Comparative evaluation of methods that adjust for reporting biases in participatory surveillance systems

Over the past decade the widespread proliferation of mobile devices and wearable technology has significantly changed the landscape of epidemiological data gathering and evolved into a field known as Digital Epidemiology. One source of active digital data collection is online participatory syndromic surveillance systems. These systems actively engage the general public in reporting health-related information and provide timely information about disease trends within the community. This dissertation comprehensively addresses how researchers can effectively use this type of data to answer questions about Influenza-like Illness (ILI) disease burden in the general population. We assess the representativeness and reporting habits of volunteers for these systems and use this information to develop statistically rigorous methods that adjust for potential biases. Specifically, we evaluate how different missing data methods, such as complete case and multiple imputation models, affect estimates of ILI disease burden using both simulated data as well as data from the Australian system, Flutracking.net. We then extend these methods to data from the American system, Flu Near You, which has different patterns. Finally, we provide examples of how this data has been used to answer questions about ILI in the general community and promote better understanding of disease surveillance and data literacy among volunteers

Maternal and infant renal safety following tenofovir disoproxil fumarate exposure during pregnancy in a randomized control trial

Background 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. Trial design 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. Methods 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. Results 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). Conclusions 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. Trial Registration: NCT01061151 on 10/02/2010 for PROMISE (1077BF). NCT01066858 on 10/02/2010 for P1084s

Evaluation of approaches that adjust for biases in participatory surveillance systems

ObjectiveTo 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.IntroductionBecause 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 Canada1, to estimate and compare Influenza-like Illness (ILI) ARs using different approaches to adjust for reporting biases in participatory surveillance data.MethodsThis 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.ResultsDuring 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.ConclusionsAs 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.References1. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wójcik O, et al. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 20152. 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

Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System

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<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor’s degree or higher. Conclusions: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data

Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons

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

Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America

Abstract Background Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems. Methods We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012–16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution. Results In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity. Conclusions With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions

Density and morphology of coronary artery calcium for the prediction of cardiovascular events:insights from the Framingham Heart Study

Objectives To investigate the association between directly measured density and morphology of coronary artery calcium (CAC) with cardiovascular disease (CVD) events, using computed tomography (CT). Methods 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). Results 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 rho = 0.75; p <0.001) and lower number of calcified segments (rho = - 0.86; p <0.001; controlled for CAC volume). In the survival analysis, CAC density was associated with CVD events independent of Framingham risk score (HR (per SD) = 2.09; 95%CI, 1.30-3.34; p = 0.002) but not after adjustment for CAC volume (p = 0.648). The extent of spherically shaped and pericardially sided calcifications was associated with fewer CVD events accounting for the number of calcified segments (HR (per count) = 0.55; 95%CI, 0.31-0.98; p = 0.042 and HR = 0.66; 95%CI, 0.45-0.98; p = 0.039, respectively). Conclusions Directly measured CAC density does not predict CVD events due to the strong correlation with CAC volume. The spherical shape and pericardial-sided location of CAC are associated with fewer CVD events and may represent morphological features related to stable coronary plaques

Nanocrystals of a Metal–Organic Complex Exhibit Remarkably High Conductivity that Increases in a Single-Crystal-to-Single-Crystal Transformation

Ag­(I) is used to form a π-stacked metal–organic solid that exhibits remarkably high electrical conductivity. The solid undergoes a single-crystal-to-single-crystal [2+2] photodimerization to generate a 1D coordination polymer with over 40% higher conductivity. The Ag­(I) complex represents the first example of an increase in conductivity resulting from a [2+2] photodimerization. Density of states calculations show a higher contribution from Ag­(I) ions to the valence band in the photodimerized solid, supporting the increase in conductivity

Prevalence of neurotoxicity symptoms among postpartum women on isoniazid preventive therapy and efavirenz-based treatment for HIV: an exploratory objective of the IMPAACT P1078 randomized trial

Abstract Background This exploratory analysis investigates the prevalence and risk factors of neurocognitive toxicity in postpartum women on HIV treatment in response to a concern of an Isoniazid Preventive Therapy (IPT)/Efavirenz interaction. Trial Design Pregnant women on HIV treatment from countries with high TB prevalence were randomized in IMPAACT P1078 to 28 weeks of IPT started either during pregnancy or at 12 weeks postpartum. Partway through study implementation, the Patient Health Questionnaire 9, the cognitive complaint questionnaire, and the Pittsburg Sleep Quality Index were added to evaluate depression, cognitive function, and sleep quality at postpartum weeks. Screening for peripheral neuropathy was conducted throughout the study. Methods We summarized percentages of women with depression symptoms, cognitive dysfunction, poor sleep quality and peripheral neuropathy and assessed the association of 11 baseline risk factors of neurotoxicity using logistic regression, adjusted for gestational age stratum. Results Of 956 women enrolled, 749 (78%) had at least one neurocognitive evaluation. During the postpartum period, the percentage of women reporting at least mild depression symptoms, cognitive complaint and poor sleep quality peaked at 13%, 8% and 10%, respectively, at 12 weeks, and the percentage of women reporting peripheral neuropathy peaked at 13% at 24 weeks. There was no evidence of study arm differences in odds of all four neurotoxic symptoms. Conclusions Timing of IPT initiation and EFV use were not associated with symptoms of neurotoxicity. Further study is advised to formally assess risk factors of neurotoxicity