Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.

BACKGROUND: Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates. METHODS: We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status. RESULTS: For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS. CONCLUSION: Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.

[Mental health surveillance at the Robert Koch Institute - strategies for monitoring the mental health of the population]. / Mental Health Surveillance am Robert Koch-Institut ­ Strategien zur Beobachtung der psychischen Gesundheit der Bevölkerung.

The continuous and systematic surveillance of the health of populations is fundamental for effective public health practice. In light of the growing importance of mental health within population health, a Mental Health Surveillance for Germany is being established at the Robert Koch Institute. Its aim is to continually provide reliable information on the current state and development of the mental health of the population.Three surveillance strategies are currently being pursued: 1) Regular comprehensive assessments aim to describe the mental health status of the population using a wide range of indicators and data sources and to observe long-term developments. They build on existing work in epidemiology and health services research. 2) High-frequency monitoring of a selection of indicators is used for the early detection of trends. 3) A continuous literature review collates current findings on mental health developments in the COVID-19 pandemic on a monthly basis. The latter two strategies were implemented in response to new information needs in the pandemic.This paper describes and discusses these three strategies and their functions, limitations, and potential for development. Their results are communicated through different forms of reporting and serve to identify needs for action and research in public mental health. The further development and long-term operation of the Mental Health Surveillance as a whole has the potential to facilitate the achievement of public mental health objectives and to contribute on different levels to the improvement of population health.

Covid-19 Surveillance Testing and Resident Outcomes in Nursing Homes.

BACKGROUND: Despite widespread adoption of surveillance testing for coronavirus disease 2019 (Covid-19) among staff members in skilled nursing facilities, evidence is limited regarding its relationship with outcomes among facility residents. METHODS: Using data obtained from 2020 to 2022, we performed a retrospective cohort study of testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among staff members in 13,424 skilled nursing facilities during three pandemic periods: before vaccine approval, before the B.1.1.529 (omicron) variant wave, and during the omicron wave. We assessed staff testing volumes during weeks without Covid-19 cases relative to other skilled nursing facilities in the same county, along with Covid-19 cases and deaths among residents during potential outbreaks (defined as the occurrence of a case after 2 weeks with no cases). We reported adjusted differences in outcomes between high-testing facilities (90th percentile of test volume) and low-testing facilities (10th percentile). The two primary outcomes were the weekly cumulative number of Covid-19 cases and related deaths among residents during potential outbreaks. RESULTS: During the overall study period, 519.7 cases of Covid-19 per 100 potential outbreaks were reported among residents of high-testing facilities as compared with 591.2 cases among residents of low-testing facilities (adjusted difference, -71.5; 95% confidence interval [CI], -91.3 to -51.6). During the same period, 42.7 deaths per 100 potential outbreaks occurred in high-testing facilities as compared with 49.8 deaths in low-testing facilities (adjusted difference, -7.1; 95% CI, -11.0 to -3.2). Before vaccine availability, high- and low-testing facilities had 759.9 cases and 1060.2 cases, respectively, per 100 potential outbreaks (adjusted difference, -300.3; 95% CI, -377.1 to -223.5), along with 125.2 and 166.8 deaths (adjusted difference, -41.6; 95% CI, -57.8 to -25.5). Before the omicron wave, the numbers of cases and deaths were similar in high- and low-testing facilities; during the omicron wave, high-testing facilities had fewer cases among residents, but deaths were similar in the two groups. CONCLUSIONS: Greater surveillance testing of staff members at skilled nursing facilities was associated with clinically meaningful reductions in Covid-19 cases and deaths among residents, particularly before vaccine availability.

Innovations in Public Health Surveillance for Emerging Infections.

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before.

[Integrated Surveillance in Italy: outputs and related activities]. / La sorveglianza integrata COVID-19 in Italia: output e attività correlate.

