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

Politics-evidence conflict in national health policy making in Africa: a scoping review

BACKGROUND

Generally, public health policy-making is hardly a linear process and is characterized by interactions among politicians, institutions, researchers, technocrats and practitioners from diverse fields, as well as brokers, interest groups, financiers and a gamut of other actors. Meanwhile, most public health policies and systems in Africa appear to be built loosely on technical and scientific evidence, but with high political systems and ideologies. While studies on national health policies in Africa are growing, there seems to be inadequate evidence mapping on common themes and concepts across existing literature.

PURPOSE

The study seeks to explore the extent and type of evidence that exist on the conflict between politics and scientific evidence in the national health policy-making processes in Africa.

METHODS

A thorough literature search was done in PubMed, Cochrane Library, ScienceDirect, Dimensions, Taylor and Francis, Chicago Journals, Emerald Insight, JSTOR and Google Scholar. In total, 43 peer-reviewed articles were eligible and used for this review.

RESULT

We found that the conflicts to evidence usage in policy-making include competing interests and lack of commitment; global policy goals, interest/influence, power imbalance and funding, morals; and evidence-based approaches, self-sufficiency, collaboration among actors, policy priorities and existing structures. Barriers to the health policy process include fragmentation among actors, poor advocacy, lack of clarity on the agenda, inadequate evidence, inadequate consultation and corruption. The impact of the politics-evidence conflict includes policy agenda abrogation, suboptimal policy development success and policy implementation inadequacies.

CONCLUSIONS

We report that political interests in most cases influence policy-makers and other stakeholders to prioritize financial gains over the use of research evidence to policy goals and targets. This situation has the tendency for inadequate health policies with poor implementation gaps. Addressing these issues requires incorporating relevant evidence into health policies, making strong leadership, effective governance and a commitment to public health.

Leveraging lessons from the COVID-19 pandemic to strengthen low-income and middle-income country preparedness for future global health threats

Although the global COVID-19 pandemic response was quick to develop medical countermeasures, it failed to fully prevent morbidity and mortality in high-income countries and low-income and middle-income countries (LMICs). As new variants and post-COVID-19 condition continue to emerge and affect health systems and economies, the full human and economic cost is yet to be felt. We should now learn from these shortcomings and implement more inclusive and equitable frameworks to prevent and respond to outbreaks. This Series offers specific learnings from COVID-19 vaccination campaigns and non-pharmaceutical interventions, highlighting the need for resilient, inclusive, and equitable health systems. Taking action to rebuild trust and invest in resilient local manufacturing capacity, supply chains, regulatory frameworks, and placing LMIC voices at the centre of decision making will help to ensure preparedness for future threats. It is time to move beyond mere talk of learning and implementing lessons and instead take action towards a more resilient future.

Epidemic intelligence studies: A research agenda for political scientists

This research letter introduces readers to health intelligence by conceptualizing critical components and providing a primer for research within political science broadly considered. Accordingly, a brief review of the literature is provided, concluding with possible future research agendas. The aim is to elaborate on the importance of public health intelligence to national security studies, and to political science more generally.

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

During the COVID-19 pandemic, many countries have issued stay-at-home orders (SAHOs) to reduce viral transmission. Because of their social and economic consequences, SAHOs are a politically risky decision for governments. Researchers typically attribute public health policymaking to five theoretically significant factors: political, scientific, social, economic, and external. However, a narrow focus on extant theory runs the risk of biasing findings and missing novel insights. This research employs machine learning to shift the focus from theory to data to generate hypotheses and insights "born from the data" and unconstrained by current knowledge. Beneficially, this approach can also confirm the extant theory. We apply machine learning in the form of a random forest classifier to a novel and multiple-domain data set of 88 variables to identify the most significant predictors of the issuance of a COVID-19-related SAHO in African countries (n = 54). Our data set includes a wide range of variables from sources such as the World Health Organization that cover the five principal theoretical factors and previously ignored domains. Generated using 1000 simulations, our model identifies a combination of theoretically significant and novel variables as the most important to the issuance of a SAHO and has a predictive accuracy using 10 variables of 78%, which represents a 56% increase in accuracy compared to simply predicting the modal outcome.

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

COVID-19 has presented governing authorities with challenging decisions that have required them to consider the tradeoffs between movement restriction and economic activity. We propose that non-state armed groups may make different decisions than state governments in response to these challenges. Drawing upon the case of Hezbollah in Lebanon, we investigate whether movement levels differed between areas with Hezbollah private governance in comparison to other areas of Lebanon. Using Google COVID-19 mobility data and a difference in differences model, we show that following the first COVID-related death in Lebanon, movement in districts with private Hezbollah governance decreased significantly less than in other districts. We present a number of potential reasons for this disparity, arguing that the most probable explanation is the relatively high level of economic assistance that Hezbollah provided to people living in areas under Hezbollah's authority, which led to comparatively lesser rates of movement decline.

Identifying factors related to school closures due to COVID-19 in the Middle East and North Africa region

The COVID-19 pandemic has had devastating effects on the Middle East and North Africa (MENA) region, and MENA states have taken dramatic steps in response. This study focuses on school closures, an intervention that all MENA states adopted, some much earlier than others. It seeks to identify policy factors related to MENA governments' decisions to close schools during the first wave of the pandemic. Results suggest external issues regarding temporal and geographic diffusion played the largest role. They also indicate that factors related to disease risk, the economy, political institutions, and women's position in society mattered as well, all of which suggest the decisions were complex.