Global Measles Surveillance: Trends, Challenges, and Implications for Public Health Interventions

Measles, a highly contagious disease primarily affecting children, carries serious health risks, including complications and mortality. Vaccination remains the most effective preventive measure against measles transmission. The COVID-19 pandemic has exacerbated challenges in surveillance and immunization efforts, leaving millions of people exposed to preventable diseases such as measles. Globally accelerated immunization campaigns are critical for achieving regional elimination goals and mitigating the risk of outbreaks. Our team has developed an open-access database for global measles monitoring, facilitating standardized data collection and analysis. The analysis of measles cases from 2011 to 2023 reveals fluctuating trends, with notable increases in Africa in 2019 and 2023, indicating potential gaps in control strategies. Using an automated signal detection tool developed by the European Centre for Disease Prevention and Control (ECDC) team, we identified significant variations between World Health Organization (WHO) regions, underscoring the importance of continuous monitoring to detect epidemiological changes early. These results underscore the need for robust surveillance systems and accelerated vaccination efforts to safeguard public health.

Bacterial Diversity and Antibiotic Resistance Patterns of Community-Acquired Urinary Tract Infections in Mega Size Clinical Samples of Egyptian Patients: A Cross-Sectional Study

BACKGROUND

 Community-acquired urinary tract infection (UTI) is one of the most common infectious diseases nowadays. Alarming increased levels of antimicrobial resistance are developing globally which limit treatment options and may lead to life-threatening problems.

AIM

Our study aimed to collect surveillance data on non-hospitalized Egyptian UTI cases and to develop strategies against multidrug-resistant pathogens (MDR). According to our knowledge, this is the first study to screen this high number (15,252 urine samples) in a short period (three months), providing valuable data on resistance profiles in non-hospitalized Egyptian UTI patients.

METHODS

A total of 15,252 urine samples were collected from different patients. Positive cultures were identified using a semi-quantitative method. Kirby-Bauer's disc diffusion method was used for antibiotic susceptibility testing, the double disc diffusion method was used for extended-spectrum beta-lactamases-producing strains, and the Chi-square test was used for statistical data processing.

RESULTS

The results showed 61% positive cultures, females accounted for 67.5%. Infants and elderly patients showed the highest positive cultures (74.4% and 69.2%, respectively). Despite Escherichia coli being the most common uropathogen (47.19%), Klebsiella species(24.42%) were the most MDR and extended-spectrum β-lactamase (ESBL)-producing organisms. E. coli and Klebsiella spp. displayed increased resistance to cephalosporins (75% and 81%, respectively). In contrast, both organisms displayed high sensitivity to carbapenems. Unlike Klebsiella spp., E. coli was highly sensitive (92%) to first-line treatment (nitrofurantoin) for UTI. Moreover, trimethoprim/sulfamethoxazole showed higher sensitivity rates compared to other nations.

CONCLUSION

 Despite Escherichia coli being the most often identified bacteria in our isolates Klebsiella spp. displayed higher resistance to the majority of tested antibiotics. Fortunately, trimethoprim/sulfamethoxazole significantly increased sensitivity, especially against E. coli. However, both species showed high rates of cephalosporin resistance. Moreover, It is important to promote Egypt's national action plan for antimicrobial resistance in collaboration with the World Health Organization, especially in the community to minimize the chance of bacterial resistance in the Egyptian community.

Risk of Coronavirus Disease 2019 Messenger RNA Vaccination-Associated Myocarditis and Pericarditis - A Systematic Review of Population-Based Data

Purpose

Early studies showed that the risks of mRNA vaccine-associated myocarditis and pericarditis are low but with substantial variation across studies. Study characteristics, ethnicity, vaccine types, dose intervals, and SARS-CoV-2 infection prevalence may influence the rates of myocarditis and pericarditis after mRNA vaccination in population-based studies.

Methods

We comprehensively searched MEDLINE for relevant articles published before November 30, 2022. We also searched the websites of health authorities in several countries for unpublished surveillance data on myocarditis and pericarditis after mRNA vaccination. The outcome of interest was the incidence of myocarditis and pericarditis developed after mRNA vaccination for COVID-19.

Results

A total of 17 studies form 10 countries were included for review. We noted that considerable heterogeneity in study characteristics, including surveillance method, case definition, and observation period, may partially be responsible for the widely varied reported rates. Studies from countries that adopted active surveillance reported higher rates than those using passive surveillance. Compared to BNT162b2 vaccine, mRNA-1273 may have a higher risk of myocarditis only in young men after the second dose. Our comparison of sex-, age-, vaccine type-, and dose-specific rates of myocarditis across countries did not support the hypothesis that individuals with recent SARS-CoV-2 infection and young Asian males were at higher risk. We also could not find sufficient evidence to conclude whether extending the between-dose interval could reduce myocarditis incidence following mRNA vaccination.

Conclusion

Differences in the study characteristics must be fully considered when comparing the risks of mRNA vaccine-related myocarditis and pericarditis in different countries.

Surveillance Methods Used to Detect, Characterize, and Monitor the COVID-19 Pandemic in Rocky Mountain Tribal Communities

OBJECTIVE

Data were essential to public health decision-making during the COVID-19 pandemic, yet no single data source was adequate for Tribes in Montana and Wyoming. We outlined data access, availability, and limitations for COVID-19 pandemic surveillance response to improve future data exchange.

MATERIALS AND METHODS

The Rocky Mountain Tribal Epidemiology Center (RMTEC) used various data sources to deliver data on the number of COVID-19 cases, deaths, and vaccinations at local, state, and regional levels to inform Tribes in Montana and Wyoming. RMTEC reviewed state, federal, and public datasets and then attached a score to each dataset for completeness of demographic information, including race, geographic level, and refresh rate.

