Abstract

Chagas disease vector control campaigns are being conducted in Latin America, but little is known about medium-term or long-term effectiveness of these efforts, especially in urban areas. After analyzing entomologic data for 56,491 households during the treatment phase of a Triatoma infestans bug control campaign in Arequipa, Peru, during 2003-2011, we estimated that 97.1% of residual infestations are attributable to untreated households. Multivariate models for the surveillance phase of the campaign obtained during 2009-2012 confirm that nonparticipation in the initial treatment phase is a major risk factor (odds ratio [OR] 21.5, 95% CI 3.35-138). Infestation during surveillance also increased over time (OR 1.55, 95% CI 1.15-2.09 per year). In addition, we observed a negative interaction between nonparticipation and time (OR 0.73, 95% CI 0.53-0.99), suggesting that recolonization by vectors progressively dilutes risk associated with nonparticipation. Although the treatment phase was effective, recolonization in untreated households threatens the long-term success of vector control.

Highlights

  • We computed the residual infestation for each iteration of the Monte Carlo Markov Chain (MCMC) and generated the distribution and credible interval at 95% of the residual population size for each category of participation (Table S2)

  • As the prevalence in households treated only the second time is 5 times lower than in households participating in both treatments, we consider the extreme case where the prevalence in non-participating households is 5 times lower than in households participating only in the second treatment. Even this strong modification does not qualitatively change our results: over 85% of the residual infestation is still in non-participating households, and the overall effectiveness of the treatment phase would be even higher with a decrease of 78% of the number of households presenting some residual infestation after the treatment phase (Table S3, fourth column)

  • To estimate the residual infestation post-treatment in the difference-in-difference framework, we use the results of the difference-in-difference model and extrapolate them to households not observed during the pre-treatment survey or the second phase of treatment

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Summary

Modalities of insecticide treatment in initial treatment phase

The treatment phase used pyrethroid insecticides, mainly deltamethrin (96.4%), in 5 formulations over the 9 years of the treatment covered here (Tab. S1). The formulation was diluted in water and applied, using a Hudson X-Pert compression sprayer at a target dose rate of 30 mg/m2 for deltamethrin based insecticides and 36 mg/m2 for lambda-cyhalothrin based insecticides. OTHRINE 50 SC: suspension concentrate of deltamethrin. K-OTHRINE 5% PM: wettable powder of deltamethrin. LAMBDA 10% PM: wettable powder of lambda-cyhalothrin. DEMAND 10 CS: capsule suspension of lambda-cyhalothrin

General model
Deterministic version of the model
Stochastic version of the model
Impact of considering the observation to be less than perfect
Sensitivity to independence of the two treatments
Sensitivity to the correlation of infestation and participation
Difference in difference analysis using pre-treatment survey data
Significance of the effect of the campaign
Estimation of residual infestation post-treatment
Estimating the reliability of surveillance
Infestation history
Findings
Models of infestation in surveillance: model selection

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