Etiologies of influenza-like illness and severe acute respiratory infections in Tanzania, 2017–2019

In 2016, Tanzania expanded sentinel surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) to include testing for non-influenza respiratory viruses (NIRVs) and additional respiratory pathogens at 9 sentinel sites. During 2017–2019, respiratory specimens from 2730 cases underwent expanded testing: 2475 specimens (90.7%) were tested using a U.S. Centers for Disease Control and Prevention (CDC)-developed assay covering 7 NIRVs (respiratory syncytial virus [RSV], rhinovirus, adenovirus, human metapneumovirus, parainfluenza virus 1, 2, and 3) and influenza A and B viruses. Additionally, 255 specimens (9.3%) were tested using the Fast-Track Diagnostics Respiratory Pathogens 33 (FTD-33) kit which covered the mentioned viruses and additional viral, bacterial, and fungal pathogens. Influenza viruses were identified in 7.5% of all specimens;however, use of the CDC assay and FTD-33 kit increased the number of specimens with a pathogen identified to 61.8% and 91.5%, respectively. Among the 9 common viruses between the CDC assay and FTD-33 kit, the most identified pathogens were RSV (22.9%), rhinovirus (21.8%), and adenovirus (14.0%);multi-pathogen co-detections were common. Odds of hospitalization (SARI vs. ILI) varied by sex, age, geographic zone, year of diagnosis, and pathogen identified;hospitalized illnesses were most common among children under the age of 5 years. The greatest number of specimens were submitted for testing during December–April, coinciding with rainy seasons in Tanzania, and several viral pathogens demonstrated seasonal variation (RSV, human metapneumovirus, influenza A and B, and parainfluenza viruses). This study demonstrates that expanding an existing influenza platform to include additional respiratory pathogens can provide valuable insight into the etiology, incidence, severity, and geographic/temporal patterns of respiratory illness. Continued respiratory surveillance in Tanzania, and globally, can provide valuable data, particularly in the context of emerging respiratory pathogens such as SARS-CoV-2, and guide public health interventions to reduce the burden of respiratory illnesses.

Etiologies of influenza-like illness and severe acute respiratory infections in Tanzania, 2017-2019.

In 2016, Tanzania expanded sentinel surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) to include testing for non-influenza respiratory viruses (NIRVs) and additional respiratory pathogens at 9 sentinel sites. During 2017-2019, respiratory specimens from 2730 cases underwent expanded testing: 2475 specimens (90.7%) were tested using a U.S. Centers for Disease Control and Prevention (CDC)-developed assay covering 7 NIRVs (respiratory syncytial virus [RSV], rhinovirus, adenovirus, human metapneumovirus, parainfluenza virus 1, 2, and 3) and influenza A and B viruses. Additionally, 255 specimens (9.3%) were tested using the Fast-Track Diagnostics Respiratory Pathogens 33 (FTD-33) kit which covered the mentioned viruses and additional viral, bacterial, and fungal pathogens. Influenza viruses were identified in 7.5% of all specimens; however, use of the CDC assay and FTD-33 kit increased the number of specimens with a pathogen identified to 61.8% and 91.5%, respectively. Among the 9 common viruses between the CDC assay and FTD-33 kit, the most identified pathogens were RSV (22.9%), rhinovirus (21.8%), and adenovirus (14.0%); multi-pathogen co-detections were common. Odds of hospitalization (SARI vs. ILI) varied by sex, age, geographic zone, year of diagnosis, and pathogen identified; hospitalized illnesses were most common among children under the age of 5 years. The greatest number of specimens were submitted for testing during December-April, coinciding with rainy seasons in Tanzania, and several viral pathogens demonstrated seasonal variation (RSV, human metapneumovirus, influenza A and B, and parainfluenza viruses). This study demonstrates that expanding an existing influenza platform to include additional respiratory pathogens can provide valuable insight into the etiology, incidence, severity, and geographic/temporal patterns of respiratory illness. Continued respiratory surveillance in Tanzania, and globally, can provide valuable data, particularly in the context of emerging respiratory pathogens such as SARS-CoV-2, and guide public health interventions to reduce the burden of respiratory illnesses.

