Surveillance Resources Research Articles

Adaptive policies for perimeter surveillance problems

We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms.

492. High Prevalence of Colonization with Carbapenem-Resistant Enterobacteriaceae Among Patients Admitted to Vietnamese Hospitals: Risk Factors and Burden of Disease

BackgroundCarbapenem-resistant Enterobacteriaceae (CRE) is an increasing problem worldwide, but particularly problematic in low- and middle-income countries (LMIC) due to limitations of resources for surveillance of CRE and infection prevention and control (IPC). MethodsA point prevalence survey (PPS) with screening for colonization with CRE was conducted on 2233 patients admitted to neonatal, pediatric and adult care at 12 Vietnamese hospitals located in northern, central and southern Vietnam during 2017 and 2018. CRE colonisation was determined by culturing of fecal specimens on selective agar for CRE. Risk factors for CRE colonisation were evaluated. A CRE admission and discharge screening sub-study was conducted among one of the most vulnerable patient groups; infants treated at an 80-bed Neonatal ICU from March throughout June 2017 to assess CRE acquisition, hospital-acquired infection (HAI) and treatment outcome.ResultsA total of 1165 (52%) patients were colonized with CRE, most commonly Klebsiella pneumoniae (n = 805), Escherichia coli (n = 682) and Enterobacter spp. (n = 61). Duration of hospital stay, HAI, intubation, peripheral venous catheter and treatment with a carbapenem were independent risk factors for CRE colonization. The PPS showed that the prevalence of CRE colonization increased on average 4.2% per day and mean CRE colonisation rates increased from 13% on the day of admission to 89% at day 15 of hospital stay. At the NICU CRE colonisation increased from 32% at admission to 87% at discharge, mortality was significantly associated (OR 5·5, P < 0·01) with CRE colonisation and HAI on admission.ConclusionThese data indicate that there is an epidemic spread of CRE in Vietnamese hospitals with rapid transmission to hospitalized patients. CRE colonization places a major burden on the healthcare system due to the increased risk of HAI caused by CRE and associated increased mortality. This study shows that large-scale epidemiological surveillance of CRE using affordable methods is possible in low- and middle-income countries.Disclosures All authors: No reported disclosures.

Preventing Emerging and Re-emerging Infections in the Eastern Mediterranean Region: Gaps, Challenges, and Priorities.

BackgroundThe Eastern Mediterranean Public Health Network, supported by the Biosecurity Engagement Program, contributed significantly to strengthening the preparedness and response to the emerging and re-emerging infections in the region.ObjectiveThis study aimed to determine the gaps, challenges, and priorities for preventing the emerging and re-emerging infections, with a focus on biosafety and biosecurity in four countries of the region, namely, Egypt, Iraq, Jordan, and Morocco.MethodsA total of two different methods were used to determine the gaps and priorities for preventing the emerging and re-emerging infections. The first method was a rapid assessment for the preparedness and response to the emerging and re-emerging infections in four countries of the region, with a focus on biosafety and biosecurity. The second method was a face-to-face round table meeting of the participating teams for two days, where the teams from all countries presented their countries’ profiles, findings, priorities, and gaps based on the countries’ assessments.ResultsThe assessment and meeting resulted in several priorities and recommendations for each of the countries in the areas of legislation and coordination, biosafety and biosecurity, surveillance and human resources, case management and response, infection control and prevention, and risk communication and laboratory capacity.ConclusionsMany recommendations were relatively consistent throughout, including improving communication or building collaborations to improve the overall health of the country.

Projection of Costs of Polio Eradication Compared to Permanent Control.

Despite increased efforts and spending toward polio eradication, it has yet to be eliminated worldwide. We aimed to project economic costs of polio eradication compared to permanent control. We used historical Financial Resource Requirements from the Global Polio Eradication Initiative, as well as vaccination and population data from publicly available sources, to project costs for routine immunization, immunization campaigns, surveillance and laboratory resources, technical assistance, social mobilization, treatment, and overhead. We found that cumulative spending for a control strategy would exceed that for an eradication strategy in 2032 (range, 2027-2051). Eradication of polio would likely be cost-saving compared to permanent control.

An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications.

