Surveillance Purposes Research Articles

Review on Pest Des Petits Ruminants Virus and its Socioeconomic Impact in Small Ruminants

Peste Des Petits Ruminants (PPR) are a highly contagious viral disease that mainly affects sheep and goats. It belongs to the genus Morbillivirus in the family Paramyxoviridae. Today, this disease is considered a cause of mortality and morbidity in many countries of the world. The disease occurs south of the Sahara Desert and north of the equator in Africa, most of the Middle East, and parts of Asia, including the Indian subcontinent. It is transmitted by aerosols during close contact between animals, mainly through sneezing and coughing. The disease is characterized by high fever, discharge from the eyes and nose, pneumonia, necrosis and gastrointestinal tract leading to foul smelling diarrhoea. Diagnosis can be made based on clinical, pathological and epizootological findings. This has significant economic implications for food security and livelihoods. Therefore, PPR is considered one of the most damaging animal diseases in Africa, the Middle East and Asia, and is also one of the priority diseases listed in the FAO-OIE Global Framework for the Progressive Control of Trans boundary Animal Diseases. Peste des Petits ruminants are common in Ethiopia due to economic losses due to reduced production, death, abortion and cost of disease control. There is no specific treatment for PPR. However, drugs that control bacterial and parasitic complications may reduce mortality. For surveillance purposes, circular vaccination and/or vaccination of high-risk populations may also be useful. Therefore, eradication of PPR is done through a combination of quarantine, movement control, cleaning and disinfection of infected areas and vaccination.

Shallow Convolutional Neural Network for Gender Classification Based on Hand

Gender recognition based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender recognition may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network (CNN) with a regularization method. Here, different gender recognition models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for recognizing gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

A Bayesian nonparametric approach to correct for underreporting in count data.

We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and exploiting a proxy for the reporting process. The proposed model is used to estimate the prevalence of chronic kidney disease in Apulia, Italy, based on a unique statistical database covering information on m = 258 municipalities obtained by integrating multisource register information. Accurate prevalence estimates are needed for monitoring, surveillance, and management purposes; yet, counts are deemed to be considerably underreported, especially in some areas of Apulia, one of the most deprived and heterogeneous regions in Italy. Our results agree with previous findings and highlight interesting geographical patterns of the disease. We compare our model to existing approaches in the literature using simulated as well as real data on early neonatal mortality risk in Brazil, described in previous research: the proposed approach proves to be accurate and particularly suitable when partial information about data quality is available.

Current Applications of Digital PCR in Veterinary Parasitology: An Overview

Digital PCR (dPCR) is an emerging technology that enables the absolute quantification of the targeted nucleic acids. The body of research on the potential applications of this novel tool is growing in human and veterinary medicine. Most of the research on dPCR applications in veterinary parasitology is concentrated on developing and validating new assays to detect and quantify parasites of great financial impact in the food-producing animal industry. Several studies describe the utility of dPCR for individualized medicine in companion animals. Most frequently, dPCR performance is assessed compared to quantitative PCR or Next Generation Sequencing platforms, while others also compare the accuracy of dPCR with traditional parasitological techniques considered gold standard methods. Other researchers describe dPCR assays for surveillance purposes, species identification, and quantification in mixed parasitic infections, the detection of mutations indicative of anthelmintic resistance, and the identification of new targets for drug development. This review provides an overview of the studies that employed dPCR in investigating animal parasites and parasitic diseases from a veterinary perspective and discusses how this novel technology could advance and facilitate diagnosis, surveillance, and the monitoring of response to treatment, or shed light on current gaps in our knowledge of the epidemiology of significant veterinary parasitic diseases.

Improving access to test results for participants in bio-behavioural surveys of people who inject drugs.

