Tina D Purnat

Public health

Health misinformation

Infodemic management

Digital and health policy

Health information and informatics

Tina D Purnat
Tina D Purnat
Tina D Purnat
Tina D Purnat
Tina D Purnat
Tina D Purnat
Tina D Purnat

Public health

Health misinformation

Infodemic management

Digital and health policy

Health information and informatics

Blog Post

We need fast-twitch and low cost behavioral science tools for non specialists

I’ve always felt that any really effective health programming requires multidisciplinary approaches adapted to the operational context and enough flexibility to be relevant to the health topic in question.

This is why in infodemic management, we’ve worked to transfer of approaches both ways – from infodemic management and information environment into behavioral tools (example from a recent lecture on vaccine demand promotion here), and from behavioral science and behavioral models to infodemic management and other disciplines, especially when it comes to participatory approaches in problem identification, intervention design and evaluation.

Trying to apply transdisciplinary approaches to problem-solving in health needs to take into account the current state of behavioral science in health. In many ways, behavioral science is still finding ways to find relevant hands-on applications and integration into routine health programming and policymaking.

The limitations of current behavioral approaches in health

Here’s the context:

  • Behavioral sciences are not commonly found in Ministries of Health or Institutes of Public Health, even in high-income countries, let alone in LMICs. There’s been a proliferation of nudge units, mostly in high-income countries, but the challenge is that nudging can only change behaviors by a few percentage points (when much larger changes are often needed by health programmes) and requires a strong primary health system because it assumes that healthcare access is not an issue (which is nor the case in many settings where behavioral science approaches are advocated).
  • Since we know that in LMICs, access to health care is an issue, any behavioral interventions in LMICs are bound to fail without addressing health service delivery and access issues at the same time or even first. For example, low vaccine demand in a low-income country may be related to access or hesitancy, and behavioral interventions that only address vaccine intent don’t actually shorten the distance between the caregiver and health facility or ensure that vaccines are in stock. So there’s often a disconnect between what behavioral science evidence is developed and offers to the policymaker and what a policymaker needs.
  • And this is in routine. In emergencies, socio-behavioral science is funded to address an acute problem but this is not routinized into routine health service delivery. We saw this during ebola, and also now during COVID-19. Too much socio-behavioral work is still designed as a complement to routine but has not yet been integrated into routine tools in ways that non-specialists could perform it next time around when the emergency hits.
  • Investment in behavioral science and implementation research is dominant in high-income countries which means that many interventions developed are not applicable in low- and middle-income settings.
  • Most behavioral insights are focused on high-income countries and are derived from laboratory experiments. Instead of experimentation, we should heavily invest in implementation science and related systems building with a focus on LMICs.
  • Cost is a big issue when introducing new approaches into routine. Even in high-income countries, behavioral science work in public health is costly, slow and difficult to link to improving health outcomes. The approaches and applications don’t take into account this necessary aspect of translation into practice.
  • Most of behavioral work started and was done through behavioral economics. But it’s much harder to change health behaviors than economic/household behaviors. The range of behaviors that we consider for individuals and populations in the context of the health goes beyond just performing a health behavior, which is the common assumption in behavioral science approaches and measurement. For example, in infodmeic management, we want to prevent all kinds of harm, and also promote behaviors over time that have an accumulative effect. There are many kinds of harm that can be caused, stigma, violence against health workers, vaccine refusal or use of substances like ivermectin or hydroxychloroquine that lead to overdoses and deaths.
  • So we need to think more broadly than KAP surveys and slow and expensive metrics that have been pushed as primary solutions during the pandemic and cooccurring emergencies. Surveys tell us the what is happening but not the why.  And without the why, we don’t have the information on where to point actions towards. This is why we need more types of behavioral assessment tools that can be used by nonbehavioral scientists across the organization.

Make behavioral science more practical, and less exclusive

So what could be done to address these gaps, structural issues and assymetries?

We need to change our thinking paradigm – be practical, design for low-cost, and for non-specialists to implement the approaches if you want the health system and policies to take up new practices:

  • First, foster the development of behavioral scientists from LMICs to do work in LMICs.
  • Second, embed behavioral scientists into health systems and health programs so that there are more behavioral scientists that understand and work effectively within health programming and health policy.
  • Develop tools for the operational context in LMICs. Develop practical tools and support innovation to define practical implementation of BI in public health and how this can be mainstreamed in different contexts which usually need strengthening in evaluation, need to focus on gender and on vulnerable populations, require the development of fast-twitch BI tools for use in emergencies, and need to better use digital innovation to innovate in monitoring, design and implementation of interventions, including for health systems and for patients and communities.
  • Consider the inclusion of wider sets of socio-behavioral data into health and programmatic analysis to improve policies and strategies, technical cooperation with countries, and ultimately improve population health outcomes. At the same time, foster innovation in the generation of new data sources that are less costly and more targeted and relevant to populations of focus and vulnerable groups. There’s also a need for the development of behavioral assessment tools that take into account the lens of gender in assessing the appropriateness and acceptability of health interventions.
  • Introduce behavioral frameworks into guidelines for public health intervention design, and at the same time introduce implementation research approaches and behavioral frameworks into the evaluation of innovations and programmes that are scaling up.
  • Mainstream human-centered design approaches in intervention development, implementation and testing. This is a matter of culture change to better use behaviorally-informed tools in HCD work. We need to across the board advocate more to be more inclusive of community members in the development and evaluation of health programmes.
  • We need to think more broadly than KAP surveys when it comes to routine measurement. They tell us the what but not the why, and so we need more types of behavioral assessment tools that can be used by nonbehavioral scientists across the organization. In infodemic management, we did this by making behavioral approaches more accessible – instead of assuming countries will hire specialist behavioral scientists to be placed across the health system, we need to provide guidelines and tools that enable nonspecialists to use behavioral approaches and frameworks in their practice
  • We should focus BI on identifying and addressing health inequities. This could be done through a research and review agenda for BI-informed interventions to identify best buy policies to address inequities in health care, access and service delivery and population health, heavily focused on applications, not on basic science.

 

I wrote this LinkedIn blog in the summer of 2023, after having to explain one too many times why behavioral science often is not actionable or adapted to public health practice and therefore in many cases is delivering less value to population health and wellbeing as promised. Real impact on the ground is human-centered and very interdisciplinary. Follow me on LinkedIn. if you’d like to read more of my commentaries.