A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data.

BACKGROUND: Africa reports the highest number of outbreaks globally, accounting for 39% of all outbreaks in 2022. The Integrated Disease Surveillance and Response strategy in Kenya ensures the reporting of outbreaks up to the national level. We present a summary of the burden of reported disease outbreaks in Kenya, 2007-2022. METHODS: We reviewed historical surveillance data, 2007-2022, summarized the annual caseload and deaths of reported outbreaks, and classified the outbreaks into 3 categories, that is high, moderate, and low burden. A nested Poisson regression model was fit to determine whether there was a significant increase in the number of diseases and counties reporting outbreaks over time. RESULTS: Twenty-three diseases were reported. COVID-19, cholera, epidemic malaria, kala-azar, and measles were associated with a high disease burden. The highest number of diseases reported in a single year was 10. We observed an increase in the number of outbreaks over time (IRR = 1.26, 95% CI [1.22-1.29], p < 0.001), and an increase in the number of counties reporting outbreaks over time (r = 0.97, p < 0.001). CONCLUSION: There was an increase in the frequency and geographic occurrence of outbreaks. The differences in outbreak occurrence between counties necessitate targeted and enhanced preventive, preparedness, and response interventions at the sub-national level to reduce the burden of outbreaks.

Sexually transmitted infections in Belgian general practices: a nationwide continuing surveillance study, data from 2015 to 2020.

AIM: The current study aims at describing sexually transmitted infections (STI) surveillance data collected from 2015 to 2020 as well as investigating patients' characteristics and risk factors in the sample population. BACKGROUND: Reported STI cases are continuously increasing in Europe. In Belgium, 94.1% of citizens have a regular general practitioner (GP) or are affiliated to a general practice. By using GPs for surveillance, STIs can be monitored in the general population. Between January 2015 and December 2020, the Sentinel General Practitioners (SGP) network retrospectively reported five STIs: chlamydia, gonorrhoea, genital warts, herpes, and syphilis. METHODS: In the SGP network database on STIs, participating GPs report on case-by-case basis through paper or online registration forms. We performed descriptive statistics, X2 test and logistic regression using SAS® 9.4. Multivariate multiple logistic regression was performed to investigate the relationship between STIs and patients' characteristics. FINDINGS: During the study period, 1009 cases were reported, corresponding to an episode-based incidence estimated at 121 per 100,000 inhabitants. The majority of patients (59.8%) were men, and 83.6% were under age 30. Among female patients 92.7% had heterosexual contacts whereas 64.4% of male patients did. Women were more likely to be diagnosed with chlamydia (odds ratio [OR] 1.56; 95% confidence interval [CI] 1.12-2.17) and herpes (OR 1.72, 95% CI 1.04-2.86) than men.In this study, STI surveillance data were in agreement with literature. Continuous surveillance through the SGP network remains an important tool to obtain information about populations at risk and STI incidence in the general population.

VigiNUTRI Brasil: methods of request, data extraction, treatment and consistency analysis of individual data from adolescents monitored by the Food and Nutrition Surveillance System (Sisvan Web).

OBJECTIVE: To describe the methods for requesting, extracting data, processing and analyzing the consistency of anthropometric and food consumption data of adolescents monitored by Sisvan Web. METHODS: Methodological study with individualized data from Sisvan web between 2008 and 2018. The modules of anthropometry and consumption, made available by the Ministry of Health, had a unique identifier for linkages. Implausible values and individuals outside the age range were excluded. Consistency analyses, with corrections for imputations and descriptive statistics, were performed using Stata 16.0 software. RESULTS: A database was obtained with 18,812,232 observations of anthropometric data between 2008 and 2018 and 440,534 records of food consumption between 2015 and 2018; after merging the banks, 64,976 observations were obtained. CONCLUSION: The combination of anthropometry and food consumption databases made it possible to link individual adolescent data and build a database with information for future analyzes relating to the dietary and nutritional profile of the same individual. MAIN RESULTS: The proposal for processing individual data from the Food and Nutrition Surveillance System (Sisvan Web) generated 18 million observations for anthropometric data and 65,000 observations after merging the anthropometry and food consumption databases. IMPLICATIONS FOR SERVICES: This work will enable replication of the methodology for processing data available in Sisvan Web, allowing for enhanced analyzes by healthcare teams and researchers in the field of public health. PERSPECTIVES: It is expected that these procedures will assist researchers, managers and healthcare professionals in handling and analyzing the information generated by Sisvan Web.

