Risk assessment for COVID-19 transmission at household level in sub-Saharan Africa: evidence from DHS

Abstract

Household habitat conditions matter for diseases transmission and control, especially in the case of the novel coronavirus (COVID-19). These conditions include availability and adequacy of sanitation facilities, and number of persons per room. Despite this, little attention is being paid to these conditions as a pathway to understanding the transmission and prevention of COVID-19, especially in Africa, where household habitat conditions are largely suboptimal. This study assesses household sanitation and isolation capacities to understand the COVID-19 transmission risk at household level across Africa. We conducted a secondary analysis of the Demographic and Health Surveys of 16 African countries implemented between 2015 and 2018 to understand the status of households for prevention of COVID-19 transmission in home. We assessed handwashing capacity and self-isolation capacity using multiple parameters, and identified households with elderly persons, who are most at risk of the disease. We fitted two-level random intercept logit models to explore independent relationships among the three indicators, while controlling for the selected explanatory variables. Handwashing capacity was highest in Tanzania (48.2%), and lowest in Chad (4.2%), varying by household location (urban or rural), as well as household wealth. Isolation capacity was highest in South Africa (77.4%), and lowest in Ethiopia (30.9%). Senegal had the largest proportion of households with an elderly person (42.1%), while Angola (16.4%) had the lowest. There were strong, independent relationships between handwashing and isolation capacities in a majority of countries. Also, strong associations were found between isolation capacity and presence of older persons in households. Household capacity for COVID-19 prevention varied significantly across countries, with those having elderly household members not necessarily having the best handwashing or isolation capacity. In view of the age risk factors of COVID-19 transmission, and its dependence on handwashing and isolation capacities of households, each country needs to use the extant information on its risk status to shape communication and intervention strategies that will help limit the impact of the disease in its population across Africa.

Supplementary information:
The online version contains supplementary material available at 10.1186/s41118-021-00130-w.

Full text

Risk assessment forCOVID‑19 transmission
athousehold level insub‑Saharan Africa:
evidence fromDHS
Olusesan Ayodeji Makinde1,2* , Joshua O. Akinyemi3, Lorretta F. Ntoimo4, Chukwuedozie K. Ajaero5,9,
Dorothy Ononokpono6, Pamela C. Banda7, Yemi Adewoyin8,9, Rebaone Petlele9, Henry Ugwu9 and
Clifford Obby Odimegwu9
Abstract
Household habitat conditions matter for diseases transmission and control, especially
in the case of the novel coronavirus (COVID-19). These conditions include availability
and adequacy of sanitation facilities, and number of persons per room. Despite this,
little attention is being paid to these conditions as a pathway to understanding the
transmission and prevention of COVID-19, especially in Africa, where household habitat
conditions are largely suboptimal. This study assesses household sanitation and isola-
tion capacities to understand the COVID-19 transmission risk at household level across
Africa. We conducted a secondary analysis of the Demographic and Health Surveys of
16 African countries implemented between 2015 and 2018 to understand the status
of households for prevention of COVID-19 transmission in home. We assessed hand-
washing capacity and self-isolation capacity using multiple parameters, and identified
households with elderly persons, who are most at risk of the disease. We fitted two-
level random intercept logit models to explore independent relationships among the
three indicators, while controlling for the selected explanatory variables. Handwash-
ing capacity was highest in Tanzania (48.2%), and lowest in Chad (4.2%), varying by
household location (urban or rural), as well as household wealth. Isolation capacity was
highest in South Africa (77.4%), and lowest in Ethiopia (30.9%). Senegal had the largest
proportion of households with an elderly person (42.1%), while Angola (16.4%) had the
lowest. There were strong, independent relationships between handwashing and isola-
tion capacities in a majority of countries. Also, strong associations were found between
isolation capacity and presence of older persons in households. Household capacity
for COVID-19 prevention varied significantly across countries, with those having elderly
household members not necessarily having the best handwashing or isolation capac-
ity. In view of the age risk factors of COVID-19 transmission, and its dependence on
handwashing and isolation capacities of households, each country needs to use the
extant information on its risk status to shape communication and intervention strate-
gies that will help limit the impact of the disease in its population across Africa.
Keywords: COVID-19, Communicable diseases, Emerging disease, Handwashing,
Outbreak, WASH
Open Access
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ORIGINAL ARTICLE
Makindeetal. Genus (2021) 77:24
https://doi.org/10.1186/s41118‑021‑00130‑w
Genus
*Correspondence:
sesmak@gmail.com
1 Viable Knowledge Masters,
Plot C114, First Avenue,
Gwarimpa, FCT, Abuja,
Nigeria
Full list of author information
is available at the end of the
article

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Makindeetal. Genus (2021) 77:24
Introduction
More than a year after its emergence on the global scene, the Coronavirus SARS-CoV-2
(COVID-19) disease remains a pandemic, and major public health issue in most coun-
tries. By 21st July 2021, the virus had resulted in more than 191 million infections and
over 4.1 million deaths globally. Of these numbers, 4,658,704 cases and 109,309 deaths
were recorded in the World Health Organization (WHO) Africa region (World Health
Organization, 2021). Based on current evidence, COVID-19 is transmitted through
droplets and direct contact with infected persons and/or surfaces (World Health Organ-
ization, 2020). Consequently, and following from its known mode of transmission,
strategies advocated to control the spread of the virus include: mask wearing in pub-
lic; consistent handwashing; disinfecting exposed surfaces; maintaining social or physi-
cal distancing; self-isolation following exposure; and lockdowns to reduce the chances
of people intermingling; and passing on the virus from one to another, and vaccination
(Dashtbali & Mirzaie, 2021; Ma etal., 2020; Musinguzi & Asamoah, 2020).
Compared to the rest of the world, particularly the US and Europe, Africa’s share of
COVID-19 infections and fatalities is relatively low (Okonji etal., 2021). While this has
been attributed to the relatively young population of the continent, with an average age
of 19, and the likelihood of cross immunity from other circulating coronavirus vari-
ants (Lawal, 2021; Njenga etal., 2020; Tso etal., 2021), a risk assessment of COVID-19
transmission in African households, becomes important for many reasons. Firstly, the
emergence of new and more deadly strains of the virus—such as the circulating delta
variant which has higher infectivity and mortality—poses a threat to global COVID-19
eradication drive (Hetemäki etal., 2021). is variant is also not as responsive to avail-
able vaccines like the alpha variant (Lopez Bernal etal., 2021). e risks of virus expor-
tation from Africa underlie international migration and transnational trade to restore
many national economies to pre-Covid buoyancy. In addition, current vaccination
against the virus is lowest in Africa. A July 2021 report by the website ourworldindata.
org shows that whereas about 70.4%, 68.3% and 55.8% of the population in Canada, the
United Kingdom, and the United States had received at least one dose of COVID-19 vac-
cines, only 3.1% of the African population had done the same (Ritchie etal., 2021). Poor
technology and insufficient capacity for the local manufacturing of vaccines in Africa
has contributed to vaccine nationalism by wealthy nations, with its attendant risk on the
persistent propagation of the virus on the continent (Ghebreyesus, 2021).
Furthermore, stemming from the low vaccine coverage, Africa’s most potent defence
against the continual spread of the virus remains good sanitation practices, along with
the maintenance of social distance. While studies have shown that general household
hygiene, proper sanitation, and water availability are positively correlated with good
health outcomes of household members (Kawuki etal., 2020; Seimetz etal., 2017), the
sanitation revolution of the 1840s was adjudged the most important medical milestone
of the nineteenth century, notably ahead of the medical quantum leaps of the same era
represented by anaesthesia, antibiotics, and vaccines (Ferriman, 2007). In most low- and
middle-income countries (LMIC) however, proper handwashing is poorly practised
(Luby & Halder, 2008; Wolf etal., 2019). Handwashing is recommended in a variety of
daily circumstances: before cooking; before eating food or feeding a child; after defe-
cating or cleaning up after a child; and for doctors, after contact with patients, among

