Access to more localized data on childhood vaccination coverage, such as at the school or neighborhood level, could help better predict and prevent measles outbreaks in the United States, according to a new study from the University of Michigan.
Research also shows that when people who aren’t vaccinated are grouped geographically, the likelihood and size of an outbreak increases. The study is expected to be published Oct. 26 in the journal Proceedings of the National Academy of Sciences.
“We found that even with an overall vaccination coverage of 99%, pooling of non-vaccinators allowed outbreaks to occur. This means that we really need to rethink whether herd immunity is significant or not when applied on large spatial scales such as level state or national, “said lead author Nina Masters, a doctoral student in epidemiology at the UM School of Public Health.
Since measles is a highly contagious disease, vaccination coverage of at least 95% is required to maintain herd immunity. But despite reaching that level nationwide, the United States saw 1,282 measles cases in 31 states last year, the majority of cases since 1992.
Masters and colleagues tried to present a simple and easy-to-understand model that shows what happens when unvaccinated people are clustered in neighborhoods and other small communities, even within a population that has achieved 95% overall vaccination coverage. against measles.
They also analyzed how aggregating vaccination data at the levels at which they are typically reported, such as cities and states, can inadvertently obscure important fine-scale clusters, making outbreaks appear less likely than they are in many cases.
Fine-scale vaccination data at the level that the researchers would like to be able to analyze is not widely accessible or available in the United States, so the researchers instead built a computational model representing a medium-sized city: a 16×16 square grid with 256 cells of 1,000 people each. The model had four levels: blocks (cells) of 1,000 people that approximate groups of census blocks; tracts of 4,000 people (four cells, approximate census tracts); neighborhoods of 16,000 people (16 cells); and quadrants of 64,000 people (64 cells, approximately cities or districts).
Keeping the overall vaccination rate constant at the typical herd immunity threshold of 95%, the researchers used a number of different clustering patterns to distribute 5% of non-vaccinators in the environment and examined the impact of several reasons. grouping on the size of the epidemic and frequency.
The researchers also looked at how aggregating data down to these four coarser levels would impact the predicted size of the outbreak. At the blocking level, across all clustering scenarios, the average predicted outbreak size was 3,886 cases, however, when they were “zoomed in”, outbreak details were lost. The aggregation of data at the trait level predicted 45% fewer cases, at the neighborhood level expected 76.5% fewer cases and at the quadrant level it predicted a 99% reduction.
“By keeping everything else constant, we have seen that the more you increase the fine-scale clustering, the greater the potential for epidemics,” Masters said. “And then, as it aggregates, you lose a lot of the ability to predict those outbreaks. This shows that if we can’t measure that clustering on a fine scale, we don’t know what susceptibility landscape we’re dealing with, and so it is not possible to effectively assess the risk of an epidemic “.
In the United States, public health surveillance systems typically report county and state level vaccination coverage, obscuring small-scale clustering and susceptibility to potential outbreaks.
For example, in Michigan, only 4.5 percent of daycare centers across the state had vaccination waivers for the 2018-19 school year, above the herd immunity threshold of 95 percent. However, that same year, a major measles outbreak occurred in Oakland County, where the dropout rate was about 7 percent. In that county, school district exemption rates ranged from about 0 to 23 percent, and two schools reported more than half of vaccine-exempt daycare centers.
“Vaccination data is collected at the school level, and in an ideal world it would be great to have that data available at the school level so that local and state health departments can be aware of regions that are highly susceptible to an epidemic and respond as a result, “Masters said, adding that this is especially important now, as the number of children receiving vaccinations has decreased due to the pandemic.
“I think it’s safe to say that existing non-vaccination groups have gotten worse, because more people who would normally get their vaccines are not getting vaccines right now for their children.”
Masters said it’s important for policymakers to think critically about how scientific assumptions are formulated and how they should be applied at the population level. For example, while the critical vaccination fraction to achieve herd immunity is often used as a tool for setting disease elimination goals – the World Health Organization uses a 95% measles vaccination target nationwide – this calculation assumes that the populations are homogeneous: both in terms of vaccine distribution and human contact.
However, societies are actually very heterogeneous, and humans are very clustered in terms of vaccine behavior, which questions how useful thresholds like these really are, Masters said.
“The broadcast happens in city blocks, not in towns,” he said. “We need to think more about how these definitions make sense in terms of matching the scale (of policies) to the scale of actual transmission.”
The researchers said there were 791,143 suspected cases globally in 2019, up from 484,077 in 2018, a 63% increase. In all, 187 out of 194 WHO Member States reported measles cases in 2019. To achieve global measles elimination targets and prevent a global resurgence, finer-scale data should be used to better predict and prevent measles outbreaks.
The PNAS the paper is titled “The fine-scale spatial clustering of measles non-vaccination that increases epidemic potential is overshadowed by pooled reporting data.”