This document presents a study on the epidemiology and geospatial analysis of chikungunya, dengue, and Zika in Cali, Colombia. The study aims to describe hotspots and environmental risks, geographical and social risk factors for incidence and severity of the diseases. Mixed methods will be used, including collecting case data from health authorities and conducting spatial video interviews in hotspots to map environmental risks. Analysis will identify hotspots, examine trends over time, and use geographical regression models to analyze relationships between disease patterns and risk factors like age, sex, ethnicity and location. The results could help direct future vector control efforts in Cali.

1. Chikungunya, Dengue, and Zika in
Cali, Colombia: epidemiological and
geospatial analyses
Amy R Krystosik, BS, MPH, Ph.D. (candidate)
Department of: Biostatistics, Environmental Health Sciences, and Epidemiology
College of Public Health, Kent State University
March, 2016

2. Chikungunya
Countries and territories where
cases have been reported* as of
October 20, 2015 (CDC, 2016)
*Does not include countries or
territories where only imported
cases have been documented.
This map is updated weekly if
there are new countries or
territories that report local
chikungunya virus transmission.
Colombia

3. Dengue
Cartogram of the annual
number of infections for all
ages as a proportion of
national or subnational
(China) geographical area
(Bhatt et al., 2013)

4. Zika Countries and territories showing historical distribution of Zika virus (autochthonous transmission), 1947 – 2016 as
of February 7, 2016; Colombia: >31,000 cases reported with no increase in incidence of microcephaly.

6. Dengue in
Colombia
Pattern of endemicity for
dengue 2008-2013
Source: Republic of Colombia,
Ministry of Health and Social
Protection
Cali

7. Chikungunya
in Cali
Confirmed cases by
municipality of
transmission October 17,
2015.
The accumulated
confirmed cases
nationally up to Up to the
cut-off date is 439,000
cases accumulated in 712
municipalities.
Source: Instituto Nacional
de Salud -INS

8. Chikungunya
in Cali
Confirmed cases by
municipality of
transmission for the
epidemiological weeks
1-6, 2016.
The accumulated
confirmed cases
nationally up to the cut-
off date is 6,356 in 371
municipalities.
Source: Instituto
Nacional de Salud -INS

9. Zika in Cali
Cases by territory up to
epidemiological week 8,
2015-2016
Source: Sivigila

10. Chikungunya Dengue Zika
Vector Aedes Aedes Aedes
Virus Alphavirus Flavivirus Flavivirus
Fever >80% >80% 20-80%
Relative bradycardia (pulse-temperature deficits) <20% 20-80% <20%
Neurological- headache, meningitis, etc. <20% >80% 20-80%
Myalgia (muscle pain); or muscular mass or swelling 20-80% >80% 20-80%
Pulmonary, thoracic, chest wall or cardiac <20% <20% 20-80%
Arthritis 20-80% <20% 20-80%
Hepatic dysfunction <20% 20-80% <20%
Neutropenia (An abnormally low count of a type of white blood
cell)
20-80% 20-80% <20%
Macules and/or papules (rash) 20-80% 20-80% >80%
Musculoskeletal- muscle, bone, and joint >80% >80% 20-80%
Thrombocytopenia (deficiency of platelets in the blood causing
bleeding into the tissues, bruising, and slow blood clotting after
injury)
<20% 20-80% <20%
Disease
Gideon.
Abrupt fever, leukopenia,
myalagia and prominent
bilateral joint pain;
maculopapular rash appears on
2nd to 5th days in greater than
50% of cases; fever resolves
within 7 days, but the joint pain
may persist for months.
Headache, myalgia,
arthralgia, relative
bradycardia, leukopenia and
macular rash
A mild dengue-like illness with
conjunctivitis and a pruritic
maculopapular rash that starts on
the face and spreads to the rest
of the body; joint pain is
common; myalgia; retro orbital
pain and leg edema may occur;
may be associated with
congenital neurological defect.

