Search

Studies suggest that manualized, measurement-guided, depression treatment is more efficacious than usual care but impact can wane. Our study among youth with HIV (YWH), aged 12-24 years at US clinical research sites in the International Maternal Pediatric Adolescent AIDS Clinical Trials Network, found a significant reduction in depressive symptoms among YWH who received a manualized, measurement-guided treatment. This paper reports outcomes up to 24 weeks after the intervention.Eligibility included diagnosis of ongoing nonpsychotic depression. Using restricted randomization, sites were assigned to either combination cognitive behavioral therapy and medication management algorithm tailored for YWH or to enhanced standard of care, which provided psychotherapy and medication management. Site-level mean Quick Inventory for Depression Symptomatology Self-Report (QIDS-SR) scores and proportion of youth with treatment response (gt;50% decrease from baseline) and remission (QIDS-SR ≤ 5) were compared across arms using t tests.Thirteen sites enrolled 156 YWH, with baseline demographic factors, depression severity, and HIV disease status comparable across arms. At week 36, the site-level mean proportions of youth with a treatment response and remission were greater at combination cognitive behavioral therapy and medication management algorithm sites (52.0% vs. 18.8%, P = 0.02; 37.9% vs. 19.4%, P = 0.05), and the mean QIDS-SR was lower (7.45 vs. 9.75, P = 0.05). At week 48, the site-level mean proportion with a treatment response remained significantly greater (58.7% vs. 33.4%, P = 0.047).The impact of manualized, measurement-guided cognitive behavioral therapy and medication management algorithm tailored for YWH that was efficacious at week 24 continued to be evident at weeks 36 and 48.

Tenofovir disoproxil fumarate (TDF) in combination with other antiretroviral (ARV) drugs has been in clinical use for HIV treatment since its approval in 2001. Although the effectiveness of TDF in preventing perinatal HIV infection is well established, information about renal safety during pregnancy is still limited.The IMPAACT PROMISE study was an open-label, strategy trial that randomized pregnant women to one of three arms: TDF based antiretroviral therapy (ART), zidovudine (ZDV) based ART, and ZDV alone (standard of care at start of enrollment). The P1084s substudy was a nested, comparative study of renal outcomes in women and their infants.PROMISE participants (n = 3543) were assessed for renal dysfunction using calculated creatinine clearance (CrCl) at study entry ( 14 weeks gestation), delivery, and postpartum weeks 6, 26, and 74. Of these women, 479 were enrolled in the P1084s substudy that also assessed maternal calcium and phosphate as well as infant calculated CrCl, calcium, and phosphate at birth.Among the 1338 women who could be randomized to TDF, less than 1% had a baseline calculated CrCl below 80 mL/min. The mean (standard deviation) maternal calculated CrCl at delivery in the TDF-ART arm [147.0 mL/min (51.4)] was lower than the ZDV-ART [155.0 mL/min (43.3); primary comparison] and the ZDV Alone [158.5 mL/min (45.0)] arms; the mean differences (95% confidence interval) were - 8.0 mL/min (- 14.5, - 1.5) and - 11.5 mL/min (- 18.0, - 4.9), respectively. The TDF-ART arm had lower mean maternal phosphate at delivery compared with the ZDV-ART [- 0.14 mg/dL (- 0.28, - 0.01)] and the ZDV Alone [- 0.17 mg/dL (- 0.31, - 0.02)] arms, and a greater percentage of maternal hypophosphatemia at delivery (4.23%) compared with the ZDV-ART (1.38%) and the ZDV Alone (1.46%) arms. Maternal calcium was similar between arms. In infants, mean calculated CrCl, calcium, and phosphate at birth were similar between arms (all CIs included 0).Although mean maternal calculated CrCl at Delivery was lower in the TDF-ART arm, the difference between arms is unlikely to be clinically significant. During pregnancy, the TDF-ART regimen had no observed safety concerns for maternal or infant renal function.NCT01061151 on 10/02/2010 for PROMISE (1077BF). NCT01066858 on 10/02/2010 for P1084s.

