{"391101":{"#nid":"391101","#data":{"type":"event","title":"Phd Defense by Sue Reynolds","body":[{"value":"\u003Cp\u003EPhd Defense by \u003Cstrong\u003ESue Reynolds\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;Title:\u0026nbsp; \u003Cstrong\u003EStatistical Estimation and Changepoint Detection Methods in Public Health Surveillance.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAdvisors:\u0026nbsp; Dr. David Goldsman,\u0026nbsp; Dr. Kwok-Leung Tsui (City University of Hong Kong)\u003C\/p\u003E\u003Cp\u003ECommittee members:\u0026nbsp; Dr. Christos Alexopoulos,\u0026nbsp; Dr. Xiaoming Huo,\u0026nbsp; Dr. Brani Vidakovic,\u0026nbsp; Dr. David Shay (Centers for Disease Control and Prevention)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;Date and time:\u0026nbsp; Wednesday,\u0026nbsp;April 1, 2015,\u0026nbsp; 9:00am\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;Location:\u0026nbsp; Groseclose 204 - Academic Conference Room\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;This thesis focuses on assessing and improving statistical methods implemented in two areas of public health research.\u0026nbsp; The first topic involves estimation of national influenza-associated mortality rates via mathematical modeling.\u0026nbsp; The second topic involves the timely detection of infectious disease outbreaks using statistical process control monitoring.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;For over fifty years, the Centers for Disease Control and Prevention has been estimating annual rates of U.S. deaths attributable to influenza.\u0026nbsp; These estimates have been used to determine costs and benefits associated with influenza prevention and control strategies.\u0026nbsp; Quantifying the effect of influenza on mortality, however, can be challenging since influenza infections typically are not confirmed virologically nor specified on death certificates.\u0026nbsp; Consequently, a wide range of ecologically based, mathematical modeling approaches have been applied to specify the association between influenza and mortality.\u0026nbsp; To date, all influenza-associated death estimates have been based on mortality data first aggregated at the national level and then modeled.\u0026nbsp; Unfortunately, there are a number of local-level seasonal factors that may confound the association between influenza and mortality\u0026nbsp;\u0026nbsp;thus suggesting that data be modeled at the local level and then pooled to make national estimates of death.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe first component of the thesis topic involving mortality estimation addresses this issue by introducing and implementing a two-stage hierarchical Bayesian modeling approach.\u0026nbsp; In the first stage, city-level data with varying trends in mortality and weather were modeled using semi-parametric, generalized additive models.\u0026nbsp; In the second stage, the log-relative risk estimates calculated for each city in stage 1 represented the \u201coutcome\u201d variable, and were modeled two ways: (1) assuming spatial independence across cities using a Bayesian generalized linear model, and (2) assuming correlation among cities using a Bayesian spatial correlation model.\u0026nbsp; Results from these models were compared to those from a more-conventional approach.\u003C\/p\u003E\u003Cp\u003EThe second component of this topic examines the extent to which seasonal confounding and collinearity affect the relationship between influenza and mortality at the local (city) level.\u0026nbsp; Disentangling the effects of temperature, humidity, and other seasonal confounders on the association between influenza and mortality is challenging since these covariates are often temporally collinear with influenza activity.\u0026nbsp; Three modeling strategies with varying representations of background seasonality were compared.\u0026nbsp; Seasonal covariates entered into the model may have been measured (e.g., ambient temperature) or unmeasured (e.g., time-based smoothing splines or Fourier terms).\u0026nbsp; An advantage of modeling background seasonality via time splines is that the amount of seasonal curvature can be controlled by the number of degrees of freedom specified for the spline.\u0026nbsp; A comparison of the effects of influenza activity on mortality based on these varying representations of seasonal confounding is assessed.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe third component of this topic explores the relationship between mortality rates and influenza activity using a flexible, natural cubic spline function to model the influenza term.\u0026nbsp; The conventional approach of fitting influenza-activity terms linearly in regression was found to be too constraining.\u0026nbsp; Results show that the association is best represented nonlinearly.\u003C\/p\u003E\u003Cp\u003EThe second area of focus in this thesis involves infectious disease outbreak detection.\u0026nbsp; A fundamental goal of public health surveillance, particularly syndromic surveillance, is the timely detection of increases in the rate of unusual events.\u0026nbsp; In syndromic surveillance, a significant increase in the incidence of monitored disease outcomes would trigger an alert, possibly prompting the implementation of an intervention strategy.\u0026nbsp; Public health surveillance generally monitors count data (e.g., counts of influenza-like illness, sales of over-the-counter remedies, and number of visits to outpatient clinics).\u0026nbsp; Statistical process control charts, designed for quality control monitoring in industry, have been widely adapted for use in disease and syndromic surveillance.\u0026nbsp; The behavior of these detection methods on discrete distributions, however, has not been explored in detail.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFor this component of the thesis, a simulation study was conducted to compare the CuSum and EWMA methods for detection of increases in negative binomial rates with varying amounts of dispersion.\u0026nbsp; The goal of each method is to detect an increase in the mean number of cases as soon as possible after an upward rate shift has occurred.\u0026nbsp; The performance of the CuSum and EWMA detection methods is evaluated using the conditional expected delay criterion, which is a measure of the detection delay, i.e., the time between the occurrence of a shift and when that shift is detected.\u0026nbsp; Detection capabilities were explored under varying shift sizes and times at which the shifts occurred.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Statistical Estimation and Changepoint Detection Methods in Public Health Surveillance."}],"uid":"28077","created_gmt":"2015-03-26 15:58:13","changed_gmt":"2016-10-08 01:49:04","author":"Danielle Ramirez","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2015-04-01T10:00:00-04:00","event_time_end":"2015-04-01T12:00:00-04:00","event_time_end_last":"2015-04-01T12:00:00-04:00","gmt_time_start":"2015-04-01 14:00:00","gmt_time_end":"2015-04-01 16:00:00","gmt_time_end_last":"2015-04-01 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"1808","name":"graduate students"},{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}