Download Eisenburg Environmental Determinants of Infectious Disease

Environmental Determinants of Infectious
Disease
Roads and diarrheal disease
Joseph Eisenberg, PhD
University of Michigan
April 2016
How New Roads Affect the Transmission of
Enteric Pathogens
 World bank and health impact assessment
 Roads provide access to health care
 Report will emphasize hazards associated with the building of
the road
Downstream effects are ignored
How New Roads Affect the Transmission of
Enteric Pathogens
 Both ecological and social drivers of transmission
are influenced by roads.
 Past studies focused on


Roads and STDs, driven largely by social processes
Roads and vectorborne diseases, driven largely by ecological processes
 Enteric pathogens affected by both
types of drivers


Waterborne, foodborne pathogens are
mediated by environment
Also transmitted by person-person
contact
Roads: What Do They Bring?
Primary Roads
Lead to secondary
road construction
Roads Facilitate Movement
Roads Facilitate Changes in Population
Structure
Roads Cause Environmental Change
Ecologic change
Social change
 Study Site
 Coastal rainforest
 Afro-Ecuadorian and Chachi
Indians
 300-year history of living
independently
 Riverine transport system
adequate for gold mining,
rubber tapping, and tagua
 Road system needed for
logging and oil palm
plantation
 1996 begin road construction
linking coast and Andes
 Connects villages along
three rivers
Interdisciplinary approach
 Study design is interdisciplinary in scope
 Public Health


Epidemiology (Village-level cohort , within village case-control)
Molecular Biology (Strain analysis of marker pathogens)
 Environmental assessment



Water quality
Sanitary assessments
Food distribution patterns
 Systems



Social Network Theory
– mapping of contacts
Spatial analysis
– GPS (villages in relation to roads/rivers, climate)
Mathematical modeling
– Integration using GIS and models of disease transmission
Study Components
 Study components
 Mapping / Climate
 Census
 Active surveillance
Weekly visits to each house by village health promoter
 Case/control studies
Rolling village visits, once in the dry season and once in the
wet season
 Social network surveys
 Ethnography
 Health outcomes
 Diarrheal disease, antibiotic resistance, dengue
 Nutrition (anthropometry, hemoglobin, diet)
Causal links between roads and diarrheal disease
Ecologic and social drivers
Social drivers
 From remote to more proximate road access
 Increased reintroduction of pathogens from outside
of regions

People in remote villages have less contact with the outside
world
 Decreased social cohesion

