The impact of body mass index and physical disability on home-based anal self-sampling.

PURPOSE: Self-sampling is increasingly being used in screening programs, yet no studies to date have examined the impact of bodily characteristics on self-sampling experiences. Our objective was to assess whether body mass index (BMI) and physical disability were associated with anal self-sampling difficulty. METHODS: We recruited sexual minority men (SMM) and trans persons in Milwaukee, Wisconsin to participate in an anal cancer screening study. Between January 2020 and August 2022, 240 participants were randomized to a home (n = 120) or clinic (n = 120) screening arm. Home participants received a mailed at-home anal self-sampling kit and were asked to attend a baseline clinic visit where biometric measurements were collected. Participants were asked to complete a survey about their experience with the kit. This research utilized data from participants who used the at-home kit and completed a baseline clinic visit and post-swab survey (n = 82). We assessed the impact of BMI and physical disability on reported body or swab positioning difficulty. RESULTS: Most participants reported no or little difficulty with body positioning (90.3%) or swab positioning (82.9%). Higher BMI was significantly associated with greater reported difficulty with body positioning (aOR = 1.10, 95% CI 1.003-1.20, p = 0.04) and swab positioning (aOR = 1.11, 95% CI 1.02-1.20, p = 0.01). Although not significant, participants who said body positioning was difficult had 2.79 higher odds of having a physical disability. Specimen adequacy did not differ by BMI category (p = 0.76) or physical disability (p = 0.88). CONCLUSION: Anal self-sampling may be a viable option to reach obese persons who may be more likely to avoid screening due to weight-related barriers.

Concordance of Human Papillomavirus Genotypes in Mailed Home-Based Self-Collected Versus Clinician-Collected Anal Swabs Among Sexual and Gender Minority Individuals.

BACKGROUND: Home-based self-sampling may be a viable option for anal cancer screening among sexual minority men (SMM). Yet limited research has compared home-based self-collected with clinician-collected anal swabs for human papillomavirus (HPV) genotyping. METHODS: The Prevent Anal Cancer Self-Swab Study recruited SMM and transgender persons 25 years and over in Milwaukee, WI to participate in an anal cancer screening study. Participants were randomized to a home or clinic arm. Home-based participants were mailed an anal self-sampling kit to complete and return via postal mail. They were also asked to attend a clinic appointment where a clinician collected an anal swab. Swabs were HPV-genotyped using the SPF 10 -LiPA 25 assay. We analyzed 79 paired self and clinician swabs to determine HPV prevalence, percent agreement, and sensitivity and specificity of the mailed home-based anal self-swab to detect HPV genotypes using the clinician-collected swab as the reference. RESULTS: The median number of days between the home and clinic swab was 19 days (range = 2 to 70). Human papillomavirus was detected in 73.3% of self and 75.0% of clinician anal swabs ( P = 0.99). Prevalence of any HPV, any high-risk HPV, any low-risk HPV, and individual HPV types did not significantly differ between self and clinician anal swabs. Agreement between self and clinician swabs was over 90% for 21 of the 25 HPV genotypes. Mailed home-based self-collected swabs had a sensitivity of 94.1% (95% confidence interval, 82.9-99.0) for detection of high-risk HPV versus clinician-collected sampling. CONCLUSIONS: Mailed home-based self-collected and clinician-collected anal swabs demonstrated high concordance for HPV genotyping.

Incidence and Predictors of Breakthrough and Severe Breakthrough Infections of SARS-CoV-2 After Primary Series Vaccination in Adults: A Population-Based Survey of 22 575 Participants.

