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Spatial Analysis in Vaccine Trials: Spatial Effect Modifiers and Herd Immunity Measurement. Michael Emch Portland State University. Collaborators. Dr. John Clemens, Director, International Vaccine Institute (IVI), South Korea Dr. Mohammad Ali, Scientist, IVI - PowerPoint PPT Presentation
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Spatial Analysis in Vaccine Trials: Spatial Effect Modifiers and Herd
Immunity Measurement
Michael EmchPortland State University
Collaborators
• Dr. John Clemens, Director, International Vaccine Institute (IVI), South Korea
• Dr. Mohammad Ali, Scientist, IVI
• Dr. Ira Longini, Professor, Biostatistics, Rollins School of Public Health, Emory University
• Dr. Mohammad Yunus, Senior Scientist & Head, Matlab Health Research Centre, International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B)
• Dr. David Sack, Director, ICDDR,B & Professor, Johns Hopkins School of Public Health
Outline• Introduction and Objectives
• Phase III Vaccine Trial Background & Traditional Efficacy Calculations
• Local Efficacy Measurement & Spatial Effect Modifiers
• Herd Immunity Measurement Using Spatial Information
• Study Data and Methods
• Findings and Conclusions
•Clemens et al. (1996) questioned the utility of conventional vaccine trial methods.
•Traditional protective efficacy measures don’t provide information to make decisions about whether or not to vaccinate diverse populations.
Clemens, J., Brenner, R., Rao, M., Tafari, N., and Lowe, C. (1996) Evaluating new vaccines for developing countries: Efficacy of Effectiveness? Journal of the American Medical Association, 275(5): 390-7.
Introduction and Objectives
Ty21a typhoid fever vaccine trials have produced conflicting results in different settings because of varying exposure levels to disease.
Justification Example
Vaccine Trial Efficacy
Egypt (Wahdan et al., 1982) 96%
Chile (Levine et al., 1990) 77%
Indonesia (Simanjuntak et al., 1991) 53%
Wahdan, M., Sarie, C,. and Derisier, Y. (1982) A controlled field trial of live Salmonella typhii strain Ty21a oral vaccine against typhoid. Journal of Infectious Diseases, 145: 292-5.
Levine, M., Ferreccio, C., and Black, R. (1990) Large-scale field trial of Ty21a live oral typhoid vaccine in enteric-coated capsule formulation. Lancet, 336: 891-94.
Simanjuntak, C.H., Paleologo, F.P., and Punjabi, N.H. (1991) Oral immunization against typhoid fever in Indonesia with Ty21a vaccine, Lancet 338: 1055-9
This paper describes methods to calculate different efficacy values for different exposure levels within a
phase III cholera vaccine trial area in rural Bangladesh.
Introduction and Objectives
Question 1: How did the effectiveness of a cholera vaccine vary spatially in the study area?
Question 2: How does protective efficacy vary by potential effect modifiers (i.e., risk factors for the disease)?
Question 3: How does cholera incidence in the placebo group vary in areas with different vaccine coverage rates; i.e., was herd immunity important, and if so, at what level of coverage did it become important?
Research Questions
The evaluation of new vaccines conventionally proceeds through several stages of testing:
•Phase I: studies of safety and immunogenicity in low-risk individuals, usually healthy adults.
•Phase II: studies of safety and immunogenicity in the population to be targeted for the vaccine in practice.
•Phase III: randomized clinical trials that evaluate the safety and clinical protection of a vaccine in its target population.
Vaccine Trial Background
•Phase III trials serve as the final “gate-keepers” in the movement of new vaccines into public health practice.
•The conventional design for phase III trials- the randomized, double-blind, clinical trial- serves as the “gold standard” method for evaluating vaccines.
Phase III Vaccine Trials
Protective efficacy is the proportionate reduction of the incidence of the target infection by vaccination
Equation 1:
Vaccine Efficacy Measurement
1001 xincidenceenonvaccine
incidencevaccineeE
•Conventional efficacy calculations are global efficacy measures (i.e., efficacy of the vaccine for the entire study area).
•Conventional trials stratify protective efficacy by individual characteristics (e.g., age and sex).
•If locational data are available a local efficacy measure can be calculated for different subsets of the trial area.
Local Efficacy Measurement
Local Efficacy Measurement
2
3
41
56
7
11
12
1000 meters
9
8
10
1000 meters
Identification Number
Vaccinee Population
Placebo Population
Vaccinee Cholera Cases
Placebo Cholera
Cases
1 12 7 0 1
2 2 6 0 0
3 23 25 0 0
4 24 22 1 2
5 25 32 0 0
6 12 25 1 1
7 25 45 0 0
8 22 23 0 0
9 34 25 0 1
10 25 20 0 0
Total 204 230 2 5
Vaccinee Incidence 0.0098
Placebo Incidence 0.022
Efficacy 0.55
where, j = incidence rate for pixel i cj = number of cases in pixel j pj = number of people in pixel j kj = kernel values of cell j of the filter n = number of cells in the kernel filter
Local Efficacy Measurement
1000kp
kc
j
n
1jj
n
1jjj
i
The mathematical expression for computing an incidence rate in a raster system is:Equation 2
Incidence can be calculated for both vaccinee and non-vaccinee groups to calculate efficacy.