Progress in physical activity research, policy, and surveillance in Canada: The global observatory for physical activity - GoPA!

BACKGROUND: The purpose of this paper is to examine the evolution of physical activity research and the comprehensiveness of national physical activity policies and surveillance systems in Canada. METHODS: A systematic review was conducted by the Global Observatory for Physical Activity (GoPA! ) on physical activity and health publications between 1950 and 2019. Findings from Canada were extracted and included in the present analysis. The number of articles published, female researcher involvement in authorship, author institution affiliations, and publication themes were examined. Policies were evaluated by determining if there was a standalone physical activity plan and if national guidelines existed. Surveillance systems were assessed for periodicity, instruments used, and age inclusivity. RESULTS: Out of 23,000 + publications analyzed worldwide; 1,962 included data collected in Canada. Physical activity research in Canada increased considerably from the 2000s to 2010s (543 articles vs. 1,288 articles), but an apparent stabilization has been observed more recently. Most physical activity publications in Canada focused on surveillance (37%), with fewer articles on policy (8%) and interventions (7%). The proportion of female first authors increased from 38% in the 1980s to 60% in the last decade. However, females remain the minority for senior authors. With respect to policy, "A Common Vision" is Canada's national plan, which has a singular policy focus on physical activity. National surveillance data is collected regularly with both the Canadian Health Measures Survey (CHMS) and the Canadian Community Health Survey. In addition to self-report, the CHMS also collects accelerometer data from participants. CONCLUSION: Through collaborative and coordinated action, Canada remains well equipped to tackle physical inactivity. Continued efforts are needed to enhance sustained awareness of existing physical activity promotion resources to increase physical activity.

Assessing measles risk transmission in Iran: a utilization of the World Health Organization's programmatic risk assessment tool,2022.

BACKGROUND: Despite successful efforts to eliminate measles in Iran, imported measles cases continue to be reported. Because measles is endemic in neighboring countries. This research aims to evaluate the risk of measles transmission in different regions of Iran. METHODS: Measles case-based surveillance data of the Expanded Program of Immunization containing 31 provinces and 463 districts from 2019 to 2021 were assessed. The WHO Measles Programmatic Risk Assessment tool was used to evaluate the risk of disease transmission in four domains: population immunity, surveillance quality, program delivery performance, and threat assessment. scores were categorized as low, medium, high, or very high risk. RESULTS: During 2019-2021, the average incidence of measles was 1.9 per 1 million. Chabahar and Mashhad with 76 and ./6per million reported the highest and lowest incidence respectively. All 463 districts were categorized as low risk in risk assessment. Andimeshk, Chabahar, and Bojnurd obtained the highest risk scores with 27, 24, and 25 respectively. All districts were classified as low risk for population immunity. The average coverage of (MMR1) and (MMR2) was 95% or higher. All districts received the minimum points for surveillance quality. CONCLUSION: All regions are placed at a low level of disease transmission risk. However, the tool is not able to assess the risk at the rural or peripheral sectors level. The indicators used in this tool are the same for all countries with different epidemiological features (elimination, endemic). Sensitivity analysis can optimize the use of this tool for countries with different disease conditions.

Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study.

BACKGROUND: During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation. OBJECTIVE: To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness. METHODS: In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks. RESULTS: A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system. CONCLUSIONS: Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods.

Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results.

Background: Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called "LeCellPHIA" (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). Objective: This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. Methods: LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. Results: The overall response rate for the year of data collection was 75%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95% CI 782-988) persons at risk. Compared to persons in the youngest age group (15-19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (≥60 years; risk ratio 2.6, 95% CI 1.7-3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. Conclusions: LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system.

COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys.

BACKGROUND: Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic. METHODS: We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme. FINDINGS: From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44-0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7-1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529) BA.1 waves. Our estimates indicate that the England Pillar 2 COVID-19 testing programme detected 26-40% of all cases (including asymptomatic cases) over the study period with no consistent differences by deprivation levels or ethnic groups. Testing biases for PCR were generally higher than those for LFDs, in line with the general policy of symptomatic and asymptomatic use of these tests. Deprivation and age were associated with testing biases on average; however, the uncertainty intervals overlapped across deprivation levels, although the age-specific patterns were more distinct. We also found that ethnic minorities and older individuals were less likely to use confirmatory PCR tests through most of the pandemic and that delays in reporting a positive LFD test were possibly longer in populations self-reporting as "Black; African; Black British or Caribbean". INTERPRETATION: Differences in testing behaviours across sociodemographic groups might be reflective of the higher costs of self-isolation to vulnerable populations, differences in test accessibility, differences in digital literacy, and differing perceptions about the utility of tests and risks posed by infection. This study shows how mass testing data can be used in conjunction with surveillance surveys to identify gaps in the uptake of public health interventions both at fine-scale levels and across sociodemographic groups. It provides a framework for monitoring local interventions and yields valuable lessons for policy makers in ensuring an equitable response to future pandemics. FUNDING: UK Health Security Agency.

