Syndromic Surveillance System Research Articles

EMS injury cause codes more accurate than emergency department visit ICD-10-CM codes for firearm injury intent in North Carolina.

The timeliness, accuracy, and completeness of data for firearm injury surveillance is crucial for public health surveillance efforts and informing injury prevention measures. While emergency department (ED) visit data can provide near real-time information on firearms injuries, there are concerns surrounding the accuracy of intent coding in these data. We examined whether emergency medical service (EMS) data provide more accurate firearm injury intent coding in comparison to ED data. We applied a firearm injury definition to EMS encounter data in NC's statewide syndromic surveillance system (NC DETECT), from January 1, 2021, through December 31, 2022. We manually reviewed each record to determine intent, and the corresponding manual classifications were compared to the injury cause codes entered in the EMS data and to ED visit records where EMS-ED record linkage was possible. We then calculated the sensitivity, specificity, positive and negative predictive values for each intent classification in SAS 9.4 using the manually reviewed intent classifications as the gold standard. We identified 9557 EMS encounters from January 1, 2021, through December 31, 2022 meeting our firearm injury definition. After removing false positives and duplicates, 8584 records were available for manual injury classification. Overall, our analysis demonstrated that manual and EMS injury cause code classifications were comparable. However, for the 3401 EMS encounters that could be linked to an ED visit record, sensitivity of the ED ICD-10-CM codes was low for assault and intentional self-harm encounters at 18.2% (CI 16.5-19.9%) and 22.2% (CI 16-28.5%), respectively. This demonstrates a marked difference in the reliability of the intent coding in the two data sources. This study illustrates both the value of examining EMS encounters for firearm injury intent, and the challenges of accurate intent coding in the ED setting. EMS coding has the potential for more accurate intent coding than ED coding within the context of existing hospital-based coding guidance. This may have implications for future firearm injury research, especially for nonfatal firearm injuries.

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

African countries from the Pasteur Network reexamine their syndromic sentinel surveillance system associated with household contact within the AFROSCREEN program.

Surveillance to better detect and respond to new pathogens remains a major challenge for global public health. The Pasteur Network recently held a brainstorming workshop located in Cameroon attended by Pasteur epidemiological teams from Niger, Central African Republic (CAR), Cameroon, Senegal, and Madagascar to discuss how the Pasteur Network in Africa could use the lessons of COVID-19 to set-up a pilot sentinel surveillance scheme given its expertise and involvement during the pandemic. The possibility of coupling sentinel syndromic and biological surveillance already implemented for influenza surveillance with the recent sequencing capacity put in place by the AFROSCREEN program prompted us to consider strengthening surveillance tools to target "Pathogen X" detection in Africa. The perspective project provided by the Pasteur Network teams and shared with other partners of the AFROSCREEN program will target strengthening of the diagnosis of severe acute respiratory infections (IRAS) and the surveillance of IRAS, the evaluation of the impact of SARS-CoV-2 on the epidemiology of IRAS, and the addition of the detection of new pathogens, called "Pathogen X," based on sequencing capacity and epidemiological criteria from One Health approaches.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

Syndromic surveillance performance in Canada throughout the COVID-19 pandemic, March 1, 2020 to March 4, 2023.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for robust surveillance of respiratory viruses. Syndromic surveillance continues to be an important surveillance component recommended by the World Health Organization (WHO). While FluWatchers, Canada's syndromic surveillance system, has been in place since 2015, the COVID-19 pandemic provided a valuable opportunity to expand the program's scope and underlying technology infrastructure. Following some structural changes to FluWatchers syndromic questionnaire, participants are now able to contribute valuable data to the non-specific surveillance of respiratory virus activity across Canada. This article examines the performance of FluWatchers' syndromic surveillance over the three years of the COVID-19 pandemic in Canada. More specifically, this article examines FluWatchers' performance with respect to the correlation between the FluWatchers influenza-like illness (ILI) and acute respiratory infection (ARI) indicators and total respiratory virus detections (RVDs) in Canada, including influenza, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and other respiratory viruses.

