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: mpf2131@columbia.edu | @piliffq | POSTER AVAILABLE AT: http://www.columbia.edu/~mpf2131/ASTMH_FernandezMP.pdf