OBJECTIVES: to describe the integrated surveillance system of COVID-19 in Italy, to illustrate the outputs used to return epidemiological information on the spread of the epidemic to the competent public health bodies and to the Italian population, and to describe how the surveillance data contributes to the ongoing weekly regional monitoring and risk assessment system. METHODS: the COVID-19 integrated surveillance system is the result of a close and continuous collaboration between the Italian National Institute of Health (ISS), the Italian Ministry of Health, and the regional and local health authorities. Through a web platform, it collects individual data of laboratory confirmed cases of SARS-CoV-2 infection and gathers information on their residence, laboratory diagnosis, hospitalisation, clinical status, risk factors, and outcome. Results, for different levels of aggregation and risk categories, are published daily and weekly on the ISS website, and made available to national and regional public health authorities; these results contribute one of the information sources of the regional monitoring and risk assessment system. RESULTS: the COVID-19 integrated surveillance system monitors the space-time distribution of cases and their characteristics. Indicators used in the weekly regional monitoring and risk assessment system include process indicators on completeness and results indicators on weekly trends of newly diagnosed cases per Region. CONCLUSIONS: the outputs of the integrated surveillance system for COVID-19 provide timely information to health authorities and to the general population on the evolution of the epidemic in Italy. They also contribute to the continuous re-assessment of risk related to transmission and impact of the epidemic thus contributing to the management of COVID-19 in Italy.

Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study.

BACKGROUND: Multiple voluntary surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of population-based COVID-19 epidemiology. During this time, testing criteria broadened and health-care policies matured. We aimed to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three surveillance platforms in three countries (two platforms per country), during periods of testing and policy changes. METHODS: For this observational study, we used data of observations from three volunteer COVID-19 digital surveillance platforms (Carnegie Mellon University and University of Maryland Facebook COVID-19 Symptom Survey, ZOE COVID Symptom Study app, and the Corona Israel study) targeting communities in three countries (Israel, the UK, and the USA; two platforms per country). The study population included adult respondents (age 18-100 years at baseline) who were not health-care workers. We did logistic regression of self-reported symptoms on self-reported SARS-CoV-2 test status (positive or negative), adjusted for age and sex, in each of the study cohorts. We compared odds ratios (ORs) across platforms and countries, and we did meta-analyses assuming a random effects model. We also evaluated testing policy changes, COVID-19 incidence, and time scales of duration of symptoms and symptom-to-test time. FINDINGS: Between April 1 and July 31, 2020, 514 459 tests from over 10 million respondents were recorded in the six surveillance platform datasets. Anosmia-ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test (robust aggregated rank one, meta-analysed random effects OR 16·96, 95% CI 13·13-21·92). Fever (rank two, 6·45, 4·25-9·81), shortness of breath (rank three, 4·69, 3·14-7·01), and cough (rank four, 4·29, 3·13-5·88) were also highly associated with test positivity. The association of symptoms with test status varied by duration of illness, timing of the test, and broader test criteria, as well as over time, by country, and by platform. INTERPRETATION: The strong association of anosmia-ageusia with self-reported positive SARS-CoV-2 test was consistently observed, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform, country, phase of illness, or testing policy. These findings show that associations between COVID-19 symptoms and test positivity ranked similarly in a wide range of scenarios. Anosmia, fever, and respiratory symptoms consistently had the strongest effect estimates and were the most appropriate empirical signals for symptom-based public health surveillance in areas with insufficient testing or benchmarking capacity. Collaborative syndromic surveillance could enhance real-time epidemiological investigations and public health utility globally. FUNDING: National Institutes of Health, National Institute for Health Research, Alzheimer's Society, Wellcome Trust, and Massachusetts Consortium on Pathogen Readiness.

Developing a sampling methodology for timely reporting of population-based COVID-19-associated hospitalization surveillance in the United States, COVID-NET 2020-2021.

BACKGROUND: The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) required a sampling methodology that allowed for production of timely population-based clinical estimates to inform the ongoing US COVID-19 pandemic response. METHODS: We developed a flexible sampling approach that considered reporting delays, differential hospitalized case burden across surveillance sites, and changing geographic and demographic trends over time. We incorporated weighting methods to adjust for the probability of selection and non-response, and to calibrate the sampled case distribution to the population distribution on demographics. We additionally developed procedures for variance estimation. RESULTS: Between March 2020 and June 2021, 19,293 (10.4%) of all adult hospitalized cases were sampled for chart abstraction. Variance estimates for select variables of interest were within desired ranges. CONCLUSIONS: COVID-NET's sampling methodology allowed for reporting of robust and timely, population-based data on the clinical epidemiology of COVID-19-associated hospitalizations and evolving trends over time, while attempting to reduce data collection burden on surveillance sites. Such methods may provide a general framework for other surveillance systems needing to quickly and efficiently collect and disseminate data for public health action.

Continuing Contributions of Field Epidemiology Training Programs to Global COVID-19 Response.

