Incorporating scale dependence in disease burden estimates:the case of human African trypanosomiasis in Uganda

The WHO has established the disability-adjusted life year (DALY) as a metric for measuring the burden of human disease and injury globally. However, most DALY estimates have been calculated as national totals. We mapped spatial variation in the burden of human African trypanosomiasis (HAT) in Uganda for the years 2000-2009. This represents the first geographically delimited estimation of HAT disease burden at the sub-country scale.Disability-adjusted life-year (DALY) totals for HAT were estimated based on modelled age and mortality distributions, mapped using Geographic Information Systems (GIS) software, and summarised by parish and district. While the national total burden of HAT is low relative to other conditions, high-impact districts in Uganda had DALY rates comparable to the national burden rates for major infectious diseases. The calculated average national DALY rate for 2000-2009 was 486.3 DALYs/100 000 persons/year, whereas three districts afflicted by rhodesiense HAT in southeastern Uganda had burden rates above 5000 DALYs/100 000 persons/year, comparable to national GBD 2004 average burden rates for malaria and HIV/AIDS.These results provide updated and improved estimates of HAT burden across Uganda, taking into account sensitivity to under-reporting. Our results highlight the critical importance of spatial scale in disease burden analyses. National aggregations of disease burden have resulted in an implied bias against highly focal diseases for which geographically targeted interventions may be feasible and cost-effective. This has significant implications for the use of DALY estimates to prioritize disease interventions and inform cost-benefit analyses

Aetiology of community-acquired neonatal sepsis in low and middle income countries

99% of the approximate 1 million annual neonatal deaths from life-threatening invasive bacterial infections occur in developing countries, at least 50% of which are from home births or community settings. Data concerning aetiology of sepsis in these settings are necessary to inform targeted therapy and devise management guidelines. This review describes and analyses the bacterial aetiology of community-acquired neonatal sepsis in developing countries

Environmental Public Health Tracking/Surveillance in Canada:A Commentary

This paper studies an infinite-server queue in a random environment, meaning that the arrival rate, the service requirements, and the server work rate are modulated by a general càdlàg stochastic background process. To prove a large deviations principle, the concept of attainable parameters is introduced. Scaling both the arrival rates and the background process, a large deviations principle for the number of jobs in the system is derived using attainable parameters. Finally, some known results about Markov-modulated infinite-server queues are generalized and new results for several background processes and scalings are established in examples

Respiratory syncytial virus: time for surveillance across all ages, with a focus on adults

Human respiratory syncytial virus (RSV), a leading cause of serious respiratory illness, can affect individuals of all ages, especially children below two years of age and adults 60 years of age and above, as well as individuals with chronic comorbidities, such as chronic pulmonary or cardiovascular conditions, and immunocompromised individuals [1,2]. In adults, clinical outcomes of RSV infection vary from mild, cold-like symptoms to more serious complications, including pneumonia, exacerbations of chronic medical conditions (e.g. asthma, chronic obstructive pulmonary disease, congestive heart failure), and can lead to death [3]. The RSV-related hospitalisation burden is especially high in older adults. A meta-analysis conducted on data from high-income countries across different continents (based on literature published between 1 January 2000 and 3 November 2021) estimated that approximately 470 000 individuals 60 years of age and above were hospitalised in 2019 due to RSV, of whom approximately 33 000 died. The pooled estimate for RSV acute respiratory infection (ARI) attack rate was 1.62% (95% CI = 0.84–3.08%), corresponding to an estimated 5.2 million RSV-associated ARI cases [2]. As RSV symptoms in adults resemble those of other common respiratory viruses (e.g. influenza), clinical diagnosis of RSV may be challenging.Circulation of the two major RSV antigenic groups (A and B) is seasonal in temperate climates, with a peak during the winter months, but has a more variable pattern in tropical climates. In addition, RSV circulation overlaps with the influenza season but usually lasts longer (16–22 vs. 6–8 weeks, respectively) [1]. Human respiratory syncytial virus circulation was impacted during the first two years of the coronavirus disease 2019 (COVID-19) pandemic, with RSV cases substantially declining after the widespread implementation of public health and social measures and re-emerging out of season when measures were gradually lifted [4].Human respiratory syncytial virus surveillance is limited, geographically heterogeneous, and does not systematically include all age groups. While the burden of RSV is highest among very young children, adults 60 years of age and above, and individuals with underlying health conditions, other populations also contribute to RSV transmission. Therefore, improved RSV surveillance systems are needed to better understand the epidemiology of RSV and inform public health measures. To identify the current challenges in RSV surveillance in adults and the ways to expand RSV surveillance systems, an advisory board among seven experts with national and international expertise in infectious diseases and surveillance was held in August 2022. The main points discussed by the group are summarised in plain language in Figure 1.<br/

Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper

A Digital One Health framework to integrate data for public health decision-making

The current implementation of One Health (OH) primarily focuses on multi-sectoral collaboration but often overlooks opportunities to integrate contextual and pathogen-related data into a unified data resource. This lack of integration hampers effective, data-driven decision-making in OH activities. In this perspective, we examine the existing strategies for data sharing and identify gaps and barriers to integration. To overcome these challenges, we propose the Digital OH (DOH) framework for data integration, which consolidates data-sharing principles within five pillars for the OH community of practice: (a) Harmonization of standards to establish trust, (b) Automation of data capture to enhance quality and efficiency, (c) Integration of data at point of capture to limit bureaucracy, (d) Onboard data analysis to articulate utility, and (e) Archiving and governance to safeguard the OH data resource. We discuss an upcoming pilot program as a use case focusing on antimicrobial resistance surveillance to illustrate the application of this framework. Our ambition is to leverage technology to create data as a shared resource using DOH not only to overcome current structural barriers but also to address prevailing ethical and legal concerns. By doing so, we can enhance the efficiency and effectiveness of decision-making processes in the OH community of practice, at a national, regional, and international level

Inferring source attribution from a multi-year multi-source dataset of Salmonella in Minnesota

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state