After 2 years of pandemicANVISA finally released the use of self-tests for covid-19which expands the detection capacity of potentially transmitting cases and significantly reduces underreporting.
Why did it take so long?
Initially, there was a flawed understanding of the role of PCR and of antigen. Taken as the “gold standard”, the bet was made in the PCR tests. And therein lies the first mistake. PCR is far from a gold standard. To have this seal, it should have 100% sensitivity and 100% specificity. It doesn’t. A meta-analysis that evaluated the sensitivity of the method concluded that up to 54% of the results may be false negatives, despite the high heterogeneity of the studies. (1).
However, even with these limitations, PCR is still the most sensitive among the different methods available to identify a case of Covid-19. That is, it is the option of choice from the individual point of view, as it is what they have the highest probability of detecting a case.
But when we talk about population medicine, there are other factors at play that are just as or more important than individual sensitivity. First let’s clarify what an individual versus population sensitivity is.
Think of two different methods. One has a sensitivity of 90% at a cost of 500 reais. Another has a sensitivity of 70% at a cost of 50 reais. A manager of a city of 1 million inhabitants has 10 million reais available to invest in one of these surveillance and diagnosis options. There is an expectation that 5 to 10% of the population will contract the infection, that is, 50 to 100 thousand cases. Which one will have the greatest ability to detect cases? What is the population sensitivity of each of them?
If option 1 is chosen, there are 20,000 tests available. It is natural that these tests are prioritized for severe cases or people with a risk factor. The average pre-test probability will be around 20% (lower in low prevalence situations, higher in peak situations). As the sensitivity is 90%, 3600 cases will be identified. This represents 3.6 to 7.2% of the total, which is the population sensitivity value. Even if the pre-test probability is 100%, the population sensitivity would be from 9 to 18% (18,000 detected cases), which is the maximum population sensitivity of this strategy.
In the second case, there are 200,000 tests available. Considering that the indications for mild or even asymptomatic cases are expanded, the average pre-test probability will be around 5%. In this way, 7 thousand cases will be identified, 7 to 14% of the total, almost double the previous strategy. If the pretest probability is 100%, all cases would be detected. Therefore the potential population sensitivity is 100%.
You can see that analytical sensitivity is just a variable of the strategy and population sensitivity depends on that, on access, which in turn is related to cost and ease of scale and pre-test probability. As access increases, there is naturally a decrease in the pre-test probability, but an increase in the ability to identify total cases.
These examples of positivity rates were drawn from real examples of countries that opted for different strategies (2). The graphs below represent the dynamics of tests, positivity, cumulative incidence rate of cases and deaths in Colombia and Denmark. There is no description of the type of test performed, but it is possible to verify the effect of scaling power.
Denmark performed 17x more tests per inhabitant than Colombia (11000 x 650 tests per 1000 inhab), with a positivity rate of 5% versus 18% in the South American country. 490 cases/1000 inhab were identified in Denmark, against 120 cases/1000 inhab in Colombia, that is, 4x more. However, Denmark accounts for 840 deaths/million inhab, while Colombia adds up to 2700 deaths/million inhab.
Realize that, to expand the capacity to identify cases by 4x, it was necessary to expand the number of tests 16x. The relationship between deaths and identified cases clearly demonstrates that the proportion of underdiagnosis was much higher in Colombia compared to Denmark.
This example demonstrates the power of testing scale. Antigen tests are much more easily scalable by definition. These tests are independent of a large laboratory structure and are much cheaper than molecular tests. Even with reduced sensitivity to identify a case, the power of the scale handily counterbalances this limitation. (4).
Returning to the initial question raised, at the beginning of the pandemic there was not a proper understanding of these characteristics and limitations of each methodology and the impact they would bring to the testing strategy. This led to an overestimation of individual analytical sensitivity and an underestimation of the power of the scale, leading to a false impression that every effort should be made to encourage PCR testing. Over time, these perceptions gradually adapted to reality and more and more investments were mobilized for antigen tests.
