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Health & Place 13 (2007) 238–248

www.elsevier.com/locate/healthplace

Efficacy calculation in randomized trials:Global or local measures?

Michael Emcha,�, Mohammad Alib, Camilo Acostab, Mohammad Yunusc,David A. Sackc, John D. Clemensb

aDepartment of Geography, Saunders Hall, University of North Carolina, Chapel Hill, NC 27599-3220 USAbInternational Vaccine Institute, Seoul, Korea

cICDDR, B: Centre for Health and Population Research, Dhaka, Bangladesh

Received 13 June 2005; received in revised form 19 December 2005; accepted 6 January 2006

Abstract

This study tests whether the effect of a vaccine trial varies in space and why. Analytical z-score maps identify unusually

high- and low-efficacy values in a trial area. Relationships between neighborhood efficacy and ecological variables are

measured to explain why efficacy varies in space. Efficacy was found to vary regardless of neighborhood size and the

variation is related to several ecological determinants. Local efficacy measures can help public health practitioners make

better decisions about when and where to vaccinate populations. The concepts offered in this study are pertinent for any

health intervention trial, not just vaccines.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Intervention trials; Vaccines; Neighborhood determinants; Cholera; Herd immunity; Spatial analysis

Introduction

Clemens et al. (1996) questioned the utility ofconventional vaccine trial methods. In particular,they suggested that public health practitionerscannot use traditional protective efficacy measuresto make decisions about whether or not to vaccinatediverse populations. Many trials, including thosemeasuring the efficacy of the Ty21a typhoid fevervaccine, have produced conflicting results in differ-ent settings. The Ty21a vaccine, for example, had anefficacy of 96 percent in Egypt (Wahdan et al.,1982), 77 percent in Chile (Levine et al., 1990), and

e front matter r 2006 Elsevier Ltd. All rights reserved

althplace.2006.01.005

ing author. Tel.: +1 919 962 8901.

ess: emch@email.unc.edu (M. Emch).

53 percent in Indonesia (Simanjuntak et al., 1991).There are various factors that can result in differentefficacies including different vaccine formulations,potencies, study designs, case definitions, strains ofthe wild-type agent, and the ecological (i.e., socio-environmental) circumstances of the trial area.Conventional vaccine trial methods have an under-lying assumption that the effect of the vaccine is thesame throughout the trial area. This paper is a casestudy that tests whether this assumption is true forone vaccine trial. If the vaccine efficacy is spatiallyheterogeneous in a trial area then public healthdecisions based on global vaccine efficacy may notbe realistic.

This study uses three data sets, including acholera vaccine trial database, a longitudinal

.

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demographic database of the rural population fromwhich the vaccine trial participants were selected,and a household-level spatial database of the samestudy area. The cholera vaccine trial is one of thelargest vaccine trials in history and the longitudinaldemographic database of the study area is one ofthe most comprehensive in the developing world.All vital demographic events are noted through anextensive community-based data collection system.A corresponding household-level geographic infor-mation systems (GIS) database allows us to identifythe household location of all individuals who tookpart in the trial as well as the household location ofeach person in the demographic surveillance system.

Materials and methods

Background

In 1985, a community-based individually rando-mized oral cholera vaccine trial was conducted inMatlab, Bangladesh, the research site for theInternational Centre for Diarrhoeal Disease Re-search, Bangladesh-ICDDR,B (now called theCentre for Health and Population Research). Thisdouble-blind trial measured the efficacy of twovaccines, the B subunit-killed whole cell (BS–WC)and the killed whole cell (WC) only vaccines. Thecontrol agent was Escherichia coli K12 strain.Females aged 15 years and older and children aged2–15 were the target groups in the trial (Clemenset al., 1990a). Three vaccine doses were given to62,285 people in the target group in six-week intervals.

