Who knows how many people are really dying of Covid-19 in India? The limitations of publicly available data on mortality.
In India as in many other low and middle-income countries (LMICs), the Covid-19 pandemic has brought into sharp focus the lack of a comprehensive publicly available civil registration data system. There is an absence of epidemiological data, including the demographics of Covid-19 infections and fatalities. Published data are limited to regional reports, snapshot figures in official briefs, screenshots from webinars, and media accounts (Mukherji 2021). India’s limited data supply contrasts with the availability of Covid-19 information in high-income countries and also some LMICs. For example, the Centre for Disease Control shares a weekly Covid-19 data tracker in addition to providing unit-level epidemiological data (CDC, 2021). In Central America, Costa Rica has a well-established system of death registration and epidemiological surveillance, which has enabled relatively complete data on the pandemic as it unfolded (WHO, 2010; Chaves et al, 2020).
Researchers argue that the true number of Covid-19 infections and the overall burden of Covid-19 related mortality in most LMICs are much higher than official reports state (Lloyd-Sherlock et al, 2020). By May 2021, WHO had documented approximately 3.5 million officially reported COVID-19 deaths (WHO, 2021). However, it has been estimated that the true numbers of global COVID-19 deaths, both reported and unreported, may in fact be 6.9 million (IHME, 2021). In the case of India, 299,296 Covid-19 deaths had been reported on 25 May 2021, but experts estimate that the actual toll has been more than double with 654,395 deaths (IHME, 2021).
High levels of excess mortality have been reported for many countries (Freitas et al, 2020; Dahal et al, 2021). As well as deaths directly attributable to the pandemic, this measure takes into account less direct mortality effects, including reduced access to treatment for other health conditions, as well as the economic and social disruptions caused by lockdowns. There are good reasons to predict very high rates of excess mortality in India. Many people in urgent need of critical health care for pre-existing chronic conditions, new acute conditions, or for maternal and childcare, have had extremely limited access to these services (WHO, 2020).
Historically, the Indian government’s annual mortality reports have never included the detailed data needed to calculate excess mortality. Consequently, none of the Indian government’s reports, press releases, or presentations produced during the pandemic to date have referred to excess deaths. In August 2020, several Indian and global researchers asked the Government of India to release the civil registration data for deaths during the pandemic, along with mortality data for the two years preceding the pandemic, in order to calculate excess deaths (Appeal for the excess death data, 2020). However, this request was not granted.
Some individual states, including Kerala and Maharastra, have strengthened their mortality databases during the pandemic. Kerala is the only state to publish excess death data based on district-level mortality data for the years 2019 and 2020. This showed, while the first wave of the pandemic resulted in 2,646 reported COVID-19 deaths, other causes of death declined by a larger amount. This led to an 11% overall reduction in the total number of deaths (Government of Kerala, 2020). There are a number of possible explanations for this unexpected result. Historically, Kerala has established much more extensive public health and social welfare systems than other Indian states and this may have facilitated efforts to control infection. Less positively (and more plausibly), the share of deaths that are registered may have fallen in Kerala during the pandemic, as larger numbers of people have been dying at home rather than in hospital.
There are a number of reasons for the poor quality of mortality data in India. Detection of COVID-19 cases is hindered by weak surveillance systems, particularly in rural and semi-urban areas. There has been widespread misclassification of Covid-19 deaths, due to a shortage of skilled health care professionals. Several reports have highlighted significant underreporting of mortality by comparing official data to media reports and funerals. In the state of Kerala, a group of volunteers led by a physician tracked mortality data reported by seven local newspapers and five TV channels between March and December 2020. They estimated that 4,559 deaths occurred in Kerala during the first wave of the pandemic rather than the 2,646 deaths reported by official sources (COVID Kerala, 2021). Kerala is one of the most educated states in India and is assumed to have the best data in India. If Kerala shows this level of underestimation of deaths, it is likely to be worse in many other states.
Similar comparisons have been made across the country during the second wave which, to date, has generated much larger numbers of deaths than the first wave. For instance, a Gujarati newspaper reported 8,286 cremations in 7 major cities in Gujarat between 15th April and 3rd May 2021: significantly higher than the 1,061 deaths reported by official sources (The Quint, 11th May 2021). In the city of Bhopal in the government reported just 50 Covid-19 deaths in the first three weeks of April 2021, but other sources estimated that there had been at least a thousand (The Importance of Knowing How Many Have Died of COVID-19 in India – The Wire Science, 2021).
Since official Indian data on overall levels of Covid-19 mortality (as well as on indirect deaths) significantly understate the actual number deaths, it follows that any data providing a higher degree of granularity are even less reliable. Disaggregated data on infections, comorbidities, hospitalizations and fatalities by age and sex and or socio-economic status are essential for understanding the course of the pandemic, for identifying groups at most risk and evaluating the effects of policies such as targeted vaccination (Scully et al 2020).
