Today no Omicron (will be for tomorrow). In this episode, I will present some data on SARS-CoV-2 and COVID in Africa, with special focus on Kenya and South-Africa, two developed countries with similar population size (55-60 million), but with, at first view a very different epidemic. As we will see, the differences may be smaller than you would think.
The focus is really COVID itself. I will spend another Episode on data about the important “collateral damage” of COVID in other sectors of health care and African societies.
I’m not a specialist in this type of epidemiological studies and therefore, I would certainly welcome your comments and any additional information, you might have.
Ep 204-1: Sophie Oyaga in Science on the first wave: March-June 2020:
- Only 20,600 cases and 341 deaths registered
- Systematic studies in 3000 blood donors shows “weighed” prevalence of 4.3 %, with higher (> 5%) values in cities (Mombasa, Nairobi and Kisumu) and lowest (< 1 % ) in Rift Valley.
Ep 204-2: Anthony Etyang in CID on 634 HCW between July and Dec 2020: overall 20 %, but with high values in Nairobi (43.8 %) and low in rural areas (11-12 %).
- Not driven by professional cadre, but by site → background level in general population.
- No data on symptoms.
Ep 204-3: Kagucia in Open Forum ID on 830 truck drivers and assistants Oct 2020
- 7.4 % PCR positive, but all apparently asymptomatic.
- 42.3 % seropositive
- According to MOH: 93 % of COVID cases in Kenya asymptomatic until Oct 2020
Ep 204-4: Isaac Ngere in IJID reports on high seroprevalence of SARS-CoV-2, but low infection fatality ratio (IFR) in Nairobi households Nov 2020.
- Among 1,164 individuals, the adjusted seroprevalence was 34.7%
- Seropositivity increased in more densely populated areas (spearman’s r = 0.63);
- Individuals aged 20-59 years at least 2-fold higher seropositivity than those aged 0-9 years.
- The overall infection fatality rate IFR was only 0.04, but individuals ≥60 years IFR = 1.15 %.
Ep 204-5: Sophie Oyaya in JAMA follow-up of seroprevalence in 3000 blood donors Jan-March 2021:
Seroprevalence rose from 4.3 in 2020 48.5 % in 2021:
- Varied little by age or sex but was higher in Nairobi (61.8%) and lower in rural regions, Nyanza and Western, adjacent to Uganda.
- Again no data on symptoms
Ep 204-6: Samuel Brand in Science Nov 2021 tries to understand the dynamics of the first two waves (until Dec 2020) by a combination of contact patterns within two distinct social groups (high and low socio-economic status) and the third wave (April-May 2021) by the introduction of a highly transmittable variant (alpha?). The fourth wave (July-Sept 2021) was again due a new variant (delta).
See Figure for interesting details
Ep 203-7: Alimohamadi J Prev Med Hyg 2021: Meta-analyis; mainly in high income countries.
See Table: in general population between 0-7 %, but mostly < 1 %.
The chance that the infection is actually diagnosed by PCR is much higher in the high socio-economic status (SES) group, but most of the burden is in the lower socio-economic part of the society, while the diagnosis is not often made in this group, maybe even not in case of death by COVID. Hence the relatively low numbers of diagnosis and deaths in the official numbers, while seroprevalance is high.
Ep 204-7: Mutevezdi in Int J Epidem. 2021: Focus on Gauteng province of South-Africa between 4 Nov 2020-22 Jan 2021 (after end of first till after peak of second wave):
- Seroprevalence = 19 %: no difference in age groups, but strong differences according to district: much more in the “inner city”.
- As compared to officially recorded (PCR-based) infections, the seroprevalence is about 10 X higher. However also half of recorded cases were seronegative. Hence real infection number possibly 20 X higher than recorded cases.
- IFR = 0.28 % (based on recorded COVID deaths, but 0.67 % (based on over-mortality).
Ep 204-8: Kleinhans in Emerg Infect Dis Dec 2021 compares a rural and an urban area in South-Africa between July 2020 and March 2021 (first and second wave)
Post–second wave seroprevalence ranged from 18% in the rural community children <5 years of age, to 59% in urban community adults 35–59 years of age.
The second wave saw a shift in age distribution of case-patients in the urban community (from persons 35–59 years of age to persons at the extremes of age), higher attack rates in the rural community, and a higher infection–fatality ratio in the urban community
Timing (see Fig 1): BD1 in first wave; BD2 jus after 1st wave in 2020; BD3-5: second wave 2021.
- IFR are less than 1 %
- The authors estimate that 95 % of the infections were not reported to national surveillance.
Ep 204-9: Obande in IJERPH Sept 2021 State of COVID-19 Pandemic in Africa up to June 2021
Fig 1 shows that Africa, as compared to other WHO regions, is characterized by a low number of tests, a rather low number of SARS-CoV-2 diagnoses and a rather high case fatality rate.
Some first conclusions by Obande:
A study of COVID-19 age-mortality by Demombynes (2020) revealed a higher death rate due to COVID-19 in high-income countries (HIC) than in low- and middle-income countries (LMIC).
Age is important:
- In the HICs, mortality was 12.6 times higher in patients above 70 years than those between 50 and 59 years as compared to just 3.5 times in LMICs.
- Only 3% of Africans are 65 years or older compared to 18% in Europe and America.
However: According to data obtained from 26 different countries, only 37% of deaths occurred in patients aged at least 70 years in LMIC, compared to 87% in HIC.
Thus age is important, but not the only factor.
But, of course, there are also differences within Africa: See the difference between South-Africa and Kenya, with a similar population size
As can be seen, the official number of cases and deaths is over 10 times lower in Kenya, which has also a lower CFR. Clearly, however, the testing was also 6 X less in Kenya.
Ep 204-10: General features of population
In Kenya: 4 % is between 55-64 years and 3 % over 65
In South-Africa: 7 % 6 %
For more details on COVID see Ep 204-10 A and B; on population: Ep 204-10 C and D.
ATTEMPT TO SOME GENERAL CONCLUSIONS:
- According to seroprevalence data, SARS-CoV-2 has widely spread into the population of both South-Africa and Kenya.
- Rural areas are lagging behind, while “inner city” areas are most affected.
- There are also differences in the dynamics according to socio-economic status: the higher SES were probably affected earlier, while lower SES affected later, but maybe harder
- While the case-fatality ratio in Africa seems high, the actual infection-fatality ratio is low.
This is obviously due to the fact that only serious cases are diagnosed and reported, while many more largely pauci-symptomatic infections are going on unnoticed.
- At first view, there are seemingly very large differences in number of cases and case-fatality ratio between Kenya and South-Africa (the latter much more affected). Seroprevalence data, however, point to a very similar evolution.
- A real difference between the two countries is the age structure, with a more ageing population in South-Africa
- It may be that underreporting in Kenya is even more pronounced than in South-Africa?
- The level of p re-disposing factors, such as untreated HIV infection, but also non-communicable diseases (NCD) “co-morbidities” such as obesitas, hypertension, diabetes chronic pulmonary diseases may be higher in South-Africa?
28 Jan 2023 Episode 311 Will variant CH.1.1 or CD3.2 beat XBB.1.5? Are Remdesivir and Molnupiravir out?
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