The vaccine trial used a passive surveillancesystem to identify cholera cases in the study area.The surveillance took place at one hospital and twocommunity-based treatment centers. During 3 yearsof follow-up, the cumulative protective efficacy was50 percent for the BS–WC group (Po0:001) and 52percent for the WC group (Po0:001). Protectionwas lower in children who were vaccinated between2 and 5 years than in older persons. For children inthis age group, protection waned after 4–6 monthsand was not evident during the third year. Personsolder than 5 years of age who were vaccinated wereprotected even in the third year of follow-up(Clemens et al., 1990a). The study, however, didnot indicate whether the level of protection of thevaccine is homogeneous throughout the trial area.We reanalyzed the same cholera vaccine trial datausing spatial information to determine if there is anyspatial pattern for vaccine efficacy in the study area,

to identify if there are areas that have unusuallyhigh or low efficacies, and to determine why efficacyvaries. This is accomplished by calculating neigh-borhood-level efficacy and by producing analyticalmaps of the same.

Study area and data

The research site for this project is Matlab,Bangladesh, which is in south-central Bangladesh,approximately 50 km southeast of Dhaka, adjacentto where the Ganges River meets the Meghna Riverforming the Lower Meghna River. A health anddemographic surveillance system (HDSS) has re-corded all vital events of the study area populationsince 1963; the study area population has beenapproximately 200,000 since that time. The data-base is the most comprehensive longitudinal demo-graphic database of a large population in thedeveloping world. The people of the study area livein clusters of patrilineally related groups of house-holds called baris. A vector GIS database of theMatlab field research area was created (Emch, 1999;Ali et al., 2001). Features in digital format includebaris, rivers, and health facilities, and a flood-control embankment. Fig. 1 shows three features inthe GIS database including the flood-controlembankment, the Dhonagoda River, and baris.

The three map views in Fig. 1 are displayed atdifferent scales. The map view on the far right hasthe individual bari identification numbers visible.The baris are all identified by an ICDDR,B HDSScensus number within the structure of the GISdatabase. This allows attribute data to be linked tothe spatial database. In turn, demographic data,disease incidence, and vaccine status data can belinked to specific bari locations. The Matlab fieldresearch center has in- and out-patient services, amedical laboratory, and research facilities. Onehundred and twenty community health workers(CHWs) visit each household area every 2 weeks tocollect demographic, morbidity, and other data.The HDSS conducts periodic censuses and usesCHWs to update demographic data (e.g., births,deaths, and migrations).

The study uses vaccine trial data collected inMatlab from 1985 to 1990 (Clemens et al., 1986a, b,1987, 1988a–d, 1989a–c, 1990a, b, 1991; Durhamet al., 1998; Sack et al., 1991; van Loon et al., 1996).The objective of the original randomized double-blind, placebo-controlled trial was to determinewhether three doses of BS–WC and WC vaccines

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Fig. 1. Study area GIS database.

M. Emch et al. / Health & Place 13 (2007) 238–248240

reduces the incidence of laboratory confirmedcholera in children aged 2–5 years old and femalesover 15. The target group was individually rando-mized based on a simple random sampling schemederived from HDSS records.

The Matlab GIS database includes an accuratebari location for all individuals living in the studyarea including all vaccinees, controls, people whorefused vaccines, and everyone else living in thestudy area who was not part of the study. The GISdatabase also includes the locations of the treatmentfacilities that were used in the passive surveillancesystem for the vaccine trial. Individual-level vaccinedata were aggregated at the bari level so that theycould be integrated with the GIS database. Theoriginal vaccine trial was conducted in 149 villagesin the Matlab study area; in 1987, however, severeriver erosion washed away seven villages, leavingonly 142 villages that could be mapped during the1994 GIS survey. There were 15 cholera cases inthose seven villages during the three-year surveil-lance period that are not included in the efficacycalculation of the present study.

Spatial analyses

The aforementioned papers by Clemens et al.reported efficacy measures for the entire study area.

In this study we recalculated efficacy by neighbor-hood using different geographic scales. The vaccinetrial data were linked to the GIS bari locations viathe ICDDR,B HDSS identification number, aunique number assigned to all individuals in thestudy area. The GIS thus facilitates the identifica-tion of the dwelling locations of individuals whoparticipated in the trial, as well as the entirepopulation distribution of the Matlab study area.Efficacy was calculated by local neighborhood todetermine how it varies in space. The two vaccinegroups were aggregated because both of thevaccines were found to have similar efficacies(Clemens et al., 1990a). The protective efficacy ofa neighborhood was calculated as:

ai ¼ 1�Wi

li

,

where, ai is the protective efficacy in neighborhoodi, Wi the vaccine incidence rate in neighborhood i, li

the nonvaccinee incidence rate in neighborhood i.The incidence rates used to calculate the vaccine-

protective efficacy are cumulative incidence rates(i.e., often called attack rates) during the six-monthsto two-year follow-up period. We removed choleracases from the first 6 months of follow-up since thevaccine efficacy for the BS–WC was notably higherthan that for WC-only vaccine during these initial