Bio-behavioural surveys of people who inject drugs (PWID) evolved from unlinked anonymous monitoring (UAM) of human immunodeficiency virus (HIV) incidence and prevalence, which began in some high-income countries in the late 1980s. UAM was conducted purely for surveillance purposes and test results were not returned to participants. Later, the importance of collecting data on behavioural risk factors was recognised, leading to the development of bio-behavioural surveys of PWID, which today are conducted regularly in several countries. Typically, these surveys recruit participants from venues providing harm reduction services and involve behavioural questionnaires and dried blood spot (DBS) testing for HIV and hepatitis C (HCV). DBS test results are not returned to participants; instead, countries offer varied systems of on-site testing separate from the bio-behavioural testing or provide referrals to external testing services. In this commentary, we trace the history of bio-behavioural surveys of PWID from their origins to the present day to explain how the methodologies evolved, along with the ethical considerations underlying them. We highlight the dramatic improvements in treatments for HIV and HCV over the past thirty years and the corresponding need to ensure that bio-behavioural survey participants can access low-barrier and timely testing. We review the pros and cons of different strategies for providing test results to participants and argue that the return of DBS results collected as part of bio-behavioural surveys warrants consideration as an additional tool to improve testing access for participants. Any changes should be informed by the perspectives of participants, study site personnel and investigators.

Antimicrobial susceptibility testing and reporting practices of public hospital microbiology laboratories in Greece, 2022: A national observational survey and call for action.

Antimicrobial resistance (AMR) in Greece is among the highest across the European Union/European Economic Area (EU/EEA), with high AMR rates even to last-line antibiotics. To better understand the clinical microbiology laboratory practices and capacities in species identification and antimicrobial susceptibility testing (AST) across public healthcare establishments in Greece, we sent a questionnaire to 98 of 128 public hospital microbiology laboratories between 1 February and 1 April 2022. Of the 73.5% (72/98) that responded, 51.4% (37/72) reported using EUCAST guidelines. Two of three laboratories used an automated instrument for species identification and AST for all laboratory samples. Broth microdilution for colistin susceptibility testing was used by 46 of the laboratories, more frequently in larger (> 400 beds) versus smaller (< 400 beds) hospitals (90.5% (19/21) vs 52.9% (27/51) respectively, p = 0.011). MALDI-TOF mass spectrometry was available in one of 10 laboratories, and more often in larger compared to smaller hospitals (p = 0.035). Although the majority of laboratories had a laboratory information system (LIS) in place, only half had the capacity to extract data directly from the LIS for the purpose of AMR surveillance; 73.6% (53/72) used restrictive antibiograms. Public microbiology laboratory AMR capacities in Greece require improvement, prioritising interventions for EUCAST guidelines implementation.

Integration of disease surveillance in the English context: a qualitative study

ObjectivesThe world is experiencing increasing threats from infectious diseases and environmental hazards. Integration of disease surveillance systems has been put forth as one way to ensure more timely analysis of data and response. This study sought to explore the current context and state of integration of disease surveillance in England, including the barriers and facilitators to integration, as well as opportunities for improvement. Study designQualitative study with focus groups and key informant interviews. MethodsFocus group discussions (FGDs) and key informant interviews (KIIs) were conducted with key national, regional, and local stakeholders involved in surveillance activities in August and September 2022. These discussions and interviews were recorded, transcribed, and coded using a within-case content and thematic analysis. ResultsIn total, five FGDs and 10 KIIs were conducted with 27 participants. Participants had different views on what integration is, though mostly agreed that surveillance systems in England are not integrated. Lack of standardisation, governance and oversight, and structural and financial barriers were hindering the current system from being more integrated. The additional benefits of integration above and beyond the ‘status quo’ during response activities were questioned by some. ConclusionEngland does not have a single integrated disease surveillance system but has a range of disease-specific surveillance systems that have evolved largely independently to meet operational needs. Greater integration may be desired and to a certain extent is important, but it is essential that it is understood as a means to an end and the overall purpose of surveillance is kept in mind.

Development of an electronic burns register: Digitisation of routinely collected hospital data for global burns surveillance

