Objectives: Breastfeeding rates are decreasing rapidly in many low and middle-income countries, disproportionately affecting urban residences. We use data from Lao People’s Democratic Republic to identify primary mechanisms underlying the urban-rural gap in breastfeeding practices.Methods: We used data from the 2017 Lao Social Indicator Survey II. Residence was categorized as large-urban (>1 million), small-urban (<1 Million), and rural. Multivariable logistic regression provided odds ratios and 95% confidence intervals (CI) to identify factors attributing to the urban-rural differences in complying with World Health Organization’s breastfeeding recommendations for children <24 months.Results: Mothers in large-urban residences had 3.78 (95% confidence intervals: 1.19, 11.95) and 4.67 (95% CI: 2.30, 9.46) higher odds of non-compliance with exclusive and complementary breastfeeding recommendations, respectively, than mothers living in rural areas in bivariate models. Breastfeeding differentials between small urban and rural residences were largely explained by differences in maternal education and household wealth.Conclusion: Results of our paper suggest large disparities in breastfeeding practices between large-urban, small-urban, and rural residences.

Since the novel coronavirus outbreak of SARS-CoV-2 from the first cases whereof were reported in Wuhan, China, in December 2019, our globalized world has changed enormously. On the 11th of March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic, and nations around the world have taken drastic measures to reduce transmission of the disease. The situation is similar in Switzerland, a small high-income country in Central Europe, where the first COVID-19 case was registered on the 25th of February 2020. Through literature review as well as correspondence with public health professionals and experts in mathematical modeling, this case study focuses on the outbreak’s impact on Switzerland and on the measures this country has implemented thus far. Along with the rapid spread of the virus, the political organization, economy, healthcare system, and characteristics of the country greatly influence the approach taken in facing the crisis. Switzerland appears to be structurally well-prepared, but, according to mathematical modeling predictions, in order to avoid total collapse of healthcare facilities, the measures taken by the Swiss Government need to reduce the virus transmission chain by at least 70%. Fortunately, updated models on April 22nd show evidence that the non-pharmaceutical measures invoked have decreased transmission by an estimated 89%, proving effective management by the federal government and allowing for progressive deconfinement measures.

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.

BACKGROUND: In 2018, a hospital-based surveillance system for influenza (CH-SUR) was established in six tertiary care hospitals in Switzerland. From March 2020 onwards, this surveillance system was expanded to include more institutions, as well as COVID-19. AIM: To quantitatively evaluate the timeliness and completeness of CH-SUR data and to qualitatively assess stakeholder perceptions of the importance, reliability and adaptability of the surveillance system. METHODS: All patients admitted to one of the participating centres for more than 24 hours and who had a laboratory-confirmed influenza virus or SARS-CoV-2 infection were included in CH-SUR. For all cases, we evaluated the timeliness and completeness of reporting to CH-SUR. A qualitative survey among CH-SUR stakeholders assessed perceived importance, understanding, reliability and adaptability of CH-SUR. RESULTS: Up to 20 centres participated in CH-SUR. Between December 2018 and October 2023, 7375 cases of influenza were reported and between March 2020 and October 2023, 49,235 cases of COVID-19 were reported to CH-SUR. During the COVID-19 pandemic, time to data entry and completeness improved over time; the median delay of data entry in CH-SUR was 5 days (interquartile range [IQR]: 2–23) for COVID-19 and 4 days (IQR: 2–15) for influenza during the period 2018–2023. The completeness of variables was high (99.4%), with the exception of COVID-19 or annual influenza vaccination status (respectively, 15% and 72% “Unknown” responses). Stakeholders perceived the system as important, relevant, understandable and adaptable. CONCLUSION: CH-SUR provided critical epidemiological and clinical information on hospitalised influenza and COVID-19 cases across Switzerland during the pandemic. Our evaluation highlighted the importance and relevance of this system among CH-SUR stakeholders, as well as its importance for preparedness and response to future infectious disease outbreaks.

Background: In settings where hepatitis E virus (HEV) is highly endemic, anti-HEV IgG seroprevalence approaches saturation, yet how often disease occurs in previously infected individuals, and how protective natural genotype 1 immunity is, remains unmeasured. IgG concentration cannot answer this as levels rise within days of symptom onset masking any pre-existing levels; IgG avidity, maturing over months, may serve as a useful tool to understand reinfections. Methods: We enrolled 1007 suspected HEV patients and measured anti-HEV IgG avidity in IgG-positive confirmed cases and an age-stratified sample of IgG-positive test-negatives (n = 366) in enhanced surveillance around a 2022 vaccination campaign in Bentiu, South Sudan. Using high avidity (avidity index ≥50%) as a marker of past infection, we estimated (i) the reinfection fraction, (ii) protection from prior infection using a test negative design and (iii) associations with viral load and infection severity. Results: Median avidity index was 11% in cases versus 77% in test-negatives. Among unvaccinated cases, 11.6% (28/242) carried high-avidity IgG (indicative of mature immunity and therefore reinfection), rising with age (odds ratio 1.6 per decade, p=0.006). Mature immunity was associated with 95% lower odds of disease (adjusted OR 0.05, 95% CI 0.03–0.08), robust to threshold and case-definition. High-avidity cases were less often viremic (29% vs 78%) at matched time since onset, though we found no differences in liver function biomarkers between patients with high-avidity and low-avidity. Conclusions: Most HEV disease occurred in previously uninfected individuals, and prior immunity was strongly protective against detected disease. Avidity provides a population-level tool to read the immune landscape where seroprevalence may be uninformative. Short Summary: Using IgG avidity to distinguish between past from recent hepatitis E infection in a highly endemic setting, we found most disease occurred in previously uninfected individuals. Prior infection was associated with 95% lower odds of disease, demonstrating strong natural immunity.