ARTICLE IN PRESSM. Emch et al. / Health & Place 13 (2007) 238–248 241

months. The cases represent clinical and laboratoryconfirmed cholera cases using the same criteriadescribed by Clemens et al. (1992). Fig. 2 showshow efficacy was measured for one spatial neigh-borhood. The black dots represent 12 different baris

and the circle represents a 2000-m radius neighbor-hood around bari number 1. Table 1 lists theattribute data that are linked to the 10 baris withinthe neighborhood shown in Fig. 2; this attributeinformation was used to calculate neighborhood-level efficacy for the 1000-m radius area around thisbari. The example shown in Fig. 2 yields an efficacyof 0.55 in the 1000-m radius area around bari

number 1 for the follow-up period. There were 6423

2

3

41

5

6

7

11

12

1000 meters

9

8

10

1000 meters

Fig. 2. One 2000-m neighborhood.

Table 1

Neighborhood efficacy data

Identification number Vaccinee population Placebo popula

1 12 7

2 2 6

3 23 25

4 24 22

5 25 32

6 12 25

7 25 45

8 22 23

9 34 25

10 25 20

Total 204 230

Vaccinee incidence

Placebo incidence

Efficacy

baris in the study area, thus the efficacy wascomputed for 6423 neighborhoods centering aroundeach bari point. Different neighborhood sizes wereexplored ranging from 500 to 10,000m, yieldingdifferent scale-dependent spatial patterns of vaccineefficacy (as reported in Table 2).

Efficacy surface maps were created for the entirestudy area by interpolating the neighborhood-levelvalues. In other words, the neighborhood-level datawere used to create a continuous surface ofprotective efficacy. Several interpolation methods(i.e., ordinary kriging, local polynomials) were usedwith similar results, but those reported used asimple inverse distance weighted algorithm (10nearest neighbors and a 2.8 inverse power function).The maps were created so that the spatial variationof efficacy within the study area can be visualized.Descriptive and analytical statistics are also re-ported.

We wanted to see whether spatial patterns in thegeographically weighted mean of the efficacies aredue to genuine differences in local efficacy ratherthan the result of aspatial random variation.Therefore, we computed local z-scores (Fothering-ham et al., 2002) of the ratio of incidences betweenvaccinees and placebo recipients. The z-score forneighborhood i (zi) is calculated as

zi ¼x̄i � m

sffiffiffiffiffiffiffiP

j

qwij2

where, x̄i is the local (neighborhood i) mean of thelog of ratio of incidences, m the global mean of thelog of ratio of incidences, s the standard deviation

tion Vaccinee cholera cases Placebo cholera cases

0 1

0 0

0 0

1 2

0 0

1 1

0 0

0 0

0 1

0 0

2 5

0.0098

0.022

0.55

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Table 2

Neighborhood efficacy summary statistics (N ¼ 6423)

Neighborhood

Size (m)

Efficacy No placebo

cases

No vaccine and

placebo cases

No vaccine

cases

Mean Variance Minimum Maximum

250 0.48 1.3456 �1.24 0.99 1165 2651 1220

500 0.34 0.1936 �1.65 0.92 942 942 927

750 0.50 0.1024 �2.17 0.95 543 309 442

1000 0.36 0.2025 �3.2 0.95 182 150 170

1500 0.45 0.0676 �1.86 0.96 9 12 17

2000 0.47 0.0324 �0.49 0.94 0 3 0

2500 0.48 0.0196 0.01 0.92 0 0 0

3000 0.48 0.0144 0.02 0.91 0 0 0

4000 0.49 0.0121 0.22 0.91 0 0 0

5000 0.50 0.0081 0.33 0.86 0 0 0

6000 0.50 0.0064 0.38 0.77 0 0 0

7000 0.49 0.0064 0.35 0.75 0 0 0

8000 0.49 0.0049 0.37 0.72 0 0 0

9000 0.49 0.0036 0.39 0.69 0 0 0

10,000 0.49 0.0036 0.41 0.70 0 0 0

M. Emch et al. / Health & Place 13 (2007) 238–248242

of the log of ratio of incidences, and wij the weightassigned to bari j.

