Problems Of Data Availability And Quality For COVID-19 And Older People In LMICs
By Peter Lloyd-Sherlock and Aravinda Guntupali, with contributions from Liat Ayalon, Joseph Batac, Leon Geffen, Syed Moeez, Lucas Sempe, Martin McKee, Susan Nungo, Yelda Özen, Walaa Talaat
In all countries, the risk of dying from a COVID-19 infection increases markedly with advancing age. As a result, people at older ages make up the majority of reported COVID-19 deaths, even in countries where population ageing is still at a relatively early stage. It might be expected, then, that policymakers and researchers would pay particular attention to the effects of the pandemic on this age group. Yet this is clearly not the case. Global public health responses have done little to move beyond the existing situation, characterised by the exclusion of and discrimination against older people. This can be seen in many areas of policy and debate, some of which are discussed in other contributions to the Global Platform. This blog focusses specifically on data issues.
Excluding older people from routine data reporting and indicators is a time-honoured (or
arguably “dishonoured”) tradition, reflecting normalised ageism. For much of the HIV pandemic, data on infections were not collected for people aged 50 or more, based on the misguided view that older people were not at risk of infection (Albone, 2011). More recently, older people have been excluded from mortality reporting and targets for non-communicable diseases (NCDs), such as heart disease and diabetes, even though they are disproportionately at risk of dying from these conditions. The main justification put forward for this was an ageist argument that policy-makers should focus on so-called “premature” deaths among younger adults (Lloyd-Sherlock et al, 2016). More generally, most surveys of health and population, even in high-income countries, exclude older people in residential care facilities, but still claim that they are nationally representative.
Tragically, numbers of COVID-19 cases and deaths are increasing rapidly in many low and middle-income countries (LMICs). To what extent are the data that are reported capturing the experiences of people at older ages? The short answer is that the provision of specific data about people at older ages has been very limited, and much of what has been published is of questionable quality.
Several LMICs provide no age-disaggregated data at all either for reported cases or for deaths from Covid-19. Examples at the time of writing this blog include Indonesia, Turkey, Egypt and Kenya. A number of other countries, including Peru and Brazil, only provide age-breakdowns for deaths, but not for cases.
Table 1 summarises data for LMICs for which we have been able to obtain data. Not all of these data are derived from official sources. In India, for example, where the National Ministry of Health does not provide age breakdowns, a crowdsourced project has compiled these data from local government bulletins and other official sources.
Table 1 shows that older people made up more than half reported COVID-19 deaths in all these countries. Yet the range is quite wide: from 52% in Mexico to 62% in the Philippines. The final column in Table 1 provides a ratio of reported cases to reported deaths for people aged 60 and over. This indicates that in Mexico for each COVID-19 death there were 3.65 cases; whereas in Pakistan there were 10.36. Taken at face value, this suggests that older people in Mexico who become infected with COVID-19 are more than twice as likely to die than their counterparts in Pakistan. If this were really true, the potential implications for global health would be huge and we should be looking closely at the “Pakistan Miracle”. It is, however, far more likely that these variations are mainly the result of problematic data reporting. Perhaps Mexico is under-counting cases among older people; perhaps Pakistan is under-counting deaths; perhaps both counties are under-counting everything. These data come with a very large health warning and do not provide a basis for meaningful analysis.
Table 1. Age disaggregated data for reported cases and mortality attributed to Covid-19, selected countries.
*as at 13 June 2020 # denominator of cases as at 13 June, the numerator of deaths as at 22 June
The issue of data quality is relevant for people of all ages and is especially pertinent in LMICs where the vast majority of deaths occur outside of hospital settings and for whom the cause is rarely certified by a trained physician. And this issue of data quality is particularly important for older people, both because they are the group most affected by the pandemic and because the cause of death is more prone to misreporting for people at older ages. Older people are more likely to have other health conditions, such as heart disease, and, even where COVID-19 symptoms are present, death may be attributed to other comorbid conditions. The experience of high-income countries shows that COVID-19 mortality has been widely under-reported in care home settings (Comas-Herrera et al, 2020). This is likely to occur in LMICs, which contain large numbers of such facilities, many of which are unregistered and not subject to oversight by public health agencies (Lloyd-Sherlock et al, 2020a).