RESULTS

The RMTEC COVID-19 response team shared data weekly on the number of COVID-19 cases, deaths, and vaccinations distributed and the percentage of the population vaccinated with Tribal health departments in Montana and Wyoming. The Indian Health Service Epidemiology Data Mart dataset scored the highest (24 of 30), followed by datasets from Montana (18 of 30) and Wyoming (22 of 30). Publicly available datasets scored low largely due to data aggregation across larger geographic areas and lack of demographic variables.

PRACTICE IMPLICATIONS

The absence of data on race and ethnicity from publicly available data and lack of access to real-time data limited RMTEC's ability to provide Tribal-specific updates on COVID-19 cases, deaths, and vaccinations to Tribal health departments. RMTEC should be fully funded to provide the necessary resources for data management and the capacity to respond to data requests from Tribal health departments and their programs to address current and future pandemics. Federal and state agencies should also be educated on Tribal Epidemiology Centers' public health authority status to improve access to infectious disease data among those agencies.

Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data

BACKGROUND

Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors.

OBJECTIVE

The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors.

METHODS

A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted.

RESULTS

A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings.

CONCLUSIONS

Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

Estimating HIV Incident Diagnoses Among Men Who Have Sex With Men Eligible for Pre-exposure Prophylaxis but Not Taking It: Protocol and Feasibility Assessment of Data Sources and Methods

BACKGROUND

HIV incidence estimates are published each year for all Ending the HIV Epidemic (EHE) counties, but they are not stratified by the demographic variables highly associated with risk of infection. Regularly updated estimates of HIV incident diagnoses available at local levels are required to monitor the epidemic in the United States over time and could contribute to background incidence rate estimates for alternative clinical trial designs for new HIV prevention products.

OBJECTIVE

We describe methods using existing, robust data sources within areas in the United States to reliably estimate longitudinal HIV incident diagnoses stratified by race and age categories among men who have sex with other men (MSM) eligible for pre-exposure prophylaxis (PrEP) but not taking it.

METHODS

This is a secondary analysis of existing data sources to develop new estimates of incident HIV diagnoses in MSM. We reviewed past methods used to estimate incident diagnoses and explored opportunities to improve these estimates. We will use existing surveillance data sources and population sizes of HIV PrEP-eligible MSM estimated from population-based data sources (eg, US Census data and pharmaceutical prescription databases) to develop metropolitan statistical area-level estimates of new HIV diagnoses among PrEP-eligible MSM. Required parameters are number of new diagnoses among MSM, estimates of MSM with an indication for PrEP, and prevalent PrEP use including median duration of use; these parameters will be stratified by jurisdiction and age group or race or ethnicity. Preliminary outputs will be available in 2023, and updated estimates will be produced annually thereafter.

RESULTS

Data to parameterize new HIV diagnoses among PrEP-eligible MSM are available with varying levels of public availability and timeliness. In early 2023, the most recent available data on new HIV diagnoses were from the 2020 HIV surveillance report, which reports 30,689 new HIV infections in 2020, and 24,724 of them occurred in an MSA with a population of ≥500,000. Updated estimates for PrEP coverage based on commercial pharmacy claims data through February 2023 will be generated. The rate of new HIV diagnoses among MSM can be estimated from new diagnoses within each demographic group (numerator) and the total person-time at risk of diagnosis for each group (denominator) by metropolitan statistical area and year. To estimate time at risk, the person-time of individuals on PrEP or person-time after incident HIV infection but before diagnosis should be removed from stratified population size estimates of the total number of person-years with indications for PrEP.

CONCLUSIONS

Reliable, serial, cross-sectional estimates for rates of new HIV diagnoses for MSM with PrEP indications can serve as benchmark community estimates of failures of HIV prevention and opportunities to improve services and will support public health epidemic monitoring and alternative clinical trial designs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42267.

Local infectious disease experience influences vaccine refusal rates: a natural experiment

Vaccination has been critical to the decline in infectious disease prevalence in recent centuries. Nonetheless, vaccine refusal has increased in recent years, with complacency associated with reductions in disease prevalence highlighted as an important contributor. We exploit a natural experiment in Glasgow at the beginning of the twentieth century to investigate whether prior local experience of an infectious disease matters for vaccination decisions. Our study is based on smallpox surveillance data and administrative records of parental refusal to vaccinate their infants. We analyse variation between administrative units of Glasgow in cases and deaths from smallpox during two epidemics over the period 1900-1904, and vaccine refusal following its legalization in Scotland in 1907 after a long period of compulsory vaccination. We find that lower local disease incidence and mortality during the epidemics were associated with higher rates of subsequent vaccine refusal. This finding indicates that complacency influenced vaccination decisions in periods of higher infectious disease risk, responding to local prior experience of the relevant disease, and has not emerged solely in the context of the generally low levels of infectious disease risk of recent decades. These results suggest that vaccine delivery strategies may benefit from information on local variation in incidence.

Impact of chronic comorbidities on hospitalization, intensive care unit admission and death among adult vaccinated and unvaccinated COVID-19 confirmed cases during the Omicron wave

Background

Comorbidities are important risk factors of severe COVID-19 complications. Their impact during the Omicron wave among vaccinated and unvaccinated COVID-19 cases is not well documented.

Purpose

The objective of this study was to estimate the association between the number of comorbidities and the risk of hospitalization, intensive care unit (ICU) admission, and death among vaccinated and unvaccinated confirmed adult COVID-19 cases during the Omicron wave.