IntroductionBurn registers provide important data that can track injury trends and evaluate services. Burn registers are concentrated in high-income countries, but most burn injuries occur in low- and middle-income countries where surveillance data are limited. Injury surveillance guidance recommends utilisation of existing routinely collected data where data quality is adequate, but there is a lack of guidance on how to achieve this. Our aim was to develop a rigorous and reproducible method to establish an electronic burn register from existing routinely collected data that can be implemented in low resource settings. MethodsData quality of handwritten routinely collected records (register books) from a tertiary government hospital burn unit in Mysore, India was assessed prior to digitisation. Process mapping was conducted for burn patient presentations. Register and casualty records were compared to assess the case ascertainment rate. Register books from February 2016 to February 2022 were scanned and anonymised. Scans were quality checked and stored securely. An online data entry form was developed. All data underwent double verification. ResultsProcess mapping suggested data were reliable, and case ascertainment was 95%. 1930 presentations were recorded in the registers, representing 0.84% of hospital all-cause admissions. 388 pages were scanned with 4.4% requiring rescanning due to quality problems. Two-step verification estimated there to be errors remaining in 0.06% of fields following data entry. ConclusionWe have described, using the example of a newly established electronic register in India, methods to assess the suitability and reliability of existing routinely collected data for surveillance purposes, to digitise handwritten data, and to quantify error during the digitisation process. The methods are likely to be of particular interest to burn units in countries with no active national burns register. We strongly recommend mobilisation of resources for digitisation of existing high quality routinely collected data as an important step towards developing burn surveillance systems in low resource settings.

Adapting a US dietary analysis software and nutrient database for use in Brazil.

In many countries, assessing food and nutrient intake for research and surveillance purposes is difficult due to the lack of comprehensive, country-specific food and nutrient databases and/or a dietary analysis software program. In this case study, we describe the approach used to adapt a United States (US) dietary analysis software and nutrient database (Nutrition Data System for Research [NDSR]) for use in analyzing 24-hour dietary recalls collected for the Brazil Kids Nutrition and Health Study (KNHS). A team of experts that included individuals knowledgeable about Brazil and US eating traditions was assembled to devise solutions for between-country differences in eating habits, food supply, food nomenclature, and language. Solutions devised relied on several key resources, including the Brazilian Food Composition Table (TBCA) and a list of 200 foods commonly consumed in Brazil. These solutions included creating data entry rules that specified how each reported food should be entered into NDSR, creation of User Recipes for foods lacking a close nutritionally matching food in the NDSR database, and adjustment for food fortification differences as part of the analysis of study data. This case study illustrates that NDSR can be adapted for use outside of the United States through a structured process.

AN INSIGHT ON THE SUSCEPTIBILITY PATTERN OF URINARY ISOLATES OF ESCHERICHIA COLI AND KLEBSIELLA PNEUMONIAE AGAINST FLUOROQUINOLONES WITH SPECIAL REFERENCE TO PRULIFLOXACIN

Introduction: The most prevalent infections in outpatient and inpatient settings are urinary tract infections (UTIs), which can be treated with a variety of antibiotics. Fluoroquinolones are the preferred antibiotic class and Prulifloxacin is one of these agents. It can be tested in vitro for its activity against urinary isolates of the bacteria Escherichia coli(E. coli) and Klebsiella pneumoniae (K. pneumoniae) for the purpose of surveillance, along with other fluoroquinolones. Objective: Evaluation of the fluoroquinolone susceptibility of urine isolates of K. pneumoniae and E. coli with a focus on Prulifloxacin. Results: 877 urine samples processed, 257 showed positive cultures, satisfying the Kass concept/Significant bacteriuria. Of these, 106 (41.2%) contained E. coli and K. pneumoniae, of which 92 (87%) were E. coli and 14 (13%) were K. pneumoniae. E. coli exhibited the highest susceptibility to Levofloxacin (61.9%), followed by Prulifloxacin (58.6%) where as to K. pneumoniae Levofloxacin (42.8%) and Prulifloxacin (50%). Conclusion: Levofloxacin is thought to be a drug reserved for the treatment of multi-drug resistant tuberculosis (MDR-TB), the study provides insight into the susceptibility patterns of urinary isolates of E. coli and K. pneumoniae against Prulifloxacin, which can be preferred as the drug of choice for the treatment of UTIs.

