Traditional Epidemiological Surveillance Research Articles

Detection of influenza virus in urban wastewater during the season 2022/2023 in Sicily, Italy.

Seasonal influenza generally represents an underestimated public health problem with significant socioeconomic implications. Monitoring and detecting influenza epidemics are important tasks that require integrated strategies. Wastewater-based epidemiology (WBE) is an emerging field that uses wastewater data to monitor the spread of disease and assess the health of a community. It can represent an integrative surveillance tool for better understanding the epidemiology of influenza and prevention strategies in public health. We conducted a study that detected the presence of Influenza virus RNA using a wastewater-based approach. Samples were collected from five wastewater treatment plants in five different municipalities, serving a cumulative population of 555,673 Sicilian inhabitants in Italy. We used the RT-qPCR test to compare the combined weekly average of Influenza A and B viral RNA in wastewater samples with the average weekly incidence of Influenza-like illness (ILI) obtained from the Italian national Influenza surveillance system. We also compared the number of positive Influenza swabs with the viral RNA loads detected from wastewater. Our study investigated 189 wastewater samples. Cumulative ILI cases substantially overlapped with the Influenza RNA load from wastewater samples. Influenza viral RNA trends in wastewater samples were similar to the rise of ILI cases in the population. Therefore, wastewater surveillance confirmed the co-circulation of Influenza A and B viruses during the season 2022/2023, with a similar trend to that reported for the weekly clinically confirmed cases. Wastewater-based epidemiology does not replace traditional epidemiological surveillance methods, such as laboratory testing of samples from infected individuals. However, it can be a valuable complement to obtaining additional information on the incidence of influenza in the population and preventing its spread.

Law enforcement fentanyl seizures and overdose mortality in US counties, 2013–2020

BackgroundThe spread of illicitly manufactured fentanyl is driving steep increases in US overdose deaths. Fentanyl seizures are correlated with state-level opioid-related mortality; however, more granular seizure surveillance information has the potential to better inform overdose prevention and harm reduction efforts. MethodsUsing data on fentanyl pill and powder seizures from High Intensity Drug Trafficking Areas (HIDTA), we tested associations between seizure prevalence and overdose mortality, from 2013 to 2020. The primary exposure—seizure burden—was constructed by identifying counties having high (above the median) prevalence of pill, powder, or combined pill/powder seizure burden per 100,000 population. Poisson models accounted for county demographic, law enforcement and time trends. ResultsDuring the timeframe, there were 13,842 fentanyl seizures in 606 US counties. In adjusted models, counties with a high burden of pill or powder fentanyl seizures, or both (combined pills/powder) exhibited higher total overdose mortality than non-high burden counties (pills adjusted prevalence ratio [aPR]: 1.10 [95 % confidence interval [CI]: 1.08, 1.12]; powder aPR 1.12 [CI: 1.11, 1.13]; combined pills/powder aPR: 1.27 [CI: 1.25, 1.29]). A similar pattern of associations with fentanyl seizure burden was noted for overdose deaths involving synthetic opioids (pills [aPR]: 0.99 [CI: 0.96, 1.02]; powder aPR 1.29 [CI: 1.27, 1.30]; combined pills/powder aPR 1.55 [CI: 1.52, 1.58]). ConclusionsLaw enforcement data on fentanyl seizures predicts drug overdose mortality at the county-level. Integrating these data with more traditional epidemiologic surveillance approaches has the potential to inform community overdose response efforts.

Outbreak of New Delhi Metallo-β-lactamase-producing Escherichia coli in a Neonatal Intensive Care Unit, New York State

