PHLT 8072 Week 8 Measles Program Appraisal Example

Averages Do Not Stop Outbreaks: Appraising a Composite County's Two-Dose Measles Program Against the Spread It Was Built to Prevent, School by School

Student Name

Walden University

PHLT 8072: Communicable Disease Epidemiology

Instructor Name

Month Day, Year

Averages Do Not Stop Outbreaks: Appraising a Composite County's Two-Dose Measles Program Against the Spread It Was Built to Prevent, School by School

The Question

For years the composite county's annual immunization report showed two-dose measles, mumps and rubella coverage among kindergarten students at about 94%, and the program was described as successful. Then the 42-case outbreak described in last week's write-up occurred. This appraisal asks whether the program was judged by the right measure. It argues that a vaccination program exists to prevent sustained transmission, so it should be judged by whether immunity is high enough everywhere transmission can happen, not by the average across the county. All figures are composite.

How Much Immunity Prevents Spread

In a population where everyone is susceptible, each measles case would on average infect R0 others. Spread stops growing when the average number of new infections per case falls below one, which happens when the share of the population that is immune exceeds a threshold of one minus 1/R0 (Fine et al., 2011). For an R0 of 12, the threshold is about 92%; for 18, about 94%. Because estimates of measles R0 vary across settings (Guerra et al., 2017), the program should treat the threshold as a band, not a point, and immunity in the low 90s as the floor rather than the target.

Why the County Average Misled

The threshold idea comes from models in which people mix at random, such as the susceptible, infected and recovered framework first set out by Kermack and McKendrick (1927). In such a model, the average susceptible share is what matters. Real communities do not mix at random. Children spend most of their contact time with classmates, siblings and neighbors, and families who decline vaccination often share schools, congregations and social networks. When unvaccinated children cluster, local immunity can fall far below the threshold even while the county average stays above it. Modeling of measles has shown that spatial clustering of susceptible people can sustain transmission and outbreak risk that county or national coverage would not predict (Truelove et al., 2019).

Schools A and B together enroll about 8% of the county's kindergarten students. Their low coverage barely moves the county average, but inside them roughly one child in four lacked two doses. Those two schools were exactly where the outbreak amplified.

Table 1

Two-Dose MMR Coverage Among Kindergarten Students by School (Composite Figures)

Note. Composite figures. Coverage is among kindergarten students with records on file; the threshold column uses the lower bound of the threshold range for illustration.

What the Program Measured and What It Should Measure

The program reported one number each year: county-wide kindergarten coverage. That measure answered the question of whether vaccine was reaching most children, and on that question the program did well. It did not answer the question of whether any group of children was large enough and unprotected enough to sustain an outbreak. Three changes would align the program's measures with its purpose. First, report coverage by school and flag any school below the lower threshold. Second, report exemption rates by school, since exemptions, not lack of access, explained most missing doses in Schools A and B. Third, track coverage among children younger than kindergarten age in the community, since 13 of the outbreak's 42 cases were under five.

Why the Report Kept Showing Success

The annual report was not wrong; it answered a narrower question than the one that mattered. County coverage rose slightly over five years, and each year's report noted the improvement. Meanwhile, exemption rates at Schools A and B climbed, but those figures were held by the school district rather than the health department and never appeared in the immunization report. The program's data system could produce school-level coverage, yet nobody had asked it to, because the state's reporting template asked for county totals only. The lesson for the appraisal is that a program measures what its reporting requirements ask for. When those requirements reflect administrative convenience rather than the biology of transmission, the program can appear to succeed on its own terms while the conditions for an outbreak grow inside it.

A Simple Test of Local Risk

A school-level check can be done with numbers the county already holds. For each school, multiply the share of children without two doses by the lower end of the measles R0 range. If the result is above one, the school could sustain transmission if measles were introduced, ignoring for simplicity the partial protection from one dose and from prior infection. For School A, about 29% of kindergartners lacked two doses; even at an R0 of 12, the product is well above one. For Schools C and D, it is below one. A test this simple will not predict where measles appears, but it identifies where an introduction would grow.

What Worked

The appraisal does not find the program ineffective. Outside the community, coverage was high and the outbreak produced almost no spread: only a handful of cases occurred outside Schools A and B and their households, and none led to further transmission. That is the protective effect the program was built to provide, and it held where coverage was high.

Recommendations

Publish school-level coverage and exemption data annually. Set a school-level target of at least 95% two-dose coverage. Direct outreach to schools and communities below the threshold, working with trusted community members rather than through general campaigns. And judge the program each year by the number of schools below the threshold as well as by the county average. An appraisal repeated next year with these measures would show whether the outreach reached the schools that needed it, which the county average alone could never reveal, and would give the health department an early warning before the next introduction rather than an explanation after it.

References

Fine, P., Eames, K., & Heymann, D. L. (2011). "Herd immunity": A rough guide. Clinical Infectious Diseases, 52(7), 911-916. https://doi.org/10.1093/cid/cir007

Guerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R0) of measles: A systematic review. The Lancet Infectious Diseases, 17(12), e420-e428. https://doi.org/10.1016/S1473-3099(17)30307-9

Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721. https://doi.org/10.1098/rspa.1927.0118

Truelove, S. A., Graham, M., Moss, W. J., Metcalf, C. J. E., Ferrari, M. J., & Lessler, J. (2019). Characterizing the impact of spatial clustering of susceptibility for measles elimination. Vaccine, 37(5), 732-741. https://doi.org/10.1016/j.vaccine.2018.12.012