Editor’s Note: earlier this year the journal published injury data from the Global Burden of Disease project. In an accompanying editorial I noted that many of the regional or sub-national estimates were “derived from aggregation and extrapolation of limited primary sources “and yet could “become the basis for policy or programming at an intensely local level.”

I saw this as a challenge to researchers, a call to “crowd source” burden of disease data from  the subregions and subpopulations unrepresented, or simply estimated, in the global aggregate. If we identified those needs and provided resources for good data collection, data management and data reporting , the information collected would be immediately useful at the global scale and  – one hopes – at the local level too. 

Dr. Safa Abdalla, a member of our editorial board, approaches that suggestion with some caution and – in this guest post – draws distinctions between the needs and experience of researchers and public health professionals in “data-rich” and “data poor” environments. – Brian Johnston (Editor-in-Chief)

Some parts of the world, typically in the low- and middle- income country classification range, lack solid basic information about frequency and distribution of injuries in their population. That is not to say that they lack the sources or the capacity to measure them, but in those same places, the public health practice machinery had been occupied (not entirely unduly of course) with a cluster of conditions like communicable diseases that international actors have been investing heavily to tackle. In such environment, local objective assessments of all potentially impactful conditions may not have been deemed necessary. As a result, priority setting has been skewed towards those conditions of historical focus without heavy reliance on local epidemiological evidence.
The very first global burden of disease and injury assessment and subsequent versions have highlighted the need to consider the burden of all realistically possible conditions that affect human health – including injuries – in a way that allows objective comparisons and consequently objective priority setting. Arguably, data from so called ‘data-poor’ countries had not always been sufficient and/or accessible enough to feed into these global-level estimation projects and data gaps were filled with an assortment of methods that continue to evolve to date, probably at a rate that surpasses the rate of improvement in the quantity and quality of data from those countries.
The burden of disease assessment methodology is very demanding, not only computationally but in terms of data input, requiring epidemiological estimates at the very granular level of disease and injury sequelae, and synthesizing those into a range of novel summary measures (Disability-adjusted life years for example). Yet, incidence, prevalence and mortality of any condition at a broader level are key inputs for country- or locality-level policy development and health service planning and monitoring. It is in measuring those epidemiological quantities that the value of country-level estimation in data-poor settings lies, without necessarily delving into the complexities (and relatively unnecessary luxury for the time-being) of summary measure calculation. In addition, country-level assessments can uncover gaps in data systems that, when addressed, can create a seamless flow of better quality data for local decision making.
But with whom does the onus of carrying out such local-level estimation reside? Undeniably, global estimation efforts have produced country-specific estimates, stimulated country data hunts that fed data into their machinery and, in a few ‘data-rich’ countries, facilitated full burden of disease and injury assessments too. However, to date, injury burden estimates for the vast majority of ‘data-poor’ countries come from indirect estimation in these global projects. One can argue that alternatively, an approach that is driven by the need for public health action (be it strategy updating or service development) would be the most beneficial for producing estimates for those very countries at national, sub-national or subgroup levels. This approach entails that a local team of researchers, public health practitioners and other stakeholders evaluate all their data sources, use them in a simple and transparent fashion to develop the best estimates that fit their purpose, and take action based on the estimates and other relevant input while also identifying the data gaps and working on filling them. Arguably, informing local public health action should take priority over informing the global view, but global burden estimation efforts can still (and must) benefit from the products of this process. However, the process needs to be driven by local demand for estimates and not by the need to fill gaps for the global estimates. It should also be led, undertaken and owned by local teams of public health practitioners, analysts and researchers. The reason for this is that assessing and using health data are basic public health functions that all public health practitioners and analysts in any country should be capable of carrying out. Relying on external support from ‘global project’ teams to develop country estimates denies public health practitioners and researchers in those ‘data-poor’ countries the opportunity to hone their skills in public health data assessments and epidemiological estimation. It also denies them ownership of any subsequent efforts to improve data availability via epidemiological studies or administrative data collection.
This approach need not be limited to injury burden assessment but is much more needed for that latter. This is mainly because injuries in many low- and middle- income countries had been neglected for so long that epidemiological assessments of other conditions traditionally associated with those countries are likely more abundant. Hopefully as more and more country teams assess, use and improve their own injury data sources, this reality will eventually change.

Safa Abdalla
drsafa@yahoo.com
twitter: @Safa12233