Prioritizing public health? Factors affecting the issuance of stay-at-home orders in response to COVID-19 in Africa

Abstract

COVID-19 has sickened and killed millions of people globally. Conventional non-pharmaceutical interventions, particularly stay-at-home orders (SAHOs), though effective for limiting the spread of disease have significantly disrupted social and economic systems. The effects also have been dramatic in Africa, where many states are already vulnerable due to their developmental status. This study is designed to test hypotheses derived from the public health policymaking literature regarding the roles played by medical and political factors as well as social, economic, and external factors in African countries’ issuance of SAHOs in response to the early stages of the COVID-19 pandemic. Using event history analysis, this study analyzed these five common factors related to public health policy to determine their impact on African states’ varying decisions regarding the issuance of SAHOs. The results of this analysis suggest that medical factors significantly influenced decisions as did factors external to the states, while the role of political factors was limited. Social and economic factors played no discernible role. Overall, this study suggests how African leaders prioritized competing factors in the early stages of a public health crisis.

Assessing the Effects of COVID-19-Related Stay-at-Home Orders on Homicide Rates in Selected U.S. Cities

“…By April 30, 2020, the U.S. Center for Disease Control and Prevention (CDC, 2021) reported more than 60,000 cumulative deaths in the U.S. from COVID-19 with about 30,000 new cases per day. Globally, governments often took dramatic steps in response to the disease (e.g., Carr et al, 2022; Murray & Rutland, 2022). Impacts from the pandemic and related government responses were widely felt with as much as a third of the world’s population under some type of lockdown or quarantine by April of 2020 (Kaplan et al, 2020; Walters, 2020).…”

Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research

“…That is, the four most important variables identified by the machine learning classifier were originally identified in theory-based research (e.g., Ref. [ 29 ]. More broadly, we found that external forces, particularly temporal diffusion, made the strongest contribution to the prediction of SAHOs.…”

“…The dependent/outcome variable is sahoStatus , which is coded 1 when a country had issued a SAHO and 0 when it had not. Overall, 41 countries issued a SAHO, and 13 did not between January 31 and June 15, 2020 [ 29 ]. January 31 is the day after the WHO declared a public health emergency related to COVID-19, and June 15 is approximately 30 days after the last African country issued a SAHO during the first wave of the pandemic.…”

“…These variables were selected based on theoretical expectations identified by Ref. [ 29 ] as well as [ 32 ] in their evaluations of SAHOs issued in the first wave of the COVID-19 pandemic in Africa and in the Middle East and North Africa region. In these articles, the literature on public health policymaking (e.g.…”

Movement under state and non-state authorities during COVID-19: Evidence from Lebanon

“…What is interesting is that current understandings of governments’ policies regarding movement during the COVID-19 pandemic have largely assumed that higher levels of movement result from weaker governing capacity (e.g., Murray & Joshua, 2022 ). While districts with Hezbollah private governance observed lower reductions in movement compared to districts without Hezbollah private governance, this does not necessarily mean that governance was lacking.…”