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Makindeetal. Genus (2021) 77:24
others. e importance of handwashing for health underpins the necessity of its promo-
tion through official regulation, religious practice, and cultural belief (Curtis etal., 2009).
In 2015–2016, 51% of the population in high-income countries washed their hands
with soap after faecal contact, 22% in LMICs, and merely 8.4% in sub-Saharan Africa
(SSA) (Prüss-Ustün etal., 2019; Wolf etal., 2019). For this reason, the practice of hand-
washing is not universal. e unavailability, or non-use of soap can therefore be a chal-
lenge to proper handwashing practices as a control method for COVID-19 outbreak.
Social distancing as a method to prevent the spread of an infectious disease can be com-
promised by factors in the home, even during a lockdown. e number of occupants in a
household, relative to the capacity of available dwelling spaces within the household, has
been shown to influence the prevention and transmission ability of infectious diseases
across the world (Adewoyin, 2018; Ali etal., 2018; World Health Organization, 2018).
Multiple contacts in a household increase the odds of the introduction and the spread
of a disease within that unit. Crowding also affects the ability to maintain good hygiene
practices and ventilation in small informal dwellings, often found in developing coun-
tries (House & Keeling, 2009; Kawuki etal., 2020).
Despite the identified benefits of these household conditions, accompanied by evi-
dence that suboptimal or lack of handwashing elevates the risk of COVID-19 (Ran
etal., 2020), proper hand hygiene can remove 97%-100% of the virus in the palm (Ma
etal., 2020), and that social distancing is effective in reducing the risk of transmission
of COVID-19 (Musinguzi & Asamoah, 2020), sanitation and social distancing capacities
remain suboptimal in several LMICs (World Health Organization, 2018). Current evi-
dence shows that access to reliable and potable water, as well as the availability of soap
in households, is low in African countries, when compared to countries on other conti-
nents (Kumar etal., 2017). In 2017, three billion people worldwide lacked access to basic
handwashing facilities with soap and water at home, with 75% in SSA, compared to 23%
in Northern Africa and Western Asia (World Health Organization, 2019). Studies have
observed that the availability of infrastructure (such as a wash basin) in the residence
encourages frequent handwashing activity, which is a resource absent in many house-
holds in SSA (Cairncross & Valdmanis, 2006; Kumar etal., 2017).
Based on the premise that household sanitation and isolation capacities present
the major pathway to controlling the transmission of COVID-19 in Africa in the near
absence of massive vaccine coverage, we investigated country-level sanitation and isola-
tion capacities in the home across 16 countries. Since observational studies conducted
across countries have revealed that more than 80% of COVID-19 fatalities occurred in
the elderly (CDC COVID-19Response Team, 2020; Liu etal., 2020), we examined how
this at-risk population is distributed across the 16 countries, with a view to providing
important information for interventions and rapid immunisation campaigns prioritisa-
tion. In assessing the associations among the variables of interest, socio-economic fac-
tors of type of residence, geographical region, household wealth index, and educational
level of head of household were controlled.
Given that the few available studies on this subject have focused on the efficacy of
handwashing for the control of COVID-19 (Hillier, 2020; Ma etal., 2020; Yang, 2020),
and the knowledge and practice by health care providers (Alfahan etal., 2016; Lotfine-
jad etal., 2020) the focus of this study on the assessment of household capacities for

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Makindeetal. Genus (2021) 77:24
adequate sanitation and isolation is expected to draw attention to the primacy of house-
hold habitat conditions in the fight against the coronavirus. Findings from the study may
be useful in shaping risk communication and advocacy for standards in home construc-
tions to foster positive behavioural habits for the future.
Data andmethods
Instrumentation
e data for this paper were extracted from the household recode file of the Demo-
graphic and Health Survey (DHS) conducted in selected SSA countries between 2015
and 2018. e DHS is a nationally representative household survey, conducted on
quinquennial basis in developing countries since early 1990s. Survey methodologies,
sampling plan, questionnaire, data collection and processing are standardised across
countries. is uniformity of procedures facilitates multi-country and multi-survey
analysis of DHS data. Selection of households and survey respondents usually involved
application of stratified two-stage cluster sampling technique.
Variable identication
For this study, all countries in SSA with surveys conducted between 2015 and 2018 were
selected so that the study findings would be as up to date as possible. Sixteen countries
were analysed to assess three indicators of COVID-19 preventive capacities. ese were:
handwashing capacity, self-isolation capacity, and proportion of households with older
persons. Regular handwashing is one of the measures recommended by the WHO to
prevent COVID-19 (World Health Organization, 2020). We assessed capacity for self-
isolation, because it is also recommended for anyone who has had contact with infected
persons, or those deemed to have been at risk of exposure. e third indicator was
included because emerging evidence on COVID-19 prognosis showed that older per-
sons are at higher risk of being symptomatic, developing complications, and dying from
the disease (Liu etal., 2020).
We considered a few explanatory variables, such as type of and place of residence,
administrative/geographical region, wealth index, and level of education of the house-
hold head. Place of residence was categorised as either rural or urban. e DHS pro-
gramme employed principal component analysis to calculate factor scores for household
possession of certain items as a proxy for wealth status. e factor scores were ranked
and divided into tercile (poor, average and rich) to represent the household wealth
index. Education of household head was categorised as: none, primary, and post-pri-
mary (secondary/higher), respectively. e choice of explanatory variables was based
on their conceptual relevance to the indicators we assessed. e four explanatory vari-
ables (type of residence, geographical region, household wealth index and education of
household head) are all indicative of socio-economic characteristics of households in
SSA. Education of the household head and wealth index are direct measures of socio-
economic empowerment, which affects the ability of household heads to provide hand-
washing facilities. is is also related to housing type (which can suggest whether there
are enough rooms to guarantee isolation capacity should the need arise).

