1836 Eco-bio-social determinants of human infection with ...mpf2131/ASTMH_FernandezMP.pdf · Model...
Transcript of 1836 Eco-bio-social determinants of human infection with ...mpf2131/ASTMH_FernandezMP.pdf · Model...
Eco-bio-social determinants of human infection with Trypanosoma cruzi in
rural communities in the Argentine ChacoMaria P. Fernández*1, Maria S. Gaspe1, Paula Sartor2, Ricardo E. Gürtler1
1Laboratorio de Eco-epidemiología, Universidad de Buenos Aires. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA), Facultad de Ciencias
Exactas y Naturales, Ciudad Universitaria, C1428EHA, Buenos Aires, Argentina. 2Programa de Chagas Provincial de Chaco, Argentina
1836
We achieved 77% coverage of the population ≥8 months old living
in the area in 2012-2015.
The overall seroprevalence in the population was 25.3% (CI95%=
23.5-27.3%)
71% of households had at least one infected person in 2008.
17% of households had at least one infected children <15 y.o.
Total population Population <15 y.o.
OR (CI95) OR (CI95)
Age 1.1* 1.06 – 1.1 1.1* 1.04 – 1.3
Abundance of infected T. infestans (1) 1.6* 1.2 – 2.3 2.2* 1.0 – 4.8
Social vulnerability index(2) 1.5** 1.2 – 1.8 1.3 0.8 – 2.1
Interaction (1) * (2) 0.7* 0.6 – 0.9 0.5* 0.2 – 0.9
Host availability index 0.7* 0.6 – 0.9 0.5* 0.2 – 0.9
Infected mother
No
Yes 3.8* 1.4 – 11
Gender
Male
Female 0.8 0.6 – 1.1 2~ 0.9 – 4.1
Number of infected co-inhabitants 1.4** 1.3 – 1.6 1.3~ 0.9 – 1.9
Recent insecticide spraying (2006-2008)
No
Yes 0.5 0.2 – 1.3
Ethnic group
Creole
Qom 2.3* 1.1 – 4.6 1.4 0.2 – 12.5
** p<0,001 * 0,001<p<0,05 ~ 0,05<p<0,1
STUDY AREA
Gran Chaco
Surveyed houses
7 rural communities of Pampa del
Indio municipality, Chaco province
386 surveyed houses and 2389
inhabitants in 2008 [3].
Most residents were indigenous
(Qom people, 90%) and a creole
minority [3]
T. Infestans current distribution
Serosurveys
Baseline
study
Vector surveys
DATA ANALYSISSocio-economic and demographic indices (correlated variables)
Refuge availability for T. infestans (categorical variable
visually determined by a member of the research group) [3]
Presence of cardboard in the roof
Presence of mud walls
Domestic area
Time since construction
Overcrowding
Educational level (mean number of schooling years attained
by household members aged 15 years old or more) [3]
Goat-equivalent index (a small stock unit to quantify the total
number of livestock and poultry owned by the household in
terms of goat biomass) [3]
Multiple correspondence
analysis
Social vulnerability index
Total number of adults
Total number of children <15 y.o.
Total number of dogs or cats
Abundance of chicken nesting indoors
Presence of dogs or cats indoors
Multiple correspondence
analysis
Host availability index
We assessed the risk of human infection with T. cruzi for
the total population and the population under 15 y.o. in
2008 when vector-borne transmission was still occurring
(age was back-corrected to 2008).
We employed generalized linear models (GLM), using a
logit as the link function, and a multimodel inference
approach through model averaging [4].
Household clustering was also assessed using GLMM
models and including household as a random variable
Multimodel inference approach
CONCLUSIONS
T. cruzi infection prevalenceEco-bio-social determinants of human T. cruzi infection
Risk maps of human T. cruzi infection
1. Gürtler RE, Yadon ZE. Eco-bio-social research on community-based approaches for Chagas disease vector
control in Latin America. Trans R Soc Trop Med Hyg. 2015;109: 91–98.
2. Hotez PJ, Bottazzi ME, Franco-Paredes C, Ault SK, Periago MR. The neglected tropical diseases of Latin
America and the Caribbean: a review of disease burden and distribution and a roadmap for control and
elimination. PLoS Negl Trop Dis.; 2008;2: e300.