BACKGROUND: Breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are well documented. The current study estimates breakthrough incidence across pandemic waves, and evaluates predictors of breakthrough and severe breakthrough infections (defined as those requiring hospitalization). METHODS: In total, 89 762 participants underwent longitudinal antibody surveillance. Incidence rates were calculated using total person-days contributed. Bias-corrected and age-adjusted logistic regression determined multivariable predictors of breakthrough and severe breakthrough infection, respectively. RESULTS: The incidence was 0.45 (95% confidence interval [CI], .38-.50) during pre-Delta, 2.80 (95% CI, 2.25-3.14) during Delta, and 11.2 (95% CI, 8.80-12.95) during Omicron, per 10 000 person-days. Factors associated with elevated odds of breakthrough included Hispanic ethnicity (vs non-Hispanic white, OR = 1.243; 95% CI, 1.073-1.441), larger household size (OR = 1.251 [95% CI, 1.048-1.494] for 3-5 vs 1 and OR = 1.726 [95% CI, 1.317-2.262] for more than 5 vs 1 person), rural versus urban living (OR = 1.383; 95% CI, 1.122-1.704), receiving Pfizer or Johnson & Johnson versus Moderna, and multiple comorbidities. Of the 1700 breakthrough infections, 1665 reported on severity; 112 (6.73%) were severe. Higher body mass index, Hispanic ethnicity, vaccine type, asthma, and hypertension predicted severe breakthroughs. CONCLUSIONS: Breakthrough infection was 4-25 times more common during the Omicron-dominant wave versus earlier waves. Higher burden of severe breakthrough infections was identified in subgroups.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey.

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

Home-based self-sampling vs clinician sampling for anal precancer screening: The Prevent Anal Cancer Self-Swab Study.

Sexual minority men are at increased risk for anal squamous cell carcinoma. Our objective was to compare screening engagement among individuals randomized to self-collect an anal canal specimen at home or to attend a clinic appointment. Specimen adequacy was then assessed for human papillomavirus (HPV) DNA genotyping. A randomized trial recruited cisgendered sexual minority men and transgender people in the community and assigned them to use a home-based self-collection swabbing kit or attend a clinic-based swabbing. Swabs were sent for HPV genotyping. The proportions of participants completing screening in each study arm and the adequacy of their specimens for HPV genotyping were assessed. Relative risks were estimated for factors associated with screening. A total of 240 individuals were randomized. Age (median, 46 years) and HIV status (27.1% living with HIV) did not differ by study arm. A total of 89.2% and 74.2% of home-arm and clinic-arm individuals returned the swab, respectively (P = .003), difference between groups, 15.0% (95% CI 5.4%-24.6%). Among black individuals, 96.2% and 63.2% in the home and clinic arms screened (P = .006). Among individuals with HIV, 89.5% and 51.9% in the home and clinic arms screened (P < .001). Self-collected swabs and clinician-collected swabs were comparable in adequacy for HPV genotyping (96.3% and 93.3%, respectively). People at highest risk for anal cancer may be more likely to screen if they are able to self-collect swabs at home rather than attend a clinic.

Long-term immune response to SARS-CoV-2 infection and vaccination in children and adolescents.

BACKGROUND: This analysis examined the durability of antibodies present after SARS-CoV-2 infection and vaccination in children and adolescents. METHODS: Data were collected over 4 time points between October 2020-November 2022 as part of a prospective population-based cohort aged 5-to-19 years (N = 810). Results of the (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test); and (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test); and (3) self-reported antigen/PCR COVID-19 test results, vaccination and symptom status were analyzed. RESULTS: N antibody levels reached a median of 84.10 U/ml (IQR: 20.2, 157.7) cutoff index (COI) ~ 6 months post-infection and increased slightly to a median of 85.25 (IQR: 28.0, 143.0) COI at 12 months post-infection. Peak S antibody levels were reached at a median of 2500 U/mL ~6 months post-vaccination and remained for ~12 months (mean 11.6 months, SD 1.20). CONCLUSIONS: This analysis provides evidence of robust durability of nucleocapsid and spike antibodies in a large pediatric sample up to 12 months post-infection/vaccination. This information can inform pediatric SARS-CoV-2 vaccination schedules. IMPACT: This study provided evidence of robust durability of both nucleocapsid and spike antibodies in a large pediatric sample up to 12 months after infection. Little is known about the long-term durability of natural and vaccine-induced SARS-CoV-2 antibodies in the pediatric population. Here, we determined the durability of anti-SARS-CoV-2 spike (S-test) and nucleocapsid protein (N-test) in children/adolescents after SARS-CoV-2 infection and/or vaccination lasts at least up to 12 months. This information can inform future SARS-CoV-2 vaccination schedules in this age group.

