Research Articles for the Week of March 30, 2015

Research Committee Selected Articles for the Week of March_30_2015

★ ***-Article is considered for Award Nomination***

• Hansen A., Cameron S., Liu Q., Sun Y., Weinstein P., Williams C., Han G.-S., Bi P. Transmission of Haemorrhagic Fever with Renal Syndrome in China and the Role of Climate Factors: A Review

• Zhang Z., Zheng X., Zeng D.D., Leischow S.J. Information seeking regarding tobacco and lung cancer: Effects of seasonality ★

• Furuya-Kanamori L., McKenzie S.J., Yakob L., Clark J., Paterson D.L., Riley T.V., Clements A.C. Clostridium difficile infection seasonality: Patterns across hemispheres and continents - A systematic review

• Faryadres M., Karami M., Moghimbeigi A., Esmailnasab N., Pazhouhi K. Levels of alarm thresholds of meningitis outbreaks in Hamadan Province, West of Iran ★

• Yomwan P., Cao C., Rakwatin P., Suphamitmongkol W., Tian R., Saokarn A. A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm ★

• Cohn P.D., Gleason J.A., Rudowski E., Tsai S.M., Genese C.A., Fagliano J.A. Community outbreak of legionellosis and an environmental investigation into a community water system

• Roche S.E., Garner M.G., Sanson R.L., Cook C., Birch C., Backer J.A., Dube C., Patyk K.A., Stevenson Evaluating vaccination strategies to control foot-and-mouth disease: A model comparison study ★

• Yi M., Marathe A. Fairness versus efficiency of vaccine allocation strategies

• Chong N.S., Smith R.J. Modeling avian influenza using Filippov systems to determine culling of infected birds and quarantine

• Reid C.E., Jerrett M., Petersen M.L., Pfister G.G., Morefield P.E., Tager I.B., Raffuse S.M., Balmes Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning ★

• Smith C., Curran M.D., Roddick I., Reacher M. Use of linked electronic health records to assess mortality and length of stay associated with pandemic influenza A(H1N1)pdm09 at a UK teaching hospital

• Chen S.-C., You Z.-S. Social contact patterns of school-age children in Taiwan: Comparison of the term time and holiday periods ★

• Greenland S., Pearce N. Statistical foundations for model-based adjustments

Transmission of Haemorrhagic Fever with Renal Syndrome in China and the Role of Climate Factors: A Review

Haemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that poses a serious public health threat in China. HFRS is caused by hantaviruses, mainly Seoul virus in urban areas and Hantaan virus in agricultural areas. Although preventive measures including vaccination programs and rodent control measures have resulted in a decline in cases in recent years, there has been an increase in incidence in some areas and new endemic areas have emerged. This review summarises the recent literature relating to the effects of climatic factors on the incidence of HFRS in China and discusses future research directions. Temperature, precipitation and humidity affect crop yields, rodent breeding patterns and disease transmission, and these can be influenced by a changing climate. Detailed surveillance of infections caused by Hantaan and Seoul viruses and further research on the viral agents will aid in interpretation of spatiotemporal patterns and a better understanding of the environmental and ecological drivers of HFRS amid China's rapidly urbanising landscape and changing climate. © 2015 The Authors.

Information seeking regarding tobacco and lung cancer: Effects of seasonality

This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms "tobacco" and "lung cancer" from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use. © 2015 Zhang et al.

Clostridium difficile infection seasonality: Patterns across hemispheres and continents - A systematic review

Background: Studies have demonstrated seasonal variability in rates of Clostridium difficile infection (CDI). Synthesising all available information on seasonality is a necessary step in identifying large-scale epidemiological patterns and elucidating underlying causes. Methods: Three medical and life sciences publication databases were searched from inception to October 2014 for longitudinal epidemiological studies written in English, Spanish or Portuguese that reported the incidence of CDI. The monthly frequency of CDI were extracted, standardized and weighted according to the number of follow-up months. Cross correlation coefficients (XCORR) were calculated to examine the correlation and lag between the yearmonth frequencies of reported CDI across hemispheres and continents. Results: The search identified 13, 5 and 2 studies from North America, Europe, and Oceania, respectively that met the inclusion criteria. CDI had a similar seasonal pattern in the Northern and Southern Hemisphere characterized by a peak in spring and lower frequencies of CDI in summer/autumn with a lag of 8 months (XCORR = 0.60) between hemispheres. There was no difference between the seasonal patterns across European and North American countries. Conclusion: CDI demonstrates a distinct seasonal pattern that is consistent across North America, Europe and Oceania. Further studies are required to identify the driving factors of the observed seasonality. © 2015 Furuya-Kanamori et al.

