Ten-year cancer incidence in rescue/recovery workers and civilians exposed to the September 11, 2001 terrorist attacks on the World Trade Center

Background: Cancer incidence in exposed rescue/recovery workers (RRWs) and civilians (non-RRWs) was previously reported through 2008. Methods: We studied occurrence of first primary cancer among World Trade Center Health Registry enrollees through 2011 using adjusted standardized incidence ratios (SIRs), and the WTC-exposure-cancer association, using Cox proportional hazards models. Results: All-cancer SIR was 1.11 (95% confidence interval (CI) 1.03–1.20) in RRWs, and 1.08 (95% CI 1.02–1.15) in non-RRWs. Prostate cancer and skin melanoma were significantly elevated in both populations. Thyroid cancer was significantly elevated only in RRWs while breast cancer and non-Hodgkin's lymphoma were significantly elevated only in non-RRWs. There was a significant exposure dose-response for bladder cancer among RRWs, and for skin melanoma among non-RRWs. Conclusions: We observed excesses of total and specific cancers in both populations, although the strength of the evidence for causal relationships to WTC exposures is somewhat limited. Continued monitoring of this population is indicated

Monitoring Temporal Changes in the Specificity of an Oral HIV Test: A Novel Application for Use in Postmarketing Surveillance

BACKGROUND: Postmarketing surveillance is routinely conducted to monitor performance of pharmaceuticals and testing devices in the marketplace. However, these surveillance methods are often done retrospectively and, as a result, are not designed to detect issues with performance in real-time. METHODS AND FINDINGS: Using HIV antibody screening test data from New York City STD clinics, we developed a formal, statistical method of prospectively detecting temporal clusters of poor performance of a screening test. From 2005 to 2008, New York City, as well as other states, observed unexpectedly high false-positive (FP) rates in an oral fluid-based rapid test used for screening HIV. We attempted to formally assess whether the performance of this HIV screening test statistically deviated from both local expectation and the manufacturer's claim for the test. Results indicate that there were two significant temporal clusters in the FP rate of the oral HIV test, both of which exceeded the manufacturer's upper limit of the 95% CI for the product. Furthermore, the FP rate of the test varied significantly by both STD clinic and test lot, though not by test operator. CONCLUSIONS: Continuous monitoring of surveillance data has the benefit of providing information regarding test performance, and if conducted in real-time, it can enable programs to examine reasons for poor test performance in close proximity to the occurrence. Techniques used in this study could be a valuable addition for postmarketing surveillance of test performance and may become particularly important with the increase in rapid testing methods

Case Fatality Rates Based on Population Estimates of Influenza-Like Illness Due to Novel H1N1 Influenza: New York City, May–June 2009

BACKGROUND: The public health response to pandemic influenza is contingent on the pandemic strain's severity. In late April 2009, a potentially pandemic novel H1N1 influenza strain (nH1N1) was recognized. New York City (NYC) experienced an intensive initial outbreak that peaked in late May, providing the need and opportunity to rapidly quantify the severity of nH1N1. METHODS AND FINDINGS: Telephone surveys using rapid polling methods of approximately 1,000 households each were conducted May 20-27 and June 15-19, 2009. Respondents were asked about the occurrence of influenza-like illness (ILI, fever with either cough or sore throat) for each household member from May 1-27 (survey 1) or the preceding 30 days (survey 2). For the overlap period, prevalence data were combined by weighting the survey-specific contribution based on a Serfling model using data from the NYC syndromic surveillance system. Total and age-specific prevalence of ILI attributed to nH1N1 were estimated using two approaches to adjust for background ILI: discounting by ILI prevalence in less affected NYC boroughs and by ILI measured in syndromic surveillance data from 2004-2008. Deaths, hospitalizations and intensive care unit (ICU) admissions were determined from enhanced surveillance including nH1N1-specific testing. Combined ILI prevalence for the 50-day period was 15.8% (95% CI:13.2%-19.0%). The two methods of adjustment yielded point estimates of nH1N1-associated ILI of 7.8% and 12.2%. Overall case-fatality (CFR) estimates ranged from 0.054-0.086 per 1000 persons with nH1N1-associated ILI and were highest for persons>or=65 years (0.094-0.147 per 1000) and lowest for those 0-17 (0.008-0.012). Hospitalization rates ranged from 0.84-1.34 and ICU admission rates from 0.21-0.34 per 1000, with little variation in either by age-group. CONCLUSIONS: ILI prevalence can be quickly estimated using rapid telephone surveys, using syndromic surveillance data to determine expected "background" ILI proportion. Risk of severe illness due to nH1N1 was similar to seasonal influenza, enabling NYC to emphasize preventing severe morbidity rather than employing aggressive community mitigation measures

The Effects of Changes in Physical Fitness on Academic Performance Among New York City Youth

To evaluate whether a change in fitness is associated with academic outcomes in New York City (NYC) middle school students using longitudinal data, and to evaluate whether this relationship is modified by student household poverty