From the accident news, it is found that the occurrences of traffic accidents every year and the numbers of deaths and injuries have raised continually and have become a specific issue concerned in society in Taiwan. More seriously, the number of traffic accidents is positively increased with the increasing motorized vehicles. Thus, to reduce the incidence of traffic accidents through by some advanced real-time technologies is an important and interesting work. However, some serious problems against traffic safety are facing, such as the low-quality video saved by a camera, low efficiency facilities supported, inefficient management of surveillance resources, and low definition resolution for cameras, which is resulted in a dilemma problem caused from providing evidence-based images to a local authority either for criteria for judgment or basis for improvement. As a big effort to deal with the above defects for constructing a smart city, this paper makes a main purpose to develop an advanced system of intelligent cloud-based transportation vehicle surveillance (called ICTVSS) for license plate identification. This existing identification algorithm was studied and developed from a combination of improved differential algorithm and improved active contour algorithm. Given such a combination, a novel algorithm of dynamic license identification for smart monitoring was fully realized for constructing a well-defined smart city. The experimental results showed good performance and experienced that the proposed algorithm performed well in locating multi-license plate and differential methods, removing image noise of license plate, and processing constant-inconstant light source from complex environment cases, and guaranteed effective license plate identification from the benefit of high resolutions of digital cameras.

A dynamic neural network model for predicting risk of Zika in real time

BackgroundIn 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner.MethodsIn this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future.ResultsThe model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks.ConclusionsSensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.

Modeling Culicoides abundance in mainland France: implications for surveillance

BackgroundBiting midges of the genus Culicoides Latreille (Diptera: Ceratopogonidae) are involved in the transmission of several viruses affecting humans and livestock, particularly bluetongue (BTV). Over the last decade, Culicoides surveillance has been conducted discontinuously and at various temporal and spatial scales in mainland France following the BTV epizootics in 2008–2009 and its reemergence and continuous circulation since 2015. The ability to predict seasonal dynamics and spatial abundance of Culicoides spp. is a key element in identifying periods and areas at high risk of transmission in order to strengthen surveillance for early detection and to establish seasonally disease-free zones. The objective of this study was to model the abundance of Culicoides spp. using surveillance data.MethodsA mixed-effect Poisson model, adjusted for overdispersion and taking into account temperature data at each trap location, was used to model the weekly relative abundance of Culicoides spp. over a year in 24 vector zones, based on surveillance data collected during 2009–2012. Vector zones are the spatial units used for Culicoides surveillance since 2016 in mainland France.ResultsThe curves of the predicted annual abundance of Culicoides spp. in vector zones showed three different shapes: unimodal, bimodal or plateau, reflecting the temporal variability of the observed counts between zones. For each vector zone, the model enabled to identify periods of vector activity ranging from 25 to 51 weeks.ConclusionsAlthough the data were collected for surveillance purposes, our modeling approach integrating vector data with daily temperatures, which are known to be major drivers of Culicoides spp. activity, provided areas-specific predictions of Culicoides spp. abundance. Our findings provide decisions makers with essential information to identify risk periods in each vector zone and guide the allocation of resources for surveillance and control. Knowledge of Culicoides spp. dynamics is also of primary importance for modeling the risk of establishment and spread of midge-borne diseases in mainland France.

A framework for evaluating the effects of observational type and quality on vector-borne disease forecast

Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy.

Health facility preparedness for cholera outbreak response in four cholera-prone districts in Cameroon: a cross sectional study

BackgroundThe risk of cholera outbreak remains high in Cameroon. This is because of the persistent cholera outbreaks in neighboring countries coupled with the poor hygiene and sanitation conditions in Cameroon. The objective of this study was to assess the readiness of health facilities to respond to cholera outbreak in four cholera-prone districts in Cameroon.MethodologyA cross-sectional study was conducted targeting all health facilities in four health districts, labeled as cholera hotspots in Cameroon in August 2016. Data collection was done by interview with a questionnaire and by observation regarding the availability of resources and materials for surveillance and case management, access to water, hygiene, and sanitation. Data analysis was descriptive with STATA 11.Principal findingsA total of 134 health facilities were evaluated, most of which (108/134[81%]) were urban facilities. The preparedness regarding surveillance was limited with 13 (50%) health facilities in the Far North and 22(20%) in the Littoral having cholera case definition guide. ORS for Case management was present in 8(31%) health facilities in the Far North and in 94(87%) facilities in the littoral. Less than half of the health facilities had a hand washing protocol and 7(5.1%) did not have any source of drinking water or relied on unimproved sources like lake. A total of 4(3.0%) health facilities, all in the Far North region, did not have a toilet.ConclusionsThe level of preparedness of health facilities in Cameroon for cholera outbreak response presents a lot of weaknesses. These are present in terms of lack of basic surveillance and case management materials and resources, low access to WaSH. If not addressed now, these facilities might not be able to play their role in case there is an outbreak and might even turn to be transmission milieus.