Background: Outbreaks of carbapenemase-producing (CP) organisms (CPOs), including carbapenem-resistant Enterobacterales (CRE), in neonatal intensive care units (NICUs) are not well documented. The Centers for Disease Control and Prevention (CDC) identifies CP-CRE as an urgent threat to United States (US) healthcare facilities. Wadsworth Center, the New York State (NYS) Department of Health’s (NYSDOH’s) public health laboratory, participates in CDC’s Antimicrobial Resistance Laboratory Network to provide CPO identification, characterization, and surveillance. NYSDOH investigated an outbreak of CP-CRE Escherichia coli (E. coli) infections in NICU patients reported by one hospital. Method: Hospital A reported a CRE E. coli outbreak in their NICU to NYSDOH, as required by NYS Sanitary Code. In response, epidemiologists reviewed case data, conducted case finding, and provided infection control guidance to the hospital. Hospital A continued NICU clinical surveillance and conducted colonization screening to detect additional cases of CRE E. coli. The Wadsworth Center Antimicrobial Resistance Laboratory Network tested isolates from affected patients for CP genes and performed whole genome sequencing (WGS) to determine the CP gene variant, multilocus sequence type (MLST), and relatedness by mutation event (ME) analysis. NYSDOH epidemiologists assessed Hospital A’s infection control practices in affected areas and provided recommendations. Result: Hospital A identified two CRE E. coli infections in NICU patients with overlapping admissions in June-July 2023. Retrospective surveillance identified a third CRE E. coli case in an adult medical intensive care unit patient on admission to Hospital A in June 2023, with prior hospitalization April-May 2023. WGS analysis identified the blaNDM-5 gene in all three CRE E. coli patient isolates. The two NICU patients’ isolates had the same MLST (361/650) and differed by 9 MEs, indicating relatedness to each other and not the adult patient’s (MLST 167/2). NICU patient colonization screening identified no additional blaNDM-5 E. coli cases. NYSDOH’s NICU infection control assessment found that both cases were in adjacent isolettes within three feet of each other. Clean isolettes, equipment, and supplies for new admissions were stored in the clinical care space, not in a separate clean area. Conclusion: CP-CRE is an urgent threat to US healthcare facilities, including hospital NICUs. Though the incidence and prevalence of CP-CRE blaNDM-5 E. coli are not well-defined in NY, single healthcare-associated cases in NICU populations represent an outbreak. The Wadsworth Center Antimicrobial Resistance Laboratory Network’s contributions complement traditional epidemiologic surveillance and investigation methods to provide more specific, comprehensive infection

Hepatitis A in Latin America: The current scenario.

This review aims to gather and disseminate updated information regarding hepatitis A virus (HAV) in Latin America (LA) in the last 11years, including seroprevalence, post-vaccination studies, virus detection in aqueous matrices and food samples, and outbreak reports. Only 24 seroprevalence studies were published between 2012 and 2023 with 55%-100% reported prevalences of anti-HAV IgG. Among the 25 LA countries, only eight of them have introduced HAV vaccines into their immunisation programs. Outbreaks of hepatitis A occurred between 2017-2019, mainly affecting men who have sex with men in Argentina, Brazil and Chile, probably as a consequence of the abrupt decline of young adults' immunity. This could be due to that young adult have never been infected in childhood (due to socio-health improvements) and are above the cut-off ages to be included when the vaccination programs were introduced. Although scarce, studies focused on environmental and food HAV surveillance have shown viral presence in these samples. Surface waters presented HAV detections between 1.2% and 86.7%, and untreated wastewaters between 2.8% and 70.9%. Genotypes found in all cases were IA and IC. The only wastewater-based epidemiology study showed to be a useful tool as a complement of traditional epidemiological surveillance. Only four LA countries have looked for HAV in food samples, with genome detection rates between 9% and 33%. Latin American HAV circulation scenario is changing. In countries where socioeconomic and sanitary conditions have not improved, the virus persists with high endemicity and the access to the vaccine should be re-evaluated by local governments. In countries where access to clean water, better sanitary conditions and HAV immunisation programs have been implemented, the number of cases among young adults seems to be increasing, alerting health authorities.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today’s complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

The Temporary Incapacity (TI) register as a complementary system to traditional epidemiological surveillance during the COVID-19 pandemic in Spain.

During the COVID-19 pandemic, a set of social measures were adopted for the preservation of business activity and the protection of workers. One of these measures was issuing the Temporary Disability (TD) for COVID-19 cases, close contacts, and especially vulnerable workers. This study analyzes whether the TD registry could be used as a complementary source to traditional epidemiological surveillance. A longitudinal study of time series was carried out with a cross-correlation analysis of TD and COVID-19 cases reported to the National Epidemiological Surveillance Network (RENAVE). The analysis included six pandemic waves between 10/03/2020 and 31/12/2021 in Spain. Cross-correlation coefficients (r) were calculated using a time lag of -14 days. During the study period, 2,253,573 TD processes were recorded in Spain and 4,894,802 COVID-19 cases were reported to RENAVE. Significant positive correlations were observed at time lags of -7, -10, and -14, indicating that TD notification preceded RENAVE notification. In the first and sixth pandemic waves, TD notification preceded RENAVE by 12 and 7 days, respectively. Negative correlations between the two series were observed in the second and fourth waves, coinciding with a lower number of reported cases. In the third and fifth waves, TD notification also preceded RENAVE (lags -1, -5 and -14, -7, respectively). The results confirm the usefulness of TD registry as a complementary system to traditional epidemiological surveillance in Spain, by detecting COVID-19 cases in the 7, 10, and 14 days prior. A better positive correlation is observed in waves where more cases were reported.