Research Design and Study sample

We performed a cohort study of COVID-19 adult cases of primo-infection occurring during the Omicron wave, from December 5, 2021 to January 9, 2022 using surveillance database of the province of Québec, Canada. The database included all laboratory-confirmed cases in the province and the related information on 21 pre-existing comorbidities, hospitalization, ICU admission, death related to COVID-19 and vaccination status.

Analysis

We performed a robust Poisson regression model to estimate the impact of the number of comorbidities on each complication by vaccination status adjusted for age, sex, socioeconomic status, and living environment.

Results

We observed that the risk of complication increased for each additional comorbidity in both vaccinated and unvaccinated individuals and that this risk was systematically higher among unvaccinated individuals. Compared with vaccinated individuals without comorbidities (reference group), the risks of hospitalization, ICU admission, and death were respectively: 9X (95% CI [7.77-12.01]), 13X (95% CI [8.74-18.87]), and 12X (95% CI [7.57-18.91]) higher in vaccinated individuals with ≥3 comorbidities; 22X (95% CI [19.07-25.95]), 45X (95% CI [29.06-69.67]) and 38X (95% CI [23.62-61.14]) higher in unvaccinated individuals with ≥3 comorbidities.

Conclusion

Our results support the importance of promoting vaccination in all individuals, and especially those with pre-existing medical conditions, to reduce severe complications, even during the Omicron wave.

Symptomatik einer akuten SARS-CoV-2-Infektion bei Kindern im Kita-Alter

Hintergrund

Die Symptomatik einer pädiatrischen SARS-CoV-2-Infektion ist sehr variabel. Es gibt nur wenige Studien zu nichthospitalisierten Kindern bzw. Kindern im Kita-Alter.

Ziel der Arbeit

Die Arbeit soll die Häufigkeit verschiedener COVID-19-Symptome bei ein- bis 6‑jährigen Kindern beschreiben. Sie führt dazu Daten aus 2 Modulen der Corona-KiTa-Studie zusammen: 1) das Modul „COALA“ – Corona: Anlassbezogene Untersuchungen in Kitas und 2) das Modul „CATS“ – Corona KiTa Surveillance (Meldedaten).

Material und Methoden

In COALA wurden die Infektionsgeschehen in 30 Kitas, in denen ein SARS-CoV-2-Fall gemeldet wurde, untersucht (Oktober 2020 bis Juni 2021). Kita-Kinder wurden prospektiv über 12 Tage beobachtet (SARS-CoV-2-Tests, Symptomtagebuch). Die Ergebnisse wurden mit den Symptomangaben der deutschlandweit gemeldeten SARS-CoV-2-Fälle (Meldedaten) verglichen.

Ergebnisse

Aus den teilnehmenden Kitas liegen für 289 Kinder Angaben vor. Von 39 Kindern mit SARS-CoV‑2 (Wildtyp, α‑Variante) hatten 64 % mindestens ein Symptom, von den nicht mit SARS-CoV‑2 infizierten Kindern 40 %. In beiden Gruppen war Schnupfen das häufigste Symptom (36 % vs. 25 %, n. s.). Aus den Meldedaten liegen für 84.371 Kinder klinische Informationen vor, Fieber war am häufigsten (27 %), neben Schnupfen (26 %). Schwere Beschwerden wie z. B. Atemnot wurden in den Ausbruchsuntersuchungen und in den Meldedaten nur selten angegeben (3 % bzw. 1 %).

Schlussfolgerung

Kinder im Kita-Alter haben meist milde bzw. asymptomatische Verläufe einer SARS-CoV-2-Infektion. Ihre Symptome ähneln denjenigen von nicht mit SARS-CoV‑2 infizierten Kindern aus denselben Kitas. Es erscheint sinnvoll, Erkenntnisse aus den Meldedaten durch Ausbruchsuntersuchungen zu ergänzen, um methodische Limitationen der einzelnen Vorgehensweisen auszugleichen.

Zusatzmaterial online

Die Online-Version dieses Beitrags (10.1007/s00112-022-01640-3) enthält eine weitere Tabelle, die Symptome bei symptomatischen SARS-CoV-2-Fällen von Kindern im Alter von einem bis 6 Jahren in der COALA-Stichprobe und in den Meldedaten gegenüberstellt.

Assessing the effect of school closures on the spread of COVID-19 in Zurich

The effect of school closure on the spread of COVID-19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age-stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time-series endemic-epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age-specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0-14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time-dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.

Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters

The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.

A Population Health Approach to Transplant Access: Challenging the Status Quo

Transplant referral and evaluation are critical steps to waitlisting yet remain an elusive part of the transplant process. Despite calls for more data collection on pre-waitlisting steps, there are currently no national surveillance data to aid in understanding the causes and potential solutions for the extreme variation in access to transplantation. As population health scientists, epidemiologists, clinicians, and ethicists we submit that the transplant community has an obligation to better understand disparities in transplant access as a first necessary step to effectively mitigating these inequities. Our position is grounded in a population health approach, consistent with several new overarching national policy and quality initiatives. The purpose of this Perspective is to (1) provide an overview of how a population health approach should inform current multisystem policies impacting kidney transplantation and demonstrate how these efforts could be enhanced with national data collection on pre-waitlisting steps; (2) demonstrate the feasibility and concrete next steps for pre-waitlisting data collection; and (3) identify potential opportunities to use these data to implement effective population-level interventions, policies, and quality measures to improve equity in access to kidney transplantation.