We documented the contributions of Field Epidemiology Training Program (FETP) trainees and graduates to global COVID-19 preparedness and response efforts. During February-July 2021, we conducted surveys designed in accordance with the World Health Organization's COVID-19 Strategic Preparedness and Response Plan. We quantified trainee and graduate engagement in responses and identified themes through qualitative analysis of activity descriptions. Thirty-two programs with 2,300 trainees and 7,372 graduates reported near-universal engagement across response activities, particularly those aligned with the FETP curriculum. Graduates were more frequently engaged than were trainees in pandemic response activities. Common themes in the activity descriptions were epidemiology and surveillance, leading risk communication, monitoring and assessment, managing logistics and operations, training and capacity building, and developing guidelines and protocols. We describe continued FETP contributions to the response. Findings indicate the wide-ranging utility of FETPs to strengthen countries' emergency response capacity, furthering global health security.

Evaluation of integrated disease surveillance and response (IDSR) and early warning and response network (EWARN) in South Sudan 2021.

Introduction: South Sudan has been implementing the Integrated Disease Surveillance and Response (IDSR) strategy since 2006, along with Early Warning and Alert Response and Network (EWARN). The IDSR/EWARN stakeholders commissioned an independent evaluation to establish performance at national, state, county, health facility, and community levels in the first half of 2021. Methods: the evaluation was conducted between June and September 2021 (during the COVID-19 pandemic) and was based on the World Health Organization (WHO) protocols for monitoring and evaluating communicable disease surveillance and response systems and the guidelines for evaluating EWARN. Results: integrated disease surveillance and response/early warning and alert response and network indicator data showed improving timeliness and completeness from the beginning of 2021 to week 16 and then a slight depression of timeliness by week 32, while completeness remained high. Event-based surveillance was active at the beginning of 2021 and in week 32. However, there was inadequate sample collection to investigate acute watery diarrhea, bloody diarrhea, and acute jaundice syndrome alerts. Respondents in all cadres had substantial experience working in IDSR/EWARN. All respondents performed the various IDSR/EWARN tasks and duties as expected, but needed more resources and training. Conclusion: while IDSR/EWARN is performing relatively well, confirmation of priority diseases by the laboratories needs to be strengthened. Health facilities need more regular supervision from the higher levels. Community health workers need more training on IDSR/EWARN. The whole IDSR/EWARN system needs more resources, particularly for communication and transport and to confirm priority diseases. Staff at all levels requested more training in IDSR/EWARN.

Respiratory surveillance wards as a strategy to reduce nosocomial transmission of COVID-19 through early detection: The experience of a tertiary-care hospital in Singapore.

OBJECTIVES: Patients with COVID-19 may present with respiratory syndromes indistinguishable from those caused by common viruses. Early isolation and containment is challenging. Although screening all patients with respiratory symptoms for COVID-19 has been recommended, the practicality of such an effort has yet to be assessed. METHODS: Over a 6-week period during a SARS-CoV-2 outbreak, our institution introduced a "respiratory surveillance ward" (RSW) to segregate all patients with respiratory symptoms in designated areas, where appropriate personal protective equipment (PPE) could be utilized until SARS-CoV-2 testing was done. Patients could be transferred when SARS-CoV-2 tests were negative on 2 consecutive occasions, 24 hours apart. RESULTS: Over the study period, 1,178 patients were admitted to the RSWs. The mean length-of-stay (LOS) was 1.89 days (SD, 1.23). Among confirmed cases of pneumonia admitted to the RSW, 5 of 310 patients (1.61%) tested positive for SARS-CoV-2. This finding was comparable to the pickup rate from our isolation ward. In total, 126 HCWs were potentially exposed to these cases; however, only 3 (2.38%) required quarantine because most used appropriate PPE. In addition, 13 inpatients overlapped with the index cases during their stay in the RSW; of these 13 exposed inpatients, 1 patient subsequently developed COVID-19 after exposure. No patient-HCW transmission was detected despite intensive surveillance. CONCLUSIONS: Our institution successfully utilized the strategy of an RSW over a 6-week period to contain a cluster of COVID-19 cases and to prevent patient-HCW transmission. However, this method was resource-intensive in terms of testing and bed capacity.

Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015-2020.

BACKGROUND: Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19. METHODS AND FINDINGS: We used data from the World Health Organization's Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015-2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity. CONCLUSIONS: Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020.