Literature review: Substance and infectious disease surveillance from wastewater-based epidemiology

As the scale of illicit drug abuse and the expansion of pandemic diseases increase, traditional epidemiological surveillance methods are unable to meet the demand of massive real-time observations. The promising approach of wastewater-based epidemiology (WBE) as an effective supplement to traditional surveillance methods relates domestic wastewater to the information of the population within a determined sewer collection area, as its advantages of non-privacy, low-cost and high-efficiency. Resorting to detection and analysis of wastewater samples, the consumption and dispersion of an epidemic substance in a community can be inverse-calculated in an easy way. This review introduces the origin and development of WBE to emerge the great potential of WBE in applications, and recommends basic procedures and calculation methods. Then we focus on its role in monitoring the consumption of some substances (e.g., tobacco, alcohol, sedative narcotic drugs, illicit psychotropic drugs, and synthetic chemicals) and its forewarning signal in epidemic, and finally provides insight into challenges and prospectives.

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.

Epidemic season 2021–2022: Frequency of co-infection by respiratory viral pathogens

BACKGROUND: This study was conducted to determine the characteristics of various viral respiratory pathogens spreading during the epidemic season 20212022 and the frequency of co-infection with SARS-CoV-2 and influenza.
 AIM: To assess the development of the influenza epidemic and frequency of cases of co-infection with respiratory pathogens in patients with acute respiratory viral infections between 2021 and 2022.
 MATERIALS AND METHODS: Traditional and hospital epidemiological surveillance methods for acute respiratory viral infections were used.
 RESULTS: The epidemic season of 20212022 was characterized by the early activity of the influenza A(H3N2) virus and the emergence and rapid spread of the omicron variant of SARS-CoV-2. The distribution of different respiratory pathogens during the epidemic season 20212022 was clearly traced: SARS-CoV-2 (18.8%) was predominant, followed by influenza viruses (10.6%) and pathogens of other acute respiratory viral infections (0.43.7%). With respect to influenza A (H3N2) and B viruses, the heterogeneity of their populations and drift variability in relation to vaccine strains were noted.
 DISCUSSION: The frequency of co-infection with various respiratory pathogens was low, i.e., it was no more than 0.1%according to traditional surveillance, and no more than 9.2% in the hospital surveillance. The rationale for updating the composition of influenza vaccines for the countries in the Northern Hemisphere for 20222023 season was identified.
 CONCLUSION: At present, early diagnosis of influenza is important given the availability of effective drugs with a direct mechanism of action for the prevention and treatment of this pathogen. Timely use of anti-influenza drugs will reduce the risks of a severe course, complications, and death, including co-infection with SARS-CoV-2.

Early warning signals for Omicron outbreaks in China: a retrospective study.

The Omicron variant has become the dominant COVID-19 variant worldwide due to its rapid and cryptic spread; therefore, successful early warning is of great importance to be able to control epidemics in their early phase, before developing into large outbreaks. COVID-19-related Baidu search index, which reflects human behavior to a certain degree, was used to retrospectively detect the warning signs for Omicron variant outbreaks in China in 2022. The characteristics and effects of warning signs were analyzed in detail. We detected the presence of early warning signs (both high and low thresholds) and found that these occurred 4-7 days earlier than traditional epidemiological surveillance and >20 days earlier than the implementation of the local "lockdown" policy. Compared with the "high threshold" warning, the early warning effect of the "low threshold" is also vital because it indicates a complacency about epidemic prevention and control. However, there is obvious heterogeneity in the optimal threshold for detecting early warning signs and their distribution in different cities. Multi-source and multi-point early warning systems should be established via combining internet-based big data in the future to conduct effective and early real-time warning. This would create precious time for the early control of COVID-19 outbreaks. This article is protected by copyright. All rights reserved.

