10 September 2024

Strengthening decision making during public health emergencies

The following insights were developed at a 2024 meeting of winners of the inaugural Trinity Challenge on pandemic preparedness and response, facilitated by Jhpiego. Jhpiego has partnered with the Trinity Challenge to deliver ongoing strategic support to winning teams.

The World Health Organization has declared eight Public Health Emergencies of International Concern in the last 15 years, beginning with the 2009-2010 H1N1 flu pandemic and culminating in the mpox outbreak that remains active in Central and East Africa, now spilling into neighbouring regions. Each new outbreak raises similar questions around how we can be better prepared to ward off the next one – particularly given the massive advances in technology and data use that we see all around us.

This question was at the heart of the 2021 Trinity Challenge on pandemic preparedness and response, which sourced eight awardees from around the world working to improve our ability to predict, detect and respond to disease outbreaks. Members of the group were able to gather in-person in 2024 to discuss the role of advanced analytics in supporting critical national and global decision-making during public health emergencies.

We all experienced the importance and impact of this decision-making during the Covid-19 pandemic, when national governments – informed by predictive models – introduced containment measures such as lock downs, school closures, and travel bans. These interventions required complex trade-offs and had far-reaching consequences across society. Were decisions taken fast enough? Were decisions based on accurate models? Reflecting on these questions – among others – is an important step in strengthening our preparedness for future outbreaks.

Building on a workshop hosted by the WHO Pandemic and Epidemic Intelligence Hub and Imperial College London in 2023, the Trinity Challenge cohort further explored the ‘data-to-decision’ pathways that exist during health emergencies, including the roles of different actors and the barriers and facilitators that influence timely decisions based on accurate data. They collectively developed these key insights:

1. Frontline data collectors play an essential role that needs to be valued and considered

Previous work on data-to-decision pathways has described only three roles: ‘evidence generators’ (modellers producing advanced analytic outputs); ‘evidence translators’ (key individuals who translate advanced analytic outputs into actionable recommendations and facilitate information sharing); and ‘evidence consumers or decision makers’ (political decision makers, public health officials, health workers, and the public). There has been no explicit mention of the data generator/data collector role. Without considering this role, some of the important factors underpinning missing or low quality data – which undermines the usefulness of any modelling outputs – will not be understood.

For example, Community Health Workers (CHWs) are often tasked with data collection and disease surveillance activities at the household level. They may be most motivated by a desire to be recognized and accepted by the community, as well as to be praised and acknowledged by their supervisor. Their performance may also be linked to incentives. This can result in data manipulation, either to obtain financial incentives or through fear of punitive action from the supervisor or an increased work burden. It is often more important for the CHW to be seen to be delivering well in their community (‘there are no women with anaemia in my community’; ‘my community is low risk’) than it is to convey the risks or vulnerabilities in the community – even if this could ultimately result in greater attention or resource allocation; a bigger picture that the CHW may lack. This data feeds into analytic models and can create inaccuracies.

2. It is important to centre the needs, priorities and motivations of decision makers

The Trinity Challenge cohort members have learned through their activities and conversations with stakeholders that modellers often produce outputs that are not centred around decision makers’ real needs. Too often, modellers see their primary audience as academic journals and other modellers, although they may be trying to influence decision makers and other end users. Modellers expect decision makers to care about their outputs, and don’t always understand when they do not. Models may produce insights that decision makers already know through their lived experiences (e.g., the seasonal pattern and geographic distribution of disease outbreaks).

Models need to be relevant and rooted in the reality of existing surveillance systems and country needs. Rather than privileging the perspective of modellers, there needs to be a better understanding of decision makers – their motivations, and how to make them more invested in modelling processes and their outputs, including building the capacity of decision makers to plan based on different modelling scenarios.

3. Authentic communication – and particularly storytelling – is a key part of evidence translation

Honest, transparent and direct communication with both the public and with policymakers – with an acknowledgement of the power of storytelling – is a critical enabler of timely decision making. This can be facilitated by strong visualisations in the media from journalists or through dashboards (like the Johns Hopkins University Covid-19 dashboard, which was an exemplar during the pandemic).

However, at the same time as the public has more appetite for and experience with scientific information, there is also much more misinformation and some are experiencing post-Covid ‘science exhaustion’. The evidence translation role can become highly politicised and polarised; maintaining a strong, neutral and science-backed position can be very challenging. Often individuals (vs larger committees and working groups) can be particularly effective and relatable in this role, but these individuals can be subject to ad hominem attacks and may be required to debunk significant misinformation.

It would be valuable to further explore what motivates certain individuals to publicly take on this responsibility, and what characteristics make certain individuals more effective than others in this translation function.

A loss of momentum since Covid-19

Although the threat of disease outbreaks has not abated, many enablers of the use of advanced analytics for public health decision making – including funding, an open-source data culture, and new ways to regulate access to data – seem to have declined without the urgency and incentives created by the global Covid-19 pandemic. We cannot afford to become complacent. Continued investment in stronger data-to-decision pathways is crucial for empowering the decisions that save lives during emergencies.