Sample-to-answer, extraction-free, real-time RT-LAMP test for SARS-CoV-2 in nasopharyngeal, nasal, and saliva samples: Implications and use for surveillance testing.

The global COVID-19 pandemic has highlighted the need for rapid, accurate and accessible nucleic acid tests to enable timely identification of infected individuals. We optimized a sample-to-answer nucleic acid test for SARS-CoV-2 that provides results in <1 hour using inexpensive and readily available reagents. The test workflow includes a simple lysis and viral inactivation protocol followed by direct isothermal amplification of viral RNA using RT-LAMP. The assay was validated using two different instruments, a portable isothermal fluorimeter and a standard thermocycler. Results of the RT-LAMP assay were compared to traditional RT-qPCR for nasopharyngeal swabs, nasal swabs, and saliva collected from a cohort of patients hospitalized due to COVID-19. For all three sample types, positive agreement with RT-LAMP performed using the isothermal fluorimeter was 100% for samples with Ct <30 and 69-91% for samples with Ct <40. Following validation, the test was successfully scaled to test the saliva of up to 400 asymptomatic individuals per day as part of the campus surveillance program at Rice University. Successful development, validation, and scaling of this sample-to-answer, extraction-free real-time RT-LAMP test for SARS-CoV-2 adds a highly adaptable tool to efforts to control the COVID-19 pandemic, and can inform test development strategies for future infectious disease threats.

Small area population denominators for improved disease surveillance and response.

The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.

The Italian Mandatory Notification System: An Important Public Health Tool For Continuous Monitoring Of Infectious Diseases.

Infectious diseases still register significant morbidity and mortality worldwide. Surveillance through a mandatory notification system allows the continuous analysis of the situation even at a local level and its importance has been highlighted by the recent COVID-19 pandemic. This paper aimed to outline the importance of the mandatory notification system as a Public Health tool in the continuous monitoring of infectious diseases. To this aim, we carried out a cross-sectional study examining the notifications reported in the Italian territory of Messina, Sicily, in the period 2001-2020. The institutional websites were examined and the notification data were used to obtain the incidences. Overall, a significant reduction of the incidence notification trend was observed. Chickenpox was by far the most notified infectious disease, followed by scabies, pediculosis, and brucellosis. Outbreaks of brucellosis, measles and hepatitis A occurred. All the diseases decreased over time, except syphilis, for which a significant increase was observed. Surveillance of infectious diseases through a mandatory notification system remains a bulwark of public health despite underreporting. Our study reflects the situation of a typical high-income area, although some unexpected criticisms are highlighted. Continuous information about correct behaviors through education campaigns are crucial in order to improve the situation. Keywords: mandatory notifications, infectious diseases, surveillance, public health Corresponding author: Alessio Facciolà, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Italy. Email: afacciola@unime.it.

Hospitalization of Infants and Children Aged 0-4 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 2020-February 2022.

The B.1.1.529 (Omicron) variant of SARS-CoV-2, the virus that causes COVID-19, has been the predominant circulating variant in the United States since late December 2021.* Coinciding with increased Omicron circulation, COVID-19-associated hospitalization rates increased rapidly among infants and children aged 0-4 years, a group not yet eligible for vaccination (1). Coronavirus Disease 19-Associated Hospitalization Surveillance Network (COVID-NET)† data were analyzed to describe COVID-19-associated hospitalizations among U.S. infants and children aged 0-4 years since March 2020. During the period of Omicron predominance (December 19, 2021-February 19, 2022), weekly COVID-19-associated hospitalization rates per 100,000 infants and children aged 0-4 years peaked at 14.5 (week ending January 8, 2022); this Omicron-predominant period peak was approximately five times that during the period of SARS-CoV-2 B.1.617.2 (Delta) predominance (June 27-December 18, 2021, which peaked the week ending September 11, 2021).§ During Omicron predominance, 63% of hospitalized infants and children had no underlying medical conditions; infants aged <6 months accounted for 44% of hospitalizations, although no differences were observed in indicators of severity by age. Strategies to prevent COVID-19 among infants and young children are important and include vaccination among currently eligible populations (2) such as pregnant women (3), family members, and caregivers of infants and young children (4).