Molecular detection and characterization of lumpy skin disease viruses from outbreaks in Thailand in 2021

Lumpy skin disease (LSD) is one of the most important transboundary and emerging diseases in cattle. The disease causes significant economic losses in animal production and trade worldwide. The first LSD outbreak was recorded in March 2021, at Roi-Et province in the northeastern region of Thailand. Thereafter, the disease had rapidly spread into neighbouring provinces and throughout the country. The aim of the present study was to provide information regarding to the molecular detection and characterization of LSD viruses from outbreaks in Thailand in 2021. There were 1,748,112 susceptible and 604,404 affected animals (n = 588,512 [36.30%], beef cattle; n = 12,367 [15.74%], dairy cattle and n = 3524 [7.35%], buffaloes). The morbidity and mortality rates were 34.57% and 3.47%, respectively, and the case fatality rate was 10.05% (60,713 deaths). Based on real-time polymerase chain reaction results, the p32 gene of LSD virus (LSDV) was detected more frequently in skin nodule samples (54/77, 70.13%) than in nasal swabs (26/55, 42.57%) and EDTA blood (16/77, 20.78%) samples. Moreover, the copy number of the p32 gene was higher in skin nodule samples than in nasal swab and EDTA blood samples (cycle threshold value = 21.94 ± 0.62 vs. 31.52 ± 0.66 and 34.27 ± 0.32, respectively). Furthermore, 29 (53.70%) of 54 capripoxvirus-positive skin nodule samples were successfully isolated from Madin-Darby bovine kidney cells, and the cytopathic effect was observed 72 h after inoculation. Based on the phylogenetic trees of the GPCR, ANK and RPO30 gene sequences, the LSDV isolates from Thailand were distinct from both the LSDV-field and LSDV-vaccine groups and were closely correlated with the LSDV strains isolated from mainland China, Hong Kong territory and Vietnam in 2020. Additionally, they could be a potential virulent vaccine-recombinant LSDV strain.

How Can Progress Toward Ending the Human Immunodeficiency Virus Epidemic in the United States Be Monitored?

The plan for Ending the HIV (human immunodeficiency virus) Epidemic (EHE) in the United States aims to reduce new infections by 75% by 2025 and by 90% by 2030. For EHE to be successful, it is important to accurately measure changes in numbers of new HIV infections after 5 and 10 years (to determine whether the EHE goals have been achieved) but also over shorter timescales (to monitor progress and intensify prevention efforts if required). In this viewpoint, we aim to demonstrate why the method used to monitor progress toward the EHE goals must be carefully considered. We briefly describe and discuss different methods to estimate numbers of new HIV infections based on longitudinal cohort studies, cross-sectional incidence surveys, and routine surveillance data. We particularly focus on identifying conditions under which unadjusted and adjusted estimates based on routine surveillance data can be used to estimate changes in new HIV infections.

Trends of Fixed-Dose Combination Antibiotic Consumption in Hospitals in China: Analysis of Data from the Center for Antibacterial Surveillance, 2013-2019

Background: Fixed-dose combination (FDC) antibiotics can be clinically inappropriate and are concerning with regards to antimicrobial resistance, with little usage data available in low- and middle-income countries. Methods: Based on retrospective data from the Center for Antibacterial Surveillance, we investigated the consumption of FDC antibiotics in hospital inpatient settings in China from 1 January 2013 to 31 December 2019. The metric for assessing antibiotic consumption was the number of daily defined doses per 100 bed days (DDD/100BDs). FDC antibiotics were classified according to their composition and the Access, Watch, Reserve (AWaRe) classification of the World Health Organization. Results: A total of 24 FDC antibiotics were identified, the consumption of which increased sharply from 8.5 DDD/100BDs in 2013 to 10.2 DDD/100BDs in 2019 (p < 0.05) despite the reduction in the total antibiotic consumption in these hospitals. The increase was mainly driven by FDC antibiotics in the Not Recommended group of the AWaRe classification, whose consumption accounted for 63.0% (6.4 DDD/100BDs) of the overall FDC antibiotic consumption in 2019, while the consumption of FDC antibiotics in the Access group only accounted for 13.5% (1.4 DDD/100BDs). Conclusion: FDC antibiotic consumption significantly increased during the study period and accounted for a substantial proportion of all systemic antibiotic usage in hospitals in China. FDC antibiotics in the Not Recommended group were most frequently consumed, which raises concerns about the appropriateness of FDC antibiotic use.

Syphilis in Ottawa: An evolving epidemic

BACKGROUND

The incidence of infectious syphilis in Canada has declined throughout the latter decades of the last century; however, in Ottawa, an upsurge in new cases began in 2001. The local epidemic continues to involve predominantly gay, bisexual and other men who have sex with men (gbMSM), but in recent years, has expanded further into heterosexual populations. This has coincided with an increase in the number of pregnant women testing positive for syphilis on antenatal screening. The aim of this study is to understand the changing epidemiology in infectious syphilis cases diagnosed in Ottawa to strengthen primary care management and public health response.

METHODS

Surveillance data from the Ontario Ministry of Health were used to describe the evolving epidemiology of infectious syphilis in the Ottawa region from 2010 to 2019, including a comprehensive chart review of cases from 2015-2019.

RESULTS

The number of cases of infectious syphilis in Ottawa rose from 50 cases in 2010 to 171 cases in 2019. These rates were consistently high among males, and increased from 10.9/100,000 in 2010 to 30.9/100,000 in 2019. The rates among females, in comparison, increased from 0.4/100,000 in 2010 to 3.2/100,000 in 2019, with corresponding increases during antenatal screening (with no congenital syphilis cases to date).

CONCLUSION

As the syphilis epidemic continues to evolve in Ottawa, ongoing surveillance plays a crucial role. Public health resources must address the needs of populations already impacted but at the same time be flexible enough to respond to changes in trends and support clinicians providing care to populations where the epidemic is emerging.