70
12.00
60
10.00
50
8.00
Degree
% Leaving Village

People remain in remote villages for longer periods
People have more interaction in remote villages
40
30
4.00
20
2.00
10
0
0.000
6.00
0.050
0.100
0.150
Rem oteness
0.200
0.250
0.00
0.000
0.050
0.100
0.150
Rem oteness
0.200
0.250
Comparison of Infection Prevalence
 Overall infection prevalence (including
subclinical cases)
 Adjusted for age, village population, sanitation, rain
 The remoteness metric compares farthest
village with closest
E. coli
Rotavirus
Giardia
Diarrhea
OR (95% CI)
OR (95% CI)
OR (95% CI) OR (95% CI)
Far
1.00
1.00
1.00
1.00
Medium
3.0 (0.8, 11.9)
1.3 (0.5, 3.2)
1.2 (0.7, 2.0)
1.8 (1.1, 3.0)
Close
3.9 (1.1, 13.6)
4.1 (2.0, 8.4)
1.6 (1.0, 2.4)
1.8 (1.2, 2.6)
Remoteness
8.4 (1.6, 43.5)
4.0 (1.3, 12.1)
1.9 (1.3, 2.7)
2.7 (1.5, 4.8)
Eisenberg et al 2006
Comparison of Infection Prevalence
 Differences in spatial trends among marker
pathogens
 Pathogens with higher Ro less impacted by
remoteness
 Ro function of shedding rates, environmental
persistence, and infectious inoculum
Giardia
Rotavirus
E coli
Shedding
Environmental
persistence
Infectious
inoculum (ID50)
Long-term
Asym & Sym
Long
10
High (1010-1012)
Long (but depends
on temp)
10
Lower (108)
Shorter
100 - 106
Level and Type of Antibiotic Resistance
 Overall 44% of isolates tested were resistant to
one or more antibiotics
N (%)
Resistance to 1 Ab
124
(27)
All comprised tet, amp, sxt
Resistance to 2 Ab
132
(29)
95% comprised three pairs (amp-tet, ampsxt, sxt-tet)
Resistance to 3 Ab
151
(33)
87% comprised amp-sxt-tet, 8% comprised
amp-clo-tet
Ab tested for
Ampicillin
Tetracycline
Sulfamethoxazoletrimethoprim
Chloramphenicol,
Cefotaxime,
Gentamicin,
Ciprofloxin
Notes
Resistance to 4 Ab
42 (9) 90% comprised amp-clo-sxt-tet
Resistance to 5 Ab
10 (2) 50% comprised amp-sxt-gen-tet-cip
Resistance to 6 Ab
2 (0)
Total
No. Samples tested
% resistant
461
1037
44%
All comprised amp-clo-sxt-gen-tet-cip
Regional Patterns of Antibiotic Resistance
 Communities aggregated into far, medium
and close with respect to road access
 Based on seven 15-day case control studies (2003-08)
 Prevalence estimates are a weighted sum of cases (diarrhea)
and controls
Sulfamethoxazole and
Ampicillin Resistance
Remoteness
Far
Medium
Close
Multivariate model controlling for age, population size, and Ab use
Adjusting for correlation within the village
OR (95% CI)
1.0
1.1 (0.6, 1.8)
1.8 (1.3, 2.3)
Eisenberg et al 2011
Possible Explanations of Spatial Patterns
 Hypotheses
 Antibiotic use
 Spread of antibiotics, antibiotic resistant bacteria,
gene-gene transfer
 Reintroduction of antibiotic resistant bacteria
Antibiotic use
 Self-report antibiotic
use
 During the past week
 N = 2532
 Aggregated across 21
villages
No significant trend by
remoteness
Antibiotic
Amoxicillin
Ampicillin
Benzipenicillin
Sulfamethoxazoletrimethroprim
Gentamycin
Ciprofloxacin
Garamycin
Chloramphenicol
Tetracycline
Cefotaxime
Other*
Unknown
Total
%
20
1
4
2 (S)
6 (T)
7
8
1
0
1
0
28
9
267
Village Level Transmission Analysis
 Transmission rates (b and
h)
 Use E. coli prevalence in road vs.
remote villages (PNAS 2006)
 Assume SIS model of
transmission
 Antibiotic use rates (r)
l (ingestion rate)
bZ
Exposed
hY
 Survey data (20% random
sample of households)
r
Amplified
Colonized
- - Transmission
r: Antibiotic use rate
Eisenberg et al 2011
Explaining Patterns of E. coli Resistance in
Communities
 Comparing road and remote villages
 Antibiotic use determines the importance of
introduction of resistance vs. transmission
Low antibiotic use
High antibiotic use
Ecological Perspective: Do risks come from
neighboring villages?
Markov chain model: state of village k (high, medium, low
diarrheal rates) at t depends on state of 21 villages at t-1.
 4 yrs. active surveillance data across 21 villages
 Villages weighted using a gravity model (distance and size)
Goldstick et al 2014
Ecological Perspective: Regional Transmission
 Risk factors often
characterized as
static
But may vary by
environmental and
biological contexts
 Regional spread:
Environmental
transport vs. human
movement
Ecological Perspective: Climate
Outcome: Diarrhea
 Weekly visits to households over 4 years
14
12
10
8
6
4
2
0
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
Maximum 1-day rainfall in 1 week (mm)
Exposure
 Extreme rainfall:
90th percentile
over 4 year
period
200
150
100
50
0
02/04
Total rainfal in the previous 8 weeks (mm)
Contextual
variable
 8-week total
rainfall
05/04
08/04
11/04
02/05
05/05
11/04
02/05
05/05
08/05
11/05
02/06
05/06
08/06
11/06
02/07
05/07
11/05
02/06
05/06
08/06
11/06
02/07
05/07
1400
1200
1000
800
600
400
200
0
02/04
05/04
08/04
08/05
Ecological Perspective: Climate
Total 8-week rainfall
IRR (95% CI)
Low (78 - 425 mm)
1.39
(1.03, 1.87)
Medium (426 - 604 mm)
0.70
(0.44, 1.11)
High (605 - 1356 mm)
0.74
(0.59, 0.92)
Water treatment can counteract risk
associated with extreme rain events
Risk (diarrhea) associated with a 2week lagged extreme rain event
Water treatment is required to
achieve protective effect associated
with extreme rain events
3
3
Low total rainfall
IRR (solid) and 95% CI (dashed)
High total rainfall
2.5
2
1.5
1
.5
2.5
2
1.5
1
.5
0
0
0
.1
.2
.3
.4
.5
.6
Fraction of households that treat their water
.7
0
.1
.2
.3
.4
.5
.6
Fraction of households that treat their water
.7
Ecological Perspective: Social Networks
 Background
 Social networks typically seen as conduits of
transmission
 But social relationships can also be protective
 Disease spreads more slowly to and in
rural villages that are more remote due to
 Reduced contact
 Greater density of social ties between individuals in
remote communities facilitates spread of individual
and collective protective practices
Zelner et al 2012
Ecological Perspective: Social Networks
 Cross sectional survey (2007): N > 4000; 24
villages
 Self-report diarrheal disease
 Sociality networks