12. Currently
Situation
• Hypoendemic dengue
• Recent outbreak of chikungunya
• Increasing concern over zika
• Current vector control methods
implemented by mayor’s office
insufficient
Gaps
• Maps lack local detail and
community knowledge
• Geographical factor has not
been considered in
epidemiological risk analysis
• New local mapping methods are
ready to be field tested

13. Hypothesis
Dengue, chikungunya, and zika cases and risk factors have a spatial
component which can predict future disease patterns, and thus can be
used to direct control efforts

14. Specific Goals
1. Describe hotspots and environmental risks
2. Describe geographical and social risk factors for dengue,
chikungunya, and zika incidence
3. Describe the geographical and social risk factors for predicting
severity of dengue

15. Design
• Mixed methods retrospective
and prospective
• Incident case data collected
from secretary of health
• Spatial video geonarratives:
cross-sectional sample in
hotspots

17. Data
• SIVIGILA: The National System of
Vigilance in Public Health
• Available ~ 1 year after events
occurred
• Cleaned and distributed by The
Ministry of Health and Social
Protection by department
• Publically available data
• October 2014 - October 2015
Parameter Value
Chikungunya 44,877
Dengue 13,443
Neighborhoods
in Cali
303
Independent
Variables
Age, sex,
ethnicity,
occupation,
geographic
location of
cases

18. Factor Chikungunya Dengue W/O
warning signs
Dengue W/ warning
signs
Severe
dengue
Dengue death
N 2,255 10,630 2,716 84 13
Age 43.25 (24.2) 31.7 (18.8) 27.7 (21.6) 31.4 (24.5) 48.7 (30.8)
Sex (F) 1,473 (65.3%)5,085 (47.8%) 1,378 (50.7%) 37 (44.0%) 6 (46.2%)
Ethnic Group 782 (34.7%) 5,545 (52.2%) 1,338 (49.3%) 47 (56.0%) 7 (53.8%)
Indigenous8 (0.4%) 30 (0.3%) 15 (0.6%) - -
rom, gitano9 (0.4%) 18 (0.2%) 7 (0.3%) - -
raizal5 (0.2%) 11 (0.1%) 6 (0.2%) - -
negro mulato
afrocolombian
61 (2.7%) 235 (2.2%) 120 (4.4%) 3 (3.6%) -
other2,172 (96.3%)10,336 (97.2%) 2,568 (94.6%) 81 (96.4%) 13 (100.0%)

19. Factor Chikungunya Dengue W/O
warning signs
Dengue W/
warning signs
Severe
dengue
Dengue
death
N 2,255 10,630 2,716 84 13
Disabled 1 (<1%) 5 (0.1%) 5 (0.2%) - -
Displaced 4 (0.2%) 6 (0.1%) 6 (0.3%) 1 (1.3%)
Migrant - 1 (<1%) - - -
Pregnant 274 (19.8%) 101 (2.3%) 34 (3.0%) - -
youth under
government care
- 5 (0.1%) 2 (0.1%) - -
community mother 1 (0.1%) 2 (<1%) - - -
Ex-paramilitary
/Ex-guerrilla
1 (<1%) - - - -
under psych care 1 (<1%) 1 (<1%) 2 (0.1%) - -
other group 1,982 (88.6%) 10,518 (99.3%)2,668 (98.9%) 83 (98.8%) 13 (100.0%)
violence victims 1 (<1%) 4 (<1%) 2 (0.1%) - -

20. Selection Criteria
Inclusion
• Cases
• Confirmed or suspect case of
chikungunya or dengue or zika*
• Community Health Worker
• From the hotspot of interest; informed
consent to recorded interview; Spanish
or English speaking
• Hotspots
• Statistically significant at p ≤ 0.05
Exclusion
• Cases
• Incarcerated, unable to be georeferenced
• Community Health Worker
• Under 18; Speaks neither Spanish nor
English
• Hotspots
• Too dangerous to collect data; no CHW
available
*if data become available