Ultimately, the best way to measure the impact of the pandemic on older people, and people of all ages, will be through careful comparison of overall mortality rates during the pandemic and for corresponding months in preceding years. This will permit estimation of excess deaths which are potentially attributed to the pandemic: both directly as a result of COVID-19 or due to more indirect effects of the pandemic, such as reduced access to treatment for other health conditions. This will provide a more complete measure of the mortality effects of the pandemic. Another advantage of this approach is that it does not depend on accurate reporting of specific causes of death. In the UK between 7 March and 5 June, there were 51,804 recorded deaths directly attributed to COVID-19, plus a further 12,729 excess deaths for other causes. For those LMICs where estimates of excess mortality are available, the proportion of excess deaths attributed to COVID-19 is much lower than in high-income countries. For example, in Ecuador, there were 3,358 reported Covid-19 deaths during March, April and May, but excess mortality is estimated to have been 16,107.
Excess mortality data disaggregated by age group are only publicly available for some high-income countries. Data for England and Wales show excess deaths for causes not attributed to COVID-19 occurred predominantly among people at older ages (Office for National Statistics, 2020). If this pattern were to be replicated across LMICs (and there is no reason to expect that it would not be), it would mean that, even in countries where age data of reported Covid-19 cases and deaths are available, the true impact of COVID-19 on older people is being vastly understated. Failing to provide such data masks the degree to which older people in all countries are “bearing the brunt” of this global pandemic (Lloyd-Sherlock et al, 2020).
Collation of high-quality mortality data will benefit both health care providers and policymakers. Hopefully, the availability and quality of age-disaggregated data on Covid-19 and excess deaths in LMICs will quickly improve. These data will be essential, both to help our general understanding of the pandemic and to ensure that older people are being fairly treated.
The Global Platform is looking to develop a number of new activities to support this process. First, we are setting up an open online resource to contain all known sources of age-disaggregated data on Covid-19 in LMICs.
Please send us information so we can extend and update our coverage of different countries.
Please use the site as a resource for your own analysis and advocacy (and please acknowledge us when you do).
In the near future, the Global Platform plans to produce a shared statement about these issues. Please let us know if you would like to be part of that.
Albone R. HIV statistics and targets exclude older people—putting millions of people at risk. 2011. http://www.helpage.org/blogs/rachel-albone-667/hiv-statistics-and-targetsexclude-older-people-putting-millions-of-people-at-risk-285/
A.Comas-Herrera, J.Zalakain, C.Litwin, A.Hsu, N.Lane and J-L Fernandez-Plotka (2020) Mortality associated with COVID-19 outbreaks in care homes: early international evidence https://ltccovid.org/2020/04/12/mortality-associated-with-covid-19-outbreaks-in-care-homes-early-international-evidence/
Joe W, Kumar A, Rajpal S, Mishra U, Subramanian SV. Equal risk, unequal burden? Gender differentials in COVID-19 mortality in India. J Glob Health Sci. 2020 Jun;2(1):e17. https://doi.org/10.35500/jghs.2020.2.e17
Lloyd-Sherlock PG; Ebrahim S; McKee M; Prince MJ. (2016) Institutional ageism in global health policy. BMJ 354, pp. i4514. https://pubmed.ncbi.nlm.nih.gov/27582131/
P.Lloyd-Sherlock, J.Bastos, L.Geffen, K.Giacomin, N.Redondo, S.Sasat and L.Sempe (2020a) An emergency strategy for managing COVID-19 in care homes in low and middle-income countries: the CIAT Framework (Version 1) https://www.corona-older.com/post/an-emergency-strategy-for-managing-covid-19-in-care-homes-in-lmics-the-ciat-framework-version-1
Lloyd-Sherlock, P, Ebrahim, S, Geffen, L & McKee, M (2020b) ‘Bearing the brunt of COVID-19: older people in low and middle-income countries’, BMJ (Clinical research ed.), vol. 368, m1052. https://doi.org/10.1136/bmj.m1052
Office for National Statistics (2020) Analysis of death registrations not involving coronavirus (COVID-19), England and Wales: 28 December 2019 to 1 May 2020. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/analysisofdeathregistrationsnotinvolvingcoronaviruscovid19englandandwales28december2019to1may2020/technicalannex