Logistic burdens of cancer care: A qualitative study.

Cancer treatment often creates logistic conflicts with everyday life priorities; however, these challenges and how they are subjectively experienced have been largely unaddressed in cancer care. Our goal was to describe time and logistic requirements of cancer care and whether and how they interfered with daily life and well-being. We conducted interviews with 20 adults receiving cancer-directed treatment at a single academic cancer center. We focused on participants' perception of the time, effort, and energy-intensiveness of cancer care activities, organization of care requirements, and preferences in how to manage the logistic burdens of their cancer care. Participant interview transcripts were analyzed using an inductive thematic analysis approach. Burdens related to travel, appointment schedules, healthcare system navigation, and consequences for relationships had roots both at the system-level (e.g. labs that were chronically delayed, protocol-centered rather than patient-centered bureaucratic requirements) and in individual circumstances (e.g. greater stressors among those working and/or have young children versus those who are retired) that determined subjective burdensomeness, which was highest among patients who experienced multiple sources of burdens simultaneously. Our study illustrates how objective burdens of cancer care translate into subjective burden depending on patient circumstances, emphasizing that to study burdens of care, an exclusive focus on objective measures does not capture the complexity of these issues. The complex interplay between healthcare system factors and individual circumstances points to clinical opportunities, for example helping patients to find ways to meet work and childcare requirements while receiving care.

External validation of the Norwegian anterior cruciate ligament reconstruction revision prediction model using patients from the STABILITY 1 Trial.

PURPOSE: A machine learning-based anterior cruciate ligament (ACL) revision prediction model has been developed using Norwegian Knee Ligament Register (NKLR) data, but lacks external validation outside Scandinavia. This study aimed to assess the external validity of the NKLR model (https://swastvedt.shinyapps.io/calculator_rev/) using the STABILITY 1 randomized clinical trial (RCT) data set. The hypothesis was that model performance would be similar. METHODS: The NKLR Cox Lasso model was selected for external validation owing to its superior performance in the original study. STABILITY 1 patients with all five predictors required by the Cox Lasso model were included. The STABILITY 1 RCT was a prospective study which randomized patients to receive either a hamstring tendon autograft (HT) alone or HT plus a lateral extra-articular tenodesis (LET). Since all patients in the STABILITY 1 trial received HT ± LET, three configurations were tested: 1: all patients coded as HT, 2: HT + LET group coded as bone-patellar tendon-bone (BPTB) autograft, 3: HT + LET group coded as unknown/other graft choice. Model performance was assessed via concordance and calibration. RESULTS: In total, 591/618 (95.6%) STABILITY 1 patients were eligible for inclusion, with 39 undergoing revisions within 2 years (6.6%). Model performance was best when patients receiving HT + LET were coded as BPTB. Concordance was similar to the original NKLR prediction model for 1- and 2-year revision prediction (STABILITY: 0.71; NKLR: 0.68-0.69). Concordance 95% confidence interval (CI) ranged from 0.63 to 0.79. The model was well calibrated for 1-year prediction while the 2-year prediction demonstrated evidence of miscalibration. CONCLUSION: When patients in STABILITY 1 who received HT + LET were coded as BPTB in the NKLR prediction model, concordance was similar to the index study. However, due to a wide 95% CI, the true performance of the prediction model with this Canadian and European cohort is unclear and a larger data set is required to definitively determine the external validity. Further, better calibration for 1-year predictions aligns with general prediction modelling challenges over longer periods. While not a large enough sample size to elicit the true accuracy and external validity of the prediction model when applied to North American patients, this analysis provides more support for the notion that HT plus LET performs similarly to BPTB reconstruction. In addition, despite the wide confidence interval, this study suggests optimism regarding the accuracy of the model when applied outside of Scandinavia. LEVEL OF EVIDENCE: Level 3, cohort study.

Performance of the pooled cohort equations in cancer survivors: the Atherosclerosis Risk in Communities study.