3. Gaspe MS, Provecho YM, Cardinal MV, Fernández MP, Gürtler RE. Ecological and sociodemographic
determinants of house infestation by Triatoma infestans in indigenous communities of the Argentine Chaco.
PLoS Negl Trop Dis. 2015;9: e0003614.
4. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic
approach. Springer-Verlag; 2002.
REFERENCES
Household clustering of human
infection occurred even when considering
the abundance of infected T. infestans,
the social vulnerability and host
availability in the household (GLMM
model; βhousehold=1.3; CI95=1.02-1.7).
However, when considering infected co-
inhabitants, no significant clustering was
observed and no differences where
observed between the GLMM and GLM
model (Likelihood ratio test, p=1).
Sensitivity : 83%
Specificity : 72%
Sensitivity : 87%
Specificity : 68%
Population <15 y.o.
1. The risk of human infection increased 60% with each additional T. cruzi-infected vector and with household social
vulnerability, but their significant negative interaction, indicates that residents from vulnerable households were exposed
to a greater risk of infection at low vector abundance than less vulnerable residents.
2. The host availability index showed a protective effect when adjusting for the number of infected co-inhabitants, which
could indicate that the availability of domestic animals may reduce human-vector contact by diverting the vector towards
other hosts.
3. Although local T. cruzi transmission occurs at a household level, spatial heterogeneity occurred in the context of active
vector-borne transmission, in which hot-spots of households with infected children and vectors coincided.
4. Integration of the eco-bio-social determinants with the spatial component into a disease risk map revealed high-risk areas
that would benefit from targeted vector surveillance and control combined with etiologic treatment.
5. This approach is useful to develop cost-effective strategies oriented to reduce the burden of Chagas disease and other
NTDs in the affected areas.
Spatial analysis
We employed point-pattern
analysis to evaluate the occurrence
of infection hot-spots at a global and
local scales:
Global analysis (K-function)
Local analysis (G* Getis)
Total population
A cross-sectional vector survey (baseline) in 2008, followed by
a community-wide insecticide spraying and an entomological
surveillance phase (2009-2015) [3].
A serosurvey in the human population, aiming at full coverage,
was conducted using two ELISA tests (Wiener ®) (2012-2015).
2012 20152008 2009 2010 2011
Community-wide
spraying 2008
vector surveillance
(with focal sprayings)
31.9% 2.3%0.5%0.7% 0.7% 0%House infestation
prevalence
Active vector-borne
transmission
Vector survey Serosurvey
STUDY DESIGN
Objectives:1. Identify the eco-bio-social determinants of human infection with Trypanosoma cruzi in
a endemic area from the Argentine Chaco, 10 years after the last community-wide
insecticide spraying campaign.
2. Integrate the eco-bio-social determinants with the spatial component to generate risk
maps of Chagas disease in the context of structural poverty.
INTRODUCTION Chagas disease, caused by Trypanosoma cruzi, is among the most important NTDs in
Latin America and particularly, presents a disproportionally high disease burden on rural
communities in the Gran Chaco eco-region [1-2]
The multivariate association between biological, ecological, socio-economic, and
cultural factors and human infection with T. cruzi has rarely been assessed in a
comprehensive manner [1,3].
RESULTS
Spatial analysis of human and vector infection with T. cruzi
Local spatial analysis detected an infection
hot-spots of children and vector infection in
the community of Cuarta Legua, where total
human infection prevalence was also higher.
Global
aggregation of
households with
at least one
infected person
was observed at
scales larger than
2km, which is the
distance between
communities (a).
Total human
infection
Infection in
children <15 y.o.
T. Infestans infection
Aggregation of vector infection was observed
at all scales (b).
No global aggregation of households with at
least one infected child or at least one infected
vector when considering only infested houses.
a.
b.
Funding:
Fogarty International Center and the National
Institute of Environmental Health Sciences (NIH
Research Grant # R01 TW05836).
TDR (WHO/UNICEF/WB).
Universidad de Buenos Aires.
Consejo Nacional de Actividades Científicas y
Técnicas (CONICET).
-Agencia Nacional de Promoción Científica y
Tecnológica (PICTO- Glaxo).
Diagnostic kits were donated by Wiener Lab
Group, Rosario, Argentina.
*E-MAIL: [email protected] | @piliffq | POSTER AVAILABLE AT: http://www.columbia.edu/~mpf2131/ASTMH_FernandezMP.pdf