Evaluating the Effectiveness of School Closure in COVID-19-Related Syndromes From Community-Based Syndromic Surveillance: Longitudinal Observational Study.

During the COVID-19 pandemic, a school closure policy was adopted to prevent cluster transmission in schools and subsequent household transmission. However, the effectiveness of school closure is not consistent in studies conducted in different countries. This study aimed to explore the association between school closure and the daily standardized incidence of COVID-19-related syndromes in an outpatient syndromic surveillance system. We calculated the incidence of COVID-19-related syndromes derived from a community-based syndromic surveillance system between the first week of January and the second or fourth weeks after school closure in 2021 and 2022 in Taipei City, Taiwan. The effect of school closure on the standardized incidence of COVID-19-related syndromes was evaluated by interrupted time series analysis using an autoregressive integrated moving average with a distributed lag function. The exogenous variables were changes in human mobility measured by Google COVID-19 community mobility reports. Furthermore, the models quantified the influence of different age groups and the hierarchy of medical facilities, such as clinics or community hospitals. School closure was only negatively and significantly associated with the overall standardized incidence of COVID-19-related syndromes in 2021 for 2 weeks after the intervention (coefficient -1.24, 95% CI -2.40 to -0.08). However, in different age groups, school closure had a significantly negative association with the standardized incidence among people aged 13-18 years and ≥65 years for 2 weeks after the intervention in clinics in 2021. In community hospitals, school closure was significantly positively associated with the standardized incidence among people aged 19-24 years in 2021. In 2022, 2 weeks after the intervention, school closure had a significantly negative association with the standardized incidence among people aged 0-6, 7-12, and 19-24 years in community hospitals and aged >45 years in clinics. Furthermore, the standardized incidence was positively associated with movement change toward grocery and pharmacy stores in all age groups in 2022. In addition, movement changes toward residences were significantly positively associated with the standardized incidence among all age groups. Overall, school closure effectively suppresses COVID-19-related syndromes in students owing to the reduction of physical contact. In addition, school closure has a spillover effect on elderly people who stay at home.

Epidemiological surveillance of respiratory viral infections in SARS-CoV-2-negative samples during COVID-19 pandemic in Iran

BackgroundTo improve the patient care, public health surveillance, and infection control, it is crucial to identify the presence and frequency of the common respiratory infections in individuals with COVID-19 symptoms but tested negative for SARS-CoV-2. This study aimed to shed light on this during the COVID-19 pandemic in Iran.MethodsIn this cross-sectional study, a total of 1,002 patients with acute respiratory infection who had negative SARS-CoV-2 test results and referred to Valfajr Health Center, the National Collaborating Laboratory of Influenza and COVID-19 National Reference Laboratory at Pasteur Institute of Iran were recruited between January 2020 and January 2022. Nasopharyngeal and oropharyngeal swab samples were collected to detect 17 common respiratory viruses via TaqMan one-step real-time multiplex PCR. Demographic and clinical data of the participants were obtained from their electronic medical records.ResultsIn total, 218 samples (21.8%) were tested positive for at least one respiratory virus infection. Most of the common investigated respiratory viruses belonged to the years 2020 and 2022. The number of investigated patients in 2021 was few, which highlights the impact of health measures following the COVID-19 pandemic in Iran. Influenza A was the most common virus (5.8%), while adenovirus had the lowest prevalence (0.1%). Although the rate of respiratory virus infection was higher in men (24%) compared to women (19.3%), this difference was not statistically significant (P = 0.069). The prevalence of respiratory viruses had an inverse association with increasing age, with the highest rate (55.6%) observed in the age group below 2 years and the lowest rate (12.7%) in those above 65 years.ConclusionOur findings underscore the significance of adopting a comprehensive approach to respiratory infections detection and management. These results can be employed for the development of syndromic surveillance systems and implementation of the effective infection control measures. Furthermore, the results contribute to better understanding of the dynamics of respiratory viruses, both during pandemic periods and in non-pandemic contexts.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