BACKGROUND: Cancer survivors may have elevated atherosclerotic cardiovascular disease (ASCVD) risk. Therefore, we tested how accurately the American College of Cardiology/American Heart Association 2013 pooled cohort equations (PCEs) predict 10-year ASCVD risk in cancer survivors. OBJECTIVES: To estimate the calibration and discrimination of the PCEs in cancer survivors compared to non-cancer participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: We evaluated the PCEs' performance among 1244 cancer survivors and 3849 cancer-free participants who were free of ASCVD at the start of follow-up. Each cancer survivor was incidence-density matched with up to five controls by age, race, sex, and study center. Follow-up began at the first study visit at least 1 year after the diagnosis date of the cancer survivor and finished at the ASCVD event, death, or end of follow-up. Calibration and discrimination were assessed and compared between cancer survivors and cancer-free participants. RESULTS: Cancer survivors had higher PCE-predicted risk, at 26.1%, compared with 23.1% for cancer-free participants. There were 110 ASCVD events in cancer survivors and 332 ASCVD events in cancer-free participants. The PCEs overestimated ASCVD risk in cancer survivors and cancer-free participants by 45.6% and 47.4%, respectively, with poor discrimination in both groups (C-statistic for cancer survivors = 0.623; for cancer-free participants, C = 0.671). CONCLUSIONS: The PCEs overestimated ASCVD risk in all participants. The performance of the PCEs was similar in cancer survivors and cancer-free participants. IMPLICATIONS FOR CANCER SURVIVORS: Our findings suggest that ASCVD risk prediction tools tailored to survivors of adult cancers may not be needed.

Associations Between Psychosocial Needs, Carbohydrate-Counting Behavior, and App Satisfaction: A Randomized Crossover App Trial on 92 Adults With Diabetes.

To examine whether psychosocial needs in diabetes care are associated with carbohydrate counting and if carbohydrate counting is associated with satisfaction with diabetes applications' usability, a randomized crossover trial of 92 adults with type 1 or 2 diabetes requiring insulin therapy tested two top-rated diabetes applications, mySugr and OnTrack Diabetes. Survey responses on demographics, psychosocial needs (perceived competence, autonomy, and connectivity), carbohydrate-counting frequency, and application satisfaction were modeled using mixed-effect linear regressions to test associations. Participants ranged between 19 and 74 years old (mean, 54 years) and predominantly had type 2 diabetes (70%). Among the three tested domains of psychosocial needs, only competence-not autonomy or connectivity-was found to be associated with carbohydrate-counting frequency. No association between carbohydrate-counting behavior and application satisfaction was found. In conclusion, perceived competence in diabetes care is an important factor in carbohydrate counting; clinicians may improve adherence to carbohydrate counting with strategies designed to improve perceived competence. Carbohydrate-counting behavior is complex; its impact on patient satisfaction of diabetes application usability is multifactorial and warrants consideration of patient demographics such as sex as well as application features for automated carbohydrate counting.

Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors' physical activity and quality of life.

PURPOSE: This study aimed to examine the effects of a multi-component mobile health intervention (wearable, apps, and social media) on cancer survivors' (CS') physical activity (PA), quality of life, and PA determinants compared to exercise prescription only, social media only, and attention control conditions. METHODS: A total of 126 CS (age = 60.37 ± 7.41 years, mean ± SD) were recruited from the United States. The study duration was 6 months and participants were randomly placed into 4 groups. All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA. They (1) received previously established weekly personalized exercise prescriptions via email, (2) received weekly Facebook health education and interacted with one another, (3) received both Conditions 1 and 2, or (4) were part of the control condition, meaning they adopted usual care. CS PA daily steps, quality of life (i.e., physical health and mental health), and PA determinants (e.g., self-efficacy, social support) were measured at baseline, 3 months, and 6 months. RESULTS: The final sample size included 123 CS. The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention (95% confidence interval (95%CI): 368-2951; p < 0.05). Similarly, those in the multi-component condition had significantly greater increased physical health than the control condition (95%CI: -0.41 to -0.01; p < 0.05) over time. In addition, the social media condition had significantly greater increased perceived social support than the control condition (95%CI: 0.01-0.93; p < 0.05). No other significant differences on outcomes were identified. CONCLUSION: The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health. Also, offering social media intervention has the potential to improve CS perceived social support.

Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction.

BACKGROUND: Clinical tools based on machine learning analysis now exist for outcome prediction after primary anterior cruciate ligament reconstruction (ACLR). Relying partly on data volume, the general principle is that more data may lead to improved model accuracy. PURPOSE/HYPOTHESIS: The purpose was to apply machine learning to a combined data set from the Norwegian and Danish knee ligament registers (NKLR and DKRR, respectively), with the aim of producing an algorithm that can predict revision surgery with improved accuracy relative to a previously published model developed using only the NKLR. The hypothesis was that the additional patient data would result in an algorithm that is more accurate. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Machine learning analysis was performed on combined data from the NKLR and DKRR. The primary outcome was the probability of revision ACLR within 1, 2, and 5 years. Data were split randomly into training sets (75%) and test sets (25%). There were 4 machine learning models examined: Cox lasso, random survival forest, gradient boosting, and super learner. Concordance and calibration were calculated for all 4 models. RESULTS: The data set included 62,955 patients in which 5% underwent a revision surgical procedure with a mean follow-up of 7.6 ± 4.5 years. The 3 nonparametric models (random survival forest, gradient boosting, and super learner) performed best, demonstrating moderate concordance (0.67 [95% CI, 0.64-0.70]), and were well calibrated at 1 and 2 years. Model performance was similar to that of the previously published model (NKLR-only model: concordance, 0.67-0.69; well calibrated). CONCLUSION: Machine learning analysis of the combined NKLR and DKRR enabled prediction of the revision ACLR risk with moderate accuracy. However, the resulting algorithms were less user-friendly and did not demonstrate superior accuracy in comparison with the previously developed model based on patients from the NKLR alone, despite the analysis of nearly 63,000 patients. This ceiling effect suggests that simply adding more patients to current national knee ligament registers is unlikely to improve predictive capability and may prompt future changes to increase variable inclusion.

Limited clinical utility of a machine learning revision prediction model based on a national hip arthroscopy registry.

PURPOSE: Accurate prediction of outcome following hip arthroscopy is challenging and machine learning has the potential to improve our predictive capability. The purpose of this study was to determine if machine learning analysis of the Danish Hip Arthroscopy Registry (DHAR) can develop a clinically meaningful calculator for predicting the probability of a patient undergoing subsequent revision surgery following primary hip arthroscopy. METHODS: Machine learning analysis was performed on the DHAR. The primary outcome for the models was probability of revision hip arthroscopy within 1, 2, and/or 5 years after primary hip arthroscopy. Data were split randomly into training (75%) and test (25%) sets. Four models intended for these types of data were tested: Cox elastic net, random survival forest, gradient boosted regression (GBM), and super learner. These four models represent a range of approaches to statistical details like variable selection and model complexity. Model performance was assessed by calculating calibration and area under the curve (AUC). Analysis was performed using only variables available in the pre-operative clinical setting and then repeated to compare model performance using all variables available in the registry. RESULTS: In total, 5581 patients were included for analysis. Average follow-up time or time-to-revision was 4.25 years (± 2.51) years and overall revision rate was 11%. All four models were generally well calibrated and demonstrated concordance in the moderate range when restricted to only pre-operative variables (0.62-0.67), and when considering all variables available in the registry (0.63-0.66). The 95% confidence intervals for model concordance were wide for both analyses, ranging from a low of 0.53 to a high of 0.75, indicating uncertainty about the true accuracy of the models. CONCLUSION: The association between pre-surgical factors and outcome following hip arthroscopy is complex. Machine learning analysis of the DHAR produced a model capable of predicting revision surgery risk following primary hip arthroscopy that demonstrated moderate accuracy but likely limited clinical usefulness. Prediction accuracy would benefit from enhanced data quality within the registry and this preliminary study holds promise for future model generation as the DHAR matures. Ongoing collection of high-quality data by the DHAR should enable improved patient-specific outcome prediction that is generalisable across the population. LEVEL OF EVIDENCE: Level III.