PURPOSE: To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events. MATERIALS AND METHODS: Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002–2005) and were followed for more than a median of 9.1 years for composite major events. A total of 624 participants with CAC Agatston score (AS) of greater than 0 and good or excellent CT image quality were included for manual CAC segmentation and extraction of a predefined set of radiomic features reflecting intensity, shape, and texture. In a discovery cohort (n = 318), machine learning was used to select the 20 most informative and nonredundant CAC radiomic features, classify features predicting events, and define a radiomic-based score (RS). Performance of the RS was tested independently for the prediction of events in a validation cohort (n = 306). RESULTS: The RS had a median value of 0.08 (interquartile range, 0.007–0.71) and a weak and modest correlation with Framingham risk score (FRS) (r = 0.2) and AS (r = 0.39), respectively. The continuous RS unadjusted, adjusted for age and sex, FRS, AS, and FRS plus AS were significantly associated with events (hazard ratio [HR] = 2.2, P < .001; HR = 1.8, P = .002; HR = 2.0, P < .001; HR = 1.7, P = .02; and HR = 1.8, P = .01, respectively). In participants with AS of less than 300, RS association with events remained significant when unadjusted and adjusted for age and sex, FRS, AS, and FRS plus AS (HR = 2.4, 2.8, 2.8, 2.3, and 2.6; P < .001, respectively). In the same subgroup of participants, adding the RS to AS resulted in a significant improvement in the discriminatory ability for events as compared with the AS (area under the receiver operating curve: 0.80 vs 0.68, respectively; P = .03). CONCLUSION: A radiomic-based score, including the complex properties of CAC, may constitute an imaging biomarker to be further developed to identify individuals at risk for major adverse cardiovascular events in a community-based cohort. Supplemental material is available for this article. © RSNA, 2020

Background Flu Near You (FNY) is an online participatory syndromic surveillance system that collects health-related information. In this article, we summarized the healthcare-seeking behavior of FNY participants who reported influenza-like illness (ILI) symptoms. Methods We applied inverse probability weighting to calculate age-adjusted estimates of the percentage of FNY participants in the United States who sought health care for ILI symptoms during the 2015–2016 through 2018–2019 influenza season and compared seasonal trends across different demographic and regional subgroups, including age group, sex, census region, and place of care using adjusted χ 2 tests. Results The overall age-adjusted percentage of FNY participants who sought healthcare for ILI symptoms varied by season and ranged from 22.8% to 35.6%. Across all seasons, healthcare seeking was highest for the Conclusions The percentage of FNY participants who sought healthcare for ILI symptoms varied by season, geographical region, age group, and sex. FNY compliments existing surveillance systems and informs estimates of influenza-associated illness by adding important real-time insights into healthcare-seeking behavior.

To investigate the association between directly measured density and morphology of coronary artery calcium (CAC) with cardiovascular disease (CVD) events, using computed tomography (CT). Framingham Heart Study (FHS) participants with CAC in noncontrast cardiac CT (2002–2005) were included and followed until 2016. Participants with known CVD or uninterpretable CT scans were excluded. We assessed and correlated (Spearman) CAC density, CAC volume, and the number of calcified segments. Moreover, we counted morphology features including shape (cylindrical, spherical, semi-tubular, and spotty), location (bifurcation, facing pericardium, or facing myocardium), and boundary regularity. In multivariate Cox regression analyses, we associated all CAC characteristics with CVD events (CVD-death, myocardial infarction, stroke). Among 1330 included participants (57.8 ± 11.7 years; 63% male), 73 (5.5%) experienced CVD events in a median follow-up of 9.1 (7.8–10.1) years. CAC density correlated strongly with CAC volume (Spearman’s ρ = 0.75; p

Background The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. Objective The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas. Methods We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care–seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care–seeking percentages and baselines for each surveillance data source. Results We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines. Conclusions Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.

