ABSTRACT
BackgroundHow best to prioritise COVID-19 vaccination within and between countries has been a public health and an ethical challenge for decision-makers globally. We systematically reviewed epidemiological and economic modelling evidence on population priority groups to minimise COVID-19 mortality, transmission and morbidity outcomes. MethodsWe searched the National Institute of Health iSearch COVID-19 Portfolio (a database of peer-reviewed and pre-print articles), Econlit, the Centre for Economic Policy Research and the National Bureau of Economic Research for mathematical modelling studies evaluating the impact of prioritising COVID-19 vaccination to population target groups. We narratively synthesised the main study conclusions on prioritisation and the conditions under which the conclusions changed. FindingsThe search identified 1820 studies. 36 studies met the inclusion criteria and were narratively synthesised. 83% of studies described outcomes in high-income countries. We found that for countries seeking to minimise deaths, prioritising vaccination of senior adults was the optimal strategy and for countries seeking to minimise cases the young were prioritised. There were several exceptions to the main conclusion, notably reductions in deaths could be increased, if groups at high risk of both transmission and death could be further identified. Findings were also sensitive to the level of vaccine coverage. InterpretationThe evidence supports WHO SAGE recommendations on COVID-19 vaccine prioritisation. There is however an evidence gap on optimal prioritisation for low- and middle-income countries, studies that included an economic evaluation, and studies that explore prioritisation strategies if the aim is to reduce overall health burden including morbidity.
ABSTRACT
Low- and middle-income countries (LMICs) remain of high potential for hotspots for COVID-19 deaths and emerging variants given the inequality of vaccine distribution and their vulnerable healthcare systems. We aim to evaluate containment strategies that are sustainable and effective for LMICs. We constructed synthetic populations with varying contact and household structures to capture LMIC demographic characteristics that vary across communities. Using an agent- based model, we explored the optimal containment strategies for rural and urban communities by designing and simulating setting-specific strategies that deploy rapid diagnostic tests, symptom screening, contact tracing and physical distancing. In low-density rural communities, we found implementing either high quality (sensitivity > 50%) antigen rapid diagnostic tests or moderate physical distancing could contain the transmission. In urban communities, we demonstrated that both physical distancing and case finding are essential for containing COVID-19 (average infection rate < 10%). In high density communities that resemble slums and squatter settlements, physical distancing is less effective compared to rural and urban communities. Lastly, we demonstrated contact tracing is essential for effective containment. Our findings suggested that rapid diagnostic tests could be prioritised for control and monitor COVID-19 transmission and highlighted that contact survey data could guide strategy design to save resources for LMICs. An accompanying open source R package is available for simulating COVID-19 transmission based on contact network models.
ABSTRACT
BackgroundMany low- and middle-income countries have implemented control measures against coronavirus disease 2019 (COVID-19). However, it is not clear to what extent these measures explain the low numbers of recorded COVID-19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study we collect contact data from residents of informal settlements around Nairobi, Kenya to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number (R0). MethodsWe conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, four weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7pm and 5am. Respondents were asked to report all direct physical and non-physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID-19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID-19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non-pandemic periods to estimate the change in R0. FindingsWe estimate that control measures reduced physical and non-physical contacts, reducing the R0 from around 2.6 to between 0.5 and 0.7, depending on the pre-COVID-19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. 86% of respondents reported a total or partial loss of income due to COVID-19, and 74% reported eating less or skipping meals due to having too little money for food. InterpretationCOVID-19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R0 are consistent with the linear epidemic growth in Kenya and other sub-Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term. Research in context Evidence before this studyWe conducted a PubMed search on 6 June 2020 with no language restrictions for studies published since inception, using the search terms ("social mix*" OR "social cont*" OR "contact pattern*) AND ("covid*"). The search yielded 53 articles, two of which reported changes in social contacts after COVID-19 control measures. The first study reported changes in contact patterns in Wuhan and Shanghai, and the second changes in contact patterns in the UK. We found no studies examining changes in contact patterns due to control measures in sub-Saharan Africa, and no studies disaggregating contacts by socioeconomic status. Added value of this studyThis is the first study to estimate the reproduction number of COVID-19 under control measures in sub-Saharan Africa using primary contact data. This study also moves beyond existing work to i) measure contacts in densely populated informal settlements, ii) explore how social contacts vary across socioeconomic status, and iii) assess the impact of control measures on economic and food security in these areas. Implications of all the evidenceCOVID-19 control measures have substantially reduced social contacts and disease transmission. People of lower socioeconomic status face greater transmission risk as they report more contacts. Control measures have led to considerable economic and food insecurity, and may not be sustainable in the long term without efforts to reduce the burden of control measures on households.
ABSTRACT
BackgroundCoronavirus disease 2019 (COVID-19) epidemics strain health systems and households. Health systems in Africa and South Asia may be particularly at risk due to potential high prevalence of risk factors for severe disease, large household sizes and limited healthcare capacity. MethodsWe investigated the impact of an unmitigated COVID-19 epidemic on health system resources and costs, and household costs, in Karachi, Delhi, Nairobi, Addis Ababa and Johannesburg. We adapted a dynamic model of SARS-CoV-2 transmission and disease to capture country-specific demography and contact patterns. The epidemiological model was then integrated into an economic framework that captured city-specific health systems and household resource use. FindingsThe cities severely lack intensive care beds, healthcare workers and financial resources to meet demand during an unmitigated COVID-19 epidemic. A highly mitigated COVID-19 epidemic, under optimistic assumptions, may avoid overwhelming hospital bed capacity in some cities, but not critical care capacity. InterpretationViable mitigation strategies encompassing a mix of responses need to be established to expand healthcare capacity, reduce peak demand for healthcare resources, minimise progression to critical care and shield those at greatest risk of severe disease. FundingBill & Melinda Gates Foundation, European Commission, National Institute for Health Research, Department for International Development, Wellcome Trust, Royal Society, Research Councils UK. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a PubMed search on May 5, 2020, with no language restrictions, for studies published since inception, combining the terms ("cost" OR "economic") AND "covid". Our search yielded 331 articles, only two of which reported estimates of health system costs of COVID-19. The first study estimated resource use and medical costs for COVID-19 in the United States using a static model of COVID 19. The second study estimated the costs of polymerase chain reaction tests in the United States. We found no studies examining the economic implications of COVID-19 in low- or middle-income settings. Added value of this studyThis is the first study to use locally collected data in five cities (Karachi, Delhi, Nairobi, Addis Ababa and Johannesburg) to project the healthcare resource and health economic implications of an unmitigated COVID-19 epidemic. Besides the use of local data, our study moves beyond existing work to (i) consider the capacity of health systems in key cities to cope with this demand, (ii) consider healthcare staff resources needed, since these fall short of demand by greater margins than hospital beds, and (iii) consider economic costs to health services and households. Implications of all the evidenceDemand for ICU beds and healthcare workers will exceed current capacity by orders of magnitude, but the capacity gap for general hospital beds is narrower. With optimistic assumptions about disease severity, the gap between demand and capacity for general hospital beds can be closed in some, but not all the cities. Efforts to bridge the economic burden of disease to households are needed.