Predicting subjective failure of ACL reconstruction: a machine learning analysis of the Norwegian Knee Ligament Register and patient reported outcomes.

OBJECTIVES: Accurate prediction of outcome following anterior cruciate ligament (ACL) reconstruction is challenging, and machine learning has the potential to improve our predictive capability. The purpose of this study was to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can (1) identify the most important risk factors associated with subjective failure of ACL reconstruction and (2) develop a clinically meaningful calculator for predicting the probability of subjective failure following ACL reconstruction. METHODS: Machine learning analysis was performed on the NKLR. All patients with 2-year follow-up data were included. The primary outcome was the probability of subjective failure 2 years following primary surgery, defined as a Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life (QoL) subscale score of <44. Data were split randomly into training (75%) and test (25%) sets. Four models intended for this type of data were tested: Lasso logistic regression, random forest, generalized additive model (GAM), and gradient boosted regression (GBM). These four models represent a range of approaches to statistical details like variable selection and model complexity. Model performance was assessed by calculating calibration and area under the curve (AUC). RESULTS: Of the 20,818 patients who met the inclusion criteria, 11,630 (56%) completed the 2-year follow-up KOOS QoL questionnaire. Of those with complete KOOS data, 22% reported subjective failure. The lasso logistic regression, GBM, and GAM all demonstrated AUC in the moderate range (0.67-0.68), with the GAM performing best (0.68; 95% CI 0.64-0.71). Lasso logistic regression, GBM, and the GAM were well-calibrated, while the random forest showed evidence of mis-calibration. The GAM was selected to create an in-clinic calculator to predict subjective failure risk at a patient-specific level (https://swastvedt.shinyapps.io/calculator_koosqol/). CONCLUSION: Machine learning analysis of the NKLR can predict subjective failure risk following ACL reconstruction with fair accuracy. This algorithm supports the creation of an easy-to-use in-clinic calculator for point-of-care risk stratification. Clinicians can use this calculator to estimate subjective failure risk at a patient-specific level when discussing outcome expectations preoperatively. LEVEL OF EVIDENCE: Level-III Retrospective review of a prospective national register.

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity.

PURPOSE: External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision ( https://swastvedt.shinyapps.io/calculator_rev/ ). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). METHODS: The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. RESULTS: In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68-0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. CONCLUSION: The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown. LEVEL OF EVIDENCE: III.

Predicting Anterior Cruciate Ligament Reconstruction Revision: A Machine Learning Analysis Utilizing the Norwegian Knee Ligament Register.

BACKGROUND: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. METHODS: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. RESULTS: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. CONCLUSIONS: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Effect of iron supplementation from neonatal period on the iron status of6-month-old infants at-risk for early iron deficiency: a randomized interventional trial.

OBJECTIVE: To investigate the effects of iron supplementation from the second day after birth on 6-month hemoglobin (Hb), serum ferritin and motor development in infants at risk of early iron deficiency. STUDY DESIGN: Term (37-41 weeks) infants of anemic (Hb ≤ 100 g L-1; N = 100) and non-anemic (Hb > 100 g L-1; N = 100) mothers were randomized to daily iron supplementation at a dose of 2 mg kg-1 from 36 h of age (N = 50, each of anemic and non-anemic mothers) or no iron-supplementation (N = 50 each of anemic and non-anemic mothers). Hb, serum ferritin and motor development at 6 months were compared in the two groups. RESULTS: Iron-supplemented infants had higher Hb (103.7 ± 9.3 g L-1 versus 97.0 ± 9.4 g L-1, p < .0001) and serum ferritin (133.93 ± 52.41 ng mL-1 versus 78.09 ± 42.03 ng mL-1, p < .001) concentrations, compared with the no iron-supplementation group. Their motor development was closer to age-appropriate norms than the no iron-supplementation group (5.83 ± 0.69 versus 5.18 ± 1.35, p < .01). CONCLUSION: Early Iron supplementation is effective for improving iron status and motor development at 6 months in infants at risk for early iron deficiency.