The role of community pharmacies in early detection of suspected COVID-19 cases in 2020: lessons from Dar es Salaam, Tanzania.

Tanzania reported its first COVID-19 case on 16 March 2020. We conducted event-based surveillance of COVID-19 suspect cases among pharmacy clients presenting with respiratory symptoms and influenza-like illness to increase early and rapid detection of COVID-19 cases and mitigate transmission. We conveniently sampled 103 pharmacies from Dar es Salaam, the epicentre for the COVID-19 pandemic in Tanzania at the time. Between 23 April 2020 and 18 May 2020, 67% of the pharmacies (69/103) reported an observed increase in the number of clients presenting with respiratory symptoms and influenza-like illness compared with the 1 month before the COVID-19 outbreak. In the 1-month surveillance period, the participating pharmacies recorded 75 alerts of COVID-19 suspect cases and referred all suspected COVID-19 cases to rapid response teams for additional symptomatic screening and SARS-CoV-2 testing. A key implementation challenge was that some clients identified as COVID-19 suspected cases were hesitant to provide follow-up information for linkage to rapid response teams. Addressing concerns among drug dispensers in the participating pharmacies and informing them of the benefits of the surveillance activity were important implementation components. Our approach demonstrates the overall feasibility of rapidly implementing an event-based surveillance system for an emerging health threat through an existing network of pharmacies within the community. The approach and tools used in this surveillance activity could be adapted in similar settings to detect and generate alerts of disease outbreaks in the community that other surveillance systems may otherwise miss.

Drivers of COVID-19 policy stringency in 175 countries and territories: COVID-19 cases and deaths, gross domestic products per capita, and health expenditures.

Background: New data on COVID-19 may influence the stringency of containment policies, but these potential effect are not understood. We aimed to understand the associations of new COVID-19 cases and deaths with policy stringency globally and regionally. Methods: We modelled the marginal effects of new COVID-19 cases and deaths on policy stringency (scored 0-100) in 175 countries and territories, adjusting for gross domestic product (GDP) per capita and health expenditure (% of GDP), and public expenditure on health. The time periods examined were March to August 2020, September 2020 to February 2021, and March to August 2021. Results: Policy response to new cases and deaths was faster and more stringent early in the COVID-19 pandemic (March to August 2020) compared to subsequent periods. New deaths were more strongly associated with stringent policies than new cases. In an average week, one new death per 100 000 people was associated with a stringency increase of 2.1 units in the March to August 2020 period, 1.3 units in the September 2020 to February 2021 period, and 0.7 units in the March to August 2021 period. New deaths in Africa and the Western Pacific were associated with more stringency than in other regions. Higher health expenditure as a percentage of GDP was associated with less stringent policies. Similarly, higher public expenditure on health by governments was mostly associated with less stringency across all three periods. GDP per capita did not have consistent patterns of associations with stringency. Conclusions: The stringency of COVID-19 policies was more strongly associated with new deaths than new cases. Our findings demonstrate the need for enhanced mortality surveillance to ensure policy alignment during health emergencies. Countries that invest less in health or have a lower public expenditure on health may be inclined to enact more stringent policies. This new empirical understanding of COVID-19 policy drivers can help public health officials anticipate and shape policy responses in future health emergencies.

Using Population Mobility Patterns to Adapt COVID-19 Response Strategies in 3 East Africa Countries.

The COVID-19 pandemic spread between neighboring countries through land, water, and air travel. Since May 2020, ministries of health for the Democratic Republic of the Congo, Tanzania, and Uganda have sought to clarify population movement patterns to improve their disease surveillance and pandemic response efforts. Ministry of Health-led teams completed focus group discussions with participatory mapping using country-adapted Population Connectivity Across Borders toolkits. They analyzed the qualitative and spatial data to prioritize locations for enhanced COVID-19 surveillance, community outreach, and cross-border collaboration. Each country employed varying toolkit strategies, but all countries applied the results to adapt their national and binational communicable disease response strategies during the pandemic, although the Democratic Republic of the Congo used only the raw data rather than generating datasets and digitized products. This 3-country comparison highlights how governments create preparedness and response strategies adapted to their unique sociocultural and cross-border dynamics to strengthen global health security.