{'fraction_non_alphanumeric': 0.024489795918367346, 'fraction_numerical': 0.030739795918367348, 'mean_word_length': 4.985496183206107, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Estimated impact of the 2020 economic downturn on under-5 mortality for 129 countries

In low- and middle-income countries (LMICs), economic downturns can lead to increased child mortality by affecting dietary, environmental, and care-seeking factors. This study estimates the potential loss of life in children under five years old attributable to economic downturns in 2020. We used a multi-level, mixed effects model to estimate the relationship between gross domestic product (GDP) per capita and under-5 mortality rates (U5MRs) specific to each of 129 LMICs. Public data were retrieved from the World Bank World Development Indicators database and the United Nations World Populations Prospects estimates for the years 1990-2020. Country-specific regression coefficients on the relationship between child mortality and GDP were used to estimate the impact on U5MR of reductions in GDP per capita of 5%, 10%, and 15%. A 5% reduction in GDP per capita in 2020 was estimated to cause an additional 282,996 deaths in children under 5 in 2020. At 10% and 15%, recessions led to higher losses of under-5 lives, increasing to 585,802 and 911,026 additional deaths, respectively. Nearly half of all the potential under-5 lives lost in LMICs were estimated to occur in Sub-Saharan Africa. Because most of these deaths will likely be due to nutrition and environmental factors amenable to intervention, countries should ensure continued investments in food supplementation, growth monitoring, and comprehensive primary health care to mitigate potential burdens.

iterations is shown in Table 3. Because the Monte Carlo results emerge from 500 iterations they differ slightly from the single iteration estimates.

Results
Between 1990 and 2019, there has been a sustained trend of decline in global poverty and infant mortality in LMICs. However, as hypothesized above, COVID-19 related economic downturns of 2020 are likely to reverse these positive trends. Table 1 presents select summary statistics for variables used in the analysis for the years 2010, 2015, and 2019. (Appendix Table 1 presents annual statistics for the entire study period, 1990-2020. Values for 2020 are based on projections from various sources that do not take into account the 2020 pandemic.) Source: Authors elaboration Note: For a detailed description for every year, see Appendix Table 1 Under-5 Mortality The results from fitting models of U5MR and GDP for each country are shown in the Appendix Moreover, we estimate that 49% of the total under-5 lives lost would occur in Sub-Saharan Africa, a pattern that is observed across the four scenarios, where the total number of lives lost in this region increased up to over 470,000 between a no recession scenario and a 15% reduction in GDP per capita.
The estimated number of deaths is the largest in countries with a higher population. Consequently,  Figure 1 presents the number of total additional deaths from a 15% reduction in GDP per capita (e.g. Scenario 4), according to income group classification. Results show that a 15% reduction in GDP per capita will have a substantial increase in the under-5 mortality rate in LMICs, with larger estimated impacts in lower-middle income countries, where under-5 mortality rates tend to be higher.  Table 3 presents the results from the Monte Carlo experiment on the estimated logarithm of U5MR for each country in every scenario. We observe that in all the scenarios, estimations remain between the 95 percent confidence interval, thereby validating the robustness of our approach.

Discussion
We estimate that the economic downturns of 2020 significantly increased loss of life among children younger than five years old in LMICs. Many of the countries in this analysis have relatively young populations with tenuous access to stable housing, clean water, food, and primary care. The health of these children is highly susceptible to reductions in the economic well-being of their families. Children in these lower income countries are also subject to a high rate of exposure to other infectious diseases, besides COVID-19, which makes them more susceptible when the economy reduces their access to nutrition, housing, water, sanitation, and parental care. [4] Disruptions to primary health care service supply and demand will compound these threats, and thus may be a likely driver of increased mortality in these settings. Efforts to shore up the delivery of pediatric primary health care services during a recession can mitigate the mortality impact of a recession.
Our estimates match the lower range of other estimates of the indirect effects of the COVID-19 pandemic on child mortality which have primarily focused on excess mortality attributed to disruptions in delivery of key health services affecting children and mothers. Reductions in service delivery could range between 10 52% and the prevalence of wasting could increase by 10 50%. [20] The estimated death toll due to health service reductions was estimated to range from 253,500 to 1,157,000 additional child deaths over a 6 month period with 60% of these deaths, linked to reduced coverage of childbirth services and 18 23% of deaths tied to wasting. [20] Another paper which focused on malaria service delivery disruption found that 25% 75% reductions in coverage of preventative and curative supplies and care may result in anywhere from 23,600 to 382,100 additional deaths in the most and least conservative scenarios, respectively. [21] In comparison, our analysis finds that 5% 15% reductions in GDP are estimated to lead to additional loss of life in children under five between 282,996 to 911,026. Our estimates are focused on those due to the reduction in GDP and do not include any direct effects of COVID-19 on children. Because our model controls for DPT vaccine delivery (i.e., our model assumes that DPT vaccine delivery is fixed) it underestimates the potential impact of economic recession through these secondary effects on services. We find that the estimated additional lives lost from 5% and 15% recessions would equate to 1.5% and 4.7% increases above baseline, respectively.
The uncertainty surrounding the actual intensity and duration of COVID-19-induced economic effects is a significant limitation of this study. The study aimed to control for uncertainty by offering a bracketed range of likely recession magnitudes from 5% to 15%, which allows countries to situate their own estimated recession rates within this range to customize results. Hopefully these estimates of the magnitude of the non-COVID-19 related child mortality can help marshal the resources needed to mitigate the burden.

Acknowledgments
The COVID-Busters a research team, formed by Dr. David Bishai has provided invaluable feedback and support for this project. We also thank colleagues and students at the Johns Hopkins Bloomberg School of Public Health for their feedback and insight on this research.

Multiple Imputation
In order to fill in missing values in the independent control variables of interest, we performed multiple imputation using multivariate normal regression. We selected this approach because all For this study, we imputed 65 additional data-sets, since the largest share of missing values in a variable was around 55%. The number of iterations used in the burn-in period to reach stationarity was 2500. In order to reduce the correlation between sets of imputed values, 900 iterations of the Markov chain Monte Carlo were performed between imputations. We used an informative ridge prior distribution for the Markov chain Monte Carlo procedure; we selected this prior because in some countries had few observations. Appendix table 2 presents a summary of our multiple imputation and Appendix Figure 1 demonstrates convergence of the Markov chain Monte Carlo algortihm. Appendix