Discussion
Addressing the SDG target for reductions in NCD mortality requires timely and reliable data at global, regional and national levels. At the first instance, the time series of mortality rates in table 1, along with the UNESCAP rating of ‘sufficient’ primary data availability suggests that the reported data provide a reliable baseline of NCD mortality in all UNESCAP countries, for monitoring the SDG target reduction of 33% by 2030. However, the information provided in figure 1 and table 2 clearly indicate that there are critical gaps in availability and quality of cause-specific mortality data from CRVS systems in most UNESCAP countries. Most importantly though, these deficiencies are to a large extent being masked by international exercises in mortality estimation, using epidemiological models. As illustrated in this article, for many countries these models are constructed without any primary national data inputs, and for others, through complex manipulations of available data. But, the details of the methods and processes used in these estimation exercises are only available from a trail of internet sites, technical reports, scientific manuscripts, and their footnotes and appendices. Further, these details are substituted by varying cryptic descriptors across different information sources, which obfuscate the actual availability and/or quality of primary data. In all likelihood, readers of the UNESCAP SDG Progress Report 2020 would take the data availability rating of ‘Sufficient’ at face value, with only a few going on to review the rating definition, and fewer still following the complete information cascade as shown in Figure 1.
The implications of this phenomenon are manifold, and pose several challenges to the public health community. First, WHO and IHME produce separate estimates of mortality by age, sex and cause for all countries for the same time periods, which creates confusion among data users. While WHO also uses the estimates to compute the NCD mortality indicator for each country as disseminated by the GHO, the IHME does not compute or publish the NCD mortality indicator. Hence, there is no publicly available direct comparison of the measures from the two sources. We used the IHME mortality estimates to separately calculate the NCD mortality indicator for 2016 for countries with ‘Useable VR’ as noted in WHO technical report. We observed that for Fiji and Uzbekistan, the IHME estimate was higher than the WHO estimate by about 6%, while for Kyrgyzstan, it was lower by 6%, although both estimates were essentially derived from the same primary data (data not shown). These differences could be due to the IHME modelling process, or WHO adjustments from disease-specific epidemiological adjustments, or both. However, these absolute differences are not trivial for these countries, considering the implications of these baseline measurements for the degree of reductions required to meet the SDG target. Likewise, there are differences of varying extent between IHME and WHO estimates of NCD mortality rates for all countries, whether or not they are based on national primary data. These varying estimates of NCD mortality rates from different information sources create confusion at the national level as to which would be more appropriate for public health policy, and for monitoring progress towards the SDG target.37 38
Second, there are also differences between mortality indicators from primary national data and IHME estimates for individual components of the NCD mortality rate (ie, cardiovascular diseases, diabetes, cancers and chronic respiratory disease) which get masked by the comparison at the aggregate level of the composite indicator. Such differences also occur for all other causes of death, and these variations also challenge direct epidemiological inferences. For example, a recent analysis for Malaysia showed statistically significant differences between age-standardised cause-specific mortality rates for 7 out of 10 leading causes in males and females, when comparing estimates from a nationally representative epidemiological study with IHME estimates for Malaysia.39 Similar differences have also been observed for European countries with high quality mortality data, and have been the subject of debate in the international public health community.40–42 At another level too, since the IHME COD estimates are derived through indirect standardisation, the results are not directly comparable across different countries, particularly when there are differences in population age structure.36 However, the IHME GBD mortality estimates are extensively used for comparative analysis at sub national, national, regional and global levels, disregarding this limitation.43–46
Third, although this article has focused on presenting the inconsistent portrayal of data availability and estimation methodology for NCD mortality, the patterns represented in figure 1 and table 2 are applicable for all other causes of disease burden as well. The observed variations between national estimates from WHO, IHME and locally derived indicators for countries with high-quality data are a matter of concern even for countries without primary data. However, the prominent reporting of these estimates by international agencies and academic institutions in reputed scientific platforms lend an aura of credibility to these estimates for both international and national stakeholders. This aura of credibility is accentuated when the facts about data sources and methods are obscured, as demonstrated by the extensive list of references needed to establish these facts, in this article. Further, enhanced credibility also occurs from secondary analysis of these estimates at global, regional and national levels, presented in repackaged forms by various international groups with their own agenda.45 47 48 Such reports, along with detailed inferences as to their epidemiological implications, often result in their direct uptake for health policy at all levels, without any consideration as to their reliability or validity.
Finally, there is a continuous cycle of updates and release of these international estimates, often accompanied by changes to the estimated indicators for some countries for the same time periods, which are justified as resulting from additional data availability, or revised estimation methods, or both.42 In summary, for many countries, this ‘flood’ of estimates which do not have any anchorage in reliable primary data tends to divert the focus from strengthening primary data for mortality measurement.49 These estimates could be verified only through the actual collection and processing of local mortality data, aided by strengthened local capacity for data analysis and interpretation. The continued publication of these estimates suggests the urgent need for such national mortality statistics programmes.
Over the past four decades, the international community has paid increasing attention to health development. This started with WHO’s call in 1978 for ‘Health for all’ by 2000, followed by the United Nations Millennium Development Goals (UNMDGs) during 1990–2015, and the current UN SDGs agenda for 2015–2030. Concomitantly, there has been an increased requirement of information to monitor progress towards these targets, starting with life expectancy at birth and infant mortality rate to measure health status for the Health for All programme, and child, maternal and infectious disease mortality rates for the UNMDGs.50 51 These data requirements were limited, and were met to some extent through population censuses, demography and health surveys, and infectious disease surveillance programmes.52 However, the UNSDGs required detailed information to monitor a range of infectious and NCDs, as well as mortality from injuries and various social and environmental exposures.15 Well-functioning CRVS systems are a natural and optimal source of primary data for these requirements, and need to be strengthened in order to resolve the challenges in mortality measurement and interpretation as reported in this article.
The need for reliable primary data on mortality is particularly important for the UNESCAP region, given its extensive population coverage and the magnitude of potentially avertable disease burden across countries. UNESCAP has launched its regional ‘Get Everyone in the Picture’ Initiative for the CRVS decade 2015–2024.53 There is an urgent need to conduct CRVS system strengthening programmes in many UNESCAP countries with zero or low data quality scores, as shown in table 2. On reviewing the findings presented here, country officials might reflect on the status of their national vital statistics programmes, and plan the way forward to improve mortality data availability.15 At another level, the four WHO Regional Offices associated with UNESCAP, in tandem with WHO Country Offices could provide stewardship and technical guidance in strengthening the reporting of causes of death, and statistical analysis.
We propose that the global health community should facilitate countries in establishing their national mortality statistics programmes, while avoiding the distraction arising from frequent release of, and debates over, modelled mortality estimates. Individual UNESCAP countries should now develop a strategic CRVS strengthening approach customised to national requirements.15 These strategies would need to be based on CRVS functional status, availability of infrastructure for health information, and prevailing levels of data quality, and would also require adequate attention being given to building local human capacity.15 54 For countries without data, the strategy would involve a thorough CRVS situational assessment, followed by practical system design and an appropriately resourced implementation plan.15 For large countries, activities could start with a nationally representative population sample, with incremental scale up of population coverage over time.55 For countries with functional systems but problems with data quality, programmes to validate available data, to re-engineer business processes for death registration and COD ascertainment and to improve data management and analysis would be required.15