Another highly relevant point that should be discussed is which analytical sensitivity we are dealing with. When talking about reduced analytical sensitivity of Antigen compared to PCR, this is based on the ability to detect cases. That makes perfect sense. But when it comes to population benefit, this is not the most important point. The main objective is to detect transmitting individuals, as it is in this group that there is potential to reduce transmissions. In this respect, the difference in analytical sensitivity between PCR and Antigen is even smaller. One study demonstrated a greater correlation of the Antigen with the viral culture, compared to PCR (3). Viral culture is the true gold standard in identifying viable cases.
Finally we come to the self-tests. In addition to the benefits already mentioned, the antigen tests can reach the point of being done by the individual, without the need for a health professional directly involved in the process. This further increases access and scalability. Maximizes the potential for population sensitivity.
Some may question whether the collection technique performed by the patient can impact analytical sensitivity. The answer is yes, it does. But it is already known that the increase in scale far outweighs this drop. These concepts have been known since 2020/2021 and incorporated by countries that are open to innovation, such as Germany, France and other European countries. The UK was a pioneer in sending home tests, subsidized by the government. Subsequently, the US incorporated the same strategy. And Brazil, better late than never, took the first step in this direction. There is still a need for more incisive action on the part of the government.
If the strategy aims to increase population sensitivity, which will increase the ability to detect transmitting cases that, in turn, will isolate and transmit the virus less, reducing viral transmissibility and total deaths, it would be logical for these tests to be stimulated to the point of be subsidized by the government. This subsidy fell far short of the real need for scale.
Now, this concept does not just apply to Covid-19, but to every highly prevalent and underdiagnosed disease. Let’s look at some examples:
Diabetes – global prevalence of 9.3%, with a high bias. 50% of these individuals are unaware of the diagnosis;
Dislipidemia – Overall prevalence 54% in Europe. Important cardiovascular risk factor. Only 23-30% of cases under treatment. Among those treated, control is achieved in 19-52% of cases;
Hepatite B – Global prevalence of 3.7%. 90% have no diagnosis;
Hepatitis C – Global prevalence of 0.7%. Important cause of liver cirrhosis. 80% have no diagnosis;
The current model of the global health system regarding laboratory tests, in general, is simply not enough. It is a strategic bottleneck, as it refers to the basis of the entire process, which is the diagnosis. Without diagnosis, there is no treatment. Without treatment, complications, hospitalizations, and deaths occur, with the economic and social costs linked to this.
Self-tests applied to these and other relevant diseases can be a game changer and significantly decrease the unacceptable proportion of underdiagnosis. But for it to really make a difference, it must be available to everyone, not just a part of the population.
Another critical concept is the regulatory aspect. It is clear that the variables that define a public health strategy are different from those established for individual care. Today there is no such discrimination and in many situations they are evaluated by the same ruler, which is inappropriate. This partly explains the delay in the incorporation of antigen tests and self-tests in Brazil. It is hoped that Covid-19 will leave a positive legacy in this regard and that we can take advantage of the maximum potential that self-tests can offer us.
Islam KU, Iqbal J. An Update on Molecular Diagnostics for COVID-19. Front Cell Infect Microbiol. 2020 Nov 10;10:560616. doi: 10.3389/fcimb.2020.560616. PMID: 33244462; PMCID: PMC7683783;
Our World in Data;
Pekosz A, Parvu V, Li M, et al. Antigen-Based Testing but Not Real-Time Polymerase Chain Reaction Correlates With Severe Acute Respiratory Syndrome Coronavirus 2 Viral Culture. Clin Infect Dis. 2021;73(9):e2861-e2866. doi:10.1093/cid/ciaa1706;
Larremore DB, Wilder B, Lester E, et al. Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening. Sci Adv. 2021;7(1):eabd5393. Published 2021 Jan 1. doi:10.1126/sciadv.abd5393.
Bernardo Almeida is an infectious disease physician and Chief Medical Officer at Hilab, a healthtech that developed Hilab, the first decentralized laboratory using remote laboratory tests. He is a specialist in infectology at the Federal University of Paraná (UFPR), with a residency in internal medicine and internal medicine at Hospital de Clínicas — UFPR and in infectology at Hospital de Clínicas — UFPR, currently studying for a master’s degree at UFPR in internal medicine, area of infectious diseases. — Epidemiology of severe acute respiratory syndromes in adults. He has experience in the field of medicine, with an emphasis on clinical medicine and infectious and parasitic diseases, and participates in a research group in the area of respiratory viruses.