactions were included because annual assessment in MYHAT (as opposed to biannual assessment in MoVIES) could lead to larger practice effects. In Model 1, we included age at each assessment, cohort, and Cohort × Age interactions (for assessing cohort effect on age-associated cognitive trajectories) and Cohort × Practice effects (for assessing cohort differences in practice effects observed at second and third assessments). In Model 2, we added education, Age × Education, and Age × Education × Cohort interactions. In exploratory analysis, we also examined nonlinear effects (age2, age3), but these were not significant and not included in the final model; practice effects were adequate to capture nonlinearity. Age was centered at age 80 for efficient convergence. Intercept and age were treated as random effects. Model fitness was examined through visual inspection of residuals and formal statistical tests. We used SAS version 9.2 (SAS Institute, Inc.) and R version 2.11 (R Foundation) for statistical analyses.

In a phase III multi-center cancer clinical trial or a large public health study, sample size is predetermined to achieve desired power, and study participants are enrolled from tens or hundreds of participating institutions. As the accrual is closing to the target size, the coordinating data center needs to project the accrual closure date on the basis of the observed accrual pattern and notify the participating sites several weeks in advance. In the past, projections were simply based on some crude assessment, and conservative measures were incorporated in order to achieve the target accrual size. This approach often resulted in excessive accrual size and subsequently unnecessary financial burden on the study sponsors. Here we proposed a discrete-time Poisson process-based method to estimate the accrual rate at time of projection and subsequently the trial closure date. To ensure that target size would be reached with high confidence, we also proposed a conservative method for the closure date projection. The proposed method was illustrated through the analysis of the accrual data of the National Surgical Adjuvant Breast and Bowel Project trial B-38. The results showed that application of the proposed method could help to save considerable amount of expenditure in patient management without compromising the accrual goal in multi-center clinical trials.

OBJECTIVE During menopause, the ratio of the three vaginal epithelial cell types (parabasal, intermediate, and superficial) changes. The proportion of these vaginal cell types is categorized by the vaginal maturation index (VMI). The VMI provides an objective assessment of vaginal hormone response as well as overall hormonal environment. The aim of this study was to determine the concordance of self- and physician-collected samples for VMI and evaluate participant preference for self-collection. DESIGN Twenty women aged 42 to 67 years were enrolled. Each woman received instructions and self-collected a sample for VMI analysis from her posterior lateral vaginal walls. Next, she underwent a vaginal speculum examination for physician VMI sampling. Finally, she completed a survey regarding sampling preference, ease of self-sampling, and previous vaginal experience. Correlation between physician- and self-collected samples were calculated using Pearson's correlation coefficient and Cronbach's alpha. Participant characteristics, vaginal experiences, and sampling preferences were summarized using frequencies and measures of central tendencies. The relationships between preference for sampling method and previous vaginal experiences were examined using Fisher's exact test. RESULTS Average age of the 20 participants was 52.2 years (range, 42-67 y). Women had a variety of previous vaginal experiences ranging from tampon use (100%) to previous vaginal self-swabbing (15%). Average self-collected VMI was 48.4 (range, 13.8-83.5) and physician-collected VMI was 49.9 (range, 20.5-83.5) with a correlation of 0.97 (P < 0. 001), alpha = 0.94. The majority of women preferred self-collection (80%) to physician collection, and there was no difference in preference based on previous vaginal experiences. Nineteen women (95%) found the self-collection to be very easy, and one found it to be somewhat easy. CONCLUSIONS Women can reliably self-collect samples for VMI, and the majority of women in our study preferred self-collection to physician collection. Vaginal self-sampling allows this technique for measuring overall hormonal environment to be used in clinical and epidemiological research.

Objective: The determinants of experiencing menopausal symptoms are complex, representing biological, psychological, and social factors. We report the impact of one such factor, fertility, on the experience of menopausal symptoms. We hypothesize that nulliparous women will have more dissatisfaction with childbearing choices and more negative attitudes toward menopause but will experience fewer menopausal symptoms. Design: Women aged 40 to 65 years were recruited from a single internal medicine practice to participate in a 5-year longitudinal study of the impact of menopause on health-related quality of life. Women completed questionnaires including the RAND-36, attitudes toward menopause, menopausal symptoms, and fertility and childbearing. Based on self-reported pregnancy and birth history, we categorized women as parous (one or more live births), nulligravida (no pregnancies or births), and nulliparous (one or more pregnancies and no live births). Categorical variables were analyzed using univariable and multivariable ordered logistic and logistic regression. Continuous variables were analyzed using Student's t test and multivariable linear regression techniques. Results: The 728 participants were, on average, 50.8 years old with 2.4 pregnancies and 1.7 live births. Compared with parous women, nulligravida and nulliparous women were more likely to report being very dissatisfied with childbearing choices (odds ratio [OR] [95% CI]: 3.3 [2.3-4.6] and 4.0 [2.5-6.4], respectively). Nulliparous, but not nulligravida, women expressed more negative attitudes toward menopause compared with parous women (P < 0.001). Nulligravida and nulliparous women were half as likely to report hot flashes as parous women (OR [95% CI]: 0.6 [0.4-0.9] and 0.5 [0.3-0.9], respectively). Compared with parous women, nulligravida women were less likely to report vaginal dryness (OR [95% CI]: 0.5 [0.3-0.7]). Conclusions: Our study examined the impact of pregnancy and birth history on menopausal symptoms, and the findings support the hypothesis that women who experience infertility may find menopause to be a time of normalcy and experience fewer hot flashes. Both physiologic and psychosocial mechanisms deserve further study.