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Makindeetal. Genus (2021) 77:24
Variable denitions
A household was deemed to have handwashing capacity if there was: (1) a designated
place for hand washing; (2) water was available; and (3) soap was available. To derive
self-isolation capacity, we first obtained the average number of persons per sleeping
room in the household. Households where the average number of persons per sleep-
ing room was less or equal to two was categorised as having self-isolation capacity.
Next, we assessed the age of members of each household to determine the presence of
older persons. In this paper, older person referred to people aged 60years and above.
is conforms with standard practices by the United Nations Population Division.
Statistical analysis
Analysis involved the use of descriptive statistics according to frequency and percent-
age. For each country, the overall percentage for the three indicators were obtained
and disaggregated, according to the few selected explanatory variables. Maps were
also plotted so as to show the variations across administrative areas in countries.
We fitted two-level random intercept logit models to explore independent relation-
ships among the three indicators, while controlling for the selected explanatory vari-
ables (wealth index, education of household head, type of residence, sex of household
head, access to electronic media). For modelling purposes, households (level 1) were
considered to be nested in enumeration areas (level 2). e multilevel logit model
helped to account for the complex cluster sampling procedure used for selection of
participants in demographic and health surveys. Adjusted odds ratio (OR) and their
95% confidence intervals (95% CI) were reported.
Results
Handwashing capacity
Table1 indicates that the percentage of households with handwashing capacity across
West African countries was highest in Nigeria (32.4%), 28.8% in Senegal, and 24.7%
in Guinea. Both Benin and Mali were less than 20%. For Middle African region, the
indication was 26.6% in Angola, and 4.2% in Chad. Among households in East Africa,
handwashing capacity ranged from 48.2% in Tanzania to 6.0% in Rwanda, and 6.5% in
Burundi, while Ethiopia and Uganda saw 11.8% and 27.6%, respectively. In the South-
ern region, South Africa (43.9%) and Zimbabwe (41.7%) had the highest percentage
of households with handwashing capacity. e level was lower in Malawi (11.0%) and
Zambia (22.3%).
Table1 further shows the distribution of handwashing capacity according to type
of place of residence, wealth index, and educational attainment of the household
head. ere was wide rural–urban disparity across most countries. e percentage of
households with handwashing capacity in urban areas was two times higher than that
of rural areas in all countries under study, except Tanzania (urban—62.3%, rural—
41.3%); South Africa (urban—50.6%, rural—29.4%); and Zimbabwe (urban—52.4%,
rural—36.3%).
Similarly, there was disparity in handwashing capacity according to household
wealth index. Generally, the percentages were similarly very low for poor and average

Page 6 of 20
Makindeetal. Genus (2021) 77:24
Table 1 Distribution of Handwashing capacity in selected African countries
Sub-region/country Sample size Overall: n (%) Residence (%) Wealth index (%) Education: HOusehold head (%)
Urban Rural Poor Average Rich None Primary Secondary+
Western Africa
Benin 14,156 1567 (11.1) 15 8.1 4.8 7.3 19.7 7 10.4 20.9
Guinea 7912 1958 (24.7) 40 16.8 14.4 19.4 41.2 20.1 22.9 29.6
Mali 9510 1682 (17.7) 33.9 13 6.7 12.1 36.2 13 16.9 37
Nigeria 40,427 13,114 (32.4) 44 22.3 13.3 25.4 51.5 17.1 32.7 42
Senegal 8380 2415 (28.8) 46.4 10 5.5 12.6 47.9 17.4 38.7 63.2
Middle Africa
Angola 16,109 4287 (26.6) 34.2 14.7 12 19.4 43.5 17.9 19.7 38.1
Chad 17,233 728 (4.2) 11.5 2.2 1.1 2.7 10.3 2.7 3.4 10
Eastern Africa
Burundi 15,977 1031 (6.5) 19.7 4.8 2.9 4.5 14.8 3.7 5.7 22
Ethiopia 16,650 1961 (11.8) 31.2 6.8 3.2 6.7 25.2 6.5 11.2 32.3
Tanzania 12,563 6056 (48.2) 62.3 41.3 31 45.1 65.8 34.1 47.4 67.9
Uganda 19,588 5415 (27.6) 42 22.7 13 23.7 41.1 19.6 24.2 37.3
Rwanda 12,699 766 (6.0) 14.9 4.2 3.2 3.9 12.2 3.6 4.7 18.2
Southern Africa
Malawi 26,361 2900 (11.0) 17.5 9.8 5.9 9.5 19.6 7 8.7 18.9
South Africa 11,083 4862 (43.9) 50.6 29.4 20.5 33.3 74 27.5 34.7 49.7
Zambia 12,831 2863 (22.3) 32.2 15 12.1 15.3 35.3 14.6 16.3 29.4
Zimbabwe 10,534 4393 (41.7) 52.4 36.3 31.8 38.3 57.6 30.8 36 46.6