Risk Factors for Hospitalisation amongst Leptospirosis Patients in New Zealand

Leptospirosis is a neglected zoonotic disease that is widespread in tropical and subtropical regions such as Oceania, which includes New Zealand. The incidence rate of leptospirosis in New Zealand remains high in comparison to other high-income countries, with over half of the notified patients hospitalised, and the factors associated with hospitalisation are poorly understood. This study aimed to estimate the risk factors for hospitalisation amongst leptospirosis patients using passive surveillance data: notifications from 1 January 1999 to 31 December 2017 extracted from New Zealand’s notifiable disease database. There were 771 hospitalised and 673 non-hospitalised patients. Multivariable logistic regression was used to identify risk factors. The year of notification was significantly and positively associated with hospitalisation, with adjusted (adj.) OR 1.03 (95% CI:1.01–1.05). Occupation was significantly associated with hospitalisation, with the adjusted odds of hospitalisation amongst dairy farmers notified with leptospirosis at adj. OR 1.44 (95% CI: 1.02–2.02) times the adjusted odds of hospitalisation amongst farmers that worked with other livestock. Seropositivity for Leptospira interrogans Copenhageni (adj. OR 5.96, 95% CI: 1.68–21.17) and Pomona (adj. OR 1.14, 95% CI: 0.74–1.74)) was more likely to result in hospitalisation when compared to Leptospira borgpetersenii Ballum (baseline). Seropositivity for Leptospira borgpetersenii Hardjo (adj. OR 0.71, 95% CI: 0.49–1.01) and Tarassovi (adj. OR 0.39, 95% CI: 0.23–0.66) was less likely to result in hospitalisation when compared to Ballum (baseline). All the estimates were additionally adjusted for the effect of sex, age, ethnicity, reported occupational exposure, geographical location, reported season, and deprivation status Although passive surveillance data has limitations we have been able to identify that the New Zealand dairy farming population may benefit from a targeted awareness campaign.

Identification of factors impacting on the transmission and mortality of COVID-19

This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19.

Low levels of respiratory syncytial virus activity in Europe during the 2020/21 season: what can we expect in the coming summer and autumn/winter?

Since the introduction of non-pharmacological interventions to control COVID-19, respiratory syncytial virus (RSV) activity in Europe has been limited. Surveillance data for 17 countries showed delayed RSV epidemics in France (≥ 12 w) and Iceland (≥ 4 w) during the 2020/21 season. RSV cases (predominantly small children) in France and Iceland were older compared with previous seasons. We hypothesise that future RSV epidemic(s) could start outside the usual autumn/winter season and be larger than expected. Year-round surveillance of RSV is of critical importance.

A Public Health Informatics Solution to Improving Food Safety in Restaurants: Putting the Missing Piece in the Puzzle

Foodborne illnesses remain an important public health challenge in the United States causing an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths per year. Restaurants are frequent settings for foodborne illness transmission. Public health surveillance - the continual, systematic collection, analysis, and interpretation of reports of health data to prevent and control illness - is a prerequisite for an effective food control system. While restaurant inspection data are routinely collected, these data are not regularly aggregated like traditional surveillance data. However, there is evidence that these data are a valuable tool for understanding foodborne illness outbreaks and threats to food safety. This article discusses the challenges and opportunities for incorporating routine restaurant inspection data as a surveillance tool for monitoring and improving foodborne illness prevention activities. The three main challenges are: 1) lack of a national framework; 2) lack of data standards and interoperability; and 3) limited access to restaurant inspection data. Tapping into the power of public health informatics represents an opportunity to address these challenges. Advancing the food safety system by improving restaurant inspection information systems and making restaurant inspection data available to support decision-making represents an opportunity to practice smarter food safety.

Most likely causes of infection and risk factors for tuberculosis in Spanish cattle herds

INTRODUCTION

This study aims to assess the most likely causes of Bovine tuberculosis (bTB) breakdowns in Spanish cattle herds and to identify the main risk factors at farm-level.

METHODS

Causes of bTB breakdowns were assessed through a qualitative risk-assessment based on decision-trees by analysing surveillance data from 3819 bTB breakdowns detected during 2014-2016. Results were compared to veterinary officers' (VO) opinions. Risk factors were identified through a case-control study with data from 196 bTB cases and 160 controls collected during 2014-2018.

RESULTS

The decision tree analysis identified residual infections and interactions with wildlife as the most frequent causes of breakdowns (36% each), followed by purchasing infected cattle (14%). These results were not supported by VOs' opinions. According to the regression models, the risk of bTB increased by sharing pastures (odds ratios [OR] = 2.7; 95% confidence interval [CI] = 1.6-4.4) and by increasing inwards cattle movements. The presence of wildlife reservoirs represented a significant risk for extensively-managed farms if other cattle farms are situated within a one-kilometre radius (OR = 2.3; 95% CI = 1.1-5.1).

DISCUSSION

To prevent bTB breakdowns, efforts should be devoted to decrease the likelihood of residual infections and improve farm biosecurity. The adoption of biosecurity measures might be influenced by farmers' perceptions, which should be carefully evaluated to ensure the effectiveness of such strategies.

Developing consensus of evidence to target case finding surveys for podoconiosis: a potentially forgotten disease in India

BACKGROUND

Podoconiosis is a non-infectious geochemical lymphoedema of the lower legs associated with a significant burden of morbidity. There are historical reports of podoconiosis in India, but its current endemicity status is uncertain. In this investigation we aimed to prioritise the selection of districts for pilot mapping of podoconiosis in India.