Environment and Planning A

spatially censored dat

Influenza Surveillance Using Wearable Mobile Health Devices

ObjectiveTo describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.IntroductionInfluenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health [1]. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns [2]. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver “faster, more locally relevant” surveillance systems [3].MethodsDuring the 2017/2018 influenza season, surveys were conducted within the Achievement platform, a health app that integrates with a variety of wearable trackers and consumer health applications [4]. The Achievement population has given consent agreeing to participation in studies like the one presented here and permitting access to their data. Surveys queried users as to whether they had experienced flu-like (ILI) symptoms in the preceding 14 days. Respondents who had experienced symptoms were then asked to identify symptom days. Those who had not experienced symptoms were queried again two weeks later. Positive responses were re-indexed to align by date of symptom onset. Individual respondent’s measures were standardized on a per-individual level in the 6 week period centered on the index date. Population-level mean signals were directly computed across several dimensions including steps, sleep, and heart rate. Uncertainty was quantified using resampling.ResultsBeginning February 17th, 2018, surveys were distributed to Achievement users. Within the first week 31,934 users had responded to the survey. Over a 12-week period, 124,892 individuals completed the survey with 25,512 reporting flu-like symptoms in a two week period prior to the survey. Of these, 9,495 had wearable device data in the 90-day window surrounding their symptom dates and 3,362 respondents had “dense” data defined as no more than 4 consecutive missing days in the 6-week period surrounding the index date.Population-level signals to ILI were clearly evident for five measures across the three dimensions. Step count [fig. 1] and time spent active [fig. 2] decreased 1 day prior to reported symptom onset date (index date), with a minimum at day 2 of -.24 std. dev. for step count and -.25 std. dev for time spent active, and a return to baseline at day 8. Sleeplessness [fig.3] and time spent in bed [fig. 4] increased one day prior to index, peaking 4 days after index at a mean increase of .16 std. dev. for sleeplessness and .13 std. dev. for time spent in bed, and returning to baseline at 7 days. Heart rate was elevated from 1 day before index to day 6 with a peak increase of .18 std. dev. on days 2 and 3 after index.ConclusionsThe potential of mHealth devices to register illness has been recognized [5]. This study is the first to present population-level influenza signals in a large network of mHealth users. Mobile health device data linked to ILI-specific survey responses taken during the 2017/18 flu season demonstrate clear aggregate patterns across several dimensions including sleep, steps, and heart rate. These signals suggest the potential for systems to rapidly process individual-level responses to classify ILI and to use such classifiers for ILI surveillance. The data described here, high resolution individual-level behavioral and physiological data linked to timely survey responses, suggests the potential to further enhance outbreak detection and improve characterization of ILI patterns. The setting of our study, a very large network of mobile health device users who have consented to the prospective use of their data and to being queried about their health status, could provide a framework for automated prospective influenza surveillance using “real world evidence” [6]. Employed over a population-representative sample, this approach could provide adjunct to standard clinically-based sentinel systems.References[1] Althouse, Benjamin M., et al. "Enhancing disease surveillance with novel data streams: challenges and opportunities." EPJ Data Science 4.1 (2015): 17.[2] Henning KJ. What is syndromic surveillance?. Morbidity and Mortality Weekly Report. 2004 Sep 24:7-11[3] Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. The Journal of infectious diseases. 2016 Nov 14;214(suppl_4):S380-5.[4] https://www.myachievement.com/[5] Li, Xiao, et al. "Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information." PLoS biology 15.1 (2017): e2001402.[6]https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.ht

Physical fitness disparities among New York City public school youth using standardized methods, 2006-2017.

Standardized physical fitness monitoring provides a more accurate proxy for youth health when compared with physical activity. Little is known about the utilization of broad-scale individual-level youth physical fitness testing to explore health disparities. We examined longitudinal trends in population-level fitness for 4th-12th grade New York City youth during 2006/7-2016/17 (average n = 510,293 per year). Analyses were performed in 2019. The primary outcome was whether or not youth achieved sex-/age-specific performance levels (called the Healthy Fitness Zone) on the aerobic capacity, muscular strength and muscular endurance tests using the NYC FITNESSGRAM. The Cooper Institute's most recent Healthy Fitness Zone criteria were applied to all tests and years. Prevalence estimates were weighted, accounted for school clustering, adjusted for student-level sociodemographics, and run by sociodemographic subgroups and year. The overall prevalence for meeting 3 Healthy Fitness Zones increased from 15.5% (95%CI: 13.9%-17.0%) in 2006/7 to 23.3% (95%CI: 22.2%-24.4%) in 2016/17 for students in grades 4-12. Fitness for all student groups increased over time, although Hispanic and non-Hispanic black girls consistently had the lowest prevalence of meeting 3 Healthy Fitness Zones as compared to all other race/sex subgroups. Also, 9th-12th graders had a lower prevalence of meeting 3 Healthy Fitness Zones as compared to 4th-8th graders. Given forecasted sharp increases in cardiovascular disease prevalence, routine youth fitness surveillance using standardized, criterion referenced methods can identify important fitness disparities and inform interventions