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Makindeetal. Genus (2021) 77:24
income households, while rich households fared much better. In fact, the gap between
poor and rich households was more than twofold in every country (Table1).
Variations in terms of education of household head also show a gradient in many
countries. e general pattern was that the proportion of households with handwashing
capacity increased with the education of the household head, with relatively higher lev-
els for those with secondary education and above.
Table2 shows the independent association between handwashing capacity, isolation
capacity, and presence of elderly persons in household, and selected background charac-
teristics. In Western Africa, the odds of handwashing capacity were significantly higher
for households with isolation capacity in Benin (OR = 1.28), Mali (OR = 1.23), and
Table 2 Adjusted Odds Ratio (OR) for association between household characteristics and
handwashing capacity in selected African countries
a Reference category was “poor ”
b Reference category‑none
Sub-
region/
country
Isolation
capacity Presence
of older
persons
Wealth indexaEducation: Household
headbResidence
Average Rich Primary Secondary+Urban vs.
rural
Western Africa
Benin 1.28
(1.13–1.45) 1.09
(0.94–1.28) 1.31
(1.06–1.62) 2.41
(1.87–3.11) 1.27
(1.07–1.50) 2.20 (1.87–2.59) 1.20
(0.93–1.53)
Guinea 1.07
(0.94–1.22) 0.81
(0.71–0.93) 1.68
(1.40–2.03) 2.86
(2.12–3.83) 1.09
(0.88–1.34) 1.49 (1.27–1.75) 1.48
(1.04–2.10)
Mali 1.23
(1.09–1.39) 1.04
(0.90–1.20)
1.76
(1.45–2.14)
6.59
(5.05–8.60)
1.15
(0.96–1.38)
1.82 (1.56–2.12) 1.07
(0.75–1.52)
Nigeria 1.05
(0.99–1.13) 1.12
(1.03–1.22) 1.93
(1.68–2.21) 4.80
(4.06–5.66) 1.26
(1.13–1.41) 1.75 (1.57–1.94) 2.01
(1.49–2.71)
Senegal 1.22
(1.06–1.41) 1.17
(1.01–1.36) 1.43
(1.10–1.86) 3.87
(2.80–5.35) 1.51
(1.25–1.82) 2.81 (2.32–3.40) 1.57
(1.18–2.09)
Middle Africa
Angola 1.37
(1.26–1.49) 0.94
(0.84–1.07) 1.72
(1.47–2.02) 3.60
(2.90–4.47) 1.22
(1.09–1.39) 1.38 (1.21–1.68) 1.35
(1.09–1.68)
Chad 1.22
(1.03–1.45) 0.86
(0.68–1.09) 1.77
(1.31–2.40) 3.08
(2.21–4.30) 1.45
(1.13–1.86) 2.41 (1.92–3.04) 2.06
(1.43–2.98)
Eastern Africa
Burundi 1.08
(0.93–1.25) 1.12
(0.91–1.37) 1.28
(1.00–1.63) 2.85
(2.17–3.73) 1.27
(1.05–1.52) 2.83 (2.27–3.53) 2.11
(1.47–3.03)
Ethiopia 1.19
(1.07–1.34) 1.10
(0.96–1.27) 1.96
(1.56–2.47) 3.35
(2.54–4.43) 1.44
(1.24–1.67) 2.45 (2.09–2.89) 2.29
(1.69–3.10)
Tanzania 0.96
(0.89–1.05) 0.90
(0.81–0.99) 1.53
(1.37–1.71) 2.75
(2.36–3.20) 1.17
(1.05–1.31) 1.71 (1.47–1.99) 1.21
(1.05–1.39)
Uganda 1.09
(1.00–1.17) 1.01
(0.91–1.13) 1.33
(1.18–1.51) 2.19
(1.90–2.52) 1.20
(1.07–1.36) 1.57 (1.37–1.79) 1.42
(1.17–1.73)
Rwanda 1.37
(1.14–1.67) 1.12
(0.87–1.43) 1.52
(1.15–2.01) 3.63
(2.61–5.07) 1.13
(0.88–1.45) 2.65 (1.94–3.61) 2.20
(1.19–4.07)
Southern Africa
Malawi 1.23
(1.13–1.34) 0.93
(0.83–1.04) 1.44
(1.27–1.63) 2.60
(2.26–2.99) 1.20
(1.04–1.39) 1.86 (1.58–2.18) 1.06
(0.87–1.29)
South
Africa 1.27
(1.13–1.43) 1.44
(1.28–1.62) 1.68
(1.46–1.94) 7.67
(6.42–9.17) 1.27
(1.07–1.50) 1.66 (1.42–1.96) 1.53
(1.24–1.87)
Zambia 1.10
(0.99–1.22) 1.19
(1.05–1.36) 1.27
(1.07–1.50) 2.82
(2.23–3.56) 1.33
(1.10–1.62) 1.75 (1.43–2.14) 1.33
(1.03–1.70)
Zimbabwe 1.38
(1.26–1.51) 1.05
(0.93–1.18) 1.27
(1.12–1.45) 3.02
(2.44–3.73) 1.18
(0.98–1.42) 1.50 (1.23–1.82) 1.16
(0.93–1.44)

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Makindeetal. Genus (2021) 77:24
Senegal (OR = 1.22). e same pattern was replicated in Middle Africa. For East Africa,
a significant relationship was observed in Ethiopia (OR = 1.19) and Rwanda (OR = 1.37);
while Southern Africa saw a relationship between Malawi (OR = 1.23), South Africa
(1.27), and Zimbabwe (1.38).
Fewer countries exhibited independent association between handwashing capacity
and presence of older persons in the household. ese include Nigeria (OR = 1.12), S en-
egal (OR = 1.17), South Africa (OR = 1.44), and Zambia (OR = 1.19). For most countries,
a dose–response pattern was observed for wealth index and education of household
head, where the odds of handwashing capacity increased with the levels of these vari-
ables (Table2). e likelihood of handwashing capacity was significantly higher in urban
than in rural areas in most of the countries.
Isolation capacity
e overall proportion of households with isolation capacity ranged from 30.9% in
Ethiopia to 77.4% in South Africa (Table3). In addition to South Africa, from Southern
Africa, countries with the highest household isolation capacity across sub-regions were
Nigeria in West Africa (55.4%), Angola in Middle Africa (46.7%); and Rwanda in Eastern
Africa (58.4%). Generally, households in urban areas had more isolation capacity than
those in the rural areas. Urban households in 11 countries and rural households in eight
countries recorded isolation capacity of 50% and above. Variations in isolation capacity
were also observed across countries especially when considering the wealth index, with
the rich households generally having more isolation capacity. While isolation capacity
Table 3 Distribution of Isolation capacity in selected African countries
Sub-region/
country Overall: n (%) Residence
(%) Wealth index (%) Education: Household head
(%)
Urban Rural Poor Average Rich None Primary Secondary+
Western Africa
Benin 6847 (48.4) 50.6 46.7 43.1 47.8 53.2 47.3 44.4 54.6
Guinea 3890 (49.2) 46.4 50.6 44.6 54.7 48.3 49.5 47.2 49.1
Mali 5098 (53.6) 52.3 54 49.7 55.4 55.9 51 55.7 62.3
Nigeria 22413 (55.4) 56.6 54.5 49.4 55.9 59.4 53.2 55.1 57
Senegal 3593 (42.9) 48.4 37 32.5 38.9 49.8 39.3 41.7 57.4
Middle Africa
Angola 7524 (46.7) 46.4 47.2 46.2 46.2 47.5 51.1 42.7 47
Chad 6677 (39.2) 44.1 37.9 40.9 33.9 42.3 36.1 39.4 49
Eastern Africa
Burundi 8465 (53.0) 54.2 52.8 52.1 51.8 56 56.9 46.6 58.7
Ethiopia 5136 (30.9) 53.4 25.2 21.8 25.1 45.8 27.6 25.4 54
Tanzania 6801 (54.1) 61.2 50.7 40.6 55.6 64.6 50.9 51.7 67.3
Uganda 9887 (50.5) 57.6 48 38.5 50.1 59.3 53.6 45.8 56.3
Rwanda 7408 (58.4) 65.5 57 54.6 57.2 64.9 62 53.9 73.9
Southern Africa
Malawi 13165 (49.9) 60 48.1 41.2 50.4 61.1 50.1 46.9 56.7
South Africa 8576 (77.4) 78 76.1 72.3 74.4 84.6 69.8 70.9 80.7
Zambia 5780 (45.0) 48.6 42.5 38.5 45.5 49.7 47.3 41 48.1
Zimbabwe 6203 (58.9) 61.9 57.4 47.6 65 65.1 60.9 59.3 58.4