Computerized Decision Support Systems Informing Community-Acquired Pneumonia Surveillance, France, 2017-2023.

We show the value of real-time data generated by a computerized decision support system in primary care in strengthening pneumonia surveillance. The system showed a 66% (95% CI 64%-67%) increase in community-acquired pneumonia from 2018 to 2023 for the population of France, 1 month before a national alert was issued.

Improvement of the Surveillance Protocols After Experiencing a Pseudo-Epidemic After 2018 Central Sulawesi Earthquake in Indonesia.

OBJECTIVE: Following a disaster, a pseudo-epidemic can occur due to redundant and duplicated data caused by infrastructure and information system disruptions. This study aims to investigate whether there have been improvements the post-disaster surveillance system in the comparison of diarrhea incidents between Central Sulawesi, Indonesia, and Cianjur, West Java, Indonesia. METHODS: We conducted an analysis of the epidemic-prone disease diarrhea before and during disasters, comparing the data with secondary data from the Early Warning Alert and Response System (EWARS) and the District Health Information System V.02 (DHIS-2). RESULTS: In central Sulawesi in 2018 and Cianjur in 2022, we observed an upsurge in diarrhea cases in the first week after the disaster. Although diarrhea cases increased after the disaster, they remained within acceptable outbreak criteria. Multiplication and redundant data were detected in the DHIS-2 system in Central Sulawesi, likely leading to erroneous overreporting. Changes in surveillance officers and their personal experiences during the disaster contributed to data inconsistencies. As compared to Central Sulawesi, the DHIS-2 reporting form in Cianjur was simplified as an individual form to enhance efficiency and accuracy. CONCLUSIONS: Enhancing valid assessment and conducting thorough investigations are essential to improve surveillance protocols for epidemic-prone diseases following disasters.

Identity Instrumentalized: Pattern Recognition as an Epistemology of Surveillance, 1960-66.

The emergence of new surveillance practices is not solely a matter of technological and political developments. It is also intertwined with the production of scientific knowledge. This article traces how scientists in the 1960s developed some of the earliest computerized facial recognition techniques while covertly sponsored by a U.S. intelligence agency. The article situates their project within approaches from the technical field of pattern recognition. It demonstrates how the scientists, especially amid unsteady patronage relationships, reinforced a key assumption: that recognizing identity could be reduced to classifying specific features within a set of existing examples. As their ambitions grew over six years, the scientists transformed human identity into a scientific object tailored to their recognition techniques. How this transformation occurred foregrounds the epistemic underpinnings of surveillance technologies.

Progress made in digitalizing antimicrobial resistance surveillance in a One Health approach in Kenya.

Background: Antimicrobial resistance (AMR) is a major threat to global public health, affecting human and animal health, agriculture, food safety, and the environment. The control of AMR is often challenging, particularly when data are scanty or siloed in individual sectors. To develop evidence-based control policies for AMR, an electronic information system that integrates AMR data from various sectors, in a One Health approach, is critical. Methodology: Acknowledging the interconnectedness of AMR in humans, animals, and the environment and the need to assess the AMR burden using a One Health approach, Kenya's National Antimicrobial Stewardship Interagency Committee (NASIC), with support from FIND, integrated human and animal health AMR data at the national AMR data repository and developed the One Health AMR Surveillance System (OHAMRS). The OHAMRS comprises two core digital components: interoperability middleware for integrating data from various sources and a DHIS2 web portal for the analysis and visualization of AMR surveillance data from the human and animal health sectors. These components are scalable for future inclusion of data from other One Health sectors, e.g., the environment, food/feed, and aquaculture sectors. Results: The OHAMRS has 42 dashboards that facilitate the presentation, interpretation, and dissemination of actionable information relating to AMR, including 17 dashboards for human and animal health priority pathogens and 8 for drug-resistance indicators. The priority pathogen dashboards provide visualization of antimicrobial susceptibility patterns, resistance and susceptibility trends, resistance tables, and geospatial susceptibility maps. Other dashboards include surveillance sites and specimen reports, data completeness, data reconciliation, sample testing workload, a One Health intersectoral dashboard, and other reporting tools for diverse stakeholders. Discussion: Digitalizing AMR surveillance through a One Health lens is pivotal to understand AMR prevalence and patterns across various sectors. The OHAMRS provides comprehensive data analysis and presentation, informing policymaking on AMR control. Digital tools such as the OHAMRS are vital in facilitating the availability of data and actionable information on AMR required to address the AMR crisis in Kenya.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.

