This document summarizes some of the work done by James Trostle and collaborators on a long-term study of environmental change, social dynamics, and infectious disease transmission in rural coastal Ecuador. The study uses a mixed methods approach, combining epidemiological surveillance, microbiological analysis, social network analysis, ethnography and mathematical modeling across multiple villages that vary in remoteness and access to roads. Some key findings discussed are that remoteness influences pathogen prevalence, with more remote villages having lower rates of disease. Social networks, which also vary with remoteness, impact disease transmission. The relationship between rainfall, water flows, and diarrhea is complex and context-dependent. Ongoing work aims to better understand how social and hydrological dynamics interact

1. Some adventures in global health
and interscalar travel
James Trostle, PhD MPH
Professor of Anthropology
Trinity College
Faculty Research Lecture
April 7, 2016

2. Global health?
Major killers of children under 5 (~6 million
deaths in 2015):
Respiratory diseases esp. pneumonia,
Diarrheal diseases,
Malnutrition,
Prematurity and birth trauma.

3. BUT many studies of infectious disease
are inadequate [lamplight studies!]:
-examine clinics or single villages at one (or at most
two) points in time,
-look at individual or village as unit of analysis but
not larger scale
-conceptualize risk and behavior as individual
(hand washing/water boiling?) or group
(municipal water source?) but not
interdependent (influence of neighboring town)

4. Traveling across scales: Some
challenges for single-village and life
history accounts
Environmental and social changes:
spread across a landscape
vary in intensity and velocity
cause varied human responses
require systems thinking

5. A challenge for ethnographic accounts
Pathogens move with (inside or on) human
bodies, but also move through direct human
contact, animal vectors, and environmental
reservoirs such as water or food.

6. A challenge for epidemiological accounts
Epidemiologists glancing ‘upwards’ in scale worry that,
by omitting information about the landscape over
which epidemiological dynamics unfold, perhaps their
models are after all ‘importantly wrong’.
…Likewise, as we peer ‘downwards’, we are increasingly
convinced that heterogeneity documented at the level
of the individual or gene locus is necessary to capture
the broader-scale epidemiological pattern.”
(Matthews and Haydon 2007:763)
“Cross-scale influences on epidemiological dynamics: from genes to
ecosystems.”

7. Chris Jordan

8. http://www.chrisjordan.com/gallery/rtn/#skull-with-cigarette
Skull With Cigarette, 2007
98x72"
Depicts 200,000 packs of cigarettes, equal to the # of Americans who die from cigarette smoking
every six months.

9. Disease transmission is individual and communal
Epidemiologic research faces
both genetic and
sociocultural frontiers:
strain typing of
pathogens must
accompany network
descriptions of
populations

10. Road as prompt
(But could also be railroads, canals,
pipelines, power lines)

11. How do roads “work” to influence
disease transmission?

12. Primary, secondary, and tertiary roads

13. Road construction: product of political decisions &
resource availability.
Roads influence interactions between humans, hosts,
and environment, leading to pathogen transmission
and disease.
How?
- changes in water quality,
- Demography,
- and networks of human populations,
- and availability of goods and services.

14. Human
Environment
Host
interaction
Pathogens
Disease
Goods
Services
Human
Population
Forests
Water
Road
Construction
Political
decisions
Resource
availability

15. New Roads Facilitate Human
Movement (Migration Flows)

16. And resource flows

18. And Pollution Flows

19. Environmental Change and Diarrheal
Disease in Northern Ecuador
How new roads affect the transmission of diarrheal
pathogens in rural coastal Ecuador
Road access unevenly distributed across a region produces
conditions of a natural experiment
Relationship between environmental change and disease can
be observed (easily?) and systematically.
Study Design
15+ year longitudinal study at village level
Twice yearly case-control studies within
each of 21 [now 24] villages, and
commercial center, Borbón.
J. Eisenberg, Epidemiology, Michigan J. Trostle, Anthropology, Trinity

20. With thanks to:
Institutions
Centro de Biomedicina UCentral
Universidad San Francisco
University of Michigan
*Joseph Eisenberg
Trinity College
*James Trostle
Emory University
Karen Levy
Ministerio de Salud Pública
*Asociación de Promotores
Field team
Betty Corozo
Andrés Acevedo
Carmen Campaña
Karina Ponce
Jeanneth Yépez
Simón Quimi
Junior Mina
Ana Estupiñan
Maritza Rentería
Geovanny Hurtado
Denys Tenorio
Liliana Requene
José Ortiz
The Local Communities
Quito team
*William Cevallos
*Gabriel Trueba
Elizabeth Falconi
Pablo Endara
Nadia Veira
Rosana Segovia
Patricio Rojas
Maria Eloisa Hashin
Deisy Parrales
Manuel Baldeón
Nancy Castro
Funding from NIH (NIAID)
and NSF (EEID)