METHODS

Through a consultative workshop bringing together expert opinion on podoconiosis with public health and NTDs in India, we developed a framework for the prioritisation of pilot areas. The four criteria for prioritisation were predicted environmental suitability for podoconiosis, higher relative poverty, occurrence of lymphoedema cases detected by the state health authorities and absence of morbidity management and disability prevention (MMDP) services provided by the National Programme for Elimination of Lymphatic Filariasis.

RESULTS

Environmental suitability for podoconiosis in India was predicted to be widespread, particularly in the mountainous east and hilly southwest of the country. Most of the districts with higher levels of poverty were in the central east and central west. Of 286 districts delineated by state representatives, lymphoedema was known to the health system in 189 districts and not recorded in 80. Information on MMDP services was unavailable for many districts, but 169 were known not to provide such services. We identified 35 districts across the country as high priority for mapping based on these criteria.

CONCLUSIONS

Our results indicate widespread presence of conditions associated with podoconiosis in India, including areas with known lymphoedema cases and without MMDP services. This work is intended to support a rational approach to surveying for an unrecognised, geographically focal, chronic disease in India, with a view to scaling up to inform a national strategy if required.

Predicting Dengue Outbreaks in Cambodia

In Cambodia, dengue outbreaks occur each rainy season (May–October) but vary in magnitude. Using national surveillance data, we designed a tool that can predict 90% of the variance in peak magnitude by April, when typically <10% of dengue cases have been reported. This prediction may help hospitals anticipate excess patients.

Risk of Vascular Access Infection Associated With Buttonhole Cannulation of Fistulas: Data From the National Healthcare Safety Network

RATIONALE & OBJECTIVE

Compared with conventional (rope-ladder cannulation [RLC]) methods, use of buttonhole cannulation (BHC) to access arteriovenous fistulas (AVFs) may be associated with increased risk for bloodstream infection and other vascular access-related infection. We used national surveillance data to evaluate the infection burden and risk among in-center hemodialysis patients with AVFs using BHC.

STUDY DESIGN

Descriptive analysis of infections and related events and retrospective observational cohort study using National Healthcare Safety Network (NHSN) surveillance data.

SETTING & PARTICIPANTS

US patients receiving hemodialysis treated in outpatient dialysis centers.

PREDICTORS

AVF cannulation methods, dialysis facility characteristics, and infection control practices.

OUTCOMES

Access-related bloodstream infection; local access-site infection; intravenous (IV) antimicrobial start.

ANALYTIC APPROACH

Description of frequency and rate of infections; adjusted relative risk (aRR) for infection with BHC versus RLC estimated using Poisson regression.

RESULTS

During 2013 to 2014, there were 2,466 access-related bloodstream infections, 3,169 local access-site infections, and 13,726 IV antimicrobial starts among patients accessed using BHC. Staphylococcus aureus was the most common pathogen, present in half (52%) of the BHC access-related bloodstream infections. Hospitalization was frequent among BHC access-related bloodstream infections (37%). In 2014, 9% (n=271,980) of all AVF patient-months reported to NHSN were associated with BHC. After adjusting for facility characteristics and practices, BHC was associated with significantly higher risk for access-related bloodstream infection (aRR, 2.6; 95% CI, 2.4-2.8) and local access-site infection (aRR, 1.5; 95% CI, 1.4-1.6) than RLC, but was not associated with increased risk for IV antimicrobial start.

LIMITATIONS

Data for facility practices were self-reported and not patient specific.

CONCLUSIONS

BHC was associated with higher risk for vascular access-related infection than RLC among in-center hemodialysis patients. Decisions regarding the use of BHC in dialysis centers should take into account the higher risk for infection. Studies are needed to evaluate infection control measures that may reduce infections related to BHC.

People living with undiagnosed HIV infection and a low CD4 count: estimates from surveillance data, Italy, 2012 to 2014

Background and aims

Late HIV diagnosis is associated with onward HIV transmission, higher morbidity, mortality and healthcare costs. In Italy, more than half of people living with HIV were diagnosed late during the last decade, with a CD4 count < 350 cells/mm³ at diagnosis. We aimed to determine the number and characteristics of people living with undiagnosed HIV infection and low CD4 counts in Italy.

Methods

Data on newly reported HIV diagnoses from 2012 –2014 were obtained from the national HIV surveillance system. We used the European Centre for Disease Prevention and Control HIV modelling tool to calculate the undiagnosed prevalence and yearly diagnosed fraction (YDF) in people with low CD4 count.

Results

The estimated annual number undiagnosed HIV infections with low CD4 count was on average 6,028 (95% confidence interval (CI): 4,954–8,043) from 2012–2014. In 2014, most of the undiagnosed people with low CD4 count were men (82.8%), a third acquired HIV through sex between men (MSM) (35.0%), and heterosexual transmission (33.4%), respectively. The prevalence of undiagnosed HIV infection was 11.3 (95% CI: 9.3–14.9) per 100,000 residents ranging from 0.7 to 20.8 between Italian regions. Nationally the prevalence rate was 280.4 (95% CI: 173.3–450.2) per 100,000 MSM, 8.3 (95% CI: 4.9–13.6) per 100,000 heterosexual men, and 3.0 (95% CI: 1.4–5.6) per 100,000 women. The YDF was highest among heterosexual women (27.1%; 95% CI: 16.9–45.2%).

Conclusions

These findings highlight the importance of improving efforts to identify undiagnosed HIV infections primarily among men, both MSM and heterosexual men.

