COVID-19 surveillance systems in 13 African countries

Background: Surveillance forms the basis for response to disease outbreaks, including COVID-19. Herein, we identified the COVID-19 surveillance systems and the associated challenges in 13 African countries. Methods: We conducted a comprehensive narrative review of peer-reviewed literature published between January 2020 and April 2021 in PubMed, Medline, PubMed Central, and Google Scholar using predetermined search terms. Relevant studies from the search and other data sources on COVID-19 surveillance strategies and associated challenges in 13 African countries (Mauritius, Algeria, Nigeria, Angola, Cote d’Ivoire, the Democratic Republic of the Congo, Ghana, Ethiopia, South Africa, Kenya, Zambia, Tanzania, and Uganda) were identified and reviewed. Results: Our findings revealed that the selected African countries have ramped up COVID-19 surveillance ranging from immediate case notification, virological surveillance, hospital-based surveillance to mortality surveillance among others. Despite this, there exist variations in the level of implementation of the surveillance systems across countries. Integrated Disease Surveillance and Response (IDSR) strategy is also being leveraged in some African countries, but the implementation across countries remains uneven. Our study also revealed various challenges facing surveillance which included shortage of skilled human resources resulting in poor data management, weak health systems, complexities of ethical considerations, diagnostic insufficiency, the burden of co-epidemic surveillance, and geographical barriers, among others. Conclusion: With the variations in the level of implementation of COVID-19 surveillance strategies seen across countries, it is pertinent to ensure proper coordination of the surveillance activities in the African countries and address all the challenges facing COVID-19 surveillance using tailored strategies.

Preterm neonatal mortality in China during 2009–2018: A retrospective study

In this retrospective analysis, we aimed to analyze the epidemic characteristics of neonatal mortality due to preterm birth at 28–36 weeks gestation in different regions from 2009 to 2018. Data were obtained from China’s Under-5 Child Mortality Surveillance System (U5CMSS). The χ2 trend test, Poisson regression and the Cochran-Mantel-Haenszel method were used in this study. We found that 51.3%, 42.0% and 44.5% of neonate deaths were preterm infants, and immaturity was mainly attributed to 60.1%, 64.1% and 69.5% of these deaths, in the eastern, central and western regions, respectively. The preterm neonatal mortality rate due to immaturity dropped from 149.2, 216.5 and 339.5 in 2009 to 47.4, 83.8 and 170.1 per 100 000 live births in 2018, giving an average annual decline rate of 12.1%, 11.6% and 6.3% in the eastern, central and western regions, respectively, during the studying period. The relative risk of preterm neonatal mortality due to immaturity were 1.3 and 2.3 for the central regions and western regions in 2009–2010, ascending to 2.2 and 3.9 in 2017–2018. The proportion of preterm neonatal deaths with a gestational age <32 weeks was highest among the eastern region. There were significantly more preterm neonatal infants who were not delivered at medical institutions in the western region than in the eastern and central regions. The preterm infant, especially with gestational age <32 weeks, should receive the most attention through enhanced policies and programs to improve child survival. Priority interventions should be region-specific, depending on the availability of economic and healthcare resources.

The Impact of COVID-19 on Mortality in Spain: Monitoring Excess Mortality (MoMo) and the Surveillance of Confirmed COVID-19 Deaths

Measuring mortality has been a challenge during the COVID-19 pandemic. Here, we compared the results from the Spanish daily mortality surveillance system (MoMo) of excess mortality estimates, using a time series analysis, with those obtained for the confirmed COVID-19 deaths reported to the National Epidemiological Surveillance Network (RENAVE). The excess mortality estimated at the beginning of March 2020 was much greater than what has been observed in previous years, and clustered in a very short time. The cumulated excess mortality increased with age. In the first epidemic wave, the excess mortality estimated by MoMo was 1.5 times higher than the confirmed COVID-19 deaths reported to RENAVE, but both estimates were similar in the following pandemic waves. Estimated excess mortality and confirmed COVID-19 mortality rates were geographically distributed in a very heterogeneous way. The greatest increase in mortality that has taken place in Spain in recent years was detected early by MoMo, coinciding with the spread of the COVID-19 pandemic. MoMo is able to identify risk situations for public health in a timely manner, relying on mortality in general as an indirect indicator of various important public health problems.