Environmental proteomics: a potential tool in wastewater-based epidemiology

Wastewater-based epidemiology (WBE) is a new complementary alternative to traditional epidemiological surveillance approaches for community health surveys. It provides objective, flexible, and comprehensive data on diseases, exposure to toxicants and pathogens, social habits, and lifestyle. Wastewater is regarded as an integrated pooled sample of the entire population served by a certain wastewater system, thus providing an average picture of its health status. WBE relies on the extraction, analysis, and subsequent interpretation of appropriate health status indicators, including both chemical and biological components, either exogenous or endogenous, altogether constituting the exposome. Instrumental development, especially mass spectrometry (MS) for the chemical analysis of small and large molecules, has been critical to the success achieved through WBE. Whereas all previous studies have been restricted to small molecules, the use of proteomics in WBE is an obvious extension of the intensive ongoing pursuit of biomarkers for community surveillance of diseases and health status. The relevance of human proteins present in wastewater as potential WBE indicators of population-prevalent diseases has been previously postulated, although no real examples to demonstrate their feasibility have been available until recently. These findings provide new insights into the characterization of wastewater proteomics useful for practical purposes related not only to WBE applications but also to environmental monitoring and wastewater treatment plant management.

Phylogeny and antimicrobial resistance of extended-spectrum beta-lactamaseproducing Escherichia coli from hospitalized oncology patients in Perú

ResumenIntroducción. Las infecciones asociadas con la atención en salud constituyen un problema de salud pública porque aumentan la morbimortalidad de los pacientes, sobre todo de aquellos con factores de riesgo, como la inmunosupresión debida a enfermedades oncológicas. Es importante conocer la diversidad genética de los principales microorganimos causantes de infecciones hospitalarias mediante la vigilancia epidemiológica tradicional y la epidemiología molecular, para hacer un mejor seguimiento y detectar brotes tempranamente.Objetivo. Determinar el grupo filogenético y la resistencia a antibióticos de las cepas de Escherichia coli aisladas de pacientes con cáncer hospitalizados.Materiales y métodos. Se hizo un estudio de tipo transversal que incluyó 67 cepas de Escherichia coli productoras de betalactamasas de espectro extendido (BLEE). Se determinó el grupo filogenético, el perfil de resistencia a los antibióticos, los genes de resistencia a betalactámicos, el tipo de las muestras y los servicios de hospitalización de donde fueron recuperadas.Resultados. El grupo filogenético más frecuente fue el B2 (36 %). El 57 % de las cepas B2 fueron aisladas de muestras de orina y el 33 % provenía del servicio de urología. La resistencia a ciprofloxacino y gentamicina fue de 91 y 53 %, respectivamente, y el 79 % de las cepas tenía el gen bla CTX-M. Se encontró una relación significativa (p<0,05) entre los grupos filogenéticos y la resistencia a ciprofloxacina, así como a la edad del paciente.Conclusión. El filogrupo de E. coli predominante fue el B2. Se evidenció una gran resistencia a ciprofloxacina y gentamicina, una proporción elevada de cepas BLEE con el bla CTX-M, y una relación entre el grupo filogenético y la resistencia a ciprofloxacino.

DIGITAL ACOUSTIC SURVEILLANCE OF COUGH FOR THE DETECTION OF RESPIRATORY DISEASE

TYPE: Abstract TOPIC: Biotechnology PURPOSE: To evaluate the potential of AI-enabled syndromic surveillance tools for the detection of outbreaks of respiratory disease. METHODS: We carried out a prospective observational study (NCT04762693) between November 2020 and August 2021 in the province of Navarra, Spain. Participants recruited from the campus of the University of Navarra, in Pamplona, and neighboring towns were instructed to download a publicly available smartphone application that automatically detects and records episodes of cough, with their time and location. Clinical records from consenting participants were reviewed regularly. Aggregated cough data was compared with local incidence of covid-19, and the time of medical consultation due to respiratory symptoms, using a randomization routine. Focused group discussions were carried out to determine barriers and facilitators for the usage of the application. RESULTS: Cough rates of participants who consulted medical services were higher in the five-day period around the date of consultation, compared to the baseline (p = 0.012), suggesting that longitudinal cough records reflected changes in symptomatology. In contrast, no clear relationship between aggregated cough frequency and covid-19 incidence in the study area was observed. Interface simplicity and the possibility of observing changes of cough frequency in real time were appreciated by participants. The possibility of programming the application so it automatically starts and stops at certain times was the most common recommendation. CONCLUSIONS: Acoustic cough monitoring can capture changes in symptomatology. Adequate uptake and usage remain challenging. CLINICAL IMPLICATIONS: Upon further development, AI-enabled cough detection could offer an attractive complementary tool to traditional respiratory epidemiological surveillance. DISCLOSURE: JB is the CEO of Hyfe Inc. EK is a Hyfe employee. CCh discloses consultancy fees and equity from Hyfe. All other authors declare no conflict of interests. KEYWORD: cough

