Discussion
Emerging and re-emerging diseases with pandemic potential continue to challenge countries and health systems, causing enormous human and economic losses.1 3 Health security calls for the need for all countries to invest in improving their global health preparedness in the phase of emerging epidemics including stronger health infrastructure as the best defence against disease outbreaks and other health threats.2 73 Not surprisingly, most countries are struggling to mitigate the impact of the current COVID-19 pandemic with varied levels of success with increasing fears or re-emergence in those places that have managed to contain the pandemic.74 It remains uncertain how Africa is going to come through this pandemic. Compared with the rest of the world, the virus has arrived later, providing an opportunity to learn from other contexts that could help guide Africa’s fight. Social distancing and basic hygiene measures are proving to be the most effective tools to slow down the rate of transmission. Yet, in many contexts throughout Africa, social distancing and frequent handwashing are privileges that not everyone has access to.7 75
In Kenya, the expectation is that nearly all communities will be affected by COVID-19 to yet undetermined degrees; however, the impact of the pandemic will not be the same in each locality. Meanwhile, Kenya does not have a pre-existing granular vulnerability index such as the CDC’s US SVI.27 Such an index would have been used to assess vulnerability or identify vulnerable populations that can be used to inform decisions on the disbursement of social support measures or identify those areas that require improved health services. In this analysis, three indices have been developed to identify which communities need the most support as COVID-19 spreads in the country. Mapped to the subcounty, the vulnerability indices provide information that is useful for emergency response planning and mitigation at a relatively granular level and can help support response planning for the current epidemic.
Once introduced, outbreaks spread faster in vulnerable communities than in less vulnerable areas. Some communities in Kenya were identified to be more vulnerable than others and would exhibit compromised ability to manage the spread and limit the economic and social impact of the outbreak. The SVI identified subcounties in the northern and southeast parts of Kenya as the most vulnerable, whereas the majority of the subcounties in central and western Kenya were observed to be less vulnerable (figure 2A). The 49 most vulnerable subcounties account for 6.9 million (15%) of Kenya’s total population will require greater focus and prioritisation in terms of response.
There is a divergence between subcounties that had high SVI and those defined as epidemiologically most vulnerable (figure 2). Most of the subcounties in western, central and parts of southeast were observed to have high EVI hence when applied to COVID-19, these areas are likely to have subpopulations at higher risk of developing severe disease and increased mortality rates.18 32–35 Although the Kenyan population has hypertension, obesity and diabetes, the prevalence is generally not high compared with other settings where severe disease and increased mortality have been observed. Conversely, these regions appear to be less vulnerable with respect to social vulnerability (figure 2A).
Though sparsely populated, northern and southeastern parts of Kenya were less epidemiologically vulnerable and are therefore generally less vulnerable to severe diseases but more susceptible to infections and spread when considering their socioeconomic context. They have poor access to hospitals and urban areas, high number of poor households, constrained access to water and sanitation, and low education attainment. These metrics allow for the identification of geographic areas that are most likely to harbour large numbers of undocumented COVID-19 cases due to lack of access to care (online supplementary additional file 2, online supplementary additional file 3), which in turn can inform the geographic targeting of testing and surveillance efforts and for the deployment of temporary hospitals based on projected need.
The government of Kenya has put in place several measures to curb the spread of COVID-19. Some of these measures lead to reduced social interaction hence reduced production and demand across all the sectors that are costly to the economy and will have negative impacts on the livelihoods of people. The national government, the county governments and other stakeholders are implementing programmes to cushion against adverse socioeconomic impacts. These include reduction of taxes, provision of masks and hand sanitisers, distribution of food, water and other commodities. Further, the government will inject Kenya shillings 53.7 billion into the economy to stimulate growth and cushion families and companies during COVID-19 pandemic in eight thematic areas including infrastructure, education, small and medium enterprises, health, agriculture, tourism, environment and manufacturing.76 This analysis identifies areas that should be prioritised for different interventions when programmes to ease vulnerability and mitigate the effects of COVID-19 are being rolled out across the country. This is important especially in areas that are highly vulnerable and are yet to experience an escalation of cases. These indices have the potential to pave the way for data-driven informed planning to tackle vulnerability.
