Introduction
Methodology
Study setting
Study design
Study population
Study instrument
Data collection
Data management
Ethical considerations
Results
Literature review
Country | DRC | Nigeria | Senegal | Uganda |
---|---|---|---|---|
Population | 92 million | 206 million | 17 million | 42 million |
Life expectancy from birth (Japan and Singapore have longest, about 85years) | 65.1years | 64.3yrs | 68.5yrs | 66.2yrs |
Universal health care effective coverage index (0-100) | 45 | 38 | 50 | 53 |
DTP3 coverage in children in 2019 | 48% | 50% | 93% | 87% |
In-facility births | 85% | 49% | 84% | 79% |
Maternal mortality ratio (SDG3.1 is 140/100,000 by 2030) | 345 per 100,000 live births | 233 per 100,000 live births | 379 per 100,000 live births | 133 per 100,000 live births |
Doctors, nurses, midwives per capita. Benchmark is 4.45 to achieve universal health coverage | 1.2:1000 | 1.6:1000 | 0.4 :1000 | 1.4 : 1000 |
Total health expenditure per capita (% of GDP). Benchmark in health financing is $86/person or 5% of GDP to achieve Universal Health Coverage | $20 3.6% | $83 3.5% | $65 4.7% | $48 6.4% |
Out of pocket health expenditure (% of total health expenditure) | $9 per capita 45% | $63per capita 76% | $31 per capita 47% | $19 per capita 40% |
Morbidity and mortality due to COVID-19
Location | Reported Cases | Reported Deaths | Population (in millions) | Cases per millions | Deaths per millions |
---|---|---|---|---|---|
World*
| 491,440,000 | 6,150,000 | 7,870 | 62,410 | 781.32 |
Africa
| 11,560,000 | 251,990 | 1,370 | 8,420 | 183.47 |
DRC | 86,750 | 1,340 | 92.38 | 939.05 | 14.47 |
NIGERIA | 255,470 | 3,140 | 211.4 | 1,210 | 14.86 |
Senegal | 85,920 | 1,970 | 17.2 | 5000 | 114.27 |
Uganda | I63,940 | 3,600 | 47.12 | 3,480 | 76.31 |
Synthesis of literature review and key informant interviews
Surveillance methods and Systems
“Community health workers conducted community surveillance through active case searches as well as the national COVID-19 hotline, which managed nearly 3,000 calls per day by November 2020. Community health workers also were involved in contact tracing and supported outreach in each health zone” (MoH, Epidemiological Surveillance Directorate, DRC)Networks of laboratories coordinated and funded by the government and partners such as the CDC Atlanta, were set up and connected across regions of the countries for laboratory-based surveillance. These laboratories as well as facilities involved in facility-based surveillance were linked to SORMAS for reporting as mentioned by a respondent:“We set up laboratories almost all over the state in Nigeria and this all pulled down to the hub, the national reference laboratory situated at Gaduwa in Abuja. And this also helped to ensure testing and ensure that the laboratory-based surveillance is been done for COVID-19”(Case Management Pillar Member NC, Nigeria)“Facility based surveillance was done to obtain information from people in the hospitals. The data was included into SORMAS, and that was where we got some information for trend, transmission peculiar to Nigeria unlike the other parts of the world, assessment, demographic figures concerning COVID-19. We discovered that COVID cut across all ages, even newborns tested positive. It is also the same risk for men and women, but more in men. All these information peculiars to our environment were derived from our own data” (Laboratory Team Lead, State Emergency Operations Centre [EOC], SW Nigeria.)
“surveillance has always been daily but it has been reinforced with the arrival of this pandemic... surveillance has been reinforced, particularly at the border level because there is a flow of travelers which means that the risks were as much as possible, particularly at the level of air borders, but we have also not forgotten the surveillance of maritime and land borders...“ (Technical Manager at MoHSA-Senegal)
“Well... work-based surveillance was basically introduced through Infection Prevention and Control (IPC) [26]. So, IPC people were trained on how to identify these cases. First of all, surveillance is basically about identifying a case and reporting. That’s surveillance. Seeing and knowing institutions that this it is. So in offices, people who were seen to have had respiratory illnesses were asked to stay at home, get tested and come back to work when they are well. So, that was the form of surveillance that was done (Surveillance Officer, Uganda)
“We had some cases that their sample were taken for investigation. Some people tested positive after death. Other things might have killed the patients, but we were able to confirm COVID-19” (Laboratory Team Lead, State Emergency Operation Centre (EOC), SW Nigeria)
Key strengths of the surveillance approaches employed
DRC | Nigeria | Senegal | Uganda | |
---|---|---|---|---|
Surveillance
| On alert due to ongoing EVD epidemic: leveraging EVD preparedness for COVID-19 Existence of a well-organized and trained surveillance team at all level of the health system (including CHWs) Well established functional laboratory networks that allowed for rapid ramp up of testing capacity Experience in dealing with different outbreaks Conducted mass testing in one region of Kinshasa | Leveraged pre-existing SORMAS software and DHIS2v EWARS functional in conflict areas Engaged private labs in tracking travelers for post quarantine for COVID-19 testing Community sero-prevalence completed in 6 states (2020/2021) Strong central coordination to avoid duplication and proactive response before first case was recorded Strong financial support from federal government | Establishment of a national alert set up during the preparatory phase with a dedicated short number accessible 24/7 for case detection. Analysis of the distribution of confirmed cases was done to identify 45 high priority districts that guided response | On alert due to ongoing EVD epidemic: leveraging EVD preparedness for COVID-19 Restructuring of surveillance pillar i.e. creation of sub pillars including; health worker surveillance, alerts, quarantine and Points of Entry teams Community surveys – Two Rapid Assessments for COVID-19 prevalence conducted April and August 2020. The surveys used RT-PCR (for active infection). A third was conducted March 2021 Pre-existing functional national laboratory network Scientific advisory committee fed the NTF helping with rapid translation of data, emerging issues, research priorities, and policy shifts. The availability of a research and innovation fund [35] from government paved the way for innovations |
