ABSTRACT
The non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment versus exacerbation. Homogeneity or heterogeneity in the population response, uptick versus suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can aid decision making in the public health response.
Subject(s)
COVID-19ABSTRACT
ABSTRACT Background Deaths from COVID-19 have exceeded 1.8 million globally (January 2020). We examined trends in markers of neonatal care before and during the pandemic at two tertiary neonatal units in Zimbabwe and Malawi. Methods We analysed data collected prospectively via the NeoTree app at Sally Mugabe Central Hospital (SMCH), Zimbabwe, and Kamuzu Central Hospital (KCH), Malawi. Neonates admitted from 1 June 2019 to 25 September 2020 were included. We modelled the impact of the first cases of COVID-19 (Zimbabwe: 20 March 2020; Malawi: 3 April 2020) on number of admissions, gestational age and birth weight, source of admission referrals, prevalence of neonatal encephalopathy, and overall mortality. Findings The study included 3,450 neonates at SMCH and 3,350 neonates at KCH. Admission numbers at SMCH did not initially change after the first case of COVID-19 but fell by 48% during a nurses’ strike (Relative risk (RR) 0·52, 95%CI 0·40-0·68, p < 0·002). At KCH, admissions dropped by 42% (RR 0·58; 95%CI 0·48-0·70; p < 0·001) soon after the first case of COVID-19. At KCH, gestational age and birth weight decreased slightly (1 week, 300 grams), outside referrals dropped by 28%, and there was a slight weekly increase in mortality. No changes in these outcomes were found at SMCH. Interpretation The indirect impacts of COVID-19 are context-specific. While this study provides vital evidence to inform health providers and policy makers, national data are required to ascertain the true impacts of the pandemic on newborn health. Funding International Child Health Group, Wellcome Trust. RESEARCH IN CONTEXT Evidence before this study We searched PubMed for evidence of the indirect impact of the COVID-19 pandemic on neonatal care in low-income settings using the search terms neonat* or newborn , and COVID-19 or SARS-CoV 2 or coronavirus , and the Cochrane low and middle income country (LMIC) filters, with no language limits between 01.10.2019 and 21.11.20. While there has been a decrease in global neonatal mortality rates, the smaller improvements seen in low-income settings are threatened by the direct and indirect impact of the COVID-19 pandemic. A modelling study of this threat predicted between 250000-1.1 million extra neonatal deaths as a result of decreased service provision and access in LMICs. A webinar and survey of frontline maternal/newborn healthcare workers in >60 countries reported a decline in both service attendance and in quality of service across the ante-, peri- and post-natal journey. Reporting fear of attending services, and difficulty in access, and a decrease in service quality due to exacerbation of existing service weaknesses, confusion over guidelines and understaffing. Similar findings were reported in a survey of healthcare workers providing childhood and maternal vaccines in LMICs. One study to date has reported data from Nepal describing an increase in stillbirths and neonatal deaths, with institutional deliveries nearly halved during lockdown. Added value of this study To our knowledge, this is the first and only study in Sub-Saharan Africa describing the impact of COVID-19 pandemic on health service access and outcomes for newborns in two countries. We analysed data from the digital quality improvement and data collection tool, the NeoTree, to carry out an interrupted time series analysis of newborn admission rates, gestational age, birth weight, diagnosis of hypoxic ischaemic encephalopathy and mortality from two large hospitals in Malawi and Zimbabwe ( n ∼7000 babies). We found that the indirect impacts of COVID-19 were context-specific. In Sally Mugabe Central Hospital, Zimbabwe, initial resilience was demonstrated in that there was no evidence of change in mortality, birth weight or gestational age. In comparison, at Kamuzu Central Hospital, Malawi, soon after the first case of COVID-19, the data revealed a fall in admissions (by 42%), gestational age (1 week), birth weight (300 grams), and outside referrals (by 28%), and there was a slight weekly increase in mortality (2%). In the Zimbabwean hospital, admission numbers did not initially change after the first case of COVID-19 but fell by 48% during a nurses’ strike, which in itself was in response to challenges exacerbated by the pandemic. Implications of all the available evidence Our data confirms the reports from frontline healthcare workers of a perceived decline in neonatal service access and provision in LMICs. Digital routine healthcare data capture enabled rapid profiling of indirect impacts of COVID-19 on newborn care and outcomes in two tertiary referral hospitals, Malawi and Zimbabwe. While a decrease in service access was seen in both countries, the impacts on care provided and outcome differed by national context. Health systems strengthening, for example digital data capture, may assist in planning context-specific mitigation efforts.