Evidence of co-circulation of multiple arboviruses transmitted by Aedes species based on laboratory syndromic surveillance at a health unit in a slum of the Federal District, Brazil

BackgroundVector-borne diseases, especially arboviruses transmitted by Aedes sp. mosquitos, should be a health policy priority in Brazil. Despite this urgency, there are significant limitations in the traditional surveillance system, mainly in vulnerable areas. This study aimed to investigate the circulation of dengue (DENV), Zika (ZIKV), and chikungunya viruses (CHIKV) by laboratory syndromic surveillance (LSS) in a slum area of the Federal District of Brazil, comparing the results with traditional surveillance data.MethodsLSS for acute febrile and/or exanthematous symptoms was developed at a health unit of Cidade Estrutural, in order to identify the circulation of arboviruses transmitted by Aedes sp. mosquitos. Between June 2019 and March 2020, 131 valid participants were identified and sera tested by reverse transcription polymerase chain reaction (RT-PCR) for DENV (by serotype), ZIKV, and CHIKV acute infection and by immunoglobulin M enzyme-inked immunosorbent assay (ELISA-IgM) for DENV and CHIKV 15–21 days after symptom onset, when the participant reported no respiratory signs (cough and/or coryza). The results obtained were compared with traditional surveillance data for the study area and period.ResultsAt least three DENV-1 (2.3%), four DENV-2 (3%), and one CHIKV (0.7%) cases were confirmed in the laboratory, showing evidence of hyperendemicity even though LSS had not reached the historic peak dengue fever months in the Federal District (April–May). When the results obtained here were compared with traditional surveillance, a significant discrepancy was observed, including underreporting of CHIKV infection.ConclusionsIn addition to the risks posed to the study population, the area investigated with its respective socio-environmental profile may be a potential site for spread of the virus, given the cosmopolitan presence of Aedes sp. and human mobility in the Federal District. It is also suggested that traditional epidemiological surveillance may be reporting acute viral infections other than DENV as dengue fever, while underreporting other arboviruses transmitted by Aedes sp. mosquitos in the Federal District.Graphical

The Relationship Between Cancer Incidence and Awareness Activities and Google Online Search Volume in Turkey: A Retrospective Research

Objective: The aim of this study was to investigate the relationship between cancer incidence and Google search volumes for the six common types of cancer in Turkey. A second aim of the research is to determine whether online search activities are affected by awareness months for different cancer types. Study Design: A retrospective study was conducted by comparing the incidence of cancer between 2006 and 2017 for the cancers of lung, breast, stomach, prostate, colon and bladder published by IHME (The Institute for Health Metrics and Evaluation) and the Google Trends Search Volume designed by Google. The relationship between annual cancer incidence and Google Search Volume for selected cancer types was tested by Spearman Correlation analysis. Then, it was tested whether there was a difference in Google Search Volume averages of cancer types by months. Results: Google Search Volume has been found to have a significant and positive relationship with the incidence of stomach, colon and bladder cancers. In addition, it was concluded that cancer online searches were affected by awareness campaigns. Breast cancer had the highest Google search volume in October, which was determined as “Breast Cancer Awareness Month” and colon cancer was high in March, which was determined as “Colon Cancer Awareness Month”. Conclusion: These results indicate that online searches may reflect public awareness of the underlying cancer epidemiology. Enhanced understanding of online search patterns can increase traditional epidemiological surveillance, provide opportunities for targeted patient engagement, and enable the assessment of community awareness activities.

Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study.

BackgroundAs human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention.ObjectiveWe aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs.MethodsWe tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches.ResultsIncidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80%, 88%, 96%, 80%, and 78%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82%, 62%, 94%, 78%, and 62%, respectively.ConclusionsAn advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention.

Advantages and disadvantages of the use of eHealth technologies during the COVID-19 pandemic

Objective: to evaluate the experience and possibilities of the use of eHealth technologies during the pandemic.Material and methods. A systematic review of literature containing information about the use of telemedicine, crowdsourcing monitoring and other eHealth resources during the COVID-19 pandemic was performed. Results. The main advantages and disadvantages of the use of eHealth technologies have been highlighted, the main problems arising on the way of their implementation have been identified and options for their solution have been proposed.Conclusion. In developed countries the COVID-19 pandemic has already accelerated the implementation of eHealth as a tool to combat the pandemic. But despite this, new laws and regulations governing liability in the field of remote treatment are needed, as well as guidelines regarding the use of crowdsourcing disease monitoring systems in addition to traditional epidemiological surveillance systems.