Data management
| Introduced e-surveillance at all levels of health system August 2020 for enhanced data collection and reporting EWARS is used in some pilot health zones for management of alerts | Real time surveillance dashboard Automated epidemiological bulletins generated to rapidly analyze and share results EWARS used for line listing and management of data Testing labs linked to DHIS2 | Establishment of software (Daan COVID) facilitated data collection and analysis for clinicians and surveillance system Introduced MoH COVID 19 tracker module which is able to cover all needs for information | Innovation of electronic tools and systems including the interactive voice response systems, ODK, HMIS, RECDTS, eIDSR, DHIS2, Go Data for case reporting, detection, investigation and follow-up An electronic integrated Disease Surveillance Response (eIDSR) was integrated into DHIS2 to capture real-time data and monitoring Centralized data bases with an electronic Results Dispatch System (eRDS), with downloadable electronic results improved reporting of lab results Integration of lab data into surveillance reports |
Key challenges and gaps
DRC | Nigeria | Senegal | Uganda | |
---|---|---|---|---|
Surveillance
| Expansion of the e-surveillance Testing of all close contacts of a confirmed case (even when asymptomatic) limited by limited test kits Multiple concurrent epidemics (Ebola and Measles) shifting attention and stretching resources Inadequate funding and resources to manage multiple outbreaks Connectivity challenges affecting roll out of electronic systems, system remained mainly paper based. Multiple reporting systems | Hard to reach areas Conflict areas of the North East Logistics for adequate contact tracing Big geography, inadequate laboratory support in parts of the country | All community cases are not documented and there is delay referring to health facilities for some community cases. Limited engagement of scientific team on the national task force delayed interpretation / translation of emerging data and findings into policy with e.g. delay in conducting serological surveys | Delay in evacuation of some positive cases Limitations in timely case detection, investigation and reporting at the district level Centralized EOC with limited use of data by the subnational structures. |
Data management
| Existence of multiple data systems renders the integration and use of data very difficult Not all health zones have acquired the digital system Poor internet connectivity in some areas and lack of Tablets and air time | Risk of suboptimal reporting due to stigma Poor use of data to guide decision making at subnational level and some states faced coordination issues | Non-systematic analysis and discussion of data collected and regularly disseminated Community stigma and misinformation influenced demand Multiple reporting applications and mechanisms made coordination difficult Under-detection of cases and variants | Dwindling interest in active reporting from districts Overreliance on donor funding and foreign supplies Limited resources for district coordination |
Key Learnings
| Task-shifting to community health workers for contact tracing Develop hotline for case reporting | Leverage experience & systems from past outbreaks Adopt tech solutions that integrate disparate information systems | Leverage government leadership for national communication strategies Enhance multi-sectoral partnerships to boost capacity and innovation | Rapid response and proactive action Initiate community surveys Leverage available funding for innovation |
“When community transmission took off and we had overwhelming cases, the contacts were also too many yet the resources for the district teams to move to where cases had been identified, contact listing and monitoring were limited and it became impracticable to follow up contacts so that arm of surveillance contact tracing to detect cases has over time become very limited in its implementation” ( Surveillance Officer, Uganda ).
“Another challenge was the perception of the public… there was a lot of misinformation… We had several negative experiences with contacts and their families. Logistics was also an issue as we didn’t have enough vehicles to do contact tracing. Some of us were working with our personal cars and we wouldn’t get reimbursed when we hired vehicles. It was quite daunting as we didn’t have much technical manpower. In one day, I trained 3 sets of contact tracers” (Surveillance Pillar Member, State EOC, SS Nigeria)“People are not ready to cooperate with us most of the time because they are afraid and ashamed, it affects surveillance because the confirmed case will have exceeded the incubation period before we can get to them” (Laboratory Team Member, State EOC, SW, Nigeria)“The problem of surveillance is the non-disclosure of suspected cases, the population considers this as a denunciation / betrayal vis-à-vis their parents or their neighbors. This shows how the disease is perceived by populations, it is a shameful disease. (Member of Health District Team Management-Senegal)“ It was not easy to manage COVID-19 because in all the structures, people did not want to believe that the disease existed. For most Congolese, COVID-19 is an invention of the whites to eliminate the Africans ” (Member, National Committee for Epidemiological Surveillance, DRC).