The prevalence and pattern of mask use and its association with COVID-19-related morbidity and mortality in Tabriz, Iran

Background. Using personal protective equipment, including masks, is one of the most important ways to prevent coronavirus disease 2019 (COVID-19). The aim of this study was to estimate the prevalence and pattern of mask use and its association with COVID-19-related morbidity and mortality in different areas of Tabriz, Iran. Methods. In this population-based study, 6,600 pedestrians were selected by multi-stage proportional sampling method in nine areas of Tabriz in 2021. The data was collected using the observation method. For each participant, we recorded gender, approximate age, mask usage, type of mask, and correct or incorrect use. The data of COVID-19-related morbidity and mortality for six months (from 21 March 2021 to 22 September 2021) were obtained from the Integrated Syndromic Surveillance System (ISSS). Results. The overall prevalence of facial mask use was 56.8% (95% CI: 55.56 – 57.95). Out of 3,421 men, 1,599 (46.7%; 95% CI: 45.1-48.4) and out of 3,179 women, 2,147 (67.5%; 95% CI: 65.9-69.2) were used face mask (P&lt;0.001). The correlations between mask use and morbidity (r=0.375, P=0.320) and mortality (r=0.219, P=0.571) rates were not statistically significant. Conclusion. The percentage of using face masks in Tabriz was relatively low, especially among males and young people. Also, there was no significant relationship between COVID-19 morbidity and mortality and mask use. Practical Implications. The results of the present study showed that the percentage of mask use in men and the youth was relatively low. In times of disease epidemics, it is necessary to create the necessary sensitivity in these groups and to carry out the necessary training and interventions to increase the use of masks and protective measures.

Impact of the 7/14/2016 Nice terrorist attack on pediatric emergency department visits thanks to syndromic surveillance: a descriptive study.

Study the impact of 14th July 2016 Nice terrorist attack on Pediatric Emergency Department (PED) visits by youth under 18 years of age. PED visits diagnoses (ICD10) were clustered and analyzed based on retrospective data from the syndromic surveillance system of the Children's university hospital of Nice (Southern France). The studied period ranges from 2013 to 2019, i.e., 3 years before and after the terrorist attack of 14th July 2016. Among 416,191 PED visits, the number of visits for stress in 4-17 years old appeared to increase in the 3 years after the attack compared to the 3 years before, particularly in September 2016 (acute effect) with 11 visits compared to an average of 2.3 visits per month from September 2013 to 2016 (p = 0.001827). In September 2017, we noticed 21 visits compared to an average of 4.8 visits per month during the following period (2013-2019). In 2017, PED visits for stress among 4-17 year olds were higher in comparison to the other years of the study: 107 visits compared to an annual average of 57. To our knowledge, this is the first study of the use of the pediatric care system before and after a terrorist attack involving syndromic surveillance. This suggests acute and long-term effects of the terrorist attack on PED use by youth for mental health issues. Further studies of the pediatric care system involving syndromic surveillance are needed in the context of mass violent events, such as terrorist attacks.

Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy.

Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.

Recent Advances in the Use of the Mortality Syndromic Surveillance System-New York City, 2015-2020.

New York City's automated mortality syndromic surveillance system monitors temporal and spatial patterns in mortality. To describe the use of the syndromic surveillance system, we used the system to find mortality patterns for the 15 leading causes of death and for deaths from rare and reportable diseases in New York City from February 2015 through June 2020. We used results to find aberrations that indicate threats to public health. We used unobserved components models to analyze time series of mortality counts for leading causes of death, historical limits methods for rare and reportable diseases, and SaTScan for temporal-spatial cluster analysis. We obtained data on the number of deaths from the electronic death registry system maintained by the city's Bureau of Vital Statistics. The mortality syndromic surveillance system detected an increase in the number of deaths from heart disease by April 1, 2020, when 75.0 deaths occurred on March 24, 2020, instead of an expected 45.8 deaths (95% upper prediction limit of 61.0) and an increase in the number of deaths from all causes on March 20, 2020, when 194.0 deaths were observed while 150.1 deaths were expected (95% upper prediction limit of 178.0). The number of deaths from all causes returned to normal the week beginning June 14, 2020, when 990.0 deaths were observed and 998.8 deaths were expected. When compared with efforts from New York City to provide yearly vital statistics, the automated mortality syndromic surveillance system can provide timely mortality data with fewer resources and raise the capacity to detect anomalous increases in mortality.