Susceptible windows of exposure to fine particulate matter and fetal growth trajectories in the Spanish INMA (INfancia y Medio Ambiente) birth cohort.

While prior studies report associations between fine particulate matter (PM2.5) exposure and fetal growth, few have explored temporally refined susceptible windows of exposure. We included 2328 women from the Spanish INMA Project from 2003 to 2008. Longitudinal growth curves were constructed for each fetus using ultrasounds from 12, 20, and 34 gestational weeks. Z-scores representing growth trajectories of biparietal diameter, femur length, abdominal circumference (AC), and estimated fetal weight (EFW) during early (0-12 weeks), mid- (12-20 weeks), and late (20-34 weeks) pregnancy were calculated. A spatio-temporal random forest model with back-extrapolation provided weekly PM2.5 exposure estimates for each woman during her pregnancy. Distributed lag non-linear models were implemented within the Bayesian hierarchical framework to identify susceptible windows of exposure for each outcome and cumulative effects [ßcum, 95% credible interval (CrI)] were aggregated across adjacent weeks. For comparison, general linear models evaluated associations between PM2.5 averaged across multi-week periods (i.e., weeks 1-11, 12-19, and 20-33) and fetal growth, mutually adjusted for exposure during each period. Results are presented as %change in z-scores per 5 µg/m3 in PM2.5, adjusted for covariates. Weeks 1-6 [ßcum = -0.77%, 95%CrI (-1.07%, -0.47%)] were identified as a susceptible window of exposure for reduced late pregnancy EFW while weeks 29-33 were positively associated with this outcome [ßcum = 0.42%, 95%CrI (0.20%, 0.64%)]. A similar pattern was observed for AC in late pregnancy. In linear regression models, PM2.5 exposure averaged across weeks 1-11 was associated with reduced late pregnancy EFW and AC; but, positive associations between PM2.5 and EFW or AC trajectories in late pregnancy were not observed. PM2.5 exposures during specific weeks may affect fetal growth differentially across pregnancy and such associations may be missed by averaging exposure across multi-week periods, highlighting the importance of temporally refined exposure estimates when studying the associations of air pollution with fetal growth.

Epidemiology of Nonsyndromic, Orofacial Clefts in Texas: Differences by Cleft Type and Presence of Additional Defects.

To describe the current epidemiology of nonsyndromic cleft palate alone (CP) and cleft lip with or without cleft palate (CL ± P) in Texas and examine differences in the characteristics of infants with CP and CL ± P based on the presence/absence of additional defects.We used data from the Texas Birth Defects Registry, a statewide active birth defect surveillance system, from 1815 cases with CP and 5066 with CL ± P, without a syndrome diagnosis (1999-2014 deliveries). All live births in Texas were used for comparison. Poisson regression was used to calculate crude and adjusted prevalence ratios (aPR) for each characteristic, separately for each cleft subphenotype.The prevalence of CL ± P and CP in our study was estimated as 8.3 and 3.0 per 10 000 live births, respectively. After adjusting for several characteristics, several factors were associated with CL ± P, CP, or both, including infant sex and maternal race/ethnicity, age, smoking, and diabetes. There were several differences between infants with isolated versus nonisolated clefts. For example, maternal prepregnancy diabetes was associated with an increased prevalence of CL ± P (aPR 7.91, 95% confidence interval [CI]: 5.53, 11.30) and CP (aPR 3.24, 95% CI: 1.43, 7.36), but only when additional defects were present.Findings from this study provide a contemporary description of the distribution of orofacial clefts in Texas accounting for differences between isolated and nonisolated clefts. They may contribute to increasing our understanding of the etiology of CP and CL ± P.

Prediction of Preterm Birth among Infants with Orofacial Cleft Defects.