Page 9 of 20
Makindeetal. Genus (2021) 77:24
ranged from 84.6% in South Africa to 42.3% in Chad for the rich wealth index, it ranged
from 74.4% in South Africa to 25.1% in Ethiopia for the average wealth index, and from
72.3% in South Africa to 21.8% in Ethiopia for the poor wealth index.
ere were gaps in isolation capacity in terms of education of household head. e
common pattern was that households whose head had secondary/higher education
enjoyed better isolation capacity than those with primary or no formal education. More
than 50% of the households whose head had post-primary education in 12 countries had
Table 4 Adjusted Odds Ratio (OR) for association between household characteristics and isolation
capacity in selected African countries
a Reference category was “poor ”
b Reference category‑none
Sub-
region/
country
Handwashing Presence
of older
persons
Wealth indexaEducationb: Household
head Residence
Average Rich Primary Secondary+Urban vs.
rural
Western Africa
Benin 1.31 (1.17–1.47) 2.01
(1.84–2.19) 1.20
(1.09–1.32) 1.49
(1.31–1.69) 0.96
(0.87–1.05) 1.41 (1.28–1.56) 1.00
(0.90–1.11)
Guinea 1.07 (0.95–1.21) 1.59
(1.44–1.77) 1.55
(1.36–1.76) 1.81
(1.47–2.25) 1.01
(0.86–1.19) 1.21 (1.06–1.38) 0.63
(0.51–0.78)
Mali 1.23 (1.09–1.39) 1.94
(1.75–2.15) 1.35
(1.20–1.52) 2.42
(1.99–2.94) 1.28
(1.12–1.46) 1.75 (1.53–2.00) 0.50
(0.38–0.65)
Nigeria 1.06 (0.99–1.12) 2.27
(2.14–2.40) 1.26
(1.17–1.35) 1.63
(1.48–1.78) 0.90
(0.84–0.97) 1.09 (1.02–1.16) 0.81
(0.75–0.87)
Senegal 1.19 (1.03–1.36) 1.08
(0.98–1.18) 1.22
(1.06–1.39) 1.89
(1.58–2.66) 0.99
(0.87–1.13) 1.66 (1.43–1.92) 0.97
(0.84–1.12)
Middle Africa
Angola 1.40 (1.29–1.52) 2.29
(2.09–2.50) 1.10
(0.99–1.23) 1.23
(1.05–1.44) 0.89
(0.81–0.97) 1.07 (0.97–1.19) 0.90
(0.80–1.01)
Chad 1.26 (1.07–1.48) 2.24
(2.06–2.44) 0.88
(0.80–0.96) 1.06
(0.95–1.19) 0.98
(0.88–1.08) 1.43 (1.28–1.60) 0.88
(0.77–1.00)
Eastern Africa
Burundi 1.13 (0.98–1.29) 3.81
(3.45–4.22) 1.21
(1.11–1.32) 1.49
(1.33–1.68) 0.84
(0.78–0.91) 1.53 (1.35–1.73) 1.00
(0.88–1.14)
Ethiopia 1.21 (1.08–1.35) 1.87
(1.71–2.033) 1.13
(1.01–1.26) 1.88
(1.62–2.19) 1.16
(1.06–1.27) 2.29 (2.04–2.57) 1.44
(1.24–1.68)
Tanzania 0.97 (0.89–1.05) 2.00
(1.82–2.21) 1.89
(1.70–2.10) 2.77
(2.39–3.20) 1.00
(0.89–1.11) 1.63 (1.40–1.89) 0.96
(0.85–1.10)
Uganda 1.11 (1.03–1.20) 2.30
(2.11–2.51) 1.47
(1.34–1.60) 2.06
(1.85–2.29) 0.94
(0.86–1.03) 1.29 (1.16–1.43) 1.07
(0.96–1.18)
Rwanda 1.47 (1.23–1.74) 2.87
(2.55–3.23) 1.24
(1.13–1.37) 1.60
(1.41–1.82) 1.01
(0.91–1.11) 2.19 (1.87–2.56) 1.12
(0.98–1.28)
Southern Africa
Malawi 1.22 (1.13–1.33) 2.22
(2.07–2.38) 1.52
(1.42–1.62) 2.14
(1.97–2.34) 1.04
(0.96–1.12) 1.35 (1.23–1.49) 1.20
(1.10–1.32)
South
Africa 1.31 (1.17–1.47) 1.21
(1.07–1.37) 1.47
(1.28–1.67) 2.89
(2.42–3.44) 1.09
(0.94–1.27) 1.62 (1.40–1.88) 0.72
(0.62–0.84)
Zambia 1.15 (1.04–1.27) 2.25
(2.04–2.48) 1.52
(1.37–1.69) 2.37
(2.02–2.79) 1.00
(0.88–1.13) 1.35 (1.18–1.54) 0.95
(0.84–1.07)
Zimbabwe 1.39 (1.27–1.52) 1.69
(1.50–1.90) 2.45
(2.17–2.76) 4.64
(3.80–5.67) 1.01
(0.85–1.20) 0.95 (0.79–1.13) 0.60
(0.50–0.72)

