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Efficacy calculation in randomized trials: Global or local measures?
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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 a
Department of Geography, Saunders Hall, University of North Carolina, Chapel Hill, NC 27599-3220 USA b International Vaccine Institute, Seoul, Korea c ICDDR, 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 of conventional vaccine trial methods. In particular, they suggested that public health practitioners cannot use traditional protective efficacy measures to make decisions about whether or not to vaccinate diverse populations. Many trials, including those measuring the efficacy of the Ty21a typhoid fever vaccine, have produced conflicting results in different settings. The Ty21a vaccine, for example, had an efficacy of 96 percent