BACKGROUND: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. OBJECTIVE: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. METHODS: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. RESULTS: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. CONCLUSIONS: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data.

Background: Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. Objective: This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. Methods: PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. Results: From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Conclusions: Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

Emergency department visits for heat-related illness among workers: Occupational health surveillance using Washington syndromic surveillance data.

BACKGROUND: Information on worker occupation and industry is critical to understanding the occupational risks of heat-related illness (HRI), yet few syndromic surveillance systems capture these key data elements. This study evaluates the work data reported through Washington syndromic surveillance for its utility in characterizing HRI ED visits by industry and occupation. METHODS: Standard industry and occupation codes were assigned to employer name and occupation descriptions reported in Washington ED visit records maintained within the state's syndromic surveillance system, for visits involving HRI in 2020-2022. HRI ED visits involving workplace heat exposure were identified based on discharge diagnoses or on keywords in the triage note or chief complaint fields. HRI ED visits were summarized by patient characteristics, and visit rates were calculated by industry and occupation. RESULTS: Employer name or occupation descriptions were reported in 21.5% of HRI ED records among patients age 16 and older, and in 41.2% of records with mention of heat exposure at work. Twice as many records were classified for industry as for occupation. Agriculture, forestry, fishing, and hunting and transportation and warehousing had the highest rates of HRI ED visits. Specific industries with the highest rates included support activities for agriculture and forestry, the postal service, and fruit and vegetable preserving and specialty food manufacturing. CONCLUSION: Syndromic surveillance data are a valuable source of occupational health surveillance information when work characteristics are reported, enhancing our understanding of the occupational risks of injuries and illnesses.

Left atrial strain measured by three-dimensional echocardiography predicts atrial fibrillation in the general population.

BACKGROUND: Left atrial (LA) strain by three-dimensional echocardiography (3DE), has been proposed as a more accurate measure of LA function, providing incremental prognostic benefits over traditional two-dimensional approaches. OBJECTIVES: Our aim was to evaluate the prognostic value of LA strain by 3DE in predicting incident atrial fibrillation (AF) in the general population. METHODS: The study included 4466 participants from a prospective longitudinal cohort study in the general population, among these 3DE LA strain was analysed in 1935 participants. The endpoint was incident AF. Adjustments were made for the CHARGE-AF clinical risk score. RESULTS: Mean age was 54 ± 17 years, 43 % were male. During a median follow-up time of 4.8 years (interquartile range 4.3-5.5 years) 59 participants (3.0 %) developed AF. In univariable analysis, all three parameters were associated with incident AF (p value for all <0.01). After multivariable adjustments, only LA reservoir strain (LASr) and LA contractile strain (LASct) were associated with incident AF (LASr: HR 1.12 (1.07-1.17), p < 0.001, per 1 % decrease; LASct: HR 1.16 (1.09-1.24), p < 0.001, per 1 % decrease), whereas LA conduit strain (LAScd) was not (HR 1.04 (0.98-1.10), p = 0.17, per 1 % decrease). Both LASr (continuous net reclassification index 0.37 ± 0.14; p = 0.003) and LASct (continuous net reclassification index 0.41 ± 0.14; p = 0.002) provided incremental prognostic information beyond the CHARGE-AF risk score. CONCLUSION: LASr and LASct measured by 3DE are independently associated with incident AF and provided incremental prognostic information beyond existing risk scores.

Monkeypox pandemic in Sudan, surveillance epidemiologic report, 2022.