22. Google Earth: Eye altitude 685 miles

23. Connects villages in three river
drainages:
Onzole, Cayapas, Santiago
21 villages, ~4200 inhabitants in
June, 2003
36% illiterate (self-report)
89% Afro-Ecuadorian
7% Mestizo
<1% Chachi
The 21 villages are categorized by
river basin (Santiago, Cayapas,
Onzole, Bajo Borbón, road)
and remoteness (close,
medium, far)
1996-2002 road construction links the S. Colombian
border and Andes with the Ecuadorian coast

25. Causal pathways
Why a
road?
Distal
political &
economic
forces

26. Assembling evidence about relationships between
road-related “development” and disease
Demography
Geography
Sociology/anthro (networks)
Ethnography
Epidemiology
Microbiology

28. Environmental Change and Diarrheal Disease
in Northern Ecuador: Study Components
Mapping & GIS
(once per yr)
Villages, houses in relation
to roads/rivers, rainfall/temp
4 Network surveys
(sociometric)
(2003-4, 2007, 2010,
2013)
Counting & mapping social
contacts in all villages
A
B

29. Study Components
Active disease
surveillance (weekly)
2003-2007, 2011-14
Village cohort study
Case/control study
( twice per year)
Risk within villages
Microbiology (throughout)
Analysis of marker pathogens

30. Study Components
Census (once per year)
Population change,
migration
Ethnography
(throughout)
Behavior, context, meaning,
causal inference

31. Study Components
Mathematical modeling
Integrate GIS and disease transmission
models, causal inference

32. Some of what have we learned
(so far)

33. Pathogen Flows
Person-to-environment transmission (Sanitation)

34. Pathogen Flows
Hygiene
Within household person-to-
person transmission
Water quality
Environment-to-person

35. Example 1: Village remoteness
(increased cost and time of
transport) influences the spread of
pathogens and disease

36. Close
Medium
Remote

37. Remoteness and Disease
E. coli
(Bacteria)
Rotavirus
(Virus)
Giardia
(Protozoa)
Diarrhea
(All Causes)
Remote 1.00 1.00 1.00 1.00
Medium 3.0 1.3 1.2 1.8
Close 3.9 4.1 1.6 1.8
Continuous 8.4 4.0 1.9 2.7
(Estimates by village were adjusted for age of individual,
population of village, sanitation level, and climate)
Eisenberg et al., PNAS, 2006

38. HOW?

40. Demographic changes
Tendencies by remoteness:
More mestizos in near communities (12%) than far ones
(4%)
Shorter duration of village residence in near communities
(13 years) than far ones (21 years)

42. A
B
Spatial Layout of a Road (A) and Remote (B) Village

43. Food-sharing Networks in a Road (A)
and Remote (B) village (2004)
Trostle et al. Epidemiology 2008
A B

44. Social Support Networks [with whom can you
discuss important things?] in a Road (A) and
Remote (B) village (2007)
Village A: 306 nodes
11 components + isolates
Village B: 327 nodes
5 components + isolates
A B

45. Causal Model of Transmission
Potential
From close to medium to remote villages
0
10
20
30
40
50
60
70
0.000 0.050 0.100 0.150 0.200 0.250
Remoteness
%LeavingVillage
Decreased reintroduction of
pathogens from outside of
regions?
Increased social solidarity
and political strength?
0.00
2.00
4.00
6.00
8.00
10.00
12.00
0.000 0.050 0.100 0.150 0.200 0.250
Remoteness
Degree

46. Case Study 1A: The Complex
Relevance of Social Networks to
Disease Transmission
Human systems (food/economic resource/social
support networks) create different environments
for the possible transmission of pathogens.
Food-borne pathogens may spread more readily in
dense food-sharing networks; but host resistance
and prevention may be higher in dense social
support networks.
These (social) environments vary with remoteness.
(Trostle et al. 2008, Zelner et al. 2012)

47. “Ask when – not just whether - it’s a risk: How
regional context influences local causes of
diarrheal disease” (Goldstick et al, AJE 2014)
– Four years of active surveillance data across 21
villages
– Markov chain model where state of village k (high,
medium or low diarrheal rates) at time t depends
on the state of the 21 villages at time t-1.
Villages are weighted using a gravity model (distance and
size)
Ecological Perspective: Regional Transmission
Case study 2