The sum of the weight is 1 and since the z-scoreswere based on the ratio of the incidence betweenvaccinees and placebo recipients, a value of|zi|X1.65 was used to accept our hypothesis of asignificantly extreme value of efficacy at the 95percent confidence level (one sided). A negative z-score indicates higher protective efficacy and apositive value lower efficacy.

In an effort to determine why the vaccineprotective efficacy varied in space we measuredwhether several ecological variables were related toefficacy levels within different spatial subsets of thetrial area. We used a 2000-m neighborhood for theanalyses because it retained local-level protectiveefficacy variation and the distribution was normalthus allowing us to conduct multiple regressionanalysis.

Results

Table 2 summarizes the efficacy values for the6423 local neighborhoods around each bari pointusing 15 different neighborhood sizes. While usingthe smallest neighborhood of 250m describes local-level variation of efficacy, the summary statisticsshow that using a smaller neighborhood does notprovide a complete account of the spatial variation.Many of the local areas did not have laboratoryconfirmed cases of cholera in the placebo group,vaccine group, or both during the two-year surveil-

lance period. The number of local areas withoutlaboratory confirmed cholera cases in each group isshown in the last three columns of the table. Forinstance, a 250-m filter size resulted in 1165 (18percent) neighborhoods without placebo cases, 1220(19 percent) without vaccine cases, and 2651 (41percent) without both placebo and vaccine cases.Thus, only 22 percent of the neighborhoods hadcases. Efficacy in 250-m neighborhoods ranged from�1.24 to 0.99 with a mean of 0.48; these summarystatistics were calculated using only those neighbor-hoods with cases. A negative efficacy value meansthat cholera incidence was higher in the vaccinegroup than the placebo group. This likely happenedwhen there were very few observations in a localarea. A 10,000-m neighborhood results in efficacyvalues ranging from 0.41 to 0.70 with a mean of 0.49and a variance near zero. This mean value is thesame as the global efficacy values reported in theoriginal trial, and the low variance suggests thatlocal variation in efficacy is obscured using such alarge neighborhood. The descriptive efficacy statis-tics suggest that a 2000-m neighborhood is the bestscale for measuring local efficacy variation in thisstudy area, since it produced a large range ofefficacy (�0.49–0.94) with moderate degree ofvariation (0.03) in the data, and virtually all of theneighborhoods have cases.

Fig. 3 displays the local efficacy surface maps thatwere created using neighborhood sizes ranging from500 to 5000m. The lighter tones are mostly areaswith no cases and a few areas with higher incidence

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500 MeterFilter

1000 MeterFilter

0 5 10 Kilometer

2000 MeterFilter

5000 MeterFilter

Efficacy<00 to 0.10.1 to 0.30.3 to 0.50.5 to 0.60.6 to 0.8>0.8

Local Vaccine EfficacyNeighborhoods

After 3-Year Followup

N

Fig. 3. Local efficacy maps using 500–5000m neighborhoods.

M. Emch et al. / Health & Place 13 (2007) 238–248 243

values in the vaccine group compared with theplacebo group. The darker tones reveal areas withthe highest incidence values. Each of the mapsshows that efficacy varies in space. As expected thelarger the neighborhood, the closer the mean localefficacy value is to the global efficacy value ofapproximately 0.48 found in the original vaccinetrial. While the smaller neighborhood sizes revealpockets of local variation in efficacy, the larger sizesreveal a trend of higher efficacy values in thenorthern region of the study area. Fig. 4 shows thez-score maps for the same four neighborhood sizesthat were used to make the continuous surface maps

shown in Fig. 3. The areas with negative z-scoresindicate areas with significantly higher protective-vaccine efficacy (in red). The 2000m neighborhood,which we have said is the best size for this study,reveals that the northern part of the study area hadareas with significantly high protective efficacyvalues.

Table 3 shows the results of the regressionanalysis that measures relationships between effi-cacy and the ecological variables listed. The vari-ables included in the model explain 26% of thevariation of efficacy suggesting that there are otherreasons for the variation not included (R2

¼ 0.26).