One and a half years into the COVID-19 pandemic - exit strategies and efficacy of SARS-CoV-2 vaccines for holistic management and achieving global control

One and a half years into the pandemic, SARS-CoV-2 is still here to stay. Whilst rapid several effective COVID-19 vaccines have been developed and are being rolled out, the critical questions remain whether vaccines provide widespread protection against infection and reinfection, and what the duration of protection is. Community wide control cannot be obtained until almost everyone is immune. Vaccine production must be ramped up to cover the world population. The price of herd immunity through natural infection is high mortality in the elderly and morbidity in other age groups including children and Long-COVID. We must expect a new wave in the coming winter. The severity will depend on the proportion of the population with immunity from natural infections or immunisation. Therefore, control rests on a population wide immunisation including children, which may or may not need to be repeated if new SARS-CoV-2 variants evolve that can escape immunity from either previous infections or immunisations. Preventing long term sequelae of COVID-19 also remains a priority.

Personal data usage and privacy considerations in the COVID-19 global pandemic. / Preservação da privacidade no enfrentamento da COVID-19: dados pessoais e a pandemia global.

Data has become increasingly important and valuable for both scientists and health authorities searching for answers to the COVID-19 crisis. Due to difficulties in diagnosing this infection in populations around the world, initiatives supported by digital technologies are being developed by governments and private companies to enable the tracking of the public's symptoms, contacts and movements. Considering the current scenario, initiatives designed to support infection surveillance and monitoring are essential and necessary. Nonetheless, ethical, legal and technical questions abound regarding the amount and types of personal data being collected, processed, shared and used in the name of public health, as well as the concomitant or posterior use of this data. These challenges demonstrate the need for new models of responsible and transparent data and technology governance in efforts to control SARS-COV2, as well as in future public health emergencies.

Dados ganham cada vez mais importância e valor na busca de respostas para enfrentar a COVID-19 tanto para a ciência quanto para as autoridades sanitárias. Em virtude da dificuldade de realizar diagnóstico da infecção na população em geral, iniciativas apoiadas em tecnologias digitais vêm sendo desenvolvidas por governos ou empresas privadas para possibilitar rastreamentos de sintomas, contatos e deslocamentos de modo a apoiar estratégias de acompanhamento e avaliação na vigilância de contágios. A despeito da importância e necessidade dessas iniciativas, questionamentos acerca da quantidade e tipos de dados pessoais coletados, processados, compartilhados e utilizados em nome da saúde pública, bem como os concomitantes ou posteriores usos desses dados, suscitam questionamentos éticos, legais e técnicos. Desafios que apontam para a necessidade de novos modelos de governança de dados e de tecnologias, responsáveis e transparentes, para controlar o Sars-Cov2 e as futuras emergências de saúde pública.

Impact of the COVID-19 pandemic on gastrointestinal infection trends in England, February-July 2020.

OBJECTIVE: To establish the impact of the first 6 months of the COVID-19 outbreak response on gastrointestinal (GI) infection trends in England. DESIGN: Retrospective ecological study using routinely collected national and regional surveillance data from seven UK Health Security Agency coordinated laboratory, outbreak and syndromic surveillance systems using key dates of UK governmental policy change to assign phases for comparison between 2020 and historic data. RESULTS: Decreases in GI illness activity were observed across all surveillance indicators as COVID-19 cases began to peak. Compared with the 5-year average (2015-2019), during the first 6 months of the COVID-19 response, there was a 52% decrease in GI outbreaks reported (1544 vs 3208 (95% CI 2938 to 3478)) and a 34% decrease in laboratory confirmed cases (27 859 vs 42 495 (95% CI 40 068 to 44 922)). GI indicators began to rise during the first lockdown and lockdown easing, although all remained substantially lower than historic figures. Reductions in laboratory confirmed cases were observed across all age groups and both sexes, with geographical heterogeneity observed in diagnosis trends. Health seeking behaviour changed substantially, with attendances decreasing prior to lockdown across all indicators. CONCLUSIONS: There has been a marked change in trends of GI infections in the context of the COVID-19 pandemic. The drivers of this change are likely to be multifactorial; while changes in health seeking behaviour, pressure on diagnostic services and surveillance system ascertainment have undoubtably played a role, there has likely been a true decrease in the incidence for some pathogens resulting from the control measures and restrictions implemented. This suggests that if some of these changes in behaviour such as improved hand hygiene were maintained, then we could potentially see sustained reductions in the burden of GI illness.