Quadrotor Robust Fractional-Order Sliding Mode Control in Unmanned Aerial Vehicles for Eliminating External Disturbances

Quadrotors, commonly known as drones or unmanned aerial vehicles (UAVs), play an important role in load transportation. The complex vehicles used for transporting loads and surveillance purposes can be replaced through the ease of use and mechanical simplicity of the quadrotors. This study aims to introduce a new way of controlling the slung load system of the quadrotor after applying the fractional-order sliding mode control (FOSMC) method for performance enhancement. This method in the presence of external disturbances is likely to help stabilize and track the quadrotor with minimized swinging of the attached load. The results of this study prove the robustness of FOSMC through analysis, outcomes, and graphical representations that show the effect with various angles for a clear and conceptual understanding. The present study contributes to the literature by designing a robust FOSMC for a quadrotor with the help of external disturbances. The results of this study could be applied to the development of the design of upcoming drones which can increase the efficiency as well as the accuracy of load transportation. Further applications in the fields of rescue, agriculture, construction, research, and advancement of the fraction calculus-based control method are recommended using the FOSMC method.

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach.

Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes.

Laboratory-Based Surveillance of Tuberculosis Disease in BTKLPP Ambon

Tuberculosis is still a health problem that is a global burden. Indonesia is the third-ranked country in the world with the highest TB burden, and in Ambon City, there are 1296 TB sufferers. The purpose of this laboratory-based surveillance is to describe the situation of TB disease in the Ambon Class II BTKLPP service area during 2022. This activity is operational research that monitors and evaluates the results of an examination of TB specimens sent by health facilities or those taken by themselves by Ambon Class II BTKLPP officers in TB screening activities in special places. Materials and tools used in examining TB specimens include sputum, TCM GeneXpert-TB, and other supporting materials. The results of the TB specimen examination are recorded in the TB Information System (SITB), which is a TB surveillance tool used to monitor TB cases in Indonesia. The results of this activity included: of the 347 TB samples examined, 15% were drug-sensitive TB, 1% were rifampicin-resistant TB, 2% were error, and 82% were negative. The conclusion there are still drug-sensitive TB and resistant TB in the Ambon BTKLPP service area. This activity needs to be continued so that TB cases in service areas can be monitored.

Vector Autoregression for Forecasting the Number of COVID-19 Cases and Analyzing Behavioral Indicators in the Philippines: Ecologic Time-Trend Study.

Traditional surveillance systems rely on routine collection of data. The inherent delay in retrieval and analysis of data leads to reactionary rather than preventive measures. Forecasting and analysis of behavior-related data can supplement the information from traditional surveillance systems. We assessed the use of behavioral indicators, such as the general public's interest in the risk of contracting SARS-CoV-2 and changes in their mobility, in building a vector autoregression model for forecasting and analysis of the relationships of these indicators with the number of COVID-19 cases in the National Capital Region. An etiologic, time-trend, ecologic study design was used to forecast the daily number of cases in 3 periods during the resurgence of COVID-19. We determined the lag length by combining knowledge on the epidemiology of SARS-CoV-2 and information criteria measures. We fitted 2 models to the training data set and computed their out-of-sample forecasts. Model 1 contains changes in mobility and number of cases with a dummy variable for the day of the week, while model 2 also includes the general public's interest. The forecast accuracy of the models was compared using mean absolute percentage error. Granger causality test was performed to determine whether changes in mobility and public's interest improved the prediction of cases. We tested the assumptions of the model through the Augmented Dickey-Fuller test, Lagrange multiplier test, and assessment of the moduli of eigenvalues. A vector autoregression (8) model was fitted to the training data as the information criteria measures suggest the appropriateness of 8. Both models generated forecasts with similar trends to the actual number of cases during the forecast period of August 11-18 and September 15-22. However, the difference in the performance of the 2 models became substantial from January 28 to February 4, as the accuracy of model 2 remained within reasonable limits (mean absolute percentage error [MAPE]=21.4%) while model 1 became inaccurate (MAPE=74.2%). The results of the Granger causality test suggest that the relationship of public interest with number of cases changed over time. During the forecast period of August 11-18, only change in mobility (P=.002) improved the forecasting of cases, while public interest was also found to Granger-cause the number of cases during September 15-22 (P=.001) and January 28 to February 4 (P=.003). To the best of our knowledge, this is the first study that forecasted the number of COVID-19 cases and explored the relationship of behavioral indicators with the number of COVID-19 cases in the Philippines. The resemblance of the forecasts from model 2 with the actual data suggests its potential in providing information about future contingencies. Granger causality also implies the importance of examining changes in mobility and public interest for surveillance purposes.