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z-valuez-value

<=-1.65<=-1.65

-0.01 to -1.64-0.01 to -1.640.00 to 1.64

0.00 to 1.64 > = 1.65> = 1.65

Neighborhood size: 500m Neighborhood size: 1000m

z-value<=-1.65-0.01 to -1.640.00 to 1.64

z-value<=-1.65-0.01 to -1.640.00 to 1.64

Neighborhood size: 5000mNeighborhood size: 2000m

Fig. 4. Local z-score maps.

M. Emch et al. / Health & Place 13 (2007) 238–248244

Variables that were inversely related to neighbor-hood vaccine efficacy include vaccine coverage rate,population size, literacy, net migration into neigh-borhoods, and distance from the passive surveil-lance facilities. Those that were positively related toneighborhood efficacy include the average age ofvaccine and placebo recipients, percentage ofHindus, percentage of farmers and distance fromthe river.

Discussion

This study illustrates that protective efficacy of theoral cholera vaccines vary in space within the trial

area. Scale-dependent patterns of efficacy are shownusing different neighborhood sizes. The spatialvariation of efficacy may be attributed to variationin the disease burden due to ecological differencesand/or spatial variation of vaccinated individuals inthe study area. Several studies have shown thatcholera incidence is spatially heterogeneous in theMatlab study area (Sommer and Woodward, 1972;Glass et al., 1982; Khan, 1981; Hughes et al., 1982;Myaux et al., 1997; Emch, 1999; Emch and Ali,2001, 2003; Ali et al., 2002a–c). For instance, Emch(1999) found that tube well water availability andsocioeconomic status, were all determinants ofcholera. Ali et al. (2002c) found that proximity to

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Table 3

Regression results of efficacy versus neighborhood variables

Factor Coefficients B Std. error t Sig. 95% Confidence interval for B

Lower bound Upper bound

Vaccine coverage (%) �1.705 .062 �27.692 0.000 �1.825 �1.584

Neighborhood population size �.00085 .000 �8.145 0.000 �0.001 �0.001

Average age of vaccine or placebo recipients 2.338 .606 3.858 0.000 1.150 3.526

% of Hindus in the neighborhood .729 .034 21.139 0.000 0.661 0.796

% literate people in the neighborhood �.908 .112 �8.124 0.000 �1.127 �0.689

% farming households in the neighborhood 1.356 .073 18.499 0.000 1.212 1.499

% net migration in the neighborhood �12.261 .302 �40.590 0.000 �12.853 �11.669

Distance from the treatment center (km) �6.594 .262 �25.123 0.000 �7.108 �6.079

Distance from the river 8.121 .403 20.159 0.000 7.331 8.911

R2: 26.26%.

M. Emch et al. / Health & Place 13 (2007) 238–248 245

surface water and high population density aredeterminants of cholera incidence. All of thesestudies either explicitly or implicitly conclude thatrisk of cholera varies spatially within the Matlabstudy area. Since risk influences disease transmissionit might therefore impact vaccine efficacy.

This paper identifies several areas in the northernpart of study area that have significantly higherefficacy (shown in red in the z-score map). Aninvestigation of why the vaccine performed so wellin these areas will show the circumstances in whichthe vaccine works best. In contrast, the lowerefficacy values in the south-central part of the studyarea might help us understand why the vaccinemight fail. Several ecological factors explain theefficacy variation. Efficacy is higher when theaverage age within a neighborhood is higher. Thisis consistent with individual-level analysis thatshowed that the vaccine worked better for adultsthan children. Efficacy is lower in neighborhoodswith a larger percentage of vaccinated people. Whenmore people have been vaccinated in a neighbor-hood there is herd protection of non-vaccinatedpeople (Ali et al., 2005). In other words, there is anindirect benefit because cholera transmission isreduced in higher coverage neighborhoods. Wehave thus provided a theoretical basis for whyneighborhood age and vaccine coverage percentageare related to efficacy. Age is related because thehigh-efficacy neighborhoods are composed of anolder population; it is simply a biological composi-tional effect. Vaccine coverage rate is relatedbecause people in low-coverage neighborhoods havea higher probability of being infected.

