ABSTRACT
Policy makers in Africa need robust estimates of the current and future spread of SARS-CoV-2. Data suitable for this purpose are scant. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya. We estimate that the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 34 - 41% of residents infected, and will peak elsewhere in the country within 2-3 months. Despite this penetration, reported severe cases and deaths are low. Our analysis suggests the COVID-19 disease burden in Kenya may be far less than initially feared. A similar scenario across sub-Saharan Africa would have implications for balancing the consequences of restrictions with those of COVID-19.
ABSTRACT
BackgroundSARS-CoV-2, the virus causing coronavirus disease 2019 (COVID-19), is rapidly spreading across sub-Saharan Africa (SSA). Hospital-based care for COVID-19 is particularly often needed among older adults. However, a key barrier to accessing hospital care in SSA is travel time to the healthcare facility. To inform the geographic targeting of additional healthcare resources, this study aimed to determine the estimated travel time at a 1km x 1km resolution to the nearest hospital and to the nearest healthcare facility of any type for adults aged 60 years and older in SSA. MethodsWe assembled a unique dataset on healthcare facilities geolocation, separately for hospitals and any type of healthcare facility (including primary care facilities) and including both private- and public-sector facilities, using data from the OpenStreetMap project and the KEMRI Wellcome Trust Programme. Population data at a 1km x 1km resolution was obtained from WorldPop. We estimated travel time to the nearest healthcare facility for each 1km x 1km grid using a cost-distance algorithm. Findings9.6% (95% CI: 5.2% - 16.9%) of adults aged [≥]60 years had an estimated travel time to the nearest hospital of longer than six hours, varying from 0.0% (95% CI: 0.0% - 3.7%) in Burundi and The Gambia, to 40.9% (95% CI: 31.8% - 50.7%) in Sudan. 11.2% (95% CI: 6.4% - 18.9%) of adults aged [≥]60 years had an estimated travel time to the nearest healthcare facility of any type (whether primary or secondary/tertiary care) of longer than three hours, with a range of 0.1% (95% CI: 0.0% - 3.8%) in Burundi to 55.5% (95% CI: 52.8% - 64.9%) in Sudan. Most countries in SSA contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of more than 12 hours and to the nearest healthcare facility of any type of more than six hours. The median travel time to the nearest hospital for the fifth of adults aged [≥]60 years with the longest travel times was 348 minutes (equal to 5.8 hours; IQR: 240 - 576 minutes) for the entire SSA population, ranging from 41 minutes (IQR: 34 - 54 minutes) in Burundi to 1,655 minutes (equal to 27.6 hours; IQR: 1065 - 2440 minutes) in Gabon. InterpretationOur high-resolution maps of estimated travel times to both hospitals and healthcare facilities of any type can be used by policymakers and non-governmental organizations to help target additional healthcare resources, such as new make-shift hospitals or transport programs to existing healthcare facilities, to older adults with the least physical access to care. In addition, this analysis shows precisely where population groups are located that are particularly likely to under-report COVID-19 symptoms because of low physical access to healthcare facilities. Beyond the COVID-19 response, this study can inform countries efforts to improve care for conditions that are common among older adults, such as chronic non-communicable diseases. FundingBill & Melinda Gates Foundation Research in context Evidence before this studyWe searched MEDLINE from January 1966 until May 2020 for studies with variations of the words physical access, distance, travel time, hospital, and healthcare facility in the title or abstract. To date, the only studies to systematically map physical access to healthcare facilities in sub-Saharan Africa at a high resolution examined access to emergency hospital care (with a focus on women of child-bearing age), access to care for children with fever, travel time to the nearest healthcare facility for specific populations at risk of viral haemorrhagic fevers, and travel time to the nearest regional- or district-level hospital. Added value of this studyThe added value of this study is threefold. First, we assembled a new dataset of GPS-tagged healthcare facilities, which combines two unique data sources for the geolocation of healthcare facilities across sub-Saharan Africa: one-based on crowd-sourced data from OpenStreetMap and one based on information from ministries of health, health management information systems, government statistical agencies, and international organizations. Second, this is the first study to comprehensively map both hospitals and primary healthcare facilities, and including both public- and private-sector facilities, across sub-Saharan Africa. Third, because the COVID-19 epidemic causes a far higher need for hospital services among older than younger population groups, we focus on physical access to healthcare for the population aged 60 years and older, which is a population group that is rarely studied in investigations of healthcare demand and supply in the region. As such, our maps can inform not only the health system response to COVID-19, but more generally to conditions that are common among older adults in the region, particularly chronic non-communicable diseases and their sequelae. Implications of all the available evidenceLow physical access to healthcare in sub-Saharan Africa will be a major barrier to receiving care for adults aged 60 years and older with COVID-19. However, there is a wide degree of variation in physical access to healthcare facilities for older adults in the region both between and within countries, which likely has an important bearing on the extent to which different population groups within countries are able to access care for COVID-19. Likewise, in those areas with a long travel time to the nearest healthcare facility of any type (which exist in most countries), symptomatic cases of COVID-19 are particularly unlikely to be reported to the healthcare system. Our high-resolution maps for each region and country in sub-Saharan Africa provide precise information about this geographic variation for local, national, and regional policymakers as well as non-governmental organizations.
