Researchers at the Harvard Center for Population Development and Studies in the US have said a demographic and health survey framework can help as a population surveillance strategy for coronavirus.
S V Subramanian and K S James from the Centre said that in the absence of universal testing, a random-sample-based population surveillance framework is urgently needed. They have proposed using the well-established Demographic and Health Survey (DHS) framework as a solution to ascertain the true prevalence of COVID-19.
In the April 29 issue of The Lancet Global Health, the duo have said that testing for COVID-19 is mainly being done among at-risk individuals — like those with influenza-like symptoms, people who have had contact with an individual testing positive for COVID-19, healthcare professionals, or those with travel history to an affected region.
“Hence an accurate value for how many individuals are truly infected is not known. Since at-risk individuals are not representative of the general population, it is impossible to obtain the true prevalence of COVID-19 in the population. Yet, establishing this value is vital to understand the morbidity and mortality risk in the population, particularly in low-income and middle-income countries (LMICs) such as India, which cannot absorb the socioeconomic and public-health fallout resulting from national shutdowns,” Subramanian said.
In 2002, India was projected to have 25 million HIV-positive individuals with a prevalence of HIV in adults of 3–4 per cent. These estimates were based on extrapolation of infection rates among selected at-risk individuals. India used the National Family Health Survey (NFHS) to test for HIV in the general population, and estimates were sharply reduced to 2·5 million, with a prevalence of HIV in adults (aged 15–49 years) of 0·28 per cent. This discrepancy showed the shortcomings of selective testing of at-risk individuals as the basis for understanding disease prevalence in a population, the researchers said.
The NFHS has state-of-the-art infrastructure with a ready sampling framework. Layering a coronavirus-focused data-collection effort onto the NFHS infrastructure would keep operational costs low, with the major expense being laboratory costs for testing samples. They have estimated the minimum required sample of individuals who would need to be tested under three scenarios of anticipated COVID-19 prevalence in the population.
Under a scenario of 0·5 per cent prevalence, only a sample of at least 3,000 individuals would be needed for tests. The minimum required sample size increases to just over 15,000 under a rarer scenario of 0·1 per cent prevalence and decreases to 1,500 if the anticipated prevalence is 1 per cent. If the anticipated prevalence of COVID-19 be higher than 1 per cent, the minimum sample size needed to reliably estimate the true prevalence would be smaller and, therefore, fewer resources would be required, the researchers said.
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