Warren B. P. Pettey

The role of heterogeneity in contact timing and duration in network models of influenza spread in schools

Influenza poses a significant health threat to children, and schools may play a critical role in community outbreaks. Mathematical outbreak models require assumptions about contact rates and patterns among students, but the level of temporal granularity required to produce reliable results is unclear. We collected objective contact data from students aged 5–14 at an elementary school and middle school in the state of Utah, USA, and paired those data with a novel, data-based model of influenza transmission in schools. Our simulations produced within-school transmission averages consistent with published estimates. We compared simulated outbreaks over the full resolution dynamic network with simulations on networks with averaged representations of contact timing and duration. For both schools, averaging the timing of contacts over one or two school days caused average outbreak sizes to increase by 1–8%. Averaging both contact timing and pairwise contact durations caused average outbreak sizes to increase by 10% at the middle school and 72% at the elementary school. Averaging contact durations separately across within-class and between-class contacts reduced the increase for the elementary school to 5%. Thus, the effect of ignoring details about contact timing and duration in school contact networks on outbreak size modelling can vary across different schools.

Estimates of outbreak risk from new introductions of ebola with immediate and delayed transmission control

Identifying incoming patients can have a larger risk-reduction effect than efforts to reduce transmissions from identified patients.

WRENMining: Large-Scale Data Collection for Human Contact Network Research

Wireless sensor networks (WSNs) have come a long way to reach their ubiquitous state known today through scalable cost, low-power optimizations, and data management. As WSNs scale in size, the necessity for system designs - from low-level hardware implementations to data collection and management procedures - to account for handling extensive amounts of data is crucial. Several prominent papers address these issues for limited deployments of less than 200 nodes, but there are little resources available for multiple consecutive deployments of over 500 nodes. We present the engineering perspective on sensor data collection, management, and processing while collaborating with epidemiologists for the Wireless Ranging Enabled Node (WREN) network system for human contact research. The WREN and all supporting systems (base stations, software, and data procedures) sustain multiple high density, mobile deployments with fast turnovers. The WRENs completed 13 deployments over a period of 8 months to mine over 35 million contact points. We present our design considerations, challenges/experiences, and solutions to account for and correct time synchronization issues, along with our methodology for collecting, managing, and processing data.

SaTScan on a Cloud: On-Demand Large Scale Spatial Analysis of Epidemics.

By using cloud computing it is possible to provide on- demand resources for epidemic analysis using computer intensive applications like SaTScan. Using 15 virtual machines (VM) on the Nimbus cloud we were able to reduce the total execution time for the same ensemble run from 8896 seconds in a single machine to 842 seconds in the cloud. Using the caBIG tools and our iterative software development methodology the time required to complete the implementation of the SaTScan cloud system took approximately 200 man-hours, which represents an effort that can be secured within the resources available at State Health Departments. The approach proposed here is technically advantageous and practically possible.

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.

Associations of Initial Injury Severity and Posttraumatic Stress Disorder Diagnoses With Long-Term Hypertension Risk After Combat Injury

The associations between injury severity, posttraumatic stress disorder (PTSD), and development of chronic diseases, such as hypertension, among military service members are not understood. We sought to (1) estimate the prevalence and incidence of PTSD within a severely injured military cohort, (2) assess the association between the presence and chronicity of PTSD and hypertension, and (3) determine whether or not initial injury severity score and PTSD are independent risk factors for hypertension. Administrative and clinical databases were used to conduct a retrospective cohort study of 3846 US military casualties injured in the Iraq and Afghanistan conflicts between February 1, 2002, and February 1, 2011. Development of PTSD and hypertension after combat injury were determined using the International Classification of Diseases, Ninth Revision codes. Multivariable competing risk regression models were used to assess associations between injury severity score, PTSD, and hypertension, while controlling for covariates. Overall prevalence of PTSD was 42.4%, and prevalence of hypertension was 14.3%. Unadjusted risk of hypertension increased significantly with chronicity of PTSD (1–15 diagnoses: hazard ratio, 1.77; 95% confidence interval, 1.46–2.14; P<0.001; >15 diagnoses: hazard ratio, 2.29; 95% confidence interval, 1.85–2.84; P<0.001) compared with patients never diagnosed with PTSD. The association between injury severity score (hazard ratio, 1.06 per 5-U increment; 95% confidence interval, 1.03–1.10; P<0.001) and hypertension was significant, with little change in effect in the multivariable model (hazard ratio, 1.05 per 5-U increment; 95% confidence interval, 1.01–1.09; P=0.03). In a cohort of service members injured in combat, we found that chronicity of PTSD diagnoses and injury severity were independent risk factors for hypertension.