Page 10 of 20
Makindeetal. Genus (2021) 77:24
isolation capacity, compared to primary education in six countries, and no formal edu-
cation in 10 countries.
Independent correlates of isolation capacity are summarised in Table4. e rela-
tionship between handwashing and isolation capacities followed the same pattern as
observed for the former. Except for Senegal, there was a significant relationship between
presence of elderly persons in household and isolation capacity in all the countries ana-
lysed. Wealth index and education of household head retained their pattern of dose–
response relationship. In terms of residence, urban households were less likely to have
isolation capacity.
Presence ofolder persons inhousehold
e percentage of households with older persons aged 60 and above in the study ranged
from 16.4% in Angola to 42.1% in Senegal (Table5). e percentage was about 20% in
most of the countries, with the exception of Guinea (26.3%), Senegal (42.1%), Ethiopia
(24.8%), and South Africa (26.3%).
Rural–urban disparity was not as pronounced as was observed for handwashing and
isolation capacity (Table5). For instance, no disparity was observed between rural and
urban households in Mali (urban—23.3%, rural—23.8%), Nigeria (20.9% vs. 20.6%), and
Chad (17.0% vs. 17.1%). For the remaining 13 countries, the percentage of households
with older persons was higher in rural areas.
Table 5 Percentage of households with persons aged 60 and above in selected African countries
Sub-region/
country Overall: n (%) Residence Wealth index Education: household head
Urban Rural Poor Average Rich None Primary Secondary+
Western Africa
Benin 3146 (22.2) 19.3 24.4 28.3 24.2 15.4 30.9 14.5 10.1
Guinea 2414 (30.5) 26.5 32.6 29 35.1 27.4 34 22.4 23.4
Mali 2253 (23.7) 23.3 23.8 27 22.2 21.8 27 16.3 16.1
Nigeria 8387 (20.8) 20.9 20.6 22.8 22.1 18.2 34 25.4 10.5
Senegal 3531 (42.1) 36.3 48.4 45.8 49.9 36.5 47.6 33.2 29.6
Middle Africa
Angola 2639 (16.4) 12.8 22.1 23.5 15.9 11 31.7 15.6 6.7
Chad 2946 (17.1) 17 17.1 19.1 15.9 15.5 22.5 11 6.6
Eastern Africa
Burundi 2772 (17.4) 11.3 18.1 20.3 16.7 13.4 24.7 10.9 7.1
Ethiopia 4124 (24.8) 17.2 26.7 27.2 27.5 19.1 36.2 12.4 8.1
Tanzania 2859 (22.8) 15.7 26.2 28 26 15.4 46.5 18.7 10.3
Uganda 3309 (16.9) 11.6 18.7 21.4 17.9 12.9 39.3 15.2 8
Rwanda 2186 (17.2) 10.4 18.6 19.9 17.4 13.5 36.3 11.2 6.7
Southern Africa
Malawi 5253 (19.9) 9.6 21.8 21.4 21.3 16.4 38.9 19.6 7.9
South Africa 2915 (26.3) 22.5 34.4 24.8 21.6 31.8 59.3 39.3 16.2
Zambia 2265 (17.6) 14.5 20 20.1 20.4 13.7 36.3 20.6 10.8
Zimbabwe 2305 (21.9) 11.7 27 26.9 25.1 12.1 59.4 36.2 8.3

Page 11 of 20
Makindeetal. Genus (2021) 77:24
In terms of household wealth index, although the disparity was not so wide, more
poor households appeared to include the elderly (Table5). is was followed by aver-
age households, with the lowest prevalence in rich households. However, there was an
exception in South Africa, where the percentage of households with older persons was
highest (31.8%) among the rich wealth index compared to average (21.6%) and poor
(24.8%) households. Overall, the results further show that the proportion of households
Table 6 Adjusted Odds Ratio (OR) for association between background characteristics and
household presence of older persons in selected African countries
a Reference category was “poor ”
b Reference category‑none
Sub-
region/
country
Handwashing Isolation
capacity Wealth indexaEducationb: household
head Residence
Average Rich Primary Secondary+Urban vs.
rural
Western Africa
Benin 1.12 (0.96–1.30) 2.00
(1.84–2.18) 0.81
(0.72–0.90) 0.61
(0.53–0.71) 0.42
(0.37–0.47) 0.30 (0.26–0.34) 0.99
(0.88–1.11)
Guinea 0.82 (0.72–0.94) 1.59
(1.43–1.77) 1.40
(1.22–1.59) 1.50
(1.20–1.87) 0.52
(0.43–0.63) 0.54 (0.46–0.62) 0.75
(0.61–0.92)
Mali 1.06 (0.93–1.22) 1.91
(1.73–2.12) 0.86
(0.76–0.98) 0.73
(0.59–0.89) 0.52
(0.44–0.61) 0.42 (0.36–0.49) 1.12
(0.87–1.44)
Nigeria 1.10 (1.02–1.18) 2.26
(2.13–2.39) 1.04
(0.96–1.14) 1.21
(1.09–1.36) 0.35
(0.32–0.38) 0.10 (0.09–0.11) 1.10
(1.04–1.25)
Senegal 1.16 (1.01–1.34) 1.08
(0.98–1.18) 1.04
(0.91–1.91) 0.88
(0.73–1.05) 0.57
(0.50–0.65) 0.45 (0.38–0.52) 0.89
(0.77–1.04)
Middle Africa
Angola 0.94 (0.84–1.06) 2.30
(2.10–2.51) 0.87
(0.76–0.99) 1.08
(0.88–1.32) 0.37
(0.33–0.41) 0.13 (0.11–0.15) 0.91
(0.78–1.06)
Chad 0.89 (0.71–1.13) 2.23
(2.05–2.43) 0.88
(0.79–0.98) 0.88
(0.79–0.98) 0.38
(0.33–0.43) 0.18 (0.15–0.22) 1.44
(1.24–1.68)
Eastern Africa
Burundi 1.10 (0.91–1.34) 3.83
(3.46–4.24) 0.82
(0.73–0.91) 0.81
(0.69–0.94) 0.42
(0.38–0.47) 0.20 (0.16–0.24) 0.92
(0.78–1.09)
Ethiopia 1.10 (0.96–1.26) 1.86
(1.71–2.03) 0.97
(0.87–1.09) 0.91
(0.76–1.08) 0.19
(0.17–0.22) 0.09 (1.13–1.66) 0.09
(1.13–1.66)
Tanzania 0.90 (0.81–0.99) 2.00
(1.81–2.21) 0.96
(0.85–1.08) 0.84
(0.70–1.00) 0.26
(0.23–0.29) 0.11 (0.09–0.13) 0.78
(0.67–0.92)
Uganda 1.00 (0.91–1.12) 2.31
(2.11–2.51) 0.90
(0.80–1.01) 1.02
(0.88–1.18) 0.26
(0.24–0.29) 0.13 (0.12–0.15) 0.82
(0.69–0.96)
Rwanda 1.10 (0.88–1.38) 2.88
(2.56–3.24) 0.99
(0.87–1.12) 1.20
(1.02–1.42) 0.24
(0.22–0.27) 0.12 (0.09–0.15) 0.72
(0.60–0.87)
Southern Africa
Malawi 0.98 (0.88–1.09) 2.22
(2.08–2.38) 1.25
(1.15–1.36) 1.77
(1.59–1.97) 0.37
(0.34–0.40) 0.12 (0.11–0.14) 0.5
(0.48–0.61)
South
Africa 1.15 (1.02–1.31) 1.43
(1.27–1.61) 1.72
(1.47–2.02) 1.72
(1.47–2.02) 0.32
(0.28–0.38) 0.06 (0.05–0.07) 0.52
(0.43–0.64)
Zambia 1.22 (1.08–1.28) 2.24
(2.03–2.47) 1.20
(1.05–1.37) 1.10
(0.90–1.36) 0.43
(0.37–0.49) 0.20 (0.17–0.23) 0.83
(0.71–0.96)
Zimbabwe 1.06 (0.94–1.20) 1.70
(1.51–1.91) 1.47
(1.26–1.70) 2.22
(1.65–2.99) 0.30
(0.25–0.36) 0.05 (0.04–0.06) 0.40
(0.30–0.53)

