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Some adventures in global health and interscalar travel
James Trostle, PhD MPHProfessor of Anthropology
Trinity CollegeFaculty Research Lecture
April 7, 2016
Global health?
Major killers of children under 5 (~6 million deaths in 2015):Respiratory diseases esp. pneumonia,Diarrheal diseases,Malnutrition,Prematurity and birth trauma.
BUT many studies of infectious diseaseare 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)
Traveling across scales: Some challenges for single-village and life
history accounts
Environmental and social changes:spread across a landscapevary in intensity and velocitycause varied human responsesrequire systems thinking
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.
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.”
Chris Jordan
http://www.chrisjordan.com/gallery/rtn/#skull-with-cigarette
Skull With Cigarette, 200798x72"
Depicts 200,000 packs of cigarettes, equal to the # of Americans who die from cigarette smoking every six months.
Disease transmission is individual and communal
Epidemiologic research faces both genetic and sociocultural frontiers: strain typing of pathogens must accompany network descriptions of populations
Road as prompt(But could also be railroads, canals,
pipelines, power lines)
How do roads “work” to influence disease transmission?
Primary, secondary, and tertiary roads
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.
HumanEnvironment
Host interaction
PathogensDisease
GoodsServices
Human Population
ForestsWater
Road Construction
Politicaldecisions
Resource availability
New Roads Facilitate Human Movement (Migration Flows)
And resource flows
And Pollution Flows
Environmental Change and Diarrheal Disease in Northern Ecuador
How new roads affect the transmission of diarrheal pathogens in rural coastal EcuadorRoad access unevenly distributed across a region produces
conditions of a natural experimentRelationship between environmental change and disease can
be observed (easily?) and systematically.
Study Design15+ year longitudinal study at village levelTwice yearly case-control studies within
each of 21 [now 24] villages, and commercial center, Borbón.
J. Eisenberg, Epidemiology, Michigan J. Trostle, Anthropology, Trinity
With thanks to: InstitutionsCentro de Biomedicina UCentralUniversidad San FranciscoUniversity of Michigan
*Joseph EisenbergTrinity College
*James TrostleEmory University
Karen LevyMinisterio de Salud Pública*Asociación de Promotores
Field teamBetty CorozoAndrés AcevedoCarmen CampañaKarina PonceJeanneth YépezSimón QuimiJunior MinaAna EstupiñanMaritza RenteríaGeovanny HurtadoDenys TenorioLiliana RequeneJosé Ortiz
The Local Communities
Quito team*William Cevallos*Gabriel TruebaElizabeth FalconiPablo EndaraNadia VeiraRosana SegoviaPatricio RojasMaria Eloisa HashinDeisy ParralesManuel BaldeónNancy Castro
Funding from NIH (NIAID) and NSF (EEID)
Google Earth: Eye altitude 685 miles
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
Causal pathways
Why a road?
Distal political & economic
forces
Assembling evidence about relationships between road-related “development” and disease
DemographyGeography Sociology/anthro (networks)EthnographyEpidemiologyMicrobiology
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
Study Components
Active disease surveillance (weekly)2003-2007, 2011-14Village cohort study
Case/control study ( twice per year)Risk within villages
Microbiology (throughout) Analysis of marker pathogens
Study ComponentsCensus (once per year)
Population change, migration
Ethnography(throughout)
Behavior, context, meaning, causal inference
Study Components
Mathematical modeling Integrate GIS and disease transmission
models, causal inference
Some of what have we learned (so far)
Pathogen FlowsPerson-to-environment transmission (Sanitation)
Pathogen Flows
Hygiene
Within household person-to-
person transmission
Water quality
Environment-to-person
Example 1: Village remoteness (increased cost and time of
transport) influences the spread of pathogens and disease
Close
Medium
Remote
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
HOW?
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)
A
B
Spatial Layout of a Road (A) and Remote (B) Village
Food-sharing Networks in a Road (A) and Remote (B) village (2004)
Trostle et al. Epidemiology 2008
A B
Social Support Networks [with whom can you discuss important things?] in a Road (A) and Remote (B) village (2007)
Village A: 306 nodes11 components + isolates
Village B: 327 nodes5 components + isolates
A B
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
% L
eavi
ng V
illag
e
Decreased reintroduction of pathogens from outside of
regions?
Increased social solidarity and political strength?
0.002.004.006.008.00
10.0012.00
0.000 0.050 0.100 0.150 0.200 0.250
Remoteness
Deg
ree
Case Study 1A: The Complex Relevance of Social Networks to
Disease TransmissionHuman 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)
“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 TransmissionCase study 2
The incidence of diarrhea in neighboring villages
affects the risks in your village
Cas
es o
f di
arrh
ea
When neighboring villages
have little diarrhea, treating
the water is beneficial
Water treatment
Neighbor village
Your village
Neighbor village
Lots of diarrhea
Little diarrhea
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
Cas
es o
f di
arrh
ea
Neighbor village
Your village
Neighbor village
Lots of diarrhea
Little Diarrhea
Ecological Perspective: Regional TransmissionCase study 2
Ecological Perspective: Regional TransmissionCase 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
Ecological Perspective: ClimateCase 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 villagesFour years of climate data: 4 villagesEnvironmental variables:
Climate (total rainfall)Infrastructure (water + sanitation)Behavior (hygiene)Social capital/cohesion
Ecological Perspective: ClimateCase study 3
0
2
4
6
8
10
12
14
Dia
rrhe
a in
cide
nce
(cas
es p
er 1
,000
per
son-
wee
ks)
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: DiarrheaWeekly visits to households over 4 years
Ecological Perspective: ClimateCase study 3
• Exposure– Extreme rainfall: 90th
percentile over 4 year period• Contextual variable
– 8-week total rainfall
0
50
100
150
200
Max
imum
1-d
ay ra
infa
ll in
1 w
eek
(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
Tota
l rai
nfal
in th
e pr
evio
us 8
wee
ks (m
m)
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
Ecological Perspective: Climate and RainfallCase study 3
Conclusion/InterpretationWater flows
Under dry conditions extreme rain events increases riskFlushes contamination buildup from soil to water
Under wet conditions extreme rain events decrease riskFurther dilutes pathogens
Behavior flowsWater treatment can counteract increases in risk during dry period Water treatment is required to realize protective effect during wet periods.
Where we’re going
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
In-channel Flows
Overbank Flows Runoff Hydrological Networks
Social Cohesion
Socio-behavioral Dynamics
Social Transitions
Social Networks
Village 1 Village 2
Mathematical epi model
Hydrological model Social vulnerability model
Moss et al. Nature 2010
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
Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change
Because vulnerability is a dynamic process, a systemsapproach is needed
Feedback through household proximity to river
17 feet
2003, 2010
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
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
Are there necessary limits to interdisciplinary work of this type? What are they? cost?complexity?time?
Conclusions
Natural experiments as opportunity for many disciplines
Road as transect and system provocation/perturbation
Many types/levels of “social” dataChallenges of measuring diverse flowsChallenges of integrating methods and
disseminating results
For more information:www.sph.umich.edu/scr/ecodess
OR Google: Ecodess
Local presentationsPresentations/discussions for: Village assemblies in all study villagesLocal 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)
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.
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
Media outreach (to newspapers, television, internet) www.sph.umich.edu/scr/ecodess/home.php
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