Monitoring the hepatitis C epidemic in England and evaluating intervention scale-up using routinely collected data

In England, 160 000 individuals were estimated to be chronically infected with hepatitis C virus (HCV) in 2005 and the burden of severe HCV-related liver disease has increased steadily for the past 15 years. Direct-acting antiviral treatments can clear infection in most patients, motivating HCV elimination targets. However, the current burden of HCV is unknown and new methods are required to monitor progress. We employed a Bayesian back-calculation approach, combining data on severe HCV-related liver disease and disease progression, to reconstruct historical HCV incidence and estimate current prevalence in England. We explicitly modelled infections occurring in people who inject drugs, the key risk group, allowing information on the size of this population and surveillance data on HCV prevalence to inform recent incidence. We estimated that there were 143 000 chronic infections in 2015 (95% credible interval 123 000-161 000), with 34% and 54% in those with recent and past injecting drug use, respectively. Following the planned scale-up of new treatments, chronic infections were predicted to fall to 113 400 (94 900-132 400) by the end of 2018 and to 89 500 (71 300-108 600) by the end of 2020. Numbers developing severe HCV-related liver disease were predicted to fall by at least 24% from 2015 to 2020. Thus, we describe a coherent framework to monitor progress using routinely collected data, which can be extended to incorporate additional data sources. Planned treatment scale-up is likely to achieve 2020 WHO targets for HCV morbidity, but substantial efforts will be required to ensure that HCV testing and patient engagement are sufficiently high.

Influenza forecast optimization when using different surveillance data types and geographic scale

Background

Advance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction.

Objectives

To assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams.

Methods

Here, we generate retrospective influenza forecasts with a dynamic, compartmental model‐inference system using surveillance data for influenza‐like illness (ILI), laboratory‐confirmed cases, and pneumonia and influenza mortality at state and county levels. We evaluate how specification of 3 system inputs—scaling, observational error variance (OEV), and filter divergence (lambda)—affects forecast accuracy.

Results

In retrospective forecasts, and across data types, there were no clear optimal combinations for the 3 system inputs; however, scaling was most critical to forecast accuracy, whereas OEV and lambda were not.

Conclusions

Forecasts using new data streams should be tested to determine an appropriate scaling value using historical data and analyzed for forecast accuracy.

Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent

Unravelling the ecological structure of emerging plant pathogens persisting in multi-host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space and host species, and may lead to biases of perception. The biased perception of pathogen ecology may be exacerbated by hidden fractions of the whole host population, which may act as infection reservoirs. We designed a mechanistic-statistical approach to help understand the ecology of emerging pathogens by filtering out some biases of perception. This approach, based on SIR (Susceptible-Infected-Removed) models and a Bayesian framework, disentangles epidemiological and observational processes underlying temporal counting data. We applied our approach to French surveillance data on Xylella fastidiosa, a multi-host pathogenic bacterium recently discovered in Corsica, France. A model selection led to two diverging scenarios: one scenario without a hidden compartment and an introduction around 2001, and the other with a hidden compartment and an introduction around 1985. Thus, Xylella fastidiosa was probably introduced into Corsica much earlier than its discovery, and its control could be arduous under the hidden compartment scenario. From a methodological perspective, our approach provides insights into the dynamics of emerging plant pathogens and, in particular, the potential existence of infection reservoirs.

Using HIV Surveillance Data to Link People to HIV Medical Care, 5 US States, 2012-2015

INTRODUCTION

From 2012 through 2015, the Centers for Disease Control and Prevention (CDC) provided funding to 5 health departments for demonstration projects using HIV surveillance data to link people with newly diagnosed HIV to care. We assessed how well these health departments established linkage to care, how the demonstration projects helped them with this work, and if they sustained these activities after CDC funding ended.

MATERIALS AND METHODS

We obtained quantitative and qualitative data on linkage-to-care activities from health department communications and progress reports submitted to CDC. We calculated and combined linkage-to-care results for the 5 health departments, and we compared these results with the combined linkage-to-care results for 61 health departments that received CDC funding for routine HIV prevention activities (eg, HIV testing, linkage to and reengagement in HIV care, HIV partner services) and for the same 5 health departments when they used only routine HIV prevention activities for linkage to care.

RESULTS

Of 1269 people with a new HIV diagnosis at the 5 health departments, 1124 (89%) were linked to care, a result that exceeded the 2010-2015 National HIV/AIDS Strategy goal (85%), the CDC Funding Opportunity Announcement performance standard (80%), and combined results for the 61 health departments (63%) and the same 5 health departments (66%) using routine HIV prevention activities. Benefits of the projects were improved collaboration and coordination and more accurate, up-to-date surveillance data. All health departments continued linkage-to-care activities after funding ended.

PRACTICE IMPLICATIONS

Using HIV surveillance data to link people with HIV to care resulted in substantial clinical and public health benefits. Our observations underscore the importance of collaboration among medical providers, public health staff members, community-based organizations, and people with HIV to ensure the best possible clinical and public health outcomes.

Acinetobacter spp. Infections in Malaysia: A Review of Antimicrobial Resistance Trends, Mechanisms and Epidemiology

Acinetobacter spp. are important nosocomial pathogens, in particular the Acinetobacter baumannii-calcoaceticus complex, which have become a global public health threat due to increasing resistance to carbapenems and almost all other antimicrobial compounds. High rates of resistance have been reported among countries in Southeast Asia, including Malaysia. In this review, we examine the antimicrobial resistance profiles of Acinetobacter spp. hospital isolates from Malaysia over a period of nearly three decades (1987–2016) with data obtained from various peer-reviewed publications as well as the Malaysian National Surveillance on Antibiotic Resistance (NSAR). NSAR data indicated that for most antimicrobial compounds, including carbapenems, the peak resistance rates were reached around 2008–2009 and thereafter, rates have remained fairly constant (e.g., 50–60% for carbapenems). Individual reports from various hospitals in Peninsular Malaysia do not always reflect the nationwide resistance rates and often showed higher rates of resistance. We also reviewed the epidemiology and mechanisms of resistance that have been investigated in Malaysian Acinetobacter spp. isolates, particularly carbapenem resistance and found that bla_OXA-23 is the most prevalent acquired carbapenemase-encoding gene. From the very few published reports and whole genome sequences that are available, most of the Acinetobacter spp. isolates from Malaysia belonged to the Global Clone 2 (GC2) CC92 group with ST195 being the predominant sequence type. The quality of data and analysis in the national surveillance reports could be improved and more molecular epidemiology and genomics studies need to be carried out for further in-depth understanding of Malaysian Acinetobacter spp. isolates.

