Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response.

Emerging infectious diseases are a growing threat in sub-Saharan African countries, but the human and technical capacity to quickly respond to outbreaks remains limited. Here, we describe the experience and lessons learned from a joint project with the WHO Regional Office for Africa (WHO AFRO) to support the sub-Saharan African COVID-19 response.In June 2020, WHO AFRO contracted a number of consultants to reinforce the COVID-19 response in member states by providing actionable epidemiological analysis. Given the urgency of the situation and the magnitude of work required, we recruited a worldwide network of field experts, academics and students in the areas of public health, data science and social science to support the effort. Most analyses were performed on a merged line list of COVID-19 cases using a reverse engineering model (line listing built using data extracted from national situation reports shared by countries with the Regional Office for Africa as per the IHR (2005) obligations). The data analysis platform The Renku Project ( https://renkulab.io ) provided secure data storage and permitted collaborative coding.Over a period of 6 months, 63 contributors from 32 nations (including 17 African countries) participated in the project. A total of 45 in-depth country-specific epidemiological reports and data quality reports were prepared for 28 countries. Spatial transmission and mortality risk indices were developed for 23 countries. Text and video-based training modules were developed to integrate and mentor new members. The team also began to develop EpiGraph Hub, a web application that automates the generation of reports similar to those we created, and includes more advanced data analyses features (e.g. mathematical models, geospatial analyses) to deliver real-time, actionable results to decision-makers.Within a short period, we implemented a global collaborative approach to health data management and analyses to advance national responses to health emergencies and outbreaks. The interdisciplinary team, the hands-on training and mentoring, and the participation of local researchers were key to the success of this initiative.

The World Health Organization's public health intelligence activities during the COVID-19 pandemic response, December 2019 to December 2021.

The coronavirus disease (COVID-19) presented a unique opportunity for the World Health Organization (WHO) to utilise public health intelligence (PHI) for pandemic response. WHO systematically captured mainly unstructured information (e.g. media articles, listservs, community-based reporting) for public health intelligence purposes. WHO used the Epidemic Intelligence from Open Sources (EIOS) system as one of the information sources for PHI. The processes and scope for PHI were adapted as the pandemic evolved and tailored to regional response needs. During the early months of the pandemic, media monitoring complemented official case and death reporting through the International Health Regulations mechanism and triggered alerts. As the pandemic evolved, PHI activities prioritised identifying epidemiological trends to supplement the information available through indicator-based surveillance reported to WHO. The PHI scope evolved over time to include vaccine introduction, emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, unusual clinical manifestations and upsurges in cases, hospitalisation and death incidences at subnational levels. Triaging the unprecedented high volume of information challenged surveillance activities but was managed by collaborative information sharing. The evolution of PHI activities using multiple sources in WHO's response to the COVID-19 pandemic illustrates the future directions in which PHI methodologies could be developed and used.

Implementing epidemic intelligence in the WHO African region for early detection and response to acute public health events.

Epidemic intelligence activities are undertaken by the WHO Regional Office for Africa to support member states in early detection and response to outbreaks to prevent the international spread of diseases. We reviewed epidemic intelligence activities conducted by the organisation from 2017 to 2020, processes used, key results and how lessons learned can be used to strengthen preparedness, early detection and rapid response to outbreaks that may constitute a public health event of international concern. A total of 415 outbreaks were detected and notified to WHO, using both indicator-based and event-based surveillance. Media monitoring contributed to the initial detection of a quarter of all events reported. The most frequent outbreaks detected were vaccine-preventable diseases, followed by food-and-water-borne diseases, vector-borne diseases and viral haemorrhagic fevers. Rapid risk assessments generated evidence and provided the basis for WHO to trigger operational processes to provide rapid support to member states to respond to outbreaks with a potential for international spread. This is crucial in assisting member states in their obligations under the International Health Regulations (IHR) (2005). Member states in the region require scaled-up support, particularly in preventing recurrent outbreaks of infectious diseases and enhancing their event-based surveillance capacities with automated tools and processes.

Patient- and Physician-Level Factors Associated With Adherence to C-CHANGE Recommendations in Primary Care Settings in Ontario.