OBJECTIVE: To develop risk prediction models for preterm birth among infants with orofacial clefts. DESIGN: Data from the Texas Birth Defects Registry for infants with orofacial clefts born between 1999-2014 were used to develop preterm birth predictive models. Logistic regression was used to consider maternal and infant characteristics, and internal validation of the final model was performed using bootstrapping methods. The area under the curve (AUC) statistic was generated to assess model performance, and separate predictive models were built and validated for infants with cleft lip and cleft palate alone. Several secondary analyses were conducted among subgroups of interest. SETTING: State-wide, population-based Registry data. PATIENTS/PARTICIPANTS: 6774 infants with orofacial clefts born in Texas between 1999-2014. MAIN OUTCOME MEASURE(S): Preterm birth among infants with orofacial clefts. RESULTS: The final predictive model performed modestly, with an optimism-corrected AUC of 0.67 among all infants with orofacial clefts. The optimism-corrected models for cleft lip (with or without cleft palate) and cleft palate alone had similar predictive capability, with AUCs of 0.66 and 0.67, respectively. Secondary analyses had similar results, but the model among infants with delivery prior to 32 weeks demonstrated higher optimism-corrected predictive capability (AUC = 0.74). CONCLUSIONS: This study provides a first step towards predicting preterm birth risk among infants with orofacial clefts. Identifying pregnancies affected by orofacial clefts at the highest risk for preterm birth may lead to new avenues for improving outcomes among these infants.

Identifying Sensitive Windows of Exposure to NO2 and Fetal Growth Trajectories in a Spanish Birth Cohort.

BACKGROUND: We previously identified associations between trimester-specific NO2 exposures and reduced fetal growth in the Spanish INfancia y Medio Ambiente (INMA) project. Here, we use temporally refined exposure estimates to explore the impact of narrow (weekly) windows of exposure on fetal growth. METHODS: We included 1,685 women from INMA with serial ultrasounds at 12, 20, and 34 gestational weeks. We measured biparietal diameter (BPD), femur length, and abdominal circumference (AC) and from them calculated estimated fetal weight (EFW). We calculated z-scores describing trajectories of each parameter during early (0-12 weeks), mid (12-20 weeks), and late (20-34 weeks) pregnancy, based on longitudinal growth curves from mixed-effects models. We estimated weekly NO2 exposures at each woman's residence using land-use regression models. We applied distributed lag nonlinear models to identify sensitive windows of exposure. We present effect estimates as the percentage change in fetal growth per 10 µg/m3 increase in NO2 exposure, and we calculated cumulative effect estimates by aggregating estimates across adjacent lags. RESULTS: We identified weeks 5-12 as a sensitive window for NO2 exposure on late EFW (cumulative ß = -3.0%; 95% CI = -4.1%, -1.9%). We identified weeks 6-19 as a sensitive window for late growth in BPD (cumulative ß = -2.0%; 95% CI = -2.7%, -1.4%) and weeks 8-13 for AC (cumulative ß = -0.68%; 95% CI = -0.97%, -0.40%). We found suggestive evidence that third trimester NO2 exposure is associated with increased AC, BPD, and EFW growth in late pregnancy. CONCLUSIONS: Our findings are consistent with the hypothesis that NO2 exposure is associated with alterations in growth of EFW, BPD, and AC dependent on the specific timing of exposure during gestation.

Maternal occupational exposure to chemicals and child cognitive function.

BACKGROUND: Limited data exist regarding child neurodevelopment in relation to maternal occupational exposure to endocrine-disrupting chemicals (EDCs). METHODS: We included 1058 mother-child pairs from the INfancia y Medio Ambiente (INMA) project (2003-2008). Using a job-exposure matrix, exposure probability scores for ten EDC groups were assigned to each mother based on her longest held job during pregnancy. At the child's 5-year visit, the McCarthy Scales of Children's Abilities was administered, yielding the general cognitive index and scales for specific cognitive domains. We analyzed region-specific associations between EDC exposures and each outcome separately using adjusted linear regression and combined region-specific effect estimates using random-effects meta-analyses. RESULTS: Approximately 24% of women were exposed to at least one EDC group, but exposure to most individual EDC groups was low (<5%). Maternal organic solvent exposure was associated with lower quantitative scores among children (-5.8 points, 95% confidence interval: -11.0, -0.5). Though statistically non-significant, exposures to polycyclic aromatic hydrocarbons, phthalates, alkylphenolic compounds, and miscellaneous chemicals were associated with poorer offspring performance for most or all cognitive domains. CONCLUSIONS: This study found limited evidence for a role of maternal occupational EDC exposures on child cognition. Further research is needed to better characterize exposures among pregnant workers. IMPACT: Using data from a prospective birth cohort, we help fill an important research gap regarding the potential consequences of work-related exposure to endocrine-disrupting chemicals (EDCs) among pregnant women on child neurodevelopment. We expand on existing literature-largely limited to pesticide and organic solvent exposures-by using a job-exposure matrix to estimate exposure to several EDC groups. We found limited evidence of an association between maternal occupational EDC exposure and children's overall cognition. We did observe specific associations between exposure to organic solvents and lower quantitative reasoning scores.