BACKGROUND: Mpox, is a zoonosis that is known to be endemic in several Central and West African countries. Recently, in 2022, it has emerged in Europe and United States, what raised the alarm to be declared in late June 2024 as a public health event of international concern. This study aimed to give insight about the recent spread of mpox in Sudan, and documents the epidemiologic situation. METHODS: Through a cross-sectional design, Sudan mpox data was extracted from the disease surveillance line-list at the national level at Sudan Federal Ministry of Health. the data was customized and then analyzed using Epi Info7 software. Analysis was done using frequencies and percentages and the results presented in tables and figures. Permission and ethical approval were obtained from the Health Emergency and Epidemic Control Directorate at the Federal Ministry of Health. RESULTS: The outbreak of mpox was confirmed after testing of initial specimens outside Sudan with positivity rate of 72%. Later the cases continued to be reported based on the clinical diagnosis and standard case definition. Out of 375 reported cases, 54.4% were males, while 45.6% were females. The age of cases ranged from one month to 78 years with majority (41.1%) of the cases were children under 5 years of age. Regarding the reported symptoms, all cases had the characteristic skin rash and 74.1% of them had fever. Other symptoms included, headache (31.5%), sore throat (30.9%) and lymphadenopathy (26.1%). For occupation, 35.7% were preschool and 10.4% were school children, 9% of cases were prisoners. Around 22 (5.8%) reported contact history with a confirmed case, while (5.6%) of the cases were imported cases. Cases were reported from 17 states with 42 affected localities (districts) with an overall attack rate of 2.36/ 100,000. The highest number of cases was reported from Gadaref (45.3%), West Darfur (25.9%), Khartoum (13.3%) and north Darfur (3.5%). In Gadaref, 146 (85.8%) of the cases were from a refugees' camp. Started in epi week 19, the outbreak peaked in week 38 and last in week 42. CONCLUSION: Mpox was confirmed in the new Sudan for the first time with cases reported in most of states. Although importation of the virus is hypothesized, internal hidden circulation is possible and more in-depth investigation is highly recommended. The higher rate of infection among preschool, school children and refugees, highlights the need to strengthen the prevention and control measures in schools and camps. More focus on the data completeness is required for better understanding of the disease and can be ensured by the surveillance directorate through training of staff and updating of reporting forms. Strengthening the lab capacity inside the country is a necessity to ensure testing of all the clinically diagnosed cases.

The power of interactive maps for communicating spatio-temporal data to health professionals.

While more and more health-related data is being produced and published every day, few of it is being prepared in a way that would be beneficial for daily use outside the scientific realm. Interactive visualizations that can slice and condense enormous amounts of multi-dimensional data into easy-to-digest portions are a promising tool that has been under-utilized for health-related topics. Here we present two case studies for how interactive maps can be utilized to make raw health data accessible to different target audiences: i) the European Notifiable Diseases Interactive Geovisualization (ENDIG) which aims to communicate the implementation status of disease surveillance systems across the European Union to public health experts and decision makers, and ii) the Zoonotic Infection Risk in Twente-Achterhoek Map (ZIRTA map), which aims to communicate information about zoonotic diseases and their regional occurrence to general practitioners and other healthcare providers tasked with diagnosing infectious diseases on a daily basis. With these two examples, we demonstrate that relatively straight-forward interactive visualization approaches that are already widely used elsewhere can be of benefit for the realm of public health.

A Comparison of Active and Passive Surveillance Strategies for Selected Birth Defects in New York.

BACKGROUND: The New York State Birth Defects Registry (BDR) has passive and active components. As part of statewide passive ascertainment, the BDR receives reports of International Classification of Diseases, Tenth Revision (ICD-10) codes and descriptive narratives on a wide range of birth defects. The BDR conducts enhanced active surveillance for selected birth defects in 14 counties, which includes medical record abstraction and clinician review. We sought to quantify agreement between the two surveillance approaches. METHODS: The analysis included live-born infants born with one of the 16 birth defects in 2018-2021 in the active surveillance counties (n = 1069 infants). We calculated positive predictive values (PPV) and 95% confidence intervals for each defect, defined as the percentage of cases confirmed in active surveillance among those in passive surveillance. Additionally, we calculated the percentage with each birth defect missed by passive surveillance. RESULTS: The PPV varied greatly by birth defect. The PPV was >90% for gastroschisis and cleft lip, but <70% for spina bifida, diaphragmatic hernia, truncus arteriosus, tricuspid atresia, hypoplastic left heart syndrome, coarctation of the aorta, and pulmonary atresia. The percentage missed by passive surveillance ranged from 2% for tetralogy of Fallot to 39% for tricuspid atresia. CONCLUSIONS: Active surveillance is an important strategy for ruling out false positive case reports for certain birth defects that we assessed, but not all of them. Passive surveillance programs can use our findings to develop targeted strategies for improving data quality of specific birth defects using active surveillance methods, thus optimizing limited resources.