Page 12 of 20
Makindeetal. Genus (2021) 77:24
with older persons was relatively higher where the household head had no formal educa-
tion, while it was lowest for post-primary education.
e independent associations for presence of older persons in households are shown
in Table6. Handwashing capacity was found to exhibit a strong positive association in
only South Africa (OR = 1.15) and Zambia (OR = 1.22). e association between isola-
tion capacity and presence of older persons in the household was largely sustained in
most countries. In countries such as Nigeria (OR = 1.21), Rwanda (OR = 1.20), Malawi
(OR = 1.25), South Africa (OR = 1.72), and Zimbabwe (OR = 2.22), households with rich
wealth index (relative to poor) were more likely to have older persons. In all countries,
the odds of having older persons in households decreased with education of household
head. e presence of older persons was more likely in urban compared to rural house-
holds in Nigeria (OR = 1.10) and Chad (OR = 1.44), while the reverse was the case in all
other countries.
Intra-country variations inCOVID-19 prevention capacities
We disaggregated the three indices according to geographical/administrative regions
within each country. It was observed that intra-country disparities were more predomi-
nant for handwashing capacity than they were for isolation capacity and proportion of
households with older persons. Figure 1 shows the spatial variations in handwashing
capacity for Western Africa (Fig.1a), Middle Africa (Fig.1b), Eastern Africa (Fig.1c), and
Southern Africa (Fig.1d). “Appendix” Table7 shows the details for all three indicators.
In Western Africa (Fig. 1a), regional/provincial variations were notable in Benin,
Guinea, Senegal, and Nigeria. For instance, Southern regions clearly fared better than
Northern regions in Nigeria, Guinea, as was also partly the case in Senegal.
For Middle Africa (Fig.1b), there was also wide differences across regions in Angola,
with Moxico and Luanda having 54.2% and 44.3% households with handwashing capac-
ity, while the levels were lowest in Bengo (5.9%). e pattern was different in Chad,
where N’djamena had 18.3%, and all other regions had less than 7%.
Eastern Africa was no different (Fig. 1c). e levels were generally higher across
regions in Tanzania, where household handwashing capacity was as high as 74.9% in
Kagera, 72.2% in Dar es Salaam, with no region having less than 20%. Burundi, Ethiopia,
Uganda, and Rwanda recorded significant variations across its regions.
In South Africa, the Western Cape (78.7%) had the highest percentage of house-
holds with handwashing capacity, while the lowest was Mpumalanga (33.5%). Malawi
had a monolithic pattern with all its three regions having less than 20%. In Zambia,
four regions were recorded in the 30s (Central, Copperbelt, Lusaka and Western),
while other regions recorded much lower percentage. Three regions in Zimbabwe
(Mashonaland Central, Mashonaland West, and Bulawayo) had more than 50% of
households with handwashing capacity (Fig.1d).

Page 13 of 20
Makindeetal. Genus (2021) 77:24
Fig. 1 a Handwashing capacity in selected Western African countries. b Handwashing capacity in selected
Middle Africa countries. c Handwashing capacity in selected Eastern Africa countries. d Handwashing
capacity in selected Southern Africa countries

Page 14 of 20
Makindeetal. Genus (2021) 77:24
Fig. 1 continued

Page 15 of 20
Makindeetal. Genus (2021) 77:24
Discussion andconclusions
COVID-19 prevention capacity in the home varies widely across countries in SSA, as
highlighted by our results. None of the 16 countries saw up to half of households enjoy-
ing handwashing capacity. e availability of handwashing infrastructure is known to
influence the practice of handwashing, and its absence suggests households are poorly
prepared to handle an infectious disease such as the ongoing COVID-19 outbreak.
Results also revealed regional disparities in handwashing capacity within and between
study countries. For instance, most households in Benin, Kidal (Northern Mali), regions
in Northern Nigeria, Guinea, Senegal and Zambia, the Bengo region in Angola, and
almost all regions in Chad, Burundi, Rwanda Ethiopia and Malawi had fewer than 20%
of households with handwashing capacity. us, these regions retain the potential for
propagating COVID-19 infections in clusters. A report from the global handwashing
partnership gives credence to this finding. According to the report, SSA countries lack
basic hygiene services, and evidence from 25 countries in the region showed that the
proportion of households with soap available at the handwashing place and access to
sanitation and hygiene was a meagre 4% (Global Handwashing Partnership, 2017). Insit-
uations where surge facilities are necessary to respond to a persistent transmission of the
virus, this evidence will be useful in making decisions on where to prioritise the installa-
tion of such facilities.
e rural–urban disparity in handwashing capacity established in this study could be
explained in relation to differences in socio-economic development between the urban
and rural areas in SSA. is indicates that households in rural areas are more likely to be
exposed to the risk of community and in-home transmission of the SARS-CoV-2 virus.
e low level of handwashing capacity found among poorer households is expected,
because access to clean water and soap in most households in SSA proves to be an eco-
nomic challenge. e inability of poor households to afford soap and maintain adequate
hand hygiene may further exacerbate their vulnerability to COVID-19 transmission in
these households. In corroboration, the Global Handwashing Partnership Report indi-
cated that urban areas had greater access to water and soap for handwashing than did
rural areas (Global Handwashing Partnership, 2017).
Like the capacity to practice handwashing, the capacity for household residents to
isolate is low. Across countries in SSA, South Africa had the best isolation capacity
in the homes, while those with low educational attainment had the poorest isolation
capacity in Ethiopia. Capacity to isolate is more prevalent among households in urban
settings. Intra-country analysis demonstrated that urban residents have higher house-
hold income; larger living spaces/property; and better hygiene practices when com-
pared to their rural counterparts. Having a household head with post-primary level
education, ranking amongst the high wealth index, and residing in an urban area,
were positively correlated with living in a home with good handwashing capabilities
with the capacity to successfully isolate if necessary. e income of the household
head has an impact on the availability of proper sanitation, which includes the pres-
ence of cleaning material to effectively sanitise surfaces in the household, and access
to a safe water source. Self-isolation is of great importance due to the increasing