The epidemic is shaped by many factors, testing capacity and social distancing, as well as population density, age structure, wealth and other social behavioural factors. In Kenya, the spread of the virus is uneven with most of the cases identified in the capital city, larger towns and a few border towns. By overlaying the number of confirmed cases of COVID-19 onto the vulnerability indices, we can begin to explore the spread of the virus in communities with different levels of vulnerability. We have preliminarily explored how vulnerability relates to the numbers of confirmed cases of COVID-19 using case data at the county level made available by May 14. Identified hotspots (Mvita subcounty, Dagoretti north and Kamukunji subcounties) are in highly vulnerable areas when considering their population characteristics that include being largely urban, high population density with a high proportion of people living within informal settlements. Further, they have most people within informal employment and shared sanitation facilities. This is somewhat confounded by the fact that we are unable to separate imported cases which comprise a substantial percentage of the total case count.77
Countries are starting to ease lockdown measures to limit the negative impact on the economy.74 78 These decisions need to be informed by the trends in new cases, the potential risk of resurgence and the strength of public health systems including the capacity to detect new cases. The vulnerability index constitutes a measure through which to better appreciate factors that enable communities to remain resilient, inform on their ability to carry out personal protective measures, practice both hand hygiene and hygiene in the household and the possibility of social distancing in a different context. Importantly, these indices have identified indicators that shed light on factors that would drive the continued spread of disease and inform prediction on the burden of severe disease and mortality due to COVID-19 in Kenya. Importantly, the indices developed are versatile and can be repurposed for use in varied emergency response situations.
The indices showed widespread inequities across subcounties of Kenya. Although the interim measures will help ease the pressure and reduce vulnerability, there is a need to reduce inequities in the longer term, beyond the current COVID-19 pandemic in preparation for future epidemics that are inevitable.79 Africa must invest heavily in relevant data systems and preparedness by increased government investments. Programmes to ensure access to improved water and sanitation, targeted social programmes such as raising awareness for proper hygiene, improvement of housing facilities in the informal settlements and IDP camps are needed. Strengthening health systems remains at the core of reducing health inequities.79 The system should also be redesigned to deal with surge capacity by absorbing the increase in the demand for healthcare services due to epidemics and pandemics.31
The prevalence and recurrence of epidemics and disasters in Africa should be the impetus for greater investment in preparedness. Disasters such as COVID-19 pandemic,80 81 Ebola epidemic,4 flooding in east and southern Africa,82 ongoing floods and recurrent malaria epidemics in Kenya83 have become common. Investments on measures such as early warning systems should be put into place to detect, respond and effectively contain these threats. Strategic actions that were recommended against influenza could potentially inform better preparation in case of a viral disease: capability to develop pandemic strain vaccines, stockpiles of broad-spectrum antiviral drugs, surge capacity for rapid vaccine production and developing models that could inform effective means of delivering therapies during an outbreak.84
The indicators used in this analysis to derive vulnerability indices were based on the a priori understandings of who is a risk and vulnerable from information available so far, however, COVID-19 is evolving and more insights will become increasingly available and the indices can be adapted. Further, the classification of indicators into sub domains should be adapted to context and data availability. For example, Wilkinson et al proposes categorising the indicators into epidemiological vulnerability (based on underlying health conditions), transmission vulnerability (eg, social mixing and hygiene infrastructure), health system vulnerability (eg, hospitals beds and health workers and so on) and vulnerability to control measures.85 In addition, there is a paucity of information on outcomes related to epidemiological factors in African countries, yet this will improve over time as more data become available. As we learn more about COVID-19 disease outcomes in these contexts, it will be important to adjust the created index to incorporate any observed differences.
Limitations
There were data-related limitations when computing the vulnerability indices. Several variables such as access to mobile phones, access to insurance cover and mobility between counties could not be accessed during the analysis. Further, due to the lack of granular data despite using SAE techniques, obesity, diabetes and hypertension could only be resolved at the county level. Therefore, we assumed that the estimates within subcounty were equal for these three variables. Some of the datasets are not updated to 2020 and their trends are likely to have changed between 2014 and 2020. Furthermore, the analysis was conducted at the subcounty level; it is likely that some variation and heterogeneity were masked in the relatively bigger polygons.
An equal weighting scheme was implemented for all the determinants as routinely applied.67–70 Equal weighting schemes have shown to be equally robust;56 58 60 61 65 71 however, the weighting scheme can be revised as more individual-level COVID-19 data with attributes become available in Kenya and other countries across Africa. This will allow a better definition of how the risks associated with different variables vary across different populations and settings.18
The preliminary overlay between indices and the cases may have been limited. Cases were allocated in counties where they were recorded; however, these might not have been the residential counties for the last several years. Some might have been living outside Kenya and other counties. Furthermore, some cases were imported from other countries.77 In contrast, the data layers used refer to the specific counties for the period between 2014 and 2020.