Meningococcal vaccines and protein-energy undernutrition in children in the African meningitis belt.

BACKGROUND: Vaccines to prevent meningococcal meningitis in the African meningitis belt include PsACWY, a polysaccharide-only vaccine; and PsA-TT, a polysaccharide-protein conjugate vaccine. Protein-energy undernutrition, a condition where children do not receive enough macro- or micronutrients, is related to increased risk of infectious diseases and poor immune function. Reduced immune function could affect vaccine immunogenicity. We investigated connections between protein-energy undernutrition and vaccine immunogenicity and antibody waning to PsACWY and PsA-TT in children in the African meningitis belt. METHODS: This is a secondary analysis of data collected as part of four clinical trials testing the safety and efficacy of PsA-TT in children in Mali, Ghana, and Senegal. We identified whether anthropometric growth indices (low height-for-age, weight-for-height, or weight-for-age Z-score categories) were related to reduced vaccine-elicited antibody (measured with rabbit complement) from pre- to 1 month post-vaccination, in linear regression models. We also identified whether these growth indices were related to increased waning for vaccine-elicited antibody over time, in linear regression models. RESULTS: A total of 697 children were included in our analysis, of which 350 (50.2%) were female; the mean (SD) age was 1.0 (1.1) years, and 578 (83.0%) received PsA-TT. In linear regression models, no consistent statistical relationship was seen between pre-vaccination anthropometric Z-score categories and vaccine immunogenicity, or decline in antibody over time, for either vaccine, although children with low weight-for-height had a greater decline in antibody from 1 to 6 months post-vaccination. CONCLUSIONS: Our analysis did not find protein-energy undernutrition to be associated with immunogenicity or waning of PsACWY- or PsA-TT-elicited antibody in children living in the African meningitis belt. Future studies should consider measuring antibody titers at additional time points post-vaccination, and for longer periods of time, to determine if the rate of antibody waning over a period of several years is associated with protein-energy undernutrition.

Influence of Changes in Sedentary Time on Outcomes of Supervised Exercise Therapy in Individuals with Comorbid Peripheral Artery Disease and Type 2 Diabetes.

BACKGROUND: Although supervised exercise therapy (SET) is effective in improving walking distance among adults with symptomatic peripheral artery disease (PAD), some research suggests that individuals with comorbid PAD and type 2 diabetes mellitus (T2DM) may experience a blunted response to SET. It is unknown whether free-living sedentary time changes during SET, and if increases in sedentary time could, in part, explain poor response to SET. The purposes of this pilot study were to (1) determine if older adults with PAD (with and without T2DM) engaging in SET change their sedentary behavior and (2) examine the relationship between changes in sedentary behavior and SET outcomes. We hypothesized that decreased sedentary time during SET would be associated with greater improvements in six-minute walk test (6MWT) total distance and other key SET outcomes. METHODS: Participants (n = 44) initiating a 12-week SET program completed the 6MWT, Short Physical Performance Battery, Walking Impairment Questionnaire, and accelerometer-assessed sedentary behavior at SET initiation, 6 weeks, and 12 weeks. RESULTS: Participants' mean age was 72.3 (7.1) years, mean ankle-brachial index was 0.71 (0.25), and 47.7% were female. On average, sedentary time did not change after SET, although there was substantial variability (-40% to +38% change in minutes of sedentary time/day). Participants with T2DM experienced greater improvements in claudication onset distance than participants without T2DM (mean = 35 m, P = 0.044, 95% confidence interval = 1.6 to 115.4 m). Neither changes in sedentary time from baseline to 6 weeks (P = 0.419) nor T2DM (P = 0.154) predicted changes in 6MWT total distance from baseline to 12 weeks. CONCLUSIONS: As SET availability increases, further examination of factors that may influence SET outcomes will help maximize benefits of this proven therapy.

Are Correlates of Physical Activity in Adolescents Similar Across Ethnicity/Race and Sex: Implications for Interventions.