Homozygosity for rs334 (GAG-GTG, glu6val) or sickle cell anemia is common in Saudi Arabia. In the Eastern Province the sickle hemoglobin (HbS) gene is usually on the autochthonous Arab Indian (AI) β-globin gene (HBB) haplotype [1]. The higher fetal hemoglobin (HbF) level in the AI haplotype is often associated with milder disease compared with other HbS-associated haplotypes, especially in children [2, 3]. The Xmn1 G-A (C-T) polymorphism (rs7482144) is present in both the AI haplotype and the Senegal haplotype. Rs10128556, which is in linkage disequilibrium (LD) with rs7482144, could be the functional SNP of the Senegal haplotype [4]. The minor alleles of these SNPs also characterize the AI haplotype where adults had ~20% HbF compared with ~10% HbF in the Senegal haplotype. This suggests that elements linked to rs10128556 and rs7482144 in the AI but not the Senegal haplotype might affect HbF gene expression. We compared the genome-wide distribution of SNPs in Saudi patients with the AI haplotype and Benin haplotype. Variants distinguishing these populations were limited to chromosome 11p15.5. Annotation of these SNPs narrowed the selection to 3 that we analyzed jointly using haplotype analysis. Homozygosity for a T/A/T haplotype of rs16912979 (in HS-4 of the LCR), rs7482144 and rs10128556 was exclusive to the AI haplotype. The AI haplotype might include a functionally important sub-haplotype that accounts for high HbF.

BACKGROUND The Centers for Disease Control and Prevention (CDC) track influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). Because participation in this system is voluntary, the composition, coverage, and consistency of healthcare reports varies from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. OBJECTIVE We qualitatively and quantitatively compare national and region-specific ILI activity in the United States (US) across four data sources: CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compare the yearly percent of FNY participants who sought health-care for ILI symptoms across geographical areas. METHODS We compare the national and regional 2018 ILI activity baselines, calculated using non-influenza weeks from previous years, for each surveillance data source. We also compare measures of ILI activity across geographical areas during three influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source are assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health-care seeking percents are calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. RESULTS We observe consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, while differences in ILI activity for HealthTweets.org appear to be associated with the total number of Tweets within a geographical area. The percent of FNY participants seeking health-care for ILI symptoms differs slightly across geographical areas. Specifically, regions with higher health-care seeking percentages correspond to regions with higher CDC ILINet and athenahealth ILI activity. CONCLUSIONS Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and/or aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.

PURPOSE. Children with Hutchinson-Gilford progeria syndrome (HGPS), a rare premature aging disease, exhibit extraskeletal calcifications detected by radiographic analysis and on physical examination. The aim of this study was to describe the natural history and pathophysiology of these abnormal calcifications in HGPS, and to determine whether medications and/or supplements tested in clinical trials alter their development. METHODS. Children from two successive clinical trials administering 1) lonafarnib (n=26) and 2) lonafarnib + pravastatin + zoledronic acid (n=37) were studied at baseline (pre-therapy), one year on therapy, and at end-of-therapy (3.3–4.3 years after the baseline visit). Calcium supplementation (oral calcium carbonate) was administered during the first year of the second trial and was subsequently discontinued. Information on calcifications was obtained from physical examinations, radiographs, and serum and urinary biochemical measures. The mineral content of two skin-derived calcifications was determined by x-ray diffraction. RESULTS. Extraskeletal calcifications were detected radiographically in 12/39 (31%) patients at baseline. The odds of exhibiting calcifications increased with age (p=0.045). The odds were unaffected by receipt of lonafarnib, pravastatin, and zoledronate therapies. However, administration of calcium carbonate supplementation, in conjunction with all three therapeutic agents, significantly increased the odds of developing calcifications (p=0.009), with the odds plateauing after the supplement’s discontinuation. Composition analysis of calcinosis cutis showed hydroxyapatite similar to bone. Although serum calcium, phosphorus, and parathyroid hormone (PTH) were within normal limits at baseline and on-therapy, PTH increased significantly after lonafarnib initiation (p

