Space Grant Consortium - University of Wisconsin - Green Bay

I conducted five semi-structured interviews with some of the founders of Nueva Alborada
in July and August of 2008. T he purpose of these interviews was to learn about the history of
Nueva Alborada, especially in the context of the establishment of triatomines in the area.
Data Analysis
Latitude and longitude coordinates of each house were determined using high-resolution
aerial phot ographs f rom G oogleEarth (GoogleEarth, 2007) . I converted t he l atitude a nd
longitude coordinates into the Universal Transverse Mercator (UTM) coordinate system (WGS
84, Zone 19 South) to facilitate analysis in meters. I created shapefiles in ArcGIS 9.3 to spatially
display the housing and animal types by household.
Because there were many houses without the vector, the outcome variable of number of
bugs was not normally distributed. Although there were many small integers containing zeros in
the out come, t he P oisson m odel w as n ot ap propriate b ecause t he m ean an d v ariance w ere n ot
equal. S tatistical an alyses w ere co nducted i n t he s tatistical p ackage R ( R D evelopment C ore
Team, 2008) . I used a two s tep a pproach i n a nalyzing t he da ta ba sed on t he a ssumption t hat
there may be two different mechanisms at work: one mechanism that determined whether any
triatomines were present or not, and one mechanism that determined how many triatomines were
present a mong hous es w ith a non -zero num ber of t riatomines. F irst I performed uni variate
logistic regressions using presence or absence of triatomines as the response, and the animal and
housing type (again as presence/absence of that particular animal or housing type) as predictors.
Then I ran a backward stepwise selection procedure starting with all predictors to select the best
fitting model. T he f inal m odel w as c hosen us ing t he lowest Akaike’s Information Criterion
(AIC). The second step was an analysis of just the houses with bugs using linear regression and
backward stepwise selection on the logarithmically transformed non-zero outcome data. I also
tested the correlations b etween al l predictors an d the in teractions b etween th e s ignificant
predictors from the univariate regressions.
I created semi-variograms of residuals to check for spatial autocorrelation in the residuals
for all models. Some models showed evidence of spatial patterns in the data; to account for this I
added a predictor variable measuring the distance from each house to the nearest house with 20
or m ore i nsects. T his metric w as c alculated u sing th e D istance B etween P oints f unction in
Hawth’s A nalysis T ools f or A rcGIS ( Beyer, 2004 ). I then r edid t he previously m entioned
statistical tests including this predictor. In ArcGIS, I also tested Moran’s I Index and Ripley’s K
Function t o t est f or s patial a utocorrelation a nd clustering, r espectively. Moran’s I Index i s a
spatial statistic used to evaluate whether homes with bugs are clustered, dispersed, or random.
Ripley’s K Function i s a nother w ay t o t est w hether t he hous es w ith bugs a re c lustered or
dispersed but provides a simpler method for finding the distance to which a feature is clustered.
It uses a multi-distance analysis to assess the observed clustering among infested households as
compared to what would be expected in randomly distributed infested households.
Results
499 l ots w ere i n t he c ommunity of Nueva A lborada a t t he t ime of t he i nsecticide
spraying. 452 households participated in the household sprayings; the remaining ones were not
sprayed b ecause t he lots w ere e ither uni nhabited, t he ow ners w ere una vailable, or t he ow ners
refused. O wner refusal accounted for only seven of the non-participating households and these
homes were randomly distributed. In the community 4,111 total bugs were counted, which were
distributed among 165 h ouses (37% of total). No bugs were infected with T. cruzi. Figure 3
displays a map of the surveyed houses and triatomine presence in the community.
14