BACKGROUND: This study tested for differences in personal, social, and environmental correlates of moderate to vigorous physical activity (MVPA) across ethnicity/race in male and female adolescents. METHODS: Self-reported MVPA and 47 potential correlates of MVPA were measured in an ethnically/racially diverse cross-sectional sample of adolescents, in Minnesota, who participated in EAT-2010 (Eating and Activity in Teens). Interactions of potential correlates with ethnicity/race on MVPA were tested in linear hierarchical regression models in boys and girls. RESULTS: Boys reported 1.7 more weekly hours of MVPA than girls. White adolescents reported 1.1 to 2.1 more weekly hours of MVPA than nonwhite adolescents. Among girls, neighborhood road connectivity was negatively correlated with MVPA among Hispanic and Asian participants. Among boys, sports participation was positively correlated with MVPA among all ethnicities/races, except Asians. Home media equipment was positively correlated with MVPA among Hispanic boys, but negatively correlated among white boys. CONCLUSIONS: A few correlates of physical activity among adolescents differed intersectionally by ethnicity/race and sex. Sports participation and home media equipment may have differing impacts on physical activity across ethnicities and races in boys, whereas neighborhood features like road connectivity may have differing impacts on physical activity across ethnicities and races in girls.

Developmental Trends and Determinants of Physical Activity From Adolescence to Adulthood Differ by Ethnicity/Race and Sex.

BACKGROUND: Interventions to raise population physical activity generally show modest effects; one possible reason is that trends and determinants of moderate to vigorous physical activity (MVPA) differ between population subgroups. This study examined differences in trends and determinants of reported MVPA by ethnicity/race and sex in a 15-year longitudinal study. METHODS: Participants (n = 2092) in the Project Eating and Activity in Teens and Young Adults study were surveyed on MVPA behavior and potential determinants from adolescence to young adulthood. Generalized estimating equations were used to model age trends in MVPA and associations with determinants. RESULTS: Mean MVPA declined by 2.1 hours per week over 15 years of follow-up from adolescence to young adulthood. Asian males reported the lowest levels of MVPA at each age. Nonwhite females reported less MVPA than white females at each age. The association of body mass index (BMI) with MVPA differed by sex and ethnicity/race. Asian males and females showed lower levels of MVPA at both low and high BMI. CONCLUSIONS: Interventions to increase MVPA may need to begin earlier among Asian men and nonwhite women than among other groups. Asian adolescents with lower BMI show lower MVPA and may benefit from additional intervention efforts compared with Asian adolescents with normal BMI.

Meningococcal carriage within households in the African meningitis belt: A longitudinal pilot study.

OBJECTIVES: Carriers of Neisseria meningitidis are a key source of transmission. In the African meningitis belt, where risk of meningococcal disease is highest, a greater understanding of meningococcal carriage dynamics is needed. METHODS: We randomly selected an age-stratified sample of 400 residents from 116 households in Bamako, Mali, and collected pharyngeal swabs in May 2010. A month later, we enrolled all 202 residents of 20 of these households (6 with known carriers) and collected swabs monthly for 6 months prior to MenAfriVac vaccine introduction and returned 10 months later to collect swabs monthly for 3 months. We used standard bacteriological methods to identify N. meningitidis carriers and fit hidden Markov models to assess acquisition and clearance overall and by sex and age. RESULTS: During the cross-sectional study 5.0% of individuals (20/400) were carriers. During the longitudinal study, 73 carriage events were identified from 1422 swabs analyzed, and 16.3% of individuals (33/202) were identified as carriers at least once. The majority of isolates were non-groupable; no serogroup A carriers were identified. CONCLUSIONS: Our results suggest that the duration of carriage with any N. meningitidis averages 2.9 months and that males and children acquire and lose carriage more frequently in an urban setting in Mali. Our study informed the design of a larger study implemented in seven countries of the African meningitis belt.

Phase 2 Placebo-Controlled Trial of Two Vaccines to Prevent Ebola in Liberia.