Objective To estimate and compare influenza attack rates (AR) in the United States (US) using different approaches to adjust for reporting biases in participatory syndromic surveillance data. Introduction Because the dynamics and severity of influenza in the US vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. The CDC uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. Over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time. These systems complement traditional surveillance systems by capturing individuals who do not seek medical care and allowing for a longitudinal view of illness burden. However, because not all participants report every week and participants are more likely to report when ill, the number of weekly reports is temporally and spatially inconsistent and the estimates of disease burden and incidence may be biased. In this study, we use data from Flu Near You (FNY), a participatory surveillance system based in the US and Canada 1 , to estimate and compare Influenza-like Illness (ILI) ARs using different approaches to adjust for reporting biases in participatory surveillance data. Methods This analysis uses FNY data from the 2015-16 influenza season. Four different approaches of bias adjustment were assessed. The first approach includes all FNY participants, defined as users and household members, who submitted at least one symptom report, whereas the second approach only includes FNY participants who submitted at least 10 symptom reports. The third approach includes all FNY participants who submitted at least one symptom report, but drops the first symptom report for all participants. For the first three approaches, all missing reports were assumed to be non-ILI when estimating attack rates. Finally, the fourth approach includes FNY participants who submitted at least 10 symptom reports and uses multiple imputation to account for missing reports. Age-stratified and overall estimates of ILI ARs were calculated for each of the four approaches to bias adjustment by dividing the sum of the weekly incident cases of ILI, defined as the first report of fever with cough and/or sore throat, by the population at risk at the beginning of the period. Results During the 2016-2017 influenza season, FNY received an average of 10,723 unique symptom reports per week from 46,390 registered users and their household members. For FNY, the youngest age group assessed, 5-17, had the largest ILI AR, and the ILI ARs decreased as the age group increased for all approaches. Overall, the approach that drops all first reports had the smallest ARs, whereas the approach that selects a cohort of users who submit at least 10 reports during the season and imputes the missing reports had the largest ARs. Although the influenza ARs estimated by the CDC were less than the ILI ARs estimated using FNY data for all age-groups, a similar pattern was observed across age groups, except for the 50-64 age group, which had the largest influenza AR. Conclusions As expected, the ARs estimated using FNY data were greater than the CDC’s influenza ARs because FNY estimates ARs of ILI and does not adjust for the probability of reporting ILI when experiencing non-flu illness. The approach of dropping the first report had the smallest ARs because during the 2015-16 influenza season the weekly percent of ILI cases that were first time reports ranged from 18-59%. This approach was developed to adjust for the potential correlation between symptom presence and willingness to join the platform. However, important information about the dynamics of disease may be lost when using this approach. The multiple imputation method was used only for individuals who submitted at least 10 reports to maintain a missing data rate below 30%. The imputation model also assumed that data were missing at random, which may not be appropriate in this case, because approximately 30% of FNY users have reported that they are more likely to report when ill. As shown in Table 1, the AR estimate depends on the bias adjustment approach. Simulation-based studies should be performed to further evaluate these methods. References 1. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wojcik O, et al. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015 2. Rolfes MA, Foppa IM, Garg S, Flannery B, Brammer L, Singleton JA, et al. Estimated Influenza Illnesses, Medical Visits, Hospitalizations, and Deaths Averted by Vaccination in the United States. 2016 Dec 9 [2017 Sept 25]; https://www.cdc.gov/flu/about/disease/2015-16.htm

Background: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Objective: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care–based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. Methods: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Results: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. Conclusions: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions.