BACKGROUND: We previously found large variation among family physicians in adherence to the Canadian Cardiovascular Harmonization of National Guidelines Endeavour (C-CHANGE). We assessed the role of patient- and physician-level factors in the variation in adherence to recommendations for managing cardiovascular disease risk factors. METHODS: We conducted a retrospective study using multilevel logistic regression analyses with the Electronic Medical Record Administrative data Linked Database (EMRALD) housed at ICES in Ontario. Five quality indicators based on C-CHANGE guidelines were modelled. Effects of clustering and between-group variation, patient-level (sociodemographics, comorbidities) and physician-level characteristics (demographic and practice information) were assessed to determine odds ratios of receiving C-CHANGE recommended care. RESULTS: In all, 324 Ontario physicians practicing in 41 clinics who provided care to 227,999 adult patients were studied. We found significant variation in quality indicators, with 15% to 39% of the total variation attributable to nonpatient factors. The largest variation was in performing 2-hour plasma glucose testing in prediabetic patients. Patient-level factors most frequently associated with recommendation adherence included sex, age, and multi-comorbidities. Women were more likely than men to have their body mass index measured, and their blood pressure controlled, but less likely to receive antiplatelet medications and liver-enzyme testing if overweight or obese. CONCLUSIONS: The majority of variations in adherence were attributable to patient attributes, but a substantial proportion of unexplained variation was due to differences among physicians and clinics. This finding may signal suboptimal processes or structures and warrant further investigation to improve the quality of primary care management of cardiovascular disease in Ontario.

CONTEXTE: Nous avions déjà constaté que l'observance des recommandations canadiennes en matière de prévention et de gestion des maladies cardiovasculaires de l'initiative C-CHANGE ( C anadian C ardiovascular H armonization of N ational G uidelines E ndeavour) varie beaucoup d'un médecin de famille à l'autre. Nous avons évalué l'effet de caractéristiques des patients et des médecins sur l'observance de ces recommandations pour la gestion des facteurs de risque de maladies cardiovasculaires. MÉTHODOLOGIE: Nous avons mené une étude rétrospective reposant sur des analyses de régression logistique multiniveaux au sein de la base de données liée aux dossiers médicaux électroniques EMRALD ( E lectronic M edical R ecord A dministrative data L inked D atabase) qui se trouve à l'ICES, en Ontario. Nous avons modélisé cinq indicateurs de la qualité en nous basant sur les recommandations de l'initiative C-CHANGE. Nous avons évalué les effets de regroupement, de la variation entre les groupes, des caractéristiques des patients (données sociodémographiques, maladies concomitantes), des caractéristiques des médecins (données démographiques et renseignements sur la pratique) afin de déterminer les risques relatifs approchés associés aux soins conformes aux recommandations de l'initiative C-CHANGE. RÉSULTATS: L'étude a porté sur un total de 324 médecins ontariens pratiquant dans 41 cliniques et ayant prodigué des soins à 227 999 adultes. Nous avons observé une variation significative entre les indicateurs de qualité, et de 15 % à 39 % de la variation totale était attribuable aux caractéristiques non reliées aux patients. La variation la plus importante concernait le test d'hyperglycémie provoquée (2 heures) chez les patients prédiabétiques. Les caractéristiques des patients qui étaient le plus souvent associées à l'observance des recommandations étaient le sexe, l'âge et la présence de multiples maladies concomitantes. L'indice de masse corporelle et la pression artérielle étaient plus souvent mesurés chez les femmes que chez les hommes, mais les femmes étaient moins susceptibles de recevoir un traitement antiplaquettaire ou de subir une analyse des enzymes hépatiques si elles étaient en surpoids ou obèses. CONCLUSIONS: La plus grande partie des variations dans l'observance des recommandations était liée aux caractéristiques des patients, mais une proportion importante de variations injustifiées était associée aux différences entre les médecins et entre les cliniques. Ces observations pourraient indiquer la présence de processus ou de structures sous-optimales et méritent une analyse approfondie qui permettra d'améliorer la qualité de la prise en charge des maladies cardiovasculaires par les médecins de soins primaires en Ontario.