BACKGROUND: The safety and efficacy of vaccines to prevent Ebola virus disease (EVD) were unknown when the incidence of EVD was peaking in Liberia. METHODS: We initiated a randomized, placebo-controlled, phase 3 trial of the chimpanzee adenovirus 3 vaccine (ChAd3-EBO-Z) and the recombinant vesicular stomatitis virus vaccine (rVSV∆G-ZEBOV-GP) in Liberia. A phase 2 subtrial was embedded to evaluate safety and immunogenicity. Because the incidence of EVD declined in Liberia, the phase 2 component was expanded and the phase 3 component was eliminated. RESULTS: A total of 1500 adults underwent randomization and were followed for 12 months. The median age of the participants was 30 years; 36.6% of the participants were women. During the week after the administration of vaccine or placebo, adverse events occurred significantly more often with the active vaccines than with placebo; these events included injection-site reactions (in 28.5% of the patients in the ChAd3-EBO-Z group and 30.9% of those in the rVSV∆G-ZEBOV-GP group, as compared with 6.8% of those in the placebo group), headache (in 25.1% and 31.9%, vs. 16.9%), muscle pain (in 22.3% and 26.9%, vs. 13.3%), feverishness (in 23.9% and 30.5%, vs. 9.0%), and fatigue (in 14.0% and 15.4%, vs. 8.8%) (P<0.001 for all comparisons); these differences were not seen at 1 month. Serious adverse events within 12 months after injection were seen in 40 participants (8.0%) in the ChAd3-EBO-Z group, in 47 (9.4%) in the rVSV∆G-ZEBOV-GP group, and in 59 (11.8%) in the placebo group. By 1 month, an antibody response developed in 70.8% of the participants in the ChAd3-EBO-Z group and in 83.7% of those in the rVSV∆G-ZEBOV-GP group, as compared with 2.8% of those in the placebo group (P<0.001 for both comparisons). At 12 months, antibody responses in participants in the ChAd3-EBO-Z group (63.5%) and in those in the rVSV∆G-ZEBOV-GP group (79.5%) remained significantly greater than in those in the placebo group (6.8%, P<0.001 for both comparisons). CONCLUSIONS: A randomized, placebo-controlled phase 2 trial of two vaccines that was rapidly initiated and completed in Liberia showed the capability of conducting rigorous research during an outbreak. By 1 month after vaccination, the vaccines had elicited immune responses that were largely maintained through 12 months. (Funded by the National Institutes of Allergy and Infectious Diseases and the Liberian Ministry of Health; PREVAIL I ClinicalTrials.gov number, NCT02344407 .).

Relevance of Interleukin-6 and D-Dimer for Serious Non-AIDS Morbidity and Death among HIV-Positive Adults on Suppressive Antiretroviral Therapy.

BACKGROUND: Despite effective antiretroviral treatment (ART), HIV-positive individuals are at increased risk of serious non-AIDS conditions (cardiovascular, liver and renal disease, and cancers), perhaps due in part to ongoing inflammation and/or coagulation. To estimate the potential risk reduction in serious non-AIDS conditions or death from any cause that might be achieved with treatments that reduce inflammation and/or coagulation, we examined associations of interleukin-6 (IL-6), D-dimer, and high-sensitivity C-reactive protein (hsCRP) levels with serious non-AIDS conditions or death in 3 large cohorts. METHODS: In HIV-positive adults on suppressive ART, associations of IL-6, D-dimer, and hsCRP levels at study entry with serious non-AIDS conditions or death were studied using Cox regression. Hazard ratios (HR) adjusted for age, gender, study, and regression dilution bias (due to within-person biomarker variability) were used to predict risk reductions in serious non-AIDS conditions or death associated with lower "usual" levels of IL-6 and D-dimer. RESULTS: Over 4.9 years of mean follow-up, 260 of the 3766 participants experienced serious non-AIDS conditions or death. IL-6, D-dimer and hsCRP were each individually associated with risk of serious non-AIDS conditions or death, HR = 1.45 (95% CI: 1.30 to 1.63), 1.28 (95% CI: 1.14 to 1.44), and 1.17 (95% CI: 1.09 to 1.26) per 2x higher biomarker levels, respectively. In joint models, IL-6 and D-dimer were independently associated with serious non-AIDS conditions or death, with consistent results across the 3 cohorts and across serious non-AIDS event types. The association of IL-6 and D-dimer with serious non-AIDS conditions or death was graded and persisted throughout follow-up. For 25% lower "usual" IL-6 and D-dimer levels, the joint biomarker model estimates a 37% reduction (95% CI: 28 to 46%) in the risk of serious non-AIDS conditions or death if the relationship is causal. CONCLUSIONS: Both IL-6 and D-dimer are independently associated with serious non-AIDS conditions or death among HIV-positive adults with suppressed virus. This suggests that treatments that reduce IL-6 and D-dimer levels might substantially decrease morbidity and mortality in patients on suppressive ART. Clinical trials are needed to test this hypothesis.