Comparison of statistical methods for the early detection of disease outbreaks in small population settings

ObjectivesThis study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases. MethodsTo address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions. ResultsThe study found that while the Early Aberration Reporting System–C1 algorithm developed by Hutwagner et al. outperformed others, no single approach provided reliable monitoring across all outbreak types. Furthermore, aberration detection approaches could only detect very large and acute outbreaks with any reliability. ConclusionThe findings of this study suggest that algorithm-based approaches to outbreak signal detection perform poorly when applied to settings with small numbers of background cases and should not be relied upon in these contexts. This highlights the need for alternative approaches for accurate and timely outbreak detection in small population settings, particularly those that are resource-constrained.

Pregnancy and Emergency Department Utilization in North Carolina, 2016–2021: A Population-Based Surveillance Study

Pregnancy-associated complaints are a common reason for emergency department visits for women of reproductive age. Emergency department utilization during pregnancy is associated with worse birth outcomes for both mothers and infants. We used statewide North Carolina emergency department surveillance data between 2016 and 2021 to describe the sociodemographic factors associated with the use of emergency department for pregnancy-associated problems and subsequent hospital admission. North Carolina Disease Event Tracking and Epidemiologic Collection Tool is a syndromic surveillance system that includes all emergency department encounters at civilian acute-care facilities in North Carolina. We analyzed all emergency department visits between January 1, 2016 and December 31, 2021 for female patients aged 15-44 years residing in North Carolina with at least 1 ICD-10-CM code (analysis occurred in July 2021-October 2022). Each emergency department visit was categorized as pregnancy-associated if assigned ICD-10-CM code(s) indicated pregnancy. We stratified visits by age, race, ethnicity, county of residence, and insurance and compared them with estimated pregnant population proportions using 1-sample t-tests. We used multivariable logistic regression to determine whether pregnancy-associated visits were more likely to be associated with hospital admission and then to determine sociodemographic predictors of admission among pregnancy-associated emergency department visits. More than 6.4 million emergency department visits were included (N=6,471,197); 10.1% (n=655,476) were pregnancy-associated, significantly higher than the proportion of women estimated to be pregnant at any given time in North Carolina (4.6%, p<0.0001) and increased over time (8.6% in 2016 vs 11.1% in 2021, p<0.0001). Pregnancy-associated visits were lower than expected for ages 25-44 years and higher than expected for those aged 15-24 years, for those of Black race, and for patients residing in rural or suburban areas. The proportion admitted was higher for pregnancy-associated emergency department visits than for nonpregnancy associated (15.6% vs 7.0%, AOR=3.06 [95% CI=3.03, 3.09]). Pregnancy-associated emergency department visits for patients of Black race had 0.58 times (95% CI=0.57, 0.59) the odds of admission compared with White patients. Emergency department utilization during pregnancy is common. The proportion of pregnancy-associated emergency department visits among reproductive-age women is increasing, as are inpatient admissions from the emergency department for pregnancy-associated diagnoses. Use of public health surveillance databases such as the North Carolina Disease Event Tracking and Epidemiologic Collection Tool may help identify opportunities for improving disparities in maternal health care, especially related to access to care.

Communicable Disease Surveillance in Lebanon during the Syrian Humanitarian Crisis, 2013-2019.