ABSTRACT
BackgroundResponse to the COVID-19 pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya. MethodsGeospatial indicators were assembled to create three vulnerability indices; social (SVI), epidemiological (EVI) and a composite of the two (SEVI) resolved at 295 sub-counties in Kenya. SVI included nineteen indicators that affect the spread of disease; socio-economic inequities, access to services and population dynamics while EVI comprised five indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1-2 denoted low-vulnerability and 6-7 high-vulnerability. The population within vulnerabilities classes was quantified. ResultsThe spatial variation of each index was heterogeneous across Kenya. Forty-nine north-western and partly eastern sub-counties (6.9 m people) were highly vulnerable while 58 sub-counties (9.7 m people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 sub-counties (7.2 m people) in central and the adjacent areas and 81 sub-counties (13.2 m people) in northern Kenya were the most and least vulnerable respectively. Overall (SEVI), 46 sub-counties (7.0 m people) around central and south-eastern were more vulnerable while 81 sub-counties (14.4 m people) that were least vulnerable. ConclusionThe vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritization and improved planning especially in highly vulnerable sub-counties where cases have not been confirmed. The heterogeneous nature of the vulnerability highlights the need to address social determinants of health disparities, strengthen the health system and establish programmes to cushion against the negative effects of the pandemic. SummaryO_ST_ABSKey questionsC_ST_ABSO_LSTWhat is already known?C_LSTO_LIDisasters and adverse health events such as epidemics and pandemics disproportionately affect population with significantly higher impacts on the most vulnerable and less resilient communities. C_LIO_LISignificant health, socio-economic, demographic and epidemiological disparities exist within Kenya when considering individual determinants, however, little is known about the spatial variation and inequities of their concurrence. C_LI O_LSTWhat are the new findings?C_LSTO_LISub-counties in the north-western and partly eastern Kenya are most vulnerable when considering social vulnerability index while central and south-east regions are most vulnerable based on the epidemiological vulnerability index affecting approximately 6.9 million and 7.2 million people respectively. C_LIO_LIThe combined index of social and epidemiological vulnerabilities shows that on average, 15% (7.0 million) of Kenyans reside in the most vulnerable sub-counties mainly located in the central and south-eastern parts of Kenya. C_LI O_LSTWhat do the new findings imply?C_LSTO_LITargeted interventions that cushion against negative effects to the most vulnerable sub-counties are essential to respond to the current COVID-19 pandemic. C_LIO_LIImplementation of strategies that address the socioeconomic determinants of health disparities and strengthening health systems is crucial to effectively prevent, detect and respond to future adverse health events or disasters in the country. C_LIO_LINeed for better quality data to define a robust vulnerability index at high spatial resolution that can be adapted and used in response to future disasters and adverse health events in the long run. C_LI
ABSTRACT
IntroductionThe COVID-19 pandemic will test the capacity of health systems worldwide. Health systems will need surge capacity to absorb acute increases in caseload due to the pandemic. We assessed the capacity of the Kenyan health system to absorb surges in the number of people that will need hospitalization and critical care because of the COVID-19. MethodsWe assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. ResultsThe capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. ConclusionSignificant gaps exist in Kenyas capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.