Assessing phenotypic virulence of Salmonella enterica across serovars and sources.

Whole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica. To test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed. P(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis.

The performance of the 2018 European Federation of Periodontology/American Academy of Periodontology (EFP/AAP) classification of periodontitis for epidemiology surveillance purposes remains to be investigated. This study assessed the surveillance use of the 2018 EFP/AAP classification and its agreement with the unsupervised clustering method compared with the 2012 Centers for Disease Control and Prevention(CDC)/AAP case definition. Participants (n=9424) in the National Health and Nutrition Examination Survey (NHANES) were staged by the 2018 EFP/AAP classification and classified into subgroups via k-medoids clustering. Concordance levels between periodontitis definitions and the clustering method were evaluated through the multiclass area under the receiver operating characteristic curve (multiclass AUC) among "periodontitis cases" and the general population, respectively. The multiclass AUC of the 2012 CDC/AAP definition versus clustering was used as a reference. The associations of periodontitis with chronic diseases were estimated using multivariable logistic regression. All the participants were identified as "periodontitis cases" by the 2018 EFP/AAP classification, and the prevalence of stage III-IV was 30%. The optimal numbers of clusters were three and four. The 2012 CDC/AAP definition versus clustering yielded a multiclass AUC of 0.82 and 0.85 among the general population and "periodontitis cases," respectively. The multiclass AUC of the 2018 EFP/AAP classification versus clustering was 0.77 and 0.78 for different target populations. Similar patterns prevailed in associations with chronic diseases between the 2018 EFP/AAP classification and clustering. The validity of the 2018 EFP/AAP classification was verified by the unsupervised clustering method, which performed better in distinguishing "periodontitis cases" than classifying the general population. For surveillance purposes, the 2012 CDC/AAP definition showed a higher agreement level with the clustering method than the 2018 EFP/AAP classification.

European regulations on the use of antibiotics in veterinary medicine

Antimicrobial resistance endangers the successful combat of bacterial infections in humans and animals. The common use of antibiotic classes including those of high clinical value in human as well as veterinary medicine is a critical factor contributing to or suspected to promote the emergence of antibiotic resistance. New legal provisions laid down in veterinary drug legislations and related guidelines and advice are in force in the European Union to safeguard the effectiveness, accessibility and availability of antibiotics.Categorisation of antibiotics in classes of importance for treatment of infections of humans by the WHO was one of the first steps. This task is also undertaken for antibiotics for treatment of animals by the EMA's Antimicrobial Advice Ad Hoc Expert Group. The new veterinary Regulation (EU) 2019/6 has extended restrictions for use of some antibiotics in animals to a full ban of certain antibiotics. While some (but not all) antibiotic compounds not being authorized in veterinary medicine may still be used in companion animals more strict provisions were already applicable for treatment of food producing animal species. Distinct regulations are in place for the treatment of animals kept in large numbers in flocks. Initial regulations focussed on the protection of consumers from residues of veterinary drugs in food commodities, new regulations address prudent (not routinely) and responsible selection, prescription and use of antibiotics, and have improved the practicality for cascade use outside the terms of marketing authorisation. Mandatory recording of use of veterinary medicinal products for food safety reasons is extended to rules for veterinarians and owners or holders of animals to regularly report the use of antibiotics for the purpose of official surveillance of consumption. National sales data of antibiotic veterinary medicinal products have been collected on a voluntary basis until 2022 by ESVAC, which has created awareness of major differences between EU member states. A significant decline in sales was reported for third and fourth generation cephalosporines, polymyxins (colistin), and (fluoro)quinolones since the initiation in 2011.