Factors associated with the Journal Impact Factor (JIF) for Urology and Nephrology Journals

Purpose: The Journal Impact Factor (JIF) is an index used to compare a journal's quality among academic journals and it is commonly used as a proxy for journal quality. We sought to examine the JIF in order to elucidate the main predictors of the index while generating awareness among scientific community regarding need to modify the index calculation in the attempt to turn it more accurate. Materials and Methods: Under the Urology and Nephrology category in the Journal Citations Report Website, the top 17 Journals by JIF in 2011 were chosen for the study. All manuscripts’ abstracts published from 2009-2010 were reviewed; each article was categorized based on its research design (Retrospective, Review, etc). T and correlation tests were performed for categorical and continuous variables respectively. The JIF was the dependent variable. All variables were then included in a multivariate model. Results: 23,012 articles from seventeen journals were evaluated with a median of 1,048 (range=78-6,342) articles per journal. Journals with a society affiliation were associated with a higher JIF (p=0.05). Self-citations (rho=0.57, p=0.02), citations for citable articles (rho=0.73, p=0.001), citations to non-citable articles (rho=0.65, p=0.0046), and retrospective studies (rho=-0.51, p=0.03) showed a strong correlation. Slight modifications to include the non-citable articles in the denominator yield drastic changes in the JIF and the ranking of the journals. Conclusion: The JIF appears to be closely associated with the number of citable articles published. A change in the formula for calculating JIF to include all types of published articles in the denominator would result in a more accurate representation.

Adjudicated morbidity and mortality outcomes by age among individuals with HIV infection on suppressive antiretroviral therapy.

BACKGROUND: Non-AIDS conditions such as cardiovascular disease and non-AIDS defining cancers dominate causes of morbidity and mortality among persons with HIV on suppressive combination antiretroviral therapy. Accurate estimates of disease incidence and of risk factors for these conditions are important in planning preventative efforts. METHODS: With use of medical records, serious non-AIDS events, AIDS events, and causes of death were adjudicated using pre-specified criteria by an Endpoint Review Committee in two large international trials. Rates of serious non-AIDS which include cardiovascular disease, end-stage renal disease, decompensated liver disease, and non-AIDS cancer, and other serious (grade 4) adverse events were determined, overall and by age, over a median follow-up of 4.3 years for 3,570 participants with CD4+ cell count ≥300 cells/mm³ who were taking antiretroviral therapy and had an HIV RNA level ≤500 copies/mL. Cox models were used to examine the effect of age and other baseline factors on risk of a composite outcome of all-cause mortality, AIDS, or serious non-AIDS. RESULTS: Five-year Kaplan-Meier estimates of the composite outcome, overall and by age were 8.3% (overall), 3.6% (<40), 8.7% (40-49) and 16.1% (≥50), respectively (p<0.001). In addition to age, smoking and higher levels of interleukin-6 and D-dimer were significant predictors of the composite outcome. The composite outcome was dominated by serious non-AIDS events (overall 65% of 277 participants with a composite event). Most serious non-AIDS events were due to cardiovascular disease and non-AIDS cancers. CONCLUSIONS: To date, few large studies have carefully collected data on serious non-AIDS outcomes. Thus, reliable estimates of event rates are scarce. Data cited here, from a geographically diverse cohort, will be useful for planning studies of interventions aimed at reducing rates of serious non-AIDS events among people with HIV.