Streamlining Disease Surveillance System implementation in Tanzania: Lessons Learnt

Streamlining Disease Surveillance System implementation in Tanzania: Lessons Learnt

Hurricane Harvey AAR: Preparedness Recommendations Based on Surveillance Data

ObjectiveTo provide recommendations for future preparedness response efforts based on an assessment of the Post-Hurricane Harvey After-Action Report (AAR).IntroductionOn August 25, 2017 Hurricane Harvey moved onshore near Port Aransas, Texas, eventually overwhelming areas of Houston with between 41-60 inches of rain (Houston Health Department [HHD], 2017). As a category 4 storm, with wind speeds as high as 130 mph, Harvey broke several rainfall records across the state and ended the prolonged period of twelve years in which no major hurricanes had made landfall in the United States (Mersereau, 2017). Harvey ambled at a leisurely pace through Houston and resulted in devastating flooding that destroyed homes and required the evacuation of approximately 37,000 Houstonians to over 78 shelter facilities across the affected area (HHD, 2017). Through concerted efforts, the American Red Cross and the HHD established the shelter at the George R. Brown Convention Center (GRB) and “delivered or coordinated social services, medical and mental health services, disease surveillance and food/sanitary inspection services” for the duration of the need for the shelter (HHD, 2017).MethodsSyndromic surveillance data is essential to understanding the health status of affected communities during and after a disaster. For this abstract, we reviewed data collected from different surveillance systems and programs within the Houston Health Department (HHD), namely Real-Time Outbreak and Surveillance (RODS), Houston Electronic Disease Surveillance System (HEDSS) and other program systems, and reports compiled into the AAR. The AAR contained an assessment of the data collected daily during shelter surveillance and helped identify gaps in the implementation of preparedness plans, current procedures, and best practices. HHD’s Informatics team was responsible for data collection, training of staff and maintaining a cloud based repository of information on surveillance data and resources. A review of the AAR indicated a need for resources for the general shelter population to address the need for pharmacy data, enhanced behavioral support for individuals with mental health needs, dialysis treatment plans and pharmaceutical needs for patients with respiratory illness or hypertension.ResultsFrom August 30, 2017 to September 8, 2017 approximately 3,500 evacuees residing at the GRB shelter were assessed for a variety of medical conditions and complaints. Patient encounters peaked on September 4, 2017, with 705 patient encounters recorded. Data from the AAR suggested there were four most prevalent conditions of immediate need; affecting almost 25% of the shelter population were hypertension (10.4%), mental and behavioral issues (7.9%), diabetes (5.7%) and dialysis or renal failure (0.3%). There were challenges with supply of medications and synchronization of data collection by HHD and partner agencies. The department’s Continuity of Operation Plan (COOP) was voluminous and was not easily accessible during the disaster response. The findings from the After-Action Report indicate that disasters present multidimensional health challenges that can overwhelm advance preparations and more needs to be done to address the problems identified from previous disaster responses to improve on future outcomes.ConclusionsSyndromic surveillance can be strengthened in the following recommended areas for better incorporation into disaster response plans; pharmacy and health related data and data collection.The ingestion of pharmacy data by the syndromic surveillance systems could highlight gaps in the supply of needed medications at pharmacies during and post disaster, data from behavioral health clinics could show whether victims of the disaster who suffer mental health issues are able to access care, and whether dialysis treatment plans were continued. Based on the gaps identified, recommendations include integration of pharmacy data into the City’s disease surveillance system “ESSENCE” for tracking prescriptions and OTC purchases, to ensure adequate preparation for disaster stock levels and identification of sources for reordering when stocks run short.Additionally, it is recommended to revise and standardize data collection tools used during shelter surveillance to streamline the data collection process and to align the data tools of partner agencies, particularly DMAT and Red Cross, to prevent unnecessary duplication of efforts.Finally, the City’s Continuity of Operation Plan (COOP) has been revised since Hurricane Harvey and is periodically assessed and updated. The revised and updated COOP provides a concise and readily accessible document which can be easily reviewed and implemented to support an emergency response.