Local Efficacy Measurementin a raster GIS
Event and Population Image Used to Calculate Incidence
Uni
tary
ker
nel f
ilter
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 0 0 0 1 2 0
0 0 0 0 5 1 3 0 0 3 0 4 1 2 0 0 0 3 0 0 0
0 1 1 1 0 0 0 0 2 3 0 10 0 5 0 0 0 6 2 0 4
Attributes of case events for the pixels (white cells are outside study area)
0 0 0 0 1 8 1 0 0 0 5 31 6 6 0 0 10 0 12 6 13 0 0 1 31 0 5 0
0 5 8 3 1 0 0 0 10 13 1 51 0 10 0 2 0 15 9 1 16
Attributes of population for the pixels (white cells are outside study area)
Uni
tary
ker
nel f
ilter
•Effect modifiers are variables that modify the effect of a vaccine.
•There may be differences in the protective efficacy of vaccines in populations with one characteristic versus another.
•Some effect modifiers are spatial in nature.
•For instance, protective efficacy of the cholera vaccine might be worse near rivers because there are more cholera bacteria in rivers and the vaccine might not work as well when exposure to the bacteria is greater.
Spatial Effect Modifiers
•Efficacy might differ in different parts of a trial area since exposure to the disease varies within the study area.
•We can calculate relationships between socio-environmental risk factors and efficacy by neighborhood.
•Example: local sanitation environment is a risk factor for cholera and may modify the effect of the vaccine.
Differential Exposure and Spatial Effect Modifiers
Differential Exposure (Force of Infection) and Spatial Effect Modifiers
Efficacy stratified by:
1. total risk (force of infection)
2. specific risk factors
Satellite remote sensing allows us
to model environmentalvariables
GIS allows us to model neighborhood-level
variables andintegrate
environmental, health & demographic
data
Spatial Effect ModifiersAre Risk
Factors that Vary in Space
John Jensen, 2004
High Incidence Areas of O139 and El Tor
HIA=High Incidence AreaLIA=Low Incidence Area
Risk Factors Sig. b
Dependent Variable: Incidence Rate of O139
(constant) .000
Population Density .000
Distance to Nearest Waterbody
.015
Flood control .006
Dependent Variable: Incidence Rate of El Tor
(constant) .000
Educational status .000
Sanitation .001
Distance to Nearest Waterbody
.021
Flood control .000
Anthropogenic and Environmental Risk Factors of Cholera
Variable P value
Number of open latrines 0.00**
Number of non-septic latrines 0.27
Number of ring septic latrines 0.35
Number of concrete septic latrines 0.65
Number of other households using latrines 0.11
Latrines per person (excluding open) 0.26
Number of tube wells in bari 0.15
Number of households sharing a common tube well in bari
0.00**
Tube wells per person 0.68
Household area (sq. ft.) 0.02*
Bari population 0.01**
Population density around baris 0.00**
Total household assets 0.35
Annual income 0.27
Mid-arm circumference (children under 5 years old)
0.10
Distance from main river 0.30
Flood control 0.00**
C holera transm iss ion
M u ltip le h ou seh o ld s u se la trin esEn viron m en tal variab le
F lood -con tro lled a reaEn viron m en tal variab le
L arg e n u m b er o f h ou seh o ld s u se tu b ew e llsCu ltu ral/ b eh avioral an den viron m en tal variab le
H ou seh o ld a rea sm allSocioecon om ic an d
en viron m en tal variab le
B ari p op u la tion is la rg eCu ltu ral/ b eh avioral, en viron m en tal,
an d socioecon om ic variab le
P op u la tion d en s ity is h ig h w ith ina h a lf k ilom eter rad iu s o f b ari
Cu ltu ral/ b eh avioral, en viron m en tal,an d socioecon om ic variab le
Spatial Patterns of Sanitation
Status
Ecological Spatial Variables Calculated Using a GIS
Cloud Penetrating Radar Satellite Image: Can easily differentiate between flooded/non-flooded areas
• Herd immunity is protection of an individual from a disease because others are immune to the disease.
• When enough people are vaccinated, the probability that the disease agent will come into contact with an individual who is not vaccinated is lower.
• This is called herd immunity because non-immunized people in the population are protected since most people in the population, i.e., the herd, are protected.
Herd Immunity
We measured whether herd immunity is important for the cholera vaccine by using spatial information. This was done in three steps:
1. calculated incidence in the placebo group by neighborhood
2. calculated vaccine coverage rate by neighborhood
3. measured relationship between incidence in the placebo group and vaccine coverage rate.
Herd Immunity Measurement
Study Area
Meghna R
iver
'] Matlab
MatlabResearch
Area
•In 1985, a community based individually randomized oral cholera vaccine trial was conducted in Matlab, Bangladesh.