Evaluation of bacterial meningitis surveillance data of the northern region, Ghana, 2010-2015

INTRODUCTION

Bacterial meningitis is a disease of major public health importance especially for countries such as Ghana; whose northern part lies within the meningitis belt. The Northern region of Ghana has been recording cases with outbreaks over the years. In order to generate evidence to improve surveillance, we described the epidemiology of bacterial meningitis using surveillance data of the northern region.

METHODS

Bacterial meningitis datasets from January 2010 to December 2015 for all the 26 districts of the Northern region were retrieved from line lists. Data were analyzed in terms of person, place, time, and identity of causative organisms using descriptive statistics. The results were presented as proportions, rates, tables and graphs.

RESULTS

A total of 1,176 cases were reported. Of these, 53.5% (629/1,176) were males. The proportion of cases aged 0 to 29 years was 77.4%. The Overall Case Fatality Rate (CFR) was 9.7% (114/1,176). About 65% of all cases were recorded from January to April. Only 23.7% (279/1,176) of cases were laboratory-confirmed. Neisseria meningitides and Streptococcus pneumonia accounted for 91.4% of confirmed cases. Over the period, the incidence reduced from 9.0/100,000 population to 3.8/100,000 population and CFR reduced from 16.6% to 5.7%.

CONCLUSION

Most cases of bacterial meningitis were recorded in the dry season and in persons younger than 30 years. Less than a quarter of cases were laboratory confirmed, and no new bacteria species were identified. Both morbidity and mortality rates were on the decline. There is the need to consolidate these gains by intensifying meningitis surveillance and improving on the rate of laboratory case confirmation.

Four sexually transmitted infections (STIs) in Belgian general practice: first results (2013-2014) of a nationwide continuing surveillance study

OBJECTIVES

To describe and explore data from the surveillance of chlamydia, syphilis, gonorrhoea and genital warts by the Belgian Network of Sentinel General Practices (SGP) over the first 2 years (2013 and 2014) and to estimate the incidence of these 4 sexually transmitted infections (STIs). A special focus is put on data quality.

DESIGN

Retrospective observational study.

SETTING

General practices from the nationwide representative SGP network.

OUTCOME MEASURES

Agreement between data distributions by year, agreement between SGP-based incidence and incidence based on mandatory notification, missingness of patient age or gender and incompleteness of sexual risk history of patients.

RESULTS

306 new STI episodes were reported from 298 patients, corresponding with an episode-based incidence of 91.9/100 000 (95% CI 81.9 to 102.8) general practice patients, with almost half of it due to chlamydia. The incidence of chlamydia in men was significantly higher in 2014 than in 2013. Population characteristics were similarly distributed in 2013 and 2014. The SGP-based incidence of gonorrhoea and syphilis in Flanders were in agreement with the incidence based on mandatory notification of cases. Patient age or gender was missing from 35 episodes (11.4%). Independent determinants of missingness of patient age or gender were the Flemish region (OR 3.46; 95% CI 1.02 to 11.73) and genital warts infection (OR 2.23; 95% CI 1.07 to 4.63). An incomplete sexual risk history was reported for 54.6% STI episodes. The odds for an incomplete sexual history were higher for older patients (OR 1.72; 95% CI 1.06 to 2.76) and for patients infected with syphilis, gonorrhoea or co-infection(s) (OR 1.71; 95% CI 1.03 to 2.83).

CONCLUSIONS

Incompleteness of reports about patients with STI sexual risk histories is important from the perspective of quality of data and of quality of care. Together with the low rates of both HIV testing and discussion of partner notification, this suggests that a general practice guideline is needed.

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼ 10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.

Using surveillance data to reduce healthcare-associated infection: a qualitative study in Sweden

INTRODUCTION

Healthcare-associated infection (HAI) surveillance data can be used to estimate the scope, spread and location of infections, monitor trends, evaluate preventive efforts, and improve practices, policy and facility planning. In Sweden, national point prevalence surveys (PPS) have been conducted twice yearly in all county councils since 2008.

AIM

The aim of this study was to identify key obstacles concerning the HAI surveillance process.

METHODS

Twenty-two infection control practitioners (ICPs) from all county councils in Sweden were interviewed, using semi-structured interview guides. Data were analysed using qualitative content analysis.

RESULTS

Sixteen types of obstacles pertaining to four surveillance stages were identified. Most obstacles were associated with the first two stages, which meant that the latter stages of this process, i.e. the use of the results to reduce HAI, were underdeveloped. The ICPs observed scepticism towards both the PPS methodology itself and the quality of the HAI data collected in the PPS, which hinders HAI surveillance realising its full potential in Swedish healthcare.

Early-season avian deaths from West Nile virus as warnings of human infection

An analysis of 2001 and 2002 West Nile virus (WNV) surveillance data shows that counties that report WNV-infected dead birds early in the transmission season are more likely to report subsequent WNV disease cases in humans than are counties that do not report early WNV-infected dead birds.