Surgical site infection surveillance in German hospitals: a national survey to determine the status quo of digitalization

BackgroundSurveillance of surgical site infections (SSI) relies on access to data from various sources. Insights into the practices of German hospitals conducting SSI surveillance and their information technology (IT) infrastructures are scarce. The aim of this study was to evaluate current SSI surveillance practices in German hospitals with a focus on employed IT infrastructures.MethodsGerman surgical departments actively participating in the national SSI surveillance module “OP-KISS” were invited in August 2020 to participate in a questionnaire-based online survey. Depending on whether departments entered all data manually or used an existing feature to import denominator data into the national surveillance database, departments were separated into different groups. Selected survey questions differed between groups.ResultsOf 1,346 invited departments, 821 participated in the survey (response rate: 61%). Local IT deficits (n = 236), incompatibility of import specifications and hospital information system (n = 153) and lack of technical expertise (n = 145) were cited as the most frequent reasons for not using the denominator data import feature. Conversely, reduction of workload (n = 160) was named as the main motivation to import data. Questions on data availability and accessibility in the electronic hospital information system (HIS) and options to export data from the HIS for the purpose of surveillance, yielded diverse results. Departments utilizing the import feature tended to be from larger hospitals with a higher level of care.ConclusionsThe degree to which digital solutions were employed for SSI surveillance differed considerably between surgical departments in Germany. Improving availability and accessibility of information in HIS and meeting interoperability standards will be prerequisites for increasing the amount of data exported directly from HIS to national databases and laying the foundation for automated SSI surveillance on a broad scale.

Can we use meat inspection data for animal health and welfare surveillance?

Ante- and post-mortem inspections at abattoir were originally introduced to provide assurance that animal carcasses were fit for human consumption. However, findings at meat inspection can also represent a valuable source of information for animal health and welfare surveillance. Yet, before making secondary use of meat inspection data, it is important to assess that the same post-mortem findings get registered in a consistent way among official meat inspectors across abattoirs, so that the results are as much independent as possible from the abattoir where the inspection is performed. The most frequent findings at official meat inspections of pigs and beef cattle in Sweden were evaluated by means of variance partitioning to quantify the amount of variation in the probabilities of these findings due to abattoir and farm levels. Seven years of data (2012-2018) from 19 abattoirs were included in the study. The results showed that there was a very low variation between abattoirs for presence of liver parasites and abscesses, moderately low variation for pneumonia and greatest variation for injuries and nonspecific findings (e.g., other lesions). This general pattern of variation was similar for both species and implies that some post-mortem findings are consistently detected and so are a valuable source of epidemiological information for surveillance purposes. However, for those findings associated with higher variation, calibration and training activities of meat inspection staff are necessary to enable correct conclusions about the occurrence of pathological findings and for producers to experience an equivalent likelihood of deduction in payment (independent of abattoir).

Prevalence of dengue virus in Haripur district, Khyber Pakhtunkhwa, Pakistan

Dengue virus (DENV) has caused about 12 large outbreaks in Pakistan, resulting in 286,262 morbidities and 1108 deaths. The most affected province is Khyber Pakhtunkhwa (KP). This study was conducted to determine the average DENV prevalence in different areas of the Haripur endemic district of KP and the causing factors of DENV. MethodsThis work was a cross-sectional study that was performed in the DENV endemic district Haripur. A total of 761 individuals were included in this study. The data were categorized according to sex, age and symptoms (like fever, body aches, bleeding, and skin rash). For data analysis, SPSS 23 version was applied. ArcGIS version 10.8 was used to map the study area. ResultsIn this study, there were 716 confirmed cases of DENV fever, including 421 males (58.8%) and 295 females (41.2%). The most affected age range, 16–30 years, reported by 301 (42.0%), was followed by 31–45 years, 184 (25.7%), above 46 years, 132 (18.4%), and 0–15 years, 99 (13.8%). The positive IgG cases were 581(81.0%). Those whose age ranges from 1 to 15 years 82 (8.7%) cases, 16–30 years 244 (34.1%), 31–45 years 156 (21.8%), above 46-year age 99 (13.8%) cases. In addition, this suggests that those between the ages of 16 and 30 are at the highest risk for DENV infection. However, this might be the fact that individuals in this age range are more likely to be out in the environment, making them more vulnerable to the virus. ConclusionOver the past ten years, DENV fever has become increasingly prevalent in Pakistan. The risk is substantially higher for males. Dengue outbreaks hit those between the ages of 16 and 30 the hardest. The proper monitoring and assessment of DENV are necessary for prevention and controlling the disease. Disease surveillance includes identification and molecular characterization of infected persons and monitoring mosquito populations in high-risk locations for the purpose of vector surveillance. In order to assess the community's willingness to participate in DENV preventive efforts, behavioral impact surveillance is also necessary.