Long-acting Reversible Contraception Among Homeless Women Veterans With Chronic Health Conditions: A Retrospective Cohort Study

Background: US women Veterans are at increased risk of homelessness and chronic health conditions associated with unintended pregnancy. Veterans Health Administration (VHA) provision of long-acting reversible contraception (LARC) can assist in healthy pregnancy planning. Objectives: To evaluate perinatal risk factors and LARC exposure in ever-homeless women Veterans. Research Design: A retrospective cohort study of women Veterans using VHA administrative data from fiscal years 2002–2015. Subjects: We included 41,747 ever-homeless women Veterans age 18–44 years and 46,391 housed women Veterans matched by military service period. A subgroup of 7773 ever-homeless and 8674 matched housed women Veterans deployed in Iraq and Afghanistan [Operations Enduring Freedom/Iraqi Freedom/New Dawn (OEF/OIF/OND)] conflicts comprised a second analytic cohort. Measures: Descriptive statistics compared demographic, military, health conditions, and LARC exposure in ever-homeless versus housed women Veterans. Multivariable logistic regression explored factors associated with LARC exposure in the OEF/OIF/OND subgroup. Results: All health conditions were significantly higher in ever-homeless versus housed Veterans: mental health disorder in 84.5% versus 48.7% (P<0.001), substance abuse in 35.8% versus 8.6% (P<0.001), and medical conditions in 74.7% versus 55.6% (P<0.001). LARC exposure among all VHA users was 9.3% in ever-homeless Veterans versus 5.4% in housed Veterans (P<0.001). LARC exposure in the OEF/OIF/OND cohort was 14.1% in ever-homeless Veterans versus 8.2% in housed Veterans (P<0.001). In the OEF/OIF/OND cohort, homelessness along Veterans with medical and mental health indicators were leading LARC exposure predictors. Conclusions: The VHA is successfully engaging homeless women Veterans and providing LARC access. The prevalence of perinatal risk factors in ever-homeless women Veterans highlights a need for further programmatic enhancements to improve reproductive planning.

Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources

During the recent Ebola crisis in West Africa, individual person-level details of disease onset, transmissions, and outcomes such as survival or death were reported in online news media. We set out to document disease transmission chains for Ebola, with the goal of generating a timely account that could be used for surveillance, mathematical modeling, and public health decision-making. By accessing public web pages only, such as locally produced newspapers and blogs, we created a transmission chain involving two Ebola clusters in West Africa that compared favorably with other published transmission chains, and derived parameters for a mathematical model of Ebola disease transmission that were not statistically different from those derived from published sources. We present a protocol for responsibly gleaning epidemiological facts, transmission model parameters, and useful details from affected communities using mostly indigenously produced sources. After comparing our transmission parameters to published parameters, we discuss additional benefits of our method, such as gaining practical information about the affected community, its infrastructure, politics, and culture. We also briefly compare our method to similar efforts that used mostly non-indigenous online sources to generate epidemiological information.

Using network projections to explore co-incidence and context in large clinical datasets

INTRODUCTION Network projections of data can provide an efficient format for data exploration of co-incidence in large clinical datasets. We present and explore the utility of a network projection approach to finding patterns in health care data that could be exploited to prevent homelessness among U.S. Veterans. METHOD We divided Veteran ICD-9-CM (ICD9) data into two time periods (0-59 and 60-364days prior to the first evidence of homelessness) and then used Pajek social network analysis software to visualize these data as three different networks. A multi-relational network simultaneously displayed the magnitude of ties between the most frequent ICD9 pairings. A new association network visualized ICD9 pairings that greatly increased or decreased. A signed, subtraction network visualized the presence, absence, and magnitude difference between ICD9 associations by time period. RESULT A cohort of 9468 U.S. Veterans was identified as having administrative evidence of homelessness and visits in both time periods. They were seen in 222,599 outpatient visits that generated 484,339 ICD9 codes (average of 11.4 (range 1-23) visits and 2.2 (range 1-60) ICD9 codes per visit). Using the three network projection methods, we were able to show distinct differences in the pattern of co-morbidities in the two time periods. In the more distant time period preceding homelessness, the network was dominated by routine health maintenance visits and physical ailment diagnoses. In the 59days immediately prior to the homelessness identification, alcohol related diagnoses along with economic circumstances such as unemployment, legal circumstances, along with housing instability were noted. CONCLUSION Network visualizations of large clinical datasets traditionally treated as tabular and difficult to manipulate reveal rich, previously hidden connections between data variables related to homelessness. A key feature is the ability to visualize changes in variables with temporality and in proximity to the event of interest. These visualizations lend support to cognitive tasks such as exploration of large clinical datasets as a prelude to hypothesis generation.

Comparison of grouping methods for template extraction from VA Medical Record Text

We investigate options for grouping templates for the purpose of template identification and extraction from electronic medical records. We sampled a corpus of 1000 documents originating from Veterans Health Administration (VA) electronic medical record. We grouped documents through hashing and binning tokens (Hashed) as well as by the top 5% of tokens identified as important through the term frequency inverse document frequency metric (TF-IDF). We then compared the approaches on the number of groups with 3 or more and the resulting longest common subsequences (LCSs) common to all documents in the group. We found that the Hashed method had a higher success rate for finding LCSs, and longer LCSs than the TF-IDF method, however the TF-IDF approach found more groups than the Hashed and subsequently more long sequences, however the average length of LCSs were lower. In conclusion, each algorithm appears to have areas where it appears to be superior.