Levels of alarm thresholds of meningitis outbreaks in Hamadan Province, West of Iran

Background: Few studies have focused on syndromic data to determine levels of alarm thresholds to detection of meningitis outbreaks. The purpose of this study was to determine threshold levels of meningitis outbreak in Hamadan Province, west of Iran. Methods: Data on both confirmed and suspected cases of meningitis (fever and neurological symptom) from 21 March 2010 to 20 March 2012 were used in Hamadan Province, Iran. Alarm threshold levels of meningitis outbreak were determined using four different methods including absolute values or standard method, relative increase, statistical cutoff points and upper control limit of exponentially weighted moving average (EWMA) algorithm. Results: Among 723 reported cases, 41 were diagnosed to have meningitis. Standard level of alarm thresholds for meningitis outbreak was determined as incidence of 5/100000 persons. Increasing 1.5 to two times in reported cases of suspected meningitis per week was known as the threshold levels according to relative increase method. An occurrence four cases of suspected meningitis per week that equals to 90th percentile was chosen as alarm thresholds by statistical cut off point method. The corresponding value according to EWMA algorithm was 2.57 i.e. three cases. Conclusions: Policy makers and staff of syndromic surveillance systems are highly recommended to apply the above different methods to determine the levels of alarm threshold. © 2015 Health Hamadan University of Medical Sciences. All rights reserved.

A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm

Flood disasters are closely associated with an increased risk of infection, particularly from waterborne diseases. Most studies of waterborne diseases have relied on the direct determination of pathogens in contaminated water to assess disease risk. In contrast, this study aims to use an indirect assessment that employs a back propagation neural network (BPNN) for modelling diarrheal outbreaks using data from remote sensing and dissolved-oxygen (DO) measurements to reduce cost and time. Our study area is in Ayutthaya province, which was very severely affected by the catastrophic 2011 Thailand flood. BPNN was used to model the relationships among the parameters of the flood and the water quality and the risk of people becoming infected. Radarsat-2 scenes were utilized to estimate flood area and duration, while the flood water quality was derived from the interpolation of DO samples. The risk-ratio function was applied to the diarrheal morbidity to define the level of outbreak detection and the outbreak periods. Tests of the BPNN prediction model produced high prediction accuracy of diarrheal-outbreak risk with low prediction error and a high degree of correlation. With the promising accuracy of our approach, decision-makers can plan rapid and comprehensively preventive measures and countermeasures in advance. © 2013, Taylor & Francis.

Community outbreak of legionellosis and an environmental investigation into a community water system

During two legionellosis outbreak investigations, one at a geriatric centre and the other in high-rise housing for seniors, it was observed that additional cases of legionellosis occurred in nearby smaller residential settings. This apparent geographical cluster of legionellosis occurred in the same general area of a community water storage tank. No potential airborne sources in or near the area could be identified, but a community water system storage tank that was centrally located among case residences spurred an investigation of water-quality factors in the identified investigation area. Conditions conducive for Legionella growth, particularly low chlorine residuals, were found. The rate of legionellosis among residents aged ?50 years in the investigation areas (61.0 and 64.1/100 000) was eight times higher than in the rest of the service area (9.0/100 000) and almost 20 times higher than the statewide annual average incidence rate (3.2/100 000). A water mains flushing programme in the area was launched by the water utility, and water samples taken before and during flushing found L. pneumophila. © Cambridge University Press 2014.

Evaluating vaccination strategies to control foot-and-mouth disease: A model comparison study

Simulation models can offer valuable insights into the effectiveness of different control strategies and act as important decision support tools when comparing and evaluating outbreak scenarios and control strategies. An international modelling study was performed to compare a range of vaccination strategies in the control of foot-and-mouth disease (FMD). Modelling groups from five countries (Australia, New Zealand, USA, UK, The Netherlands) participated in the study. Vaccination is increasingly being recognized as a potentially important tool in the control of FMD, although there is considerable uncertainty as to how and when it should be used. We sought to compare model outputs and assess the effectiveness of different vaccination strategies in the control of FMD. Using a standardized outbreak scenario based on data from an FMD exercise in the UK in 2010, the study showed general agreement between respective models in terms of the effectiveness of vaccination. Under the scenario assumptions, all models demonstrated that vaccination with 'stamping-out' of infected premises led to a significant reduction in predicted epidemic size and duration compared to the 'stamping-out' strategy alone. For all models there were advantages in vaccinating cattle-only rather than all species, using 3-km vaccination rings immediately around infected premises, and starting vaccination earlier in the control programme. This study has shown that certain vaccination strategies are robust even to substantial differences in model configurations. This result should increase end-user confidence in conclusions drawn from model outputs. These results can be used to support and develop effective policies for FMD control. © Cambridge University Press 2014.