Birth defect co-occurrence patterns in the Texas Birth Defects Registry.

BACKGROUND: The population-level landscape of co-occurring birth defects among infants without a syndromic diagnosis is not well understood. METHODS: We analyzed data from 40,771 infants with two or more major birth defects in the Texas Birth Defects Registry (TBDR; 1999-2014). We calculated adjusted observed-to-expected (O/E) ratios for all two, three, four, and five-way combinations of 138 major defects. RESULTS: Among 530 patterns with the highest adjusted O/E ratios (top 5% of 10,595 patterns), 66% included only defects co-occurring within one organ system and 28% were suggestive of known patterns (e.g., midline developmental defects). Of the remaining patterns, the combination of defects with the highest O/E ratio (193.8) encompassed the diaphragm, spine, spleen, and heart defects. Fourteen patterns involved heart and spine defects with or without rib defects. Ten additional patterns primarily involved two hallmark components of VACTERL association (specifically, vertebral defects, anal atresia, cardiac defects, renal, or limb defects, but not tracheoesophageal fistula). CONCLUSIONS: Our analyses provide a description of the birth defect co-occurrence patterns in a multi-ethnic, population-based sample, and revealed several patterns of interest. This work complements prior work that has suggested etiologic connections between select defects (e.g., diaphragmatic hernia and heart and spleen anomalies; heart and spine defects). IMPACT: In this large-scale, population-based study of birth defect co-occurrence patterns, we found several birth defect combinations of potential interest that warrant further investigation: congenital diaphragmatic hernia, heart, spine, and spleen defects and scimitar syndrome with vertebral defects. The majority of patterns of co-occurring defects observed more frequently than expected involved multiple defects within the same system and combinations suggestive of known associations. Nearly all of the top patterns (beyond the same system and those suggestive of known associations) involved organ systems that are components of the VACTERL association, with heart, spine, and rib defect patterns being the most common.

Birth Defect Co-Occurrence Patterns Among Infants With Cleft Lip and/or Palate.

OBJECTIVE: To investigate 2- to 5-way patterns of defects co-occurring with orofacial clefts using data from a population-based registry. DESIGN: We used data from the Texas Birth Defects Registry for deliveries between 1999 and 2014 to Texas residents, including 1884 cases with cleft palate (CP) and 5289 cases with cleft lip with or without cleft palate (CL±P) without a known syndrome. We identified patterns of defects co-occurring with CP and with CL±P observed more frequently than would be expected if these defects occurred independently. We calculated adjusted observed-to-expected (O/E) ratios to account for the known tendency of birth defects to cluster nonspecifically. RESULTS: Among infants without a syndrome, 23% with CP and 21% with CL±P had at least 1 additional congenital anomaly. Several combinations of defects were observed much more often than expected. For example, the combination of CL±P, congenital hydrocephaly, anophthalmia, and other nose anomalies had an O/E ratio of 605. For both CP and CL±P, co-occurrence patterns with the highest O/E ratios involved craniofacial and brain abnormalities, and many included the skeletal, cardiovascular, and renal systems. CONCLUSIONS: The patterns of defects we observed co-occurring with clefts more often than expected may help improve our understanding of the relationships between multiple defects. Further work to better understand some of the top defect combinations could reveal new phenotypic subgroups and increase our knowledge of the developmental mechanisms that underlie the respective defects.

Patterns of congenital anomalies among individuals with trisomy 13 in Texas.