•This double-blind trial measured the efficacy of two vaccines, the B subunit-killed whole cell (BS-WC) and the killed whole cell only (WC) vaccine.
•The control agent was Escherichia coli K12 strain.
Study Data
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>
?
?
?
?
SylhetRajshahi
KhulnaChittagong
Barisal
Legend
" Bari
Embankment
> ICDDR,B Hospital
? ICDDR,B Subcenter
Main river
Villages
KM
43210
Matlab Study Area
E
BANGLADESH
Dhonagoda River
Dhonagoda R
iver
Dhaka
Matlab
@
Bay of Bengal
Divisional boundaries
Meg
hn
a River
Main rivers
•Women over 15 and children aged 2 to 15 were the target group in the trial.
•Three vaccine doses were given to 62,285 people in the target group in six-week intervals.
•The vaccine trial used a passive surveillance system to identify cholera cases from the study area.
•The surveillance took place at one hospital and two community-based treatment centers.
Data
•During 5 years of follow-up, the protective efficacy for the BS-WC group was 49% (P < 0.001) and 47% for the WC group (P < 0.001). •Protection was lower in children who were vaccinated at 2 to 5 years than in older persons.
•For children in this age group, protection waned after 4 to 6 months and was not evident during the third year.
•Persons vaccinated who were older than 5 years were protected even in the third year of follow-up.
Data
What we do not know from this study:
1) Whether efficacy varies within the study area.
2) Whether the spatial variance is related to different socio-environmental characteristics that are responsible for spatially heterogeneous exposure.
3) If there was herd immunity in areas with high vaccine coverage rates.
Data
Question 1: Findings
Spatial Pattern of Vaccine Efficacy 3 year cumulative
Question 2: Findings
Regression Model: Dependent Variable Efficacy by 1000 meter neighborhoods
Variable Prob. Value
Direction
Population Density by 100 meter neighborhood (cholera risk factor)
0.0000 Negative
Education: Socio-economic Status in Neighborhood- 250 meters (cholera risk factor)
0.0003 Positive
Vaccine coverage in Neighborhood- 250 meters 0.0419 Positive
Distance to Passive Surveillance Facility (Possible confounder) 0.0195 Negative
•As local-level population density increases the vaccine is not as effective
•The vaccine is more effective in neighborhoods with higher socio-economic status.
•In neighborhoods with higher vaccine coverage efficacy is higher.
•Vaccine efficacy is lower farther away from passive surveillance facilities.
Question 3: Findings
Spatial distribution of oral cholera vaccine coverage
based on 2 doses
Vaccine coverage
(%)
Total baris
% baris
Incidence rate/1000 Protective Efficacy (PE)
P-value
Vaccine group Placebo group
Rate % changed Rate % changed
≤10 320 4.98 9.15 -- 3.66 -- -1.50 0.6696
11-20 768 11.96 10.16 1.00* 17.10 1.00* 0.40 0.0804
21-30 1446 22.51 9.70 -4.53 18.63 +8.90 0.48 0.0002
31-40 2412 37.55 5.54 -45.47 12.29 -28.13 0.54 0.0000
41-50 630 9.81 3.80 -62.60 10.74 -37.19 0.65 0.0001
51+ 847 13.19 4.98 -50.98 10.75 -37.13 0.54 0.0001
•As vaccine coverage rates got larger the cholera incidence rate went down.
•This pattern is true for both the placebo and vaccine group.
* this is used as the reference group
Question 3: Findings
Incidence rate and protective efficacy by vaccine coverage (based on two doses)
•In the original trial, the cholera vaccines were less effective in the third year (Clemens et al., 1990)
• Using spatial information we found that the vaccines remained effective (PE60%) in the third year in areas where the coverage is better (40% and over).
•Non-vaccinees in high coverage areas benefited from reduced probability of infection.
Question 3: Findings
•Vaccine efficacy varies in space no matter how it is measured at multiple scales
•Vaccine efficacy varies by different socio-environmental circumstances (i.e., these variables are spatial effect modifiers)
•Herd immunity is present is some areas
•Incorporating a spatial component into vaccine efficacy measurement provides specific information that is necessary to determine whether or not to vaccinate different populations.
Conclusions
•All vaccine researchers can use these methods in future phase III trials.
•Using spatial information policy makers can make better decisions about the effectiveness of a vaccine in different circumstances.
•For example, if poor sanitation modifies the effect of the cholera vaccine then public health officials can stratify efficacy measures for different sanitation circumstances.
•They can therefore know how well the vaccine will perform in another area with a similar sanitation environment.
Conclusions
•Explore more effect modifiers and total risk (force of infection). We are just getting started on the spatial effect modification question.
•Explore herd immunity further
•Create spatially autoregressive models to control for spatially autocorrelated variables.
•Investigate local relationships by mapping geographically weighted regression (GWR) results.
Next
This project is being funded by:
•National Institutes of Health (NIH): National Institute of Allergies and Infectious Disease.
•National Science Foundation (NSF): Geography and Regional Science
Acknowledgements
Questions?