High prevalence of colonisation with carbapenem-resistant Enterobacteriaceae among patients admitted to Vietnamese hospitals: Risk factors and burden of disease

Carbapenem-resistant Enterobacteriaceae (CRE) is an increasing problem worldwide, but particularly problematic in low- and middle-income countries (LMIC) due to limitations of resources for surveillance of CRE and infection prevention and control (IPC). A point prevalence survey (PPS) with screening for colonisation with CRE was conducted on 2233 patients admitted to neonatal, paediatric and adult care at 12 Vietnamese hospitals located in northern, central and southern Vietnam during 2017 and 2018. CRE colonisation was determined by culturing of faecal specimens on selective agar for CRE. Risk factors for CRE colonisation were evaluated. A CRE admission and discharge screening sub-study was conducted among one of the most vulnerable patient groups; infants treated at an 80-bed Neonatal ICU from March throughout June 2017 to assess CRE acquisition, hospital-acquired infection (HAI) and treatment outcome. A total of 1165 (52%) patients were colonised with CRE, most commonly Klebsiella pneumoniae (n = 805), Escherichia coli (n = 682) and Enterobacter spp. (n = 61). Duration of hospital stay, HAI and treatment with a carbapenem were independent risk factors for CRE colonisation. The PPS showed that the prevalence of CRE colonisation increased on average 4.2% per day and mean CRE colonisation rates increased from 13% on the day of admission to 89% at day 15 of hospital stay. At the NICU, CRE colonisation increased from 32% at admission to 87% at discharge, mortality was significantly associated (OR 5·5, P<0·01) with CRE colonisation and HAI on admission. These data indicate that there is an epidemic spread of CRE in Vietnamese hospitals with rapid transmission to hospitalised patients.

Utilizing Electronic Laboratory Reporting Data to Assess the Burden of Hepatitis C in Arizona.

Hepatitis C virus (HCV) infections must be reported to public health departments in Arizona; however, Arizona Department of Health Services has not had the resources to conduct comprehensive HCV surveillance since 2008 and thus monitoring HCV is difficult. Cases were traditionally reported via mail, fax, or telephone; however, beginning in 2009, clinical laboratories could report HCV results through electronic laboratory reporting (ELR). To assess completeness of ELR in capturing HCV case reports and its utility for HCV surveillance and describing the current burden of HCV. Two components of study: (1) HCV reporting from all sources for 2 months in 2015 was de-duplicated at the patient level and cross-matched with the 1998-2008 database and 2009-2015 ELR data to identify cases newly reported during the 2-month period and calculate the proportion reported through ELR. (2) HCV ELR results during 2009-2015 were similarly de-duplicated and compared with the 1998-2008 database to identify newly reported cases. Hepatitis C virus patients reported to Arizona Department of Health Services. Hepatitis C virus case patients reported during 1998-2008 and through ELR during 2009-2015. Hepatitis C virus patients through paper reports for January and June 2015. (1) Using 2 months of all HCV reporting in 2015 to examine the proportion of cases captured by ELR and the differences in the type of reports captured by ELR and non-ELR sources only. (2) Compared sex, birth year, viral load, and genotype from ELR-only data to other surveillance data. Electronic laboratory reporting accounted for 1260 (64%) HCV cases newly reported during the 2 months, with 698 (36%) newly identified from non-ELR sources only. Based on these findings, an estimated 11 534 HCV cases were newly reported in 2015 (172 cases per 100 000 population). During 2009-2015, a substantial amount (23%) of newly reported cases were among persons born after 1978. Utilizing ELR data alone can provide meaningful HCV surveillance and offers a less resource-intensive means to describe HCV burden and identify trends in newly reported cases. An assessment like this one can provide a tool for HCV monitoring in other jurisdictions that lack resources for HCV surveillance as more laboratories transition to ELR.

Is dynamic traffic sensor network profitable for network-level real-time information prediction?

Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. The aims of the study are twofold. First, the proposed optimization model maximizes the network-level travel time surveillance benefits by taking into account the values of real-time measurements induced from both temporal and spatial traffic state patterns. Second, the mechanism of sensor relocation operations is designed and incorporated into the proposed model. This mechanism enables one to evaluate whether appropriately relocating sensors can optimize the utilization of the surveillance resources and further enhance the surveillance benefits over a given time horizon. We applied the proposed optimization framework to a mixed arterial-freeway commuting network located in Washington. D.C.-Baltimore metropolitan area. The study empirically revealed and illustrated two interesting findings: (1) relocation operations do bring additional surveillance benefits on travel time prediction, and the marginal benefit is highly correlated with the sensor fleet size; (2) spatial measurement is not a trivial part in travel time prediction, and one should fully recognize the effectiveness of temporal and spatial measurements when designing a traffic sensor network for real-time travel time surveillance.

A spatial assessment of Nipah virus transmission in Thailand pig farms using multi-criteria decision analysis

BackgroundThailand’s Central Plain is identified as a contact zone between pigs and flying foxes, representing a potential zoonotic risk. Nipah virus (NiV) has been reported in flying foxes in Thailand, but it has never been found in pigs or humans. An assessment of the suitability of NiV transmission at the spatial and farm level would be useful for disease surveillance and prevention. Multi-criteria decision analysis (MCDA), a knowledge-driven model, was used to map contact zones between local epizootic risk factors as well as to quantify the suitability of NiV transmission at the pixel and farm level.ResultsSpatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density). The resulting high suitable areas were concentrated around the bat colonies in three provinces in the East of Thailand, including Chacheongsao, Chonburi, and Nakhonnayok. The suitability of NiV transmission in pig farms in the study area was quantified as ranging from very low to medium suitability.ConclusionsWe believe that risk-based surveillance in the identified priority areas may increase the chances of finding out NiV and other bat-borne pathogens and thereby optimize the allocation of financial resources for disease surveillance. In the long run, improvements of biosecurity in those priority areas may also contribute to preventing the spread of potential emergence of NiV and other bat-borne pathogens.

Spatial dynamics of transmission and persistence of African swine fever in wild boar populations: insight for control

Spatial dynamics of transmission and persistence of African swine fever in wild boar populations: insight for control

Providing Infectious Disease Information to Child-Rearing Families and Its Evaluation

Background: Earlier study without actual trial indicated that caregivers needed delivery of information about outbreak situations independently of their job status. This report describes, for about two months in winter, actual delivery outbreak information to families with preschool children. The study objective was to confirm the usefulness of this information delivery. Method: Participants receiving outbreak information were recruited from the Child Care Support Service in a ward in Tokyo, Japan. Outbreak information was obtained from the Nursery School Absenteeism Surveillance System (NSASSy) covering approximately 40% of all nursery schools in Japan, prescription surveillance and other resources. Delivery of outbreak information started in December 2017 and ceased at the end of February in 2018. After the delivery period, a questionnaire survey was administered to participants. Results: For this area, NSASSy showed the most dominant disease was influenza, with 707 patients, followed by group A streptococcal pharyngitis with 98 patients. The outbreak peak was inferred to be as Monday, 22 January, and it was announced by e-mail on 23 January. Of the 202 persons joined this trial, 60 participants responded to the questionnaire survey after the delivery period. Of those respondents, 98% wanted delivery of that information to continue. Conclusion: We found that almost all respondents confirmed usefulness of the information about infectious diseases which was delivered.

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

BackgroundBiting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe.MethodsWe modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present.ResultsThe distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups.ConclusionsThe duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain.

688. Impact of Expanded Influenza Post-exposure Prophylaxis on Healthcare Worker Absenteeism at a Tertiary Care Center During the 2017–2018 Season