Few population-based studies have analyzed patterns of co-occurring birth defects among those with trisomy 13. We evaluated the frequency of all possible combinations of any one, two, three, or four additional co-occurring birth defects among 736 individuals with trisomy 13 using data from the Texas Birth Defects Registry for deliveries during 1999-2014. We calculated the observed-to-expected ratio for each combination, adjusting for the known tendency for birth defects to cluster non-specifically. To address potential ascertainment differences among live births and non-live births, we repeated analyses specifically among live births. The combination of defects with the largest observed-to-expected ratio was microcephalus, reduction deformities of brain (e.g., holoprosencephaly), anomalies of nose, and polydactyly. As expected, most of the highest 30 observed-to-expected ratios involved combinations with documented features of trisomy 13, including defects of the scalp (e.g., aplasia cutis) and heart. Results were similar among sensitivity analyses restricted to live births. Our findings may help further delineate the phenotypic spectrum for trisomy 13 and may inform future research related to improving screening and counseling for the condition.

A machine learning compatible method for ordinal propensity score stratification and matching.

Although machine learning techniques that estimate propensity scores for observational studies with multivalued treatments have advanced rapidly in recent years, the development of propensity score adjustment techniques has not kept pace. While machine learning propensity models provide numerous benefits, they do not produce a single variable balancing score that can be used for propensity score stratification and matching. This issue motivates the development of a flexible ordinal propensity scoring methodology that does not require parametric assumptions for the propensity model. The proposed method fits a one-parameter power function to the cumulative distribution function (CDF) of the generalized propensity score (GPS) vector resulting from any machine learning propensity model, and is henceforth called the GPS-CDF method. The estimated parameter from the GPS-CDF method, ã , is a scalar balancing score that can be used to group similar subjects in outcome analyses. Specifically, subjects who received different levels of the treatment are stratified or matched based on their ã value to produce unbiased estimates of the average treatment effect (ATE). Simulation studies presented show remediation of covariate balance, minimal bias in ATEs, and maintain coverage probability. The proposed method is applied to the Mexican-American Tobacco use in Children (MATCh) study to determine whether an ordinal treatment of exposure to smoking imagery in movies causes cigarette experimentation in Mexican-American adolescents.

Propensity score stratification methods for continuous treatments.

Continuous treatments propensity scoring remains understudied as the majority of methods are focused on the binary treatment setting. Current propensity score methods for continuous treatments typically rely on weighting in order to produce causal estimates. It has been shown that in some continuous treatment settings, weighting methods can result in worse covariate balance than had no adjustments been made to the data. Furthermore, weighting is not always stable, and resultant estimates may be unreliable due to extreme weights. These issues motivate the current development of novel propensity score stratification techniques to be used with continuous treatments. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) and the nonparametric GPS-CDF approaches are introduced. Empirical CDFs are used to stratify subjects based on pretreatment confounders in order to produce causal estimates. A detailed simulation study shows superiority of these new stratification methods based on the empirical CDF, when compared with standard weighting techniques. The proposed methods are applied to the "Mexican-American Tobacco use in Children" study to determine the causal relationship between continuous exposure to smoking imagery in movies, and smoking behavior among Mexican-American adolescents. These promising results provide investigators with new options for implementing continuous treatment propensity scoring.

Evaluation of Noninvasive Hemoglobin Measurements in Trauma Patients: A Repeat Study.

INTRODUCTION: Reliable, accurate, and non-invasive hemoglobin measurements would be useful in the trauma setting. The aim of this study was to re-examine the ability of the Masimo Radical 7 in this setting after recent hardware and software improvements. METHODS: Level 1 Trauma patients were prospectively enrolled in the study over a 9-mo period with the goal of obtaining 3 paired data points from 150 patients admitted to the ICU or IMU. Hospital laboratory hemoglobin values were compared with cyanomethemoglobin (HiCN) and Masimo device hemoglobin (SpHb) values using comparison plots and Bland-Altman analysis. RESULTS: A total of 380 patients were enrolled in the study with 150 of those being admitted to the ICU or IMU. Comparison of hospital lab hemoglobin and HiCN (n = 494) found a correlation of R2 = 0.92. Comparison of hospital lab hemoglobin and Masimo device hemoglobin (n = 218) found a correlation of R2 = 0.27. Bland-Altman analysis of the 218 of the comparable hospital hemoglobin and Masimo device hemoglobin values had a bias of 0.505 g/dL with 95% of values within the limits of agreement of 4.06 g/dL to -3.60 g/dL. CONCLUSIONS: The Masimo Radical 7 device has the potential to provide timely, useful clinical information, but it is not currently able to serve as an initial noninvasive diagnostic tool for trauma patients. There was poor correlation between clinical Hgb and SpHb, and because of that, SpHb should not be used to evaluate hemoglobin levels in trauma patients.