The other neighborhood-level variables that arerelated to cholera vaccine efficacy include popula-

tion density, percentage of Hindus, literacy rates,percentage of farming households, net migrationinto the area, distance to the river, and treatmentcenter distance. Treatment center distance wasincluded in the model because people are less likelyto report to passive surveillance facilities if they livefurther away; it is essentially a variable to controlfor this bias. The underlying biological mechanismsbehind why the other neighborhood-level variablesaffect efficacy are not well understood. In otherwords, we do not have a clear theory of why thesecontexts might be related to efficacy. Efficacy ishigher in neighborhoods where a larger percentageof people are illiterate, have more Hindus, have alarge percentage of farmers, have high rates of out-migration, a lower population density, and are farfrom the main river. These neighborhood-levelvariables were chosen for this analysis because theywere found to be related to spatial variation ofcholera incidence and they were available for thesame time period that the trial was conducted.Literacy is one measure of socio-economic status(SES) and probably a better proxy than assets andincome in this community. The study area iscomprised of mostly very poor people and there isnot much variability to SES here. However, therewas a SES gradient to efficacy in which the vaccinedid not work as well in poorer communities. Thereis no plausible biological basis for why the vaccinewould work better in predominantly Hindu com-munities compared to Muslim communities. How-ever, it is possible that there could be someunknown behavioral variable that is related toreligious persuasion.

While this study did not test the exact mechanismbehind why all of these neighborhood-level vari-

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ables might be related to efficacy, it is still importantto understand the context in which a vaccine works.If all vaccine trials collected neighborhood-levelinformation about socio-environmental variation inthe population then we might know why there issuch large trial-to-trial variation in efficacy for thesame vaccine. If we find consistency in how well thevaccine works in populations with similar charac-teristics then we will at least know the context inwhich the vaccine will most likely work at thepopulation level. While neighborhood-level vari-ables clearly affect the efficacy of the oral choleravaccine, they only explain 26% of the variation. Onereason is that we did not have data for allneighborhood-level variables including some weknow are related to disease burden such asneighborhood-level sanitation. Also, neighbor-hood-level studies do not typically explain a largeamount of variation compared with individual-levelstudies (Mayer and Jencks, 1989; Macintyre et al.,1993; Diez Roux, 2001). One reason for this is thatthere might also be unknown individual-level effectsthat are not specified.

Inclusion of spatial information into a vaccinetrial can provide new valuable insights into theeffectiveness of a vaccine. Since individually rando-mized phase III vaccine trials are already expensive,adding a spatial component would not be costprohibitive. Inexpensive global positioning systemreceivers can be used to collect household-levelgeographic data during the initial census for thetrial. Alternatively, satellite imagery can be used todevelop household geographic databases in urbanareas (Ali et al., 2004). Deciding whether or not tovaccinate diverse populations is a difficult decisionfor developing world public health advisors withlimited resources. In this study, we observed widevariation in efficacy between neighborhoods. Vac-cine trials must give public health practitionersdetailed information concerning the levels of effi-cacy that a particular vaccine might achieve indifferent settings. Measuring neighborhood varia-tion in efficacy is as important as understandingindividual-level efficacy variation, which traditionalphase III trials have always calculated (Wahdanet al., 1982; Levine et al., 1990; Simanjuntak et al.,1991; Clemens et al., 1990a).

Developing country health policy makers mustcarefully evaluate trial results because of limitedresources. This study suggests that vaccinating allpeople in a population may not be worthwhilebecause disease incidence and ecological factors

vary in space. Policy makers should criticallyexamine local efficacy variation and identify targetareas to introduce vaccines so that programs arecost effective. This study shows that efficacy variesin space, and therefore the global efficacy estimatemay not be realistic. The issues addressed in thisstudy do not only pertain to vaccine trials. Anyindividually randomized trial assumes that effect ofthe trial is the same throughout the area. If localvariation of the outcome exists then the two reasonsidentified in this study that explain the variation,namely indirect protection and differential socio-environmental circumstances risk, are likely to beimportant. Therefore, the need for local-levelefficacy measures shown in this study also existsfor other health interventions.

Acknowledgments

Funding for this study was provided by GrantNo. 1R03AI53214-01 National Institute of Allergiesand Infectious Disease, National Institutes ofHealth and Grant No. 0323131 Geography andRegional Science Program, National Science Foun-dation.

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