Who do you talk to for important matters?
 Contact networks

Who have you spent time with during the last week
(outside your household)?
– For anyone or for infectious individuals
Ecological Perspective: Social Networks
 Heterogeneous social landscape across
villages
 Networks for similar size villages
Remote village
Close village
Isolates not shown
Ecological Perspective: Social Networks
 Risks and protective effects are mediated through
a number of social processes
OR = 0.49 (0.29, 0.84)
+
Within-village
Infectious
contacts
OR = 0.89 (0.81, 0.98)
An Ecological Perspective
 The presence of road causes environmental
changes (social and ecological)
 These changes occur differentially across the
landscape of villages



Affects social structure
Spread of microorganisms differentially through water
sanitation and hygiene pathways
Affects movement and migration patterns at multiple
scales
Affects climate and hydrological processes
Regional patterns of environmental change will vary
over time.
Remoteness
relative to Borbón
Close
Medium
Far
Acknowledgments
Ecuador
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
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William Cevallos (Project director)
Gabriel Trueba (PI: Microbiologist)
Diana Lopez (Microbiologist)
Eugenia Meja (Microbiologist)
Maria Ines Baquero (Microbiologist)
Andres Acevedo (Field anthropologist)
Vilma Requene (Field assistant)
Mariuxi Ayovi (Field assistant)
Deni Tenorio (Field assistant)
Mauricio Ayovi (Field assistant)
Maritza Renteria (Field assistant)
Jose Ortiz (Transportation coordinator)
Emel Bustamante (Data entry)
United States
•
•
•
•
James Trostle
Betsy Foxman, Carl Marrs, Lixin
Zhang, Karen Levy
James Fuller
Ian Spicknell, Jason Goldstick,
Jon Zelner, Robert Wood
Health Promoters
Deni Orobio, Pastor Mercado, Cecilia
Mercado, Carmen Nazareno, Ludis
Castillo, Mirtha Campaz, Estela Arroyo,
Ramona Sabando, Maria Ayovi, Blanca
Vega, Jorge Peralta, Santos Mina,
Amelia Preciado, Marco B., Ereccni
Cuero, Julio Valdez, Lucrecio Palacio,
Heroina Arboleda, Juliana Mina,
Adalin Valencia, Mariuxci C., Dominga
A., Maria Arroyo, Gonzolo M., Gabriel
Ayovi, Maria Corozo