The Covid-19 pandemic in India has brought two important lessons. First, the need to strengthen the regular civil registration surveillance system. This system remains very incomplete in rural areas and there are also gaps for semi-urban ones. Second, rapid population-based epidemiological surveillance surveys are crucial for collecting the data needed to assess excess mortality for high-risk populations during and after the pandemic (Balbo et al 2020). Existing nation-wide longitudinal surveys – such as the Demographic Health Survey (DHS), Health and Retirement Study (HRS), Living Standards Measurement Surveys (LSMS) – may provide a useful platform for these rapid Covid-19 surveys (Adjiwanou et al 2020, World Bank 2020, Subramanian and James 2020). However, none of these include high-risk groups such as older people living in long-term care facilities.
Appeal for the excess death data, 2020. appeal_signatories_release_data_on_excess_deaths.pdf – Google Drive
Adjiwanou V, Alam N., Alkema., Asiki G. Bawah A, et al 2020. Measuring excess mortality during the COVID-19 pandemic in low- lower-middle income countries: the need for mobile phone surveys. Unpublished working paper https://osf.io/preprints/socarxiv/4bu3q/
Balbo, N, Kashnitsky, E, Melogaro A, Mesle F, Mills M, et al 2020. Demography and the Coronavirus Pandemic, Population and Policy Brief no.25, May 2020. https://population-europe.eu/policy-brief/demography-and-coronavirus-pandemic
Bhramar, M. 2021. Why we still don’t know how many, and who exactly died of Covid in India https://timesofindia.indiatimes.com/blogs/voices/why-we-still-dont-know-how-many-and-who-exactly-died-of-covid-in-india/
Centre for Disease Control (CDC) (2021) COVID data tracker. Weekly review. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html
L. Chaves, L. Hurtado, M. Ramírez Rojas, M. Friberg, R. Rodríguez et al 2020. COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica. Math. Model. Nat. Phenom. 15:32 https://www.mmnp-journal.org/articles/mmnp/full_html/2020/01/mmnp200065/mmnp200065.html
COVID Kerala 2021 covid kerala – Google Sheets
Dahal S, Banda JM, Bento AI, Mizumoto K, Chowell G. Characterizing all-cause excess mortality patterns during COVID-19 pandemic in Mexico. BMC Infectious Diseases [Internet]. 2021 Dec;21(1):1–0. Available from: https://doi.org/10.1186/s12879-021-06122-7
Freitas ARR, Medeiros NM de, Frutuoso LCV, Beckedorff OA, Martin LMA de, Coelho MM de M, et al. Tracking excess deaths associated with the COVID-19 epidemic as an epidemiological surveillance strategy-preliminary results of the evaluation of six Brazilian capitals. Revista da Sociedade Brasileira de Medicina Tropical [Internet]. 2020;53:e20200558. Available from: www.scielo.br/rsbmtIwww.rsbmt.org.br
Government of Kerala (2020). Reduction in all-cause mortality in Kerala during COVID-19 pandemic Technical-paper-All-Cause-Mortality-Kerala.pdf
Institute for Health Metrics and Evaluation (IHME) 2021. Estimation of total mortality due to COVID-19 http://www.healthdata.org/print/8660
Lloyd-Sherlock P, Sempe L, McKee M, Guntupalli A. 2020. Problems of Data Availability and Quality for COVID-19 and Older People in Low- and Middle-Income Countries. The Gerontologist [Internet]. 2020 Oct;XX:1–4. https://academic.oup.com/gerontologist/advance-article/doi/10.1093/geront/gnaa153/5918111
E. Scully, J. Haverfield, R. Ursin, C. Tannenbaum and S. Klein 2020. Considering how biological sex impacts immune responses and COVID-19 outcomes, Perspectives. Nature Reviews. https://doi.org/10.1038/s41577-020-0348-8
Subramanian, V., K S James, 2020. Use of the Demographic and Health Survey framework as a population surveillance strategy for COVID-19 Correspondence www.thelancet.com/lancetgh Vol 8 July 2020e895 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190287/pdf/main.pdf
World Bank 2020. High Frequency Mobile Phone Surveys of Households to Assess the Impacts of COVID-19 : Overview, The World Bank Group -IBRD.IDA http://documents1.worldbank.org/curated/en/703571588695361920/pdf/Overview.pdf
World Health Organization (WHO) 2010. Improving the quality and use of birth, death and cause-of-death information: guidance for a standards-based review of country practices. https://www.who.int/healthinfo/tool_cod_2010.pdf
World Health Organization (WHO) 2021. Covid19 and NCD, World Health Organization, Geneva. https://www.who.int/publications/m/item/rapid-assessment-of-service-delivery-for-ncds-during-the-covid-19-pandemic