Lebanon has been one of the most affected countries by the Syrian humanitarian crisis. The national communicable disease surveillance was enhanced to detect outbreaks among Syrians. In this study, we aim to describe the findings of the communicable disease surveillance among Syrians in Lebanon, compare it to residents' data, and describe the implemented surveillance activities between 2013 and 2019. During the study period, data on communicable diseases was mainly collected through the routine national surveillance system and an enhanced syndromic surveillance system. Predefined case definitions and standard operating procedures were in place. Data collection included both case-based and disease-specific reporting forms. Descriptive data and incidence rates were generated. Information was disseminated through weekly reports. Activities were conducted in close collaboration with different partners. The most commonly reported diseases were: viral hepatitis A, cutaneous leishmaniasis, mumps, and measles. Hepatitis A incidence increased in 2013 and 2014 among Syrians as well as residents. For leishmaniasis, the incidence increased only among Syrians in 2013 and decreased after that. An outbreak of mumps was reported among Syrians between 2014 and 2016, with a peak in 2015 concomitant with a national outbreak. Outbreaks of measles were reported among Syrians and residents in 2013, 2018, and 2019. The infrastructure of the well-implemented surveillance system in Lebanon has been utilized to monitor the health status of Syrians in Lebanon, early detect communicable diseases among this population, and guide needed preventive and control measures. This highlights the importance of having a flexible surveillance system that can be adapted to emergencies and the importance of sharing results with involved partners.

New developments and expansion of the GP in-hours syndromic surveillance system: collaboration between UKHSA and the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub.

The UK Health Security Agency have established a partnership with the Oxford-Royal College of General Practitioners (RCGP) Clinical Informatics Digital Hub (ORCHID) to provide a new source of primary care data to enhance an existing GP in-hours syndromic surveillance system. To provide better coverage of the English population and more comprehensive information to inform public health action. We produced indicators using SNOMED CT codes grouped into syndromes and validated the outputs against the existing GP in-hours data. A daily data feed from the ORCHID system at Oxford to the GP in-hours syndromic surveillance system was established for routine surveillance. Population coverage increased from 6.8 to 18.8 million patients, representative of the English population. More granular data are available at postcode district and lower layer super output level (LSOA), providing better geographical and sociodemographic coverage, and enabling the investigation of social inequalities on conditions diagnosed in primary care. New indicators can be implemented rapidly in response to new public health threats. For decades the RCGP has run sentinel surveillance with integrated virology and now RCGP data are being used for syndromic surveillance. The addition of ORCHID data to the GP in-hours syndromic surveillance system has improved the utility of the system. There is better population coverage, additional syndromic indicators, and greater ability to interrogate the data. The new system will provide enhanced information to support public health in England and highlights the value of GP records for use in protecting the health security of the nation.

Benefits of Using a Transdisciplinary Approach for the Design and Operationalization of a Surveillance System

Abstract During the preparatory phase of a surveillance project in Western Kenya, it was noted that several animal health activities, including meat inspection, were recorded using paper-based forms. Through discussions with the meat inspectors and other slaughterhouse actors, an electronic meat inspection form was developed, tested, and used in two slaughterhouses in Bungoma County. The meat inspectors completed and submitted a form for every animal slaughtered at their facility over a 2.75-year period. In a feedback meeting with the meat inspectors and other stakeholders, areas for improvement were highlighted, such as the timely provision of feedback to the data providers, and the inclusion of data fields that allow for more detailed animal movement information. The meat inspectors remarked that the form made them feel appreciated and added value to their work as the data could be used to educate farmers on prevailing livestock conditions and provide economic incentives for disease control interventions. The data submitted via the electronic forms were of sufficient quality to be summarized and identify trends in the animal slaughterhouse throughput, as well as frequent reasons for meat condemnation and associated costs. These data could therefore set the foundation for a syndromic surveillance system based on slaughterhouse data. Other non-traditional data sources, such as livestock and meat transport certificates, could also be leveraged to create a post-farm animal health information system. Information © The Authors 2023

Development and application syndromic surveillance and early warning system in border area in Yunnan Province

