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Field
Secondary Outcomes (Explanation)
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Before
The spatial-temporal model predicts the probability of infestation for each house, conditional on covariates (prior infestation, prior participation in insecticide campaigns), spatial position (a spatially correlated random effect estimated through INLA) and an intercept.
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After
The spatial-temporal model predicts the probability of infestation for each house, conditional on covariates (prior infestation, prior participation in insecticide campaigns), spatial position (a spatially correlated random effect estimated through INLA) and an intercept.
Difference in Difference Analysis of the estimated mean probability of infestation.
The average probability of infestation for an area is estimated by a spatiotemporal statistical model [Gutfraind & Peterson et al.] (https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006883) using the history of infestation in the area. We obtain the difference in the average probability for each area as follows:
Difference in average probability of infestation = average probability of infestation before inspection - average probability of infestation after inspection
We consider five potential covariates::
1. The number of houses in the search zone
- An integer value
2. The number of houses that were previously infested during the vector control campaign conducted by the Arequipa Regional Ministry of Health, including the insecticide application (attack) phase and the post-treatment surveillance phase
- An integer value
3. The percent of houses that were not previously sprayed with insecticide during the vector-control spray campaign conducted by the Arequipa Regional Ministry of Health
- A decimal value between 0 and 1
4. The area of the convex hull enclosing the search zone
- A real number value, in meters
- This was estimated using the “chull” command that is part of the grDevices package in R
5. The index of dissimilarity [White 1986] (https://www.jstor.org/stable/pdf/3644339.pdf) in the search zone. This measures the clustering of the higher risk houses (within the top two quantiles, which are shown as red and dark red in the vectorpoint application). The index is potentially useful because in areas in which the index of dissimilarity is low, and high probability houses are near to each other, it may be easier to decrease the average probability of infestation due to the effect of the spatial autocorrelation term in the model.
- A real number value
- The index of dissimilarity in a given search zone is estimated by first dividing a search zone into 5 areas using a 5-k-means clustering algorithm using the “kmeans” function that is part of the stats package in R. We will then calculate the index of dissimilarity using the following formula adapted from [White 1986] (https://www.jstor.org/stable/pdf/3644339.pdf): D = ½ summation from i=1 to i=n of the absolute value of h_i / H_T - l_i / L_T, where i is the number of each cluster identified by the 5-k-means clustering algorithm, l_i is the number of houses within the two highest quantiles of probabilities of household infestation in cluster i, H_T is the total number of houses within the two highest quantiles of probabilities of household infestation in the entire search zone, l_i is the number of houses within the lowest 3 quantiles of probabilities of household infestation in cluster i, L_T is the total number of houses with the lowest 60% probabilities of household infestation in the entire search zone.
We will conduct univariate analyses testing for associations between each variable and the difference in the mean probability of infestation before and after the search. Each of these tests will be done using the “glm” function of the stats package in R. Those variables found to have an association with a p-value of <.2 will be kept for consideration in the full difference-in-difference model.
Any zone in which infestation is detected during the trial will be excluded from the difference in difference analyse (because the mean probability of infestation will increase in that zone due to the new information).
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