21. Spatial Video Geonarratives
Mechanics
• Contour2+ Cameras with video and GPS
coordinates
• Mounted inside polarized windows of car
• Collect video of ground, buildings, animals,
greenery, water, and people
• Collect GPS coordinate trail of video
• Likely to code: road condition (cement/paved in
good condition, cement/paved with potholes,
unpaved dirt), abandoned tires, planters, trash,
puddles, lakes, canals, unkempt parks or green
areas, and individuals who appear to be living on
the street
• Interviews will be translated, transcribed, and
coded as a map layer of incidence.
Previous examples
• Described by Curtis et al 2013 and
Curtis et al 2015
• collected and mapped fine street-level data for
environmental risks related to cholera
transmission including standing water, trash
accumulation, presence of dogs, cohort specific
population characteristics, and other cultural
phenomena in a challenging urban environment
in Haiti
• Used previously to capture
environmental risks for vector-borne
diseases
• Lewis, Fotheringham, & Winstanley,
2011; Mills, Curtis, Kennedy, Kennedy, &
Edwards, 2010; Schultz, 2015

22. Spatial Video Geonarratives
Benefits
• Allows us to capture data in hard
to reach areas at low cost and
create time capsules for time-
series data
• Applies to other diseases
• First time field testing this
method for Aedes
Conditions
• Collected during drought October 14 –
December 1, 2015 and January 25 –
February 9, 2016
• Interviewed: vector specialist, TB
community health nurse, and local
community members

23. Mapping Cali
• Projection: EPSG Projection 3115
- MAGNA-SIRGAS / Colombia
West zone
• Layers include: cases,
environmental risks, schools,
clinics, bodies of water, and
potential vector breeding sites

24. Mapping Cali

25. Analysis
• Spatial: ArcGIS 10.3 and spatial analyst extension
• Hotspot analysis
• Kernel density analysis
• Geographically weighted regressions
• Epidemiological: Stata 12; R 3.1.2
• Univariate analysis- normality (Shapiro Wilk), mean and standard deviation for
normal and median and interquartile ranges for non-normal
• Proportions and strata (categorical variables)(chi-squared tests and Fisher’s exact
test)
• Cumulative Incidence and severity by social risk groups (displaced, immigrant,
pregnant, children), sex, age, ethnicity, occupation, and location
• Incidence trend line estimated over time
• Disease specific mortality rate
• Case-fatality rate

26. Analysis
• Incidence: Geographically weighted Poisson regression
• Severity: Geographically weighted mixed multinomial ordered logit
regression
• Variables with more than 30 subjects in each cell will be included if
found to be significant at p < 0.05 in the bivariate analysis
• The final GWRs will likely include the independent variables of race,
age, gender, ethnicity, occupation, social strata, and pregnant women.
• The parametric, semiparametric, and non-parametric models will be
compared and the most parsimonious model chosen

27. Sample size
Epidemiological analysis
• All eligible dengue, chikungunya,
and possible zika cases with will
be included.
• Due to the descriptive nature of
the analysis and the high
number of cases a power
calculation was not performed.
Geographical analysis
• Moran’s I: n ≥ 25
• Nearest Neighbor Index: 30 events per
cluster
• Kernel Density Analysis: the minimal
sample size is set by the user and can
be set during analysis. We will test
various bandwidths and select that
which minimizes error.
• Hotspots analysis: all safe and
statistically significant hotspots as
confirmed by nearest neighbor index
and Moran’s I.

28. Anticipated limitations and solutions
• Severity of chikungunya
• 5% sample of chikungunya cases
• Cross-sectional nature of environmental data
• Semi-structured interviews
• CHW knowledge level varies
• Cases not able to be geocoded excluded from analysis
• Reliable data for zika in the region are not yet available
• Safety and social acceptance of field work

29. Chronogram
*Data collection, cleaning, and analysis can be reopened if zika data becomes available.
*
*

30. Chronogram
*Data collection, cleaning, and analysis can be reopened if zika data becomes available.

31. Collaborators
• KSU
• Dr Madhav Bhatta
• Dr Mark James
• Dr Andrew Curtis
• ICESI
• Dr Diana Dávalos
• Secretary of Health
• Paola Buritica
• SIVIGILA
• Jorge Humberto Rojas Palacios
• Valle de Lilli
• Robinson Pacheco
• Dr Sarita Rodriguez
• Cañaveralejo Hospital
• Dr Javier Colorado

32. Thank you! Questions?