Page 16 of 20
Makindeetal. Genus (2021) 77:24
number of cases of community transmission of the virus across countries in the
region. Overcrowding diminishes the ability to self-isolate in homes when the house-
hold structure (number of residents vs. availability of sleeping rooms) is unfavourable
(House & Keeling, 2009; Karlsson etal., 2020; Kawuki etal., 2020). e inability of
household members to self-isolate in homes, when necessary, can result in all house-
hold members contracting the disease, including the elderly in the home, which can
have undesirable outcomes.
COVID-19 infection has been found to have worse outcomes in the elderly. Sene-
gal had the oldest population in the region, but handwashing and isolation capacities
in the country were not optimal. Households with lower income often had more older
members than did wealthier homes. Unfortunately, these poorer households have been
shown to have lower capacity for handwashing and isolation. us, the risk of transmis-
sion of the virus to the elderly in these homes is magnified once a household inhabitant
contracts the disease. Furthermore, lower income households are less likely to have easy
access to health facilities for care in countries that rely more on out-of-pocket payment
for care, such as Nigeria and Ethiopia (Ifeagwu etal., 2021). is can influence the deci-
sion to present in health facilities for care and the delay can further result in exposure to
other members of the household and community, with eventually poor outcomes among
those that might contract the disease. Older members are a high-risk population for
morbidity and mortality, as they often harbour other chronic diseases such as hyperten-
sion, diabetes, and debilitating diseases that have been shown to lead to poor outcomes
in COVID-19 infected patients (Liu etal., 2020). For these reasons, efforts must be made
to reduce as far as possible the chances of the introduction of the virus to homes that
harbour elderly citizens, through awareness campaigns and other preventative strate-
gies, including physical distancing.
When compared with available evidence on the case fatality rate (CFR) of COVID-
19 across the 16 countries under study (see Additional file1), the current impact
of the disease does not seem to be dependent on any single factor presented in this
study. Senegal, which has the highest proportion of elderly population, is not report-
ing the highest mortality rate. Mali, which currently has the highest CFR, did not have
the worst handwashing capacity, isolation capacity, or a particularly sizable elderly
population. Similarly, Burundi which has the lowest case fatality rate had poor hand-
washing capacity, a fair isolation capacity, and a relatively young population. us,
factors that can influence the outcome of COVID-19 are complex and may extend
beyond mere household characteristics. In such a scenario, proactive prevention of
the continued propagation of the virus remains an important strategy for its control.
While we have used available evidence to compare with the outcomes reported in
this study, it is noteworthy that some factors might affect such comparison. Testing
for COVID-19 across the continent has been poor, thereby leaving many potential
and asymptomatic cases undiagnosed. ere are also concerns that the civil regis-
tration and vital statistics system of most countries in the region is suboptimal and
cannot provide adequate data to monitor the pandemic or determine the excess mor-
tality that might be attributable to the pandemic (BBC Makinde etal., 2020; News,

Page 17 of 20
Makindeetal. Genus (2021) 77:24
2021). Despite these observations, there is a general agreement that mortalities from
COVID-19 in Africa are not as high as in Europe or the US, although the risks might
change with increasing identification of mutant virus variants, and the difficulty in
access to vaccines for the immunisation of Africa’s population.
In conclusion, this paper shows the wide differences in the household conditions across
SSA, demonstrating the need for each country to use available evidence in identifying
risks and in framing its interventions and response strategies to minimise the spread and
eventually curtail the COVID-19 outbreak in their country. For a meaningful reduction
in the risks of transmission of SARS-CoV-2 virus, especially as new and more deadly
variants of the virus are being identified with poorer response to vaccines, and future
infectious disease outbreak in households in countries across SSA, efforts to improve
handwashing capacity, particularly in disadvantaged households, through education and
behavioural change interventions should be prioritised. As part of the response to the
ongoing outbreak, construction of isolation centres should be established in regions that
have limited in-home isolation capacity and relatively high proportion of elderly popula-
tion. In the longer term, home construction standards that embed basic hygienic needs,
including access to safe and reliable water should not only be encouraged but enforced.
As vaccines against the virus are being rolled out across African states, these recom-
mendations can be prioritised to initially focus on areas with greater elderly populations,
and low household isolation capacities. Finally, with mutant strains of the virus being
more deadly and less responsive to available vaccines even with a low level of vaccina-
tion across SSA, leveraging household habitat conditions remains a major strategy for
the control of the outbreak in the region. is strategy should be emphasised in com-
munication briefs on national preparedness with the rising number of the delta variant-
associated COVID-19 infections across countries.
Appendix
See Table7.
Supplementary Information
The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s41118- 021- 00130-w.
Additional le1. Updated Covid-19 cases, number of fatalities and Case fatality rates of 16 countries in SSA.
Acknowledgements
We wish to acknowledge ICF International and USAID for free access to the Demographic and Health Survey data.
Authors’ contributions
COO conceived the study. COO, JOA, YA, OAM, DO, LFN developed the methodology. JOA analysed the data and led
creation of tables and maps and writing of results. HU, CKA, PB and DO supported development of tables and writing
the results. OAM, LFN, RP, YA, conducted the literature reviews and developed the introduction section. RP, DO, PB, HU
and CKA reviewed the results and developed the first draft of the discussion. All authors read and approved the final
manuscript.
Funding
This work was carried out under the COVID-19 Africa Rapid Grant Fund supported under the auspices of the Science
Granting Councils Initiative in Sub-Saharan Africa (SGCI) and administered by South Africa’s National Research Founda-
tion (NRF) in collaboration with Canada’s International Development Research Centre (IDRC), the Swedish International
Development Cooperation Agency (Sida), South Africa’s Department of Science and Innovation (DSI), the Fonds de
Recherche du Québec (FRQ), the United Kingdom’s Department of International Development (DFID), United Kingdom

Page 18 of 20
Makindeetal. Genus (2021) 77:24
Research and Innovation (UKRI) through the Newton Fund, and the SGCI participating councils across 15 countries in
sub-Saharan Africa”, jointly supported by the National Institute for the Humanities and Social Sciences, South Africa.
Availability of data and materials
The datasets analysed during the current study are available for download after approval from the Demographic and
Health Survey (DHS) Program (https:// dhspr ogram. com/).
Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1 Viable Knowledge Masters, Plot C114, First Avenue, Gwarimpa, FCT, Abuja, Nigeria. 2 Viable Helpers Development Organ-
ization, Abuja, Nigeria. 3 Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan,
Ibadan, Nigeria. 4 Federal University Oye, Oye, Ekiti State, Nigeria. 5 Department of Geography, University of Nigeria,
Nsukka, Nigeria. 6 Department of Sociology and Anthropology, Faculty of Social Sciences, University of Uyo, Uyo, Nigeria.
7 Provincial Education Office Ministry of Education, Lusaka, Zambia. 8 University of Nigeria, Nsukka, Nigeria. 9 Demography
and Population Studies Program, Schools of Public Health and Social Sciences, University of the Witwatersrand, Johan-
nesburg, South Africa.
Received: 31 October 2020 Accepted: 12 August 2021
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