The Future of Substance Abuse Now: Relationships among Adolescent Use of Vaping Devices, Marijuana, and Synthetic Cannabinoids.

Background: Substances that can be vaped include nicotine, marijuana, cocaine, heroin, and a range of synthetic drugs called new psychoactive substances (NPS). Due to the rising popularity of vaping among adolescents, it is crucial to understand the relationships between vaping and illicit drug use. Objectives: This paper examined the prevalence and trends of using vaping devices, marijuana vaping, marijuana products, synthetic cannabinoids and mist contents among youth. Methods: The study utilized 5 sets of public cross-sectional national data from the "Monitoring the Future" surveys during 2014-2018. It employed logistic regression to analyze the data. Results: There was an increase from 10.5% in 2017 to 20.8% in 2018 for the past 30-day use of vaping devices among 12th graders. Furthermore, there was an increase from 21.6% in 2017 to 34.5% in 2018 for the past 12-month use of marijuana via vaping device among 12th-grade marijuana users. Additionally, there were significant associations between vaping device use and marijuana vaping, between vaping device use and marijuana use, between vaping device use and synthetic cannabinoids use, and between marijuana use and synthetic cannabinoids use from 2016 to 2018. Conclusions: Vaping emerged as another major route of marijuana administration among youth. Adolescent marijuana users had higher odds of using synthetic cannabinoids. This finding highlighted the importance of understanding what adolescent substance consumption pattern would be where marijuana was legalized. It also supported the hypothesis that vaping devices use correlates with, or is associated with, marijuana and synthetic cannabinoids use.

dbMTS: A comprehensive database of putative human microRNA target site SNVs and their functional predictions.

MicroRNAs (miRNA) are short noncoding RNAs that can repress the expression of protein-coding messenger RNAs (mRNAs) by binding to the 3'-untranslated region (UTR) of the target. Genetic mutations such as single nucleotide variants (SNVs) in the 3'-UTR of the mRNAs can disrupt miRNA regulation. In this study, we presented dbMTS, a database for miRNA target site (MTS) SNVs and their functional annotations. This database can help studies easily identify putative SNVs that affect miRNA targeting and facilitate the prioritization of their functional importance. dbMTS is freely available for academic use at http://database.liulab.science/dbMTS as a web service or a downloadable attached database of dbNSFP.

A semiparametric model for wearable sensor-based physical activity monitoring data with informative device wear.

Wearable sensors provide an exceptional opportunity in collecting real-time behavioral data in free living conditions. However, wearable sensor data from observational studies often suffer from information bias, since participants' willingness to wear the monitoring devices may be associated with the underlying behavior of interest. The aim of this study was to introduce a semiparametric statistical approach for modeling wearable sensor-based physical activity monitoring data with informative device wear. Our simulation study indicated that estimates from the generalized estimating equations showed ignorable bias when device wear patterns were independent of the participants physical activity process, but incrementally more biased when the patterns of device non-wear times were increasingly associated with the physical activity process. The estimates from the proposed semiparametric modeling approach were unbiased both when the device wear patterns were (i) independent or (ii) dependent to the underlying physical activity process. We demonstrate an application of this method using data from the 2003-2004 National Health and Nutrition Examination Survey ($N=4518$), to examine gender differences in physical activity measured using accelerometers. The semiparametric model can be implemented using our R package acc, free software developed for reading, processing, simulating, visualizing, and analyzing accelerometer data, publicly available at the Comprehensive R Archive Network.