Fairness versus efficiency of vaccine allocation strategies

Objectives To develop a framework to objectively measure the degree of fairness of any allocation rule aimed at distributing a limited stockpile of vaccines to contain the spread of influenza. Methods The trade-off between the efficiency and fairness of allocation strategies was demonstrated through an illustrative simulation study of an influenza epidemic in Southwestern Virginia. A Susceptible-Exposed-Infectious-Recovered model was used to represent the disease progression within the host. Results Our findings showed that among all the criteria considered here, the household size (largest first) combined with age (youngest first)-based strategy leads to the best outcome. At 80% fairness, highest efficiency can be achieved but in order to be 100% fair, disease prevalence will have to rise by approximately 1.5%. Conclusions This research provides a framework to objectively determine the degree of fairness of vaccine allocation strategies. © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR).

Modeling avian influenza using Filippov systems to determine culling of infected birds and quarantine

The growing number of reported avian influenza cases has prompted awareness of the effectiveness of pharmaceutical or/and non-pharmaceutical interventions that aim to suppress the transmission rate. We propose two Filippov models with threshold policy: the avian-only model with culling of infected birds and the SIIR (Susceptible-Infected-Infected-Recovered) model with quarantine. The dynamical systems of these two models are governed by nonlinear ordinary differential equations with discontinuous right-hand sides. The solutions of these two models will converge to either one of the two endemic equilibria or the sliding equilibrium on the discontinuous surface. We prove that the avian-only model achieves global stability. Moreover, by choosing an appropriate quarantine threshold level Ic in the SIIR model, this model converges to an equilibrium in the region below Ic or a sliding equilibrium, suggesting the outbreak can be controlled. Therefore a well-defined threshold policy is important for us to combat the influenza outbreak efficiently. © 2015 Elsevier Ltd. All rights reserved.

Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event. © 2015 American Chemical Society.

Use of linked electronic health records to assess mortality and length of stay associated with pandemic influenza A(H1N1)pdm09 at a UK teaching hospital

Effective use of data linkage is becoming an increasingly important focus in the new healthcare system in England. We linked data from the results of a multiplex PCR assay for respiratory viruses for a population of 230 inpatients at a UK teaching hospital with their patient administrative system records in order to compare the mortality and length of stay of patients who tested positive for influenza A(H1N1)pdm09 with those positive for another influenza A virus. The results indicated a reduced risk of death among influenza A(H1N1)pdm09 patients compared to other influenza A strains, with an adjusted risk ratio of 0.25 (95% confidence interval 0.08-0.75, P = 0.01), while no significant differences were found between the lengths of stay in the hospital for these two groups. Further development of such methods to link hospital data in a routine fashion could provide a rapid means of gaining epidemiological insights into emerging infectious diseases. © Cambridge University Press 2014.

Social contact patterns of school-age children in Taiwan: Comparison of the term time and holiday periods

School closure is one of the most common interventions in the early weeks of an influenza pandemic. Few studies have investigated social contact patterns and compared individual student contact characteristics during the school term and holiday periods in Taiwan. Here, we conducted a well-used questionnaire survey in a junior high school (grades 7-8) in June 2013. All 150 diary-based effective questionnaires covering conversation and skin-to-skin contact behaviour were surveyed. Two questionnaires for each participant were designed to investigate the individual-level difference of contact numbers per day during the two periods. The questionnaire response rate was 44%. The average number of contacts during term time (20.0 contacts per day) and holiday periods (12.6 contacts per day) were significantly different (P<0.05). The dominant contact frequencies and duration were everyday contact (89.10%) and contacts lasting less than 5 minutes (37.09%). The greatest differences occurred within the 13-19 years age groups. The result presented in this study provide an indication of the likely reduction in daily contact frequency that might occur if a school closure policy was adopted in the event of an influenza pandemic in Taiwan. Comparing contact patterns during term time and holiday periods, the number of contacts decreased by 40%. This study is the first research to investigate the contact numbers and contact characteristics for school-age children during the school term and a holiday period in Taiwan. With regard to public health, this study could provide the basic contact information and database for modelling influenza epidemics for minimizing the spread of influenza that depends on personal contacts for transmission. © Cambridge University Press 2014.

Statistical foundations for model-based adjustments

Most epidemiology textbooks that discuss models are vague on details of model selection. This lack of detail may be understandable since selection should be strongly influenced by features of the particular study, including contextual (prior) information about covariates that may confound, modify, or mediate the effect under study. It is thus important that authors document their modeling goals and strategies and understand the contextual interpretation of model parameters and model selection criteria. To illustrate this point, we review several established strategies for selecting model covariates, describe their shortcomings, and point to refinements, assuming that the main goal is to derive the most accurate effect estimates obtainable from the data and available resources. This goal shifts the focus to prediction of exposure or potential outcomes (or both) to adjust for confounding; it thus differs from the goal of ordinary statistical modeling, which is to passively predict outcomes. Nonetheless, methods and software for passive prediction can be used for causal inference as well, provided that the target parameters are shifted appropriately. Copyright © 2015 by Annual Reviews. All rights reserved.

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