Study Objective This study identified the prevalence of menarche and coincident sexual characteristics in female adolescents with Hutchinson-Gilford Progeria Syndrome (HGPS). Design Data were examined to determine the prevalence of menarche in female adolescents older than 12 years; all were participants in clinical trials between 2007 and 2016. Setting Pediatric hospital in Boston, Massachusetts. Participants Fifteen female adolescents, median age 15 (range, 12.0-20.3) years with a confirmed diagnosis of HGPS. Interventions and Main Outcome Measures Report of menarche, anthropometric and serum hormonal measures, Tanner pubertal staging, and body composition using dual-energy x-ray absorptiometry. Results Nine of 15 (60%) participants reported spontaneous menarche at a median age of 14.4 years (range, 12.0-16.5 years). In those experiencing menarche vs not, median age was older (16.5 vs 13.6 years; P = .02), whereas body mass index did not differ (10.5 vs 10.4; P = .53) nor percentage body fat (19.4% vs. 19.3%; P = .98) or serum leptin levels (0.40 vs 0.40 ng/mL; P = .23). Among those who achieved menarche, 2 of 9 (22%) had Tanner II breast development and 2 of 9 (22%) exhibited Tanner II Pubic hair, all reflecting minimal pubertal development. Only early signs of puberty were similarly seen in the non-menstruating group, including 1 of 6 (17%) with Tanner II breasts and 2 of 6 (33%) with Tanner II pubic hair, and Tanner staging did not differ between those who reported menarche vs those who did not (each P = 1.0). None of the participants achieved Tanner IV or V thelarche over the course of the study. Conclusion Menarche was achieved in more than half of adolescent girls with HGPS, in the setting of little to no physical signs of pubertal development and minimal body fat.

Background— The presence and extent of coronary artery calcium (CAC) are associated with increased risk for cardiovascular events. We determined whether information on the distribution of CAC and coronary dominance as detected by cardiac computed tomography were incremental to traditional Agatston score (AS) in predicting incident major coronary heart disease (CHD). Methods and Results— We assessed total AS and the presence of CAC per coronary artery, per segment, and coronary dominance by computed tomography in participants from the offspring and third-generation cohorts of the Framingham Heart Study. The primary outcome was major CHD (myocardial infarction or CHD death). We performed multivariable Cox proportional hazards analysis and calculated relative integrated discrimination improvement. In 1268 subjects (mean age, 56.2±10.3 years, 63.2% men) with AS >0 and no history of major CHD, a total of 42 major CHD events occurred during median follow-up of 7.4 years. The number of coronary arteries with CAC (hazard ratio, 1.68 per artery; 95% confidence interval, 1.10–2.57; P =0.02) and the presence of CAC in the proximal dominant coronary artery (hazard ratio, 2.59; 95% confidence interval, 1.15–5.83; P =0.02) were associated with major CHD events after multivariable adjustment for Framingham risk score and categories of AS. In addition, measures of CAC distribution improved discriminatory capacity for major CHD events (relative integrated discrimination improvement, 0.14). Conclusions— Distribution of coronary atherosclerosis, especially CAC in the proximal dominant coronary artery and an increased number of coronary arteries with CAC, predict major CHD events independently of the traditional AS in community-dwelling men and women.

Background Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. Objective Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY’s high-participation users, and summarize results from a user survey of a cohort of FNY participants. Methods We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY’s 2015 and 2016 end-of-season user surveys were calculated. Results During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P

Objectives. We summarized Flu Near You (FNY) data from the 2012–2013 and 2013–2014 influenza seasons in the United States. Methods. FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenza-like illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012–2013 and 2013–2014 seasons. We compared raw and noise-filtered ILI rates with ILI rates from the Centers for Disease Control and Prevention ILINet surveillance system. Results. More than 61 000 participants submitted at least 1 report during the 2012–2013 season, totaling 327 773 reports. Nearly 40 000 participants submitted at least 1 report during the 2013–2014 season, totaling 336 933 reports. Rates of ILI as reported by FNY tracked closely with ILINet in both timing and magnitude. Conclusions. With increased participation, FNY has the potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems. Although many established systems have the benefits of specificity and credibility, participatory systems offer advantages in the areas of speed, sensitivity, and scalability.