Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

Abstract

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.

Keywords: disease dynamics; epidemiology; measles; simulation model; vaccination clustering.

Fig. 1.

Impact of spatial aggregation of vaccination data on coverage estimates. (A) Vaccination coverage data from Oakland County, Michigan, at five different levels of spatial scale: block groups, census tracts, school districts, congressional districts, and the county level. (B) Schematic illustrating the spatial model used in this study with a 256-grid cell environment, each of which contains 1,000 individual people, divided into spatial scales of “blocks” (all grid cells), “tracts” (groups of 4 cells), “neighborhoods” (groups of 16 cells), “quadrants” (groups of 64 cells), and, finally, the entire vaccination “environment” (all 256 cells aggregated to one unit), the level at which overall vaccination percentages are fixed for analysis (i.e., at 95 and 98%). (C) Example data from one simulated set of vaccination conditions, fixed at 95% overall vaccination, showing the impact of aggregation to these different scales on loss of granularity of block-level data.

Fig. 2.

Distribution of nonvaccination at baseline (Left) and case burden after 1 y (Right) for four selected clustering motifs with 95% overall vaccination coverage (n = 12,800 nonvaccinated individuals at baseline). In each case, a seed infection was introduced into the top left quadrant and cases spread throughout and beyond the demarcated boundaries of high-risk unvaccinated regions, as can be seen for motifs 1 and 2, where there is a band of nonzero case burden around the high-risk cluster of nonvaccinated individuals, even though the vaccination rate in this region is 0 to 1%. For motif 3, the four foci of nonvaccination with >25% unvaccinated proportions are the hardest hit in terms of attack rate after 1 y, with >150 cases per cell (>15% attack rate), but the surrounding cells, with 5 to 10% nonvaccination, see 10 to 50 cases after 1 y, representing a 1 to 5% attack rate. Finally, for motif 4, a fine-scale clustering pattern creates local cells with high attack rates, but all cells have a nonzero attack rate.

Fig. 3.

Impact of aggregation on estimated outbreak risk. Cumulative incidence at four different levels of vaccination coverage: 94, 95, 98, and 99%, including nonaggregated vaccination data (block-level) resolution to tract level (4-cell) resolution, to neighborhood (16 cell), and, finally, quadrant-level (64 cells) shows the reduction in estimated case burden as aggregation increases, a pattern that holds true across all levels of vaccination.

Fig. 4.

Aggregated vaccine coverage systematically downplays outbreak risk. Aggregation from “true” 256-cell (block-level) resolution to tract level (4-cell) resolution to neighborhood (16-cell), and, finally, to quadrant-level (64-cell) resolution using a starting vaccination motif with overall vaccination at 95%. Three different motifs with different clustering patterns were subsequently aggregated up these three levels and yielded the same aggregate motif at the quadrant level, illustrating that large-scale vaccination data can mask significant heterogeneity at finer scales.

Fig. 5.

Underestimate of outbreak risk grows with intensity of isolation of nonvaccinators. (A) Proportion of estimated cases identified, treating the block-level or individual-cell level simulation results as “truth,” in gray, when motifs are aggregated to the tract, neighborhood, and quadrant levels, sorted by the isolation index of the starting motif. (B) Difference in number of estimated cases, or cumulative incidence, by aggregation level and isolation index of initial motif, illustrating greater loss in predicted number of cases as both aggregation level and isolation index increase.