Assessing Community Empowerment for an Innovative Epidemiologic Approach.

Community empowerment can be a process, but also the result of nursing care. To analyze it as a result there is an instrument that allows to quantify its level in nine domains. According to Melo (2020), health centers can be considered communities, becoming the potential target of community and public health nurses care, especially in the public health unit. One of the main functions of a public health unit is the epidemiological surveillance of the population’s health state. However, traditional epidemiological surveillance is focused on diseases and Melo (2020) proposes a new approach for epidemiology focused on people in what concerns nursing diagnosis. The aim of this research is to identify the level of empowerment of four Portuguese primary healthcare structures, named as ACeS, so as to improve the epidemiological surveillance of nursing diagnoses. As methodology, we developed four focus group with all nursing leaders from all primary care units of the four ACeS, using the Portuguese version of the empowerment assessment rating scale. The results present the level of community empowerment of each ACeS according to the nine domains of the scale. The needs of intervention to improve the ACeS empowerment were also identified in order to develop the epidemiological surveillance of nursing diagnoses.

1232. Potential Health and Cost Outcomes of Optimized Statistical Process Control Use for Surgical Site Infection Surveillance

BackgroundSurgical site infections (SSIs) are common (160,000–300,000 per year in the United States) and costly ($6,000–$25,500 per event) healthcare-associated infections with potentially lethal outcomes (2.1%–6.7% mortality rate). A prior analysis by our group suggested that statistical process control (SPC) can detect SSI outbreaks earlier than traditional epidemiological surveillance methods. This study aimed to quantify the potential impact of SPC surveillance on patient outcomes (prevented SSIs and deaths) and healthcare costs.MethodsWe retrospectively analyzed 30 SSI outbreaks occurring over a period of 8 years in a network of 50 community hospitals from the Southeastern United States. We applied 24 control chart variations, including 2 optimized for SSI surveillance, 6 with expert-defined pre-outbreak baselines (used in our pilot study), 4 with lagged rolling baselines (idem), and 12 common practice ones (using rolling baselines with no lag or fixed baselines). The charts used procedure-specific data from either the outbreak hospital or the entire network to compute baseline SSI rates. We calculated the average SSI rates during, before and after the outbreaks, and the months elapsed between SPC and traditional detection. We then used these values to estimate the number of SSIs that could have been prevented by SPC, and corresponding deaths avoided and cost savings (Figure 1).ResultsOptimized charts detected 96% of the outbreaks earlier than traditional surveillance, while pilot study and common practice charts did so only 65% (58%) of the time (Figure 2). Optimized charts could potentially prevent 15.2 SSIs, 0.64 deaths, and save $226,000 in excess care costs per outbreak. Overall, charts using network baselines performed better than those relying on local hospital data. Commonly used variations were the least effective, but were still able to improve on traditional surveillance (Figure 3).ConclusionSPC methods provide a great opportunity to prevent infections and deaths and generate cost savings, ultimately improving patient safety and care quality. While common practice SPC charts can also speed up outbreak detection, optimized SPC methods have a significantly higher potential to prevent SSIs and reduce healthcare costs. Disclosures All authors: No reported disclosures.

Outbreak of Shiga toxin-producing Escherichia coli O157:H7 linked to raw drinking milk resolved by rapid application of advanced pathogen characterisation methods, England, August to October 2017.

An outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 occurred on the Isle of Wight between August and October 2017. Of the seven cases linked to the outbreak, five were identified through the statutory notification process and two were identified through national surveillance of whole genome sequencing data. Enhanced surveillance questionnaires established a common link to a farm, and link to the likely food vehicle, raw drinking milk (RDM). Microbiological investigations, including PCR, identified the presence of STEC O157:H7 in samples of RDM. Analysis of core genome single nucleotide polymorphism (SNP) data of STEC O157:H7 from human stool specimens, animal faecal samples and RDM demonstrated a one SNP difference between isolates, and therefore close genetic relatedness. Control measures that were put in place included suspension of sales and recall of RDM, as well as restrictions on public access to parts of the farm. Successful integration of traditional epidemiological surveillance and advanced laboratory methods for the detection and characterisation of STEC O157:H7 from human, animal and environmental samples enabled prompt identification of the outbreak vehicle and provided evidence to support the outbreak control team’s decision-making, leading to implementation of effective control measures in a timely manner.