48. The incidence of diarrhea in neighboring villages
affects the risks in your village
Casesof
diarrhea
When neighboring villages
have little diarrhea, treating
the water is beneficial
Water treatment
Neighbor
village
Your
village
Neighbor
village
Lots of
diarrhea
Little
diarrhea

49. Water treatment
The incidence of diarrhea in neighboring villages
affects the risks in your village
When neighboring villages
have lots of diarrhea, treating
the water is not as effective
Casesof
diarrhea
Neighbor
village
Your
village
Neighbor
village
Lots of
diarrhea
Little
Diarrhea

50. Ecological Perspective: Regional Transmission
Case study 2

51. Ecological Perspective: Regional Transmission
Case study 2
• Risk factors are often characterized as static
– But they may vary by social and biological
contexts
– Need to shift question from: ‘is variable X a
risk?’ to ‘when (under what conditions ) is
variable X a risk?’
• Environmental transport vs. human movement

53. Ecological Perspective: Climate
Case study 3
Social and environmental contexts modify the
effect of extreme rainfall on diarrhea
incidence in Northern Coastal Ecuador
(Carlton et al, 2013)
Four years of active surveillance data: 21 villages
Four years of climate data: 4 villages
Environmental variables:
Climate (total rainfall)
Infrastructure (water + sanitation)
Behavior (hygiene)
Social capital/cohesion

54. Ecological Perspective: Climate
Case study 3
0
2
4
6
8
10
12
14
Diarrheaincidence(casesper1,000person-weeks)
02/04 05/04 08/04 11/04 02/05 05/05 08/05 11/05 02/06 05/06 08/06 11/06 02/07 05/07
Outcome: Diarrhea
Weekly visits to households over 4 years

55. Ecological Perspective: Climate
Case study 3
• Exposure
– Extreme rainfall: 90th
percentile over 4 year period
• Contextual variable
– 8-week total rainfall
0
50
100
150
200
Maximum1-dayrainfallin1week(mm)
02/04 05/04 08/04 11/04 02/05 05/05 08/05 11/05 02/06 05/06 08/06 11/06 02/07 05/07
0
200
400
600
800
1000
1200
1400
Totalrainfalintheprevious8weeks(mm)
02/04 05/04 08/04 11/04 02/05 05/05 08/05 11/05 02/06 05/06 08/06 11/06 02/07 05/07

56. Ecological Perspective: Climate
and Rainfall
Case study 3
Conclusion/Interpretation
Water flows
Under dry conditions extreme rain events increases risk
Flushes contamination buildup from soil to water
Under wet conditions extreme rain events decrease risk
Further dilutes pathogens
Behavior flows
Water treatment can counteract increases in risk during
dry period
Water treatment is required to realize protective effect
during wet periods.

57. Where we’re going

58. How do social dynamics interact with hydrodynamics to
drive patterns of waterborne diseases?
• Based on this understanding, what are the consequences of a more variable and
changing climate?
Data: GI illness data, surface water quality/dynamics, social structure/dynamics

59. In-channel
Flows
Overbank Flows Runoff Hydrological
Networks
Social Cohesion
Socio-behavioral
Dynamics
Social
Transitions
Social Networks
Village 1 Village 2

60. Mathematical epi model
Hydrological model Social vulnerability model

61. Moss et al. Nature 2010

62. Components of the social environment
influencing disease risk
• Demographic changes
– Migration
– Movement patterns
• Social cohesion
– Social network degree
• Outside contacts
• Social capital
• Infrastructure
– Sanitation
– Hygiene
– Water projects

63. Vulnerability is the degree to which a system is susceptible
to, or unable to cope with, adverse effects of climate change

64. Because vulnerability is a dynamic process, a systems
approach is needed

65. Feedback through household
proximity to river

66. 17 feet

67. 2003,
2010

68. Models, including agent-based simulations, can be
used to study systems
• Can incorporate & investigate:
• Heterogeneity and
stochasticity
• Population-level (emergent)
outcomes from individual-level
behaviors and objectives
• Multiple scales and context-
specific details
• Our model analyses will
explore:
• Relative impact of different climate
conditions on adaptation decision-
making
• Relationship between vulnerability and
disease outcomes
• Alternative functions for combining
exposure, sensitivity, and adaptive
capacity
• Relationship between social
environment and disease
transmission
• For example, Human movement
patterns, environmental cues (e.g.,
flood or drought conditions) and
diarrheal disease

69. Some methodological challenges:
Different rhythms of data collection
(periodicity and duration of measurement)
Different rhythms of analysis (movement and
serotype analysis)
Challenge of “thick description” of ecology or
systems

