Assessing effects of cholera vaccination in the presence of interference.
Interference occurs when the treatment of one person affects the outcome of another. For example, in infectious diseases, whether one individual is vaccinated may affect whether another individual becomes infected or develops disease. Quantifying such indirect (or spillover) effects of vaccination could have important public health or policy implications. In this article we use recently developed inverse-probability weighted (IPW) estimators of treatment effects in the presence of interference to analyze an individually-randomized, placebo-controlled trial of cholera vaccination that targeted 121,982 individuals in Matlab, Bangladesh. Because these IPW estimators have not been employed previously, a simulation study was also conducted to assess the empirical behavior of the estimators in settings similar to the cholera vaccine trial. Simulation study results demonstrate the IPW estimators can yield unbiased estimates of the direct, indirect, total, and overall effects of vaccination when there is interference provided the untestable no unmeasured confounders assumption holds and the group-level propensity score model is correctly specified. Application of the IPW estimators to the cholera vaccine trial indicates the presence of interference. For example, the IPW estimates suggest on average 5.29 fewer cases of cholera per 1000 person-years (95% confidence interval 2.61, 7.96) will occur among unvaccinated individuals within neighborhoods with 60% vaccine coverage compared to neighborhoods with 32% coverage. Our analysis also demonstrates how not accounting for interference can render misleading conclusions about the public health utility of vaccination.
Department of Biological Sciences, Meredith College, Raleigh, North Carolina, U.S.A.
Carolina
C
Perez-Heydrich
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A.
Michael G
MG
Hudgens
Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A.|Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
M Elizabeth
ME
Halloran
Department of Epidemiology, University of California, Los Angeles, California, U.S.A.
John D
JD
Clemens
Department of International Health, Johns Hopkins University, Baltimore, Maryland, U.S.A.
Mohammad
M
Ali
Department of Geography, University of North Carolina, Chapel Hill, North Carolina, U.S.A.
Michael E
ME
Emch
D016477:Artifacts; D001459:Bangladesh / Q000453:epidemiology; D015982:Bias; D002771:Cholera / Q000453:epidemiology* / Q000517:prevention & control*; D022121:Cholera Vaccines / Q000627:therapeutic use*; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D032541:Mass Vaccination / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D057216:Propensity Score; D016896:Treatment Outcome
D022121:Cholera Vaccines
Causal inference; Interference; Inverse‐probability weighted estimators; Spillover effect; Two‐stage randomization; Vaccine