Establishing an ad hoc COVID-19 mortality surveillance during the first epidemic wave in Belgium, 1 March to 21 June 2020

Background COVID-19-related mortality in Belgium has drawn attention for two reasons: its high level, and a good completeness in reporting of deaths. An ad hoc surveillance was established to register COVID-19 death numbers in hospitals, long-term care facilities (LTCF) and the community. Belgium adopted broad inclusion criteria for the COVID-19 death notifications, also including possible cases, resulting in a robust correlation between COVID-19 and all-cause mortality. Aim To document and assess the COVID-19 mortality surveillance in Belgium. Methods We described the content and data flows of the registration and we assessed the situation as of 21 June 2020, 103 days after the first death attributable to COVID-19 in Belgium. We calculated the participation rate, the notification delay, the percentage of error detected, and the results of additional investigations. Results The participation rate was 100% for hospitals and 83% for nursing homes. Of all deaths, 85% were recorded within 2 calendar days: 11% within the same day, 41% after 1 day and 33% after 2 days, with a quicker notification in hospitals than in LTCF. Corrections of detected errors reduced the death toll by 5%. Conclusion Belgium implemented a rather complete surveillance of COVID-19 mortality, on account of a rapid investment of the hospitals and LTCF. LTCF could build on past experience of previous surveys and surveillance activities. The adoption of an extended definition of ‘COVID-19-related deaths’ in a context of limited testing capacity has provided timely information about the severity of the epidemic.

SARS-CoV-2 Mortality Surveillance among Community Deaths brought to University Teaching Hospital Mortuary in Lusaka, Zambia, 2020

Introduction: During March-December 2020, Zambia recorded 20,725 confirmed COVID-19 cases, with the first wave peaking between July and August. Of the 388 COVID-19-related deaths occurring nationwide, most occurred in the community. We report findings from COVID-19 mortality surveillance among community deaths brought to the University Teaching Hospital (UTH) mortuary in Lusaka. Methods: In Zambia, when a person dies in the community, and is brought into a health facility mortuary, they are recorded as 'brought in dead' (BID). The UTH mortuary accepts persons BID for Lusaka District, the most populated district in Zambia. We analyzed data for persons BID at UTH during 2020. We analyzed two data sources: weekly SARS-CoV-2 test results for persons BID and monthly all-cause mortality numbers among persons BID. For all-cause mortality among persons BID, monthly deaths during 2020 that were above the upper bound of the 95% confidence interval for the historic mean (2017-2019) were considered significant. Spearman's rank test was used to correlate the overall percent positivity in Zambia with all-cause mortality and SARS-CoV-2 testing among persons BID at UTH mortuary. Results: During 2020, 7,756 persons were BID at UTH (monthly range 556-810). SARS-CoV-2 testing began in April 2020, and through December 3,131 (51.9%) of 6,022 persons BID were tested. Of these, 212 (6.8%) were SARS-CoV-2 positive with weekly percent test positivity ranging from 0-32%, with the highest positivity occurring during July 2020. There were 1,139 excess persons BID from all causes at UTH mortuary in 2020 compared to the 2017-2019 mean. The monthly number of persons BID from all causes was above the upper bound of the 95% confidence interval during June-September and December. Conclusion: Increases in all-cause mortality and SARS-CoV-2 test positivity among persons BID at UTH mortuary corresponded with the first peak of the COVID-19 epidemic in June and August 2020, indicating possible increased mortality related to the COVID-19 epidemic in Zambia. Combining all-cause mortality and SARS-CoV-2 testing for persons BID provides useful information about the severity of the epidemic in Lusaka and should be implemented throughout Zambia.

Estimating the mortality burden of large scale mining projects—Evidence from a prospective mortality surveillance study in Tanzania

We set up a mortality surveillance system around two of the largest gold mines in Tanzania between February 2019 and February 2020 to estimate the mortality impact of gold mines. Death circumstances were collected using a standardized verbal autopsy tool, and causes of death were assigned using the InSilicoVA algorithm. We compared cause-specific mortality fractions in mining communities with other subnational data as well as national estimates. Within mining communities, we estimated mortality risks of mining workers relative to other not working at mines. At the population level, mining communities had higher road-traffic injuries (RTI) (risk difference (RD): 3.1%, Confidence Interval (CI): 0.4%, 5.9%) and non-HIV infectious disease mortality (RD: 5.6%, CI: 0.8%, 10.3%), but lower burden of HIV mortality (RD: -5.9%, CI: -10.2%, -1.6%). Relative to non-miners living in the same communities, mining workers had over twice the mortality risk (relative risk (RR): 2.09, CI: 1.57, 2.79), with particularly large increases for death due to RTIs (RR: 14.26, CI: 4.95, 41.10) and other injuries (RR:10.10, CI: 3.40, 30.02). Our results shows that gold mines continue to be associated with a large mortality burden despite major efforts to ensure the safety in mining communities. Given that most of the additional mortality risk appears to be related to injuries programs targeting these specific risks seem most desirable.