Objective: To establish a dynamic syndromic surveillance system in the border areas of Yunnan Province based on information technology, evaluate its effectiveness and timeliness in the response to common communicable disease epidemics and improve the communicable disease prevention and control in border areas. Methods: Three border counties were selected for full coverage as study areas, and dynamic surveillance for 14 symptoms and 6 syndromes were conducted in medical institutions, the daily collection of information about students' school absence in primary schools and febrile illness in inbound people at border ports were conducted in these counties from January 2016 to February 2018 to establish an early warning system based on mobile phone and computer platform for a field experimental study. Results: With syndromes of rash, influenza-like illness and the numbers of primary school absence, the most common communicable disease events, such as hand foot and mouth disease, influenza and chickenpox, can be identified 1-5 days in advance by using EARS-3C and Kulldorff time-space scanning models with high sensitivity and specificity. The system is easy to use with strong security and feasibility. All the information and the warning alerts are released in the form of interactive charts and visual maps, which can facilitate the timely response. Conclusions: This system is highly effective and easy to operate in the detection of possible outbreaks of common communicable diseases in border areas in real time, so the timely and effective intervention can be conducted to reduce the risk of local and cross-border communicable disease outbreaks. It has practical application value.

Point and Lifetime Prevalence of Sexually Transmitted Diseases Based on the Definitions of the Iranian Syndrome Surveillance System: A Hospital-Based Survey

Background: Sexually transmitted infections (STIs) are among the most common infectious diseases and a globally concerning public health issue, especially in developing countries. Objectives: This study aimed to evaluate the syndrome-based point and lifetime prevalence of sexually transmitted infections in a big Iranian city. Methods: This cross-sectional study was conducted in 2019 on a hospital-based random sample of 2107 people (men and women aged 15 - 50) at Marvdasht Central Hospital (Iran). Results: The lifetime prevalence of Sexually Transmitted Diseases (STDs) was significantly higher in women than in men (81.56% vs. 33.60%, P-value &lt; 0.0001). Also, about 20.7% of men and 48.26% of women had at least one of the STIs-associated syndromes at the time of the interview (instantaneous prevalence) (P-value = 0.001). Conclusions: The estimated prevalence of syndromes associated with sexually transmitted infections is alarmingly high. The results emphasize the need for further studies on effective health care and health promotion services to reduce STIs (including early detection and treatment of infections and public education).

Interrupted time series segmented regression analysis for detecting waterborne disease outbreaks by syndromic surveillance.

Pathogens can enter the drinking water supply and cause gastroenteritis outbreaks. Such events can affect many people in a short time, making them a high risk for public health. In Australia, the Victoria State Government Department of Health is deploying a syndromic surveillance system for drinking water contamination events. We assessed the utility of segmented regression models for detecting such events and determined the number of excess presentations needed for such methods to signal a detection. The study involved an interrupted time series study of a past lapse in water treatment. The baseline period comprised the four weeks before the minimum incubation period of suspected pathogens, set at two days post-event. The surveillance period comprised the week after. We used segmented linear regression to compare the count of gastroenteritis presentations to public hospital emergency departments (EDs) between the surveillance and baseline periods. We then simulated events resulting in varying excess presentations. These were superimposed onto the ED data over fifty different dates across 2020. Using the same regression, we calculated the detection probability at p < 0.05 for each outbreak size. In the retrospective analysis, there was strong evidence for an increase in presentations shortly after the event. In the simulations, with no excess presentations (i.e., with the ED data as is) the models signalled 8% probability of detection. The models returned 50% probability of detection with 28 excess presentations and 100% probability of detection with 78 excess presentations. The transient increase in presentations after the event may be attributed to microbiological hazards or increased health-seeking behaviour following the issuing of boil water advisories. The simulations demonstrated the ability for segmented regressions to signal a detection, even without a large excess in presentations. The approach also demonstrated high specificity and should be considered for informing Victoria's syndromic surveillance system.