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Contextualising best practice: one size does not fit all

Here’s another example of how “best practice” is context dependent, this time in responses to the Covid-19 crisis:

In the response to the COVID-19 pandemic, it is now evident that many of the public health and hospital-based interventions deployed by high-income countries (HICs) may be ineffective or infeasible in low- and middle-income countries (LMICs). Social distancing including lockdowns, rapid deployment of effective test-and-trace protocols followed by quarantine, the ability to properly self-isolate, effective protection of hospital staff from infection with appropriate PPE, and upscaled hospital care – many of these interventions cannot be implemented in the time frame required, or carry consequences so significant that they are socio-politically unacceptable

In many LMICs, the health system, particularly hospitals, was already overwhelmed by demand prior to the pandemic. The clinical guidelines suggest upscaled hospital care including nasal oxygen, mechanical ventilation and, for those that survive ventilation but have complications, dialysis. But resources are extremely limited.

If more than 60 percent of the workforce is in the informal sector and must work no matter what to survive, can “stay-at-home” orders result in fewer cases, or does “stay-at-home” lead to more detentions, transmission, social disorder, and impoverishment, including more deaths from non-COVID-19 related illness?

Can people who live in the most densely occupied households and neighborhoods in the world isolate their vulnerable and elderly? In India, for example, about 73 percent of households reported living in two rooms or less in the last census.

In the few models that consider LICs, recommended interventions such as test-trace-isolate and/or social distancing measures, even when assuming a modest basic reproduction number for the virus, must reach effectiveness levels comparable or above those achieved in certain high income settings (such as South Korea and Singapore) in order to significantly flatten the curve. Is this even remotely realistic?

We have three suggestions to focus on the broader protection of life in the immediate term that are different in scale from existing guidance:

1. Start with water and sanitation: Immediately scale up low-tech clean water and handwashing interventions and more toilet facilities (which will do good beyond the COVID-19 pandemic and save lives from non-COVID-19 infectious diseases as well).

2. Protect other essential healthcare services and supply chains like vaccination, malaria control, insulin, antihypertensives, and reproductive, maternal, and child health, where most lives may be lost.   Baird et al (REStat 2011) estimated that a 1 percent decrease in per capita GDP in developing countries was associated with a 0.24 to 0.40 increase in infant mortality per 1,000 children born. For developing countries in East Asia and the Pacific, the World Bank has already projected a decline in GDP growth of 3.7 percentage points in 2020. Extrapolating these figures, a back-of-the-envelope calculation from our colleague Justin Sandefur suggests that if a similar economic downturn occurs in other regions, it would imply between 100,000 to 200,000 additional infant deaths in developing countries worldwide next year–even without any epidemic deaths in the developing world. This must be avoided at all costs.

3. Cash transfers to stay home:  The enormous economic consequences of COVID-19 are sufficient motivation for a scale-up of cash transfers in LMICs. But if social distancing is our only hope to slow spread, perhaps most of the money should go directly to households to enable “stay-at-home” and not spent on ventilators and dialysis equipment that cannot be effectively utilized in the near-or medium-term.

Amanda Glassman, Kalipso Chalkidou and Richard SullivanDoes One Size Fit All? Realistic Alternatives for COVID-19 Response in Low-Income Countries, blog post at Center for Global Development

What’s the point?

The point – especially for those working with poor or marginalised communities – is that standard “best practice” from other contexts is unlikely to transfer directly to where you’re working.

Education, health services, housing policy and politics don’t occur in a vacuum, and a straight transplant of best practice in any of these fields is unlikely to work or be sustainable without, at the very least, a complementary transplant of other inputs that that made the practices effective – or even possible – in the first place.

Other examples might be investing in technology for education in a place without the necessary infrastructure, or focusing on equipping teachers to develop higher order thinking skills before they understand the basic principles of how to teach reading.

The key questions become, “What is the most powerful intervention possible here?” and “Which other parts of the enabling ecosystem do I need to attend to in order to make this viable?”

I'd love to hear your thoughts and recommended resources...