70. Are there necessary limits to interdisciplinary
work of this type? What are they?
cost?
complexity?
time?

71. Conclusions
Natural experiments as opportunity for many
disciplines
Road as transect and system
provocation/perturbation
Many types/levels of “social” data
Challenges of measuring diverse flows
Challenges of integrating methods and
disseminating results

72. For more information:
www.sph.umich.edu/scr/ecodess
OR Google: Ecodess

73. Local presentations
Presentations/discussions for:
Village assemblies in all study villages
Local hospital and community epidemiology
program employees (Borbon)
Provincial Ministry of Health (Esmeraldas)
National Ministry of Health (Quito)
Public and private universities in Quito (FLACSO,
U Central, USFQ)

74. Degree training (* = Ecuador)
2015. Stephanie Garcia. “Unidos Somos Más.”An exploration of social cohesion as a time-dependent variable in San Miguel and Telembí,
two Afro-Ecuadorian villages in Esmeraldas, Ecuador. BA Honor’s Thesis, Anthropology.
2011. Jennifer Jimenez, Cathya Solano (Independent Studies) Anthropology, Trinity College.
2009. Katherine J. Connors. Environmental Change and Infectious Disease: How Road Access Affects the Transmission of Dengue Fever in
Rural Ecuador. MPH Thesis, Epidemiology. University of Michigan.
2008. Cristina S. Wheeler Castillo. Measurement of Socioeconomic Position and its Health Implications in Rural Ecuador.
BA thesis in International Health Studies, Trinity College. (Winner of the Grossman Senior Research Prize for Global Studies.)
2008. Owen Solberg. Population Genetic Diversity of Two Pathogens and the Role of Balancing Selection in HLA Immunogenetics.Chapter 1:
Molecular epidemiology of group A rotavirus in Ecuador. Ph.D., Integrative Biology . U.C. Berkeley.
*2007. Rosana Segovia. Evidence of Horizontal Gene Transfer of Antibiotic Resistance Genes in Communities with Limited Access to Antibiotics
MS Thesis, Universidad San Francisco de Quito.
* 2007. Eloisa Hasing. Sudden Replacement of Rotaviral Genotype G9 in Ecuador. MS Thesis, Universidad San Francisco de Quito.
* 2007. Patricio Rojas. Genotypes of Enterotoxigenic E. coli in Ecuadorian Remote Communities.
MS Thesis, Universidad San Francisco de Quito.
* 2007. Dimitri Kakabadse. Conjugative Transference of Antibiotic Resistance in E. coli Isolates from Esmeraldas Province
BS Thesis, Universidad San Francisco de Quito.
2007. Karen Levy. Environmental Drivers of Water Quality and Waterborne Disease in the Tropics with a Particular Focus in Northern Coastal
Ecuador. Ph.D. in Environmental Science, Policy, and Management. U.C. Berkeley.
2007. Marylin Rodriguez. Migración urbana en la costa de Ecuador: Tradiciones de salud en transición. (Urban migration on the
Ecuadorian coast: Health traditions in transition.)
BA honor's thesis, Trinity College, International Studies and Hispanic Studies.
* 2006. Pablo Endara. High Prevalence of P[8]G9 Rotavirus in Remote Coastal Communities of Ecuador.
MS in Microbiology, Universidad San Francisco de Quito.
* 2006. Nadia Vieira. High prevalence of Enteroinvasive Escherichia coli isolated in a remote region of northern coastal Ecuador.
MS in Microbiology, Universidad San Francisco de Quito.
* 2006. Patricio Bueno. Analisis Microbiologico del Agua de la Parroquia Borbon, Canton Eloy Alfaro y su Asociacion con la Enfermedad
Diarreica. BS, Universidad San Francisco de Quito.
2005. Sarah Bates. The relevance of social and geographic structures to disease transmission in rural Ecuador.
MS in Health, Environment, and Development, U.C. Berkeley School of Public Health
* 2005. Sonya Ontoneda.
MS in Microbiology, Universidad San Francisco de Quito.
2004. Betsy Cowan. The Social World of a Road in Northwest Ecuador. B.A. Honor’s Thesis, Anthropology, Trinity College.

76. Social connectedness can inhibit disease
transmission: Social organization, cohesion, village
context and infection risk in rural Ecuador. Jon Zelner,
James Trostle, Jason Goldstick, James House, and Joseph NS Eisenberg

77. Media outreach (to newspapers, television,
internet) www.sph.umich.edu/scr/ecodess/home.php