BackgroundA severe 2017–2018 influenza season was expected for the United States. We hypothesized that a surge in influenza cases paired with decreased vaccine effectiveness could increase healthcare worker (HCW) absenteeism. Because of a potential public health emergency during the 2017–2018 season, we offered oseltamivir post-exposure prophylaxis (PEP) to exposed HCWs regardless of vaccination status. We describe PEP uptake, cost, and impact on absenteeism at the University of Iowa Hospital and Clinics (UIHC).MethodsUIHC serves as a referral and safety net health system for Eastern Iowa. Influenza seasons were defined as the period between Week 40 from 1 year to Week 13 of the following year. During the 2016–2017 season, PEP (75 mg/day for 7 days) was offered free of charge to unvaccinated exposed HCWs. Exposure was defined as proximity within 3 ft of a confirmed influenza-infected person for ≥10 minutes without mask protection, or direct contact with respiratory secretions. During the 2017–2018 season, PEP was expanded to all exposed HCWs regardless of vaccination status. We reviewed surveillance, employee health, pharmacy, and human resources records for the 2016–2017 and 2017–2018 seasons. We defined PEP uptake as prescriptions picked up/all referrals and absenteeism rate as sick-leave requests/scheduled hours.ResultsDuring the 2016–2017 and 2017–2018 seasons, we detected 373 and 427 confirmed influenza cases among patients at UIHC. HCW vaccination rates were similar: 89.7% and 90.9%. PEP was recommended in 49 exposures during 2016–2017 and 280 exposures during 2017–2018. A total of 22 (44.9%) and 133 (47.5%) HCWs picked up oseltamivir from the pharmacy during the 2016–2017 and 2017–2018 seasons. The estimated cost of oseltamivir was $1,791 and $10,828, respectively. Overall, 6,187 sick-leave requests (median = 12 hours, absenteeism rate = 3.2%), and 6,174 sick-leave requests (median = 12 hours, absenteeism rate = 3.4%) were reported during the 2016–2017 and 2017–2018 seasons.ConclusionInfluenza case counts mildly increased from the 2016–2017 to the 2017–2018 season. Expanding PEP to all exposed HCWs, regardless of vaccination status, had moderate uptake and was costly. Absenteeism rates remained similar during both seasons.Disclosures All authors: No reported disclosures.

Factors in Premature Infants Associated With Low Risk of Developing Retinopathy of Prematurity

Most premature infants will not develop retinopathy of prematurity (ROP) of clinical relevance, yet screening evaluations often continue beyond hospital discharge, even for those infants without ROP. To identify the characteristics of infants at low risk for ROP, for whom further postdischarge screening may be of limited value. This study took place in North American neonatal intensive care units where clinicians had expertise in ROP. Infants with birth weight less than 1251 g who were born at or transferred into an Telemedicine Approaches to Evaluating Acute-Phase ROP (e-ROP) study center were enrolled. The study included post hoc analysis of prospectively collected in-hospital ROP examination results among infants enrolled in the e-ROP study. We characterized infants without ROP and performed logistic regression on the subset of infants who were 27 to 33 weeks' gestational age to determine characteristics associated with the absence of ROP during all in-hospital examinations. The main measure was the absence of ROP prior to hospital discharge; the main outcome was treatment for ROP. A total of 1257 infants born at 22 to 35 weeks' gestation (median [interquartile range (IQR)], 26 [25-28] weeks) with birth weights less than 1251 g (median [IQR], 860 [690-1040] g) underwent 4113 ROP examinations between 31 and 47 weeks' postmenstrual age. Overall, 1153 examinations (38%) showed no ROP, and 456 infants (36%) did not have ROP prior to study center discharge or study end point. Among infants without ROP during examinations at 32 and 33 weeks' postmenstrual age, 16 (9.4%) and 14 (5.3%) subsequently underwent ROP treatment, respectively. At hospital discharge, there was no ROP in 59% of infants of 27 to 33 weeks' gestational age, compared with 15% of those who were less than 27 weeks' gestational age (difference, 44% [95% CI, 38.5%-48.1%]; P ≤ .001). With more than 85% follow-up among infants without ROP by 37 weeks' postmenstrual age, none (95% CI, 0%-0.98%) were treated for ROP. In multivariate analysis of infants born at 27 to 33 weeks' gestation, larger birth weight (OR, 4.1 [95% CI, 1.6-10.3]) and higher gestational age (OR, 4.0 [95% CI, 1.5-10.8]) were significantly associated with absence of ROP. These findings suggest that, for infants of 27 weeks' gestational age or greater and birth weights larger than 750 g, if no ROP has been detected by discharge at near-term postmenstrual age, then further ROP surveillance has limited value. Studies of all infants at risk are needed to develop more specific, objective criteria for termination of ROP surveillance and focus resources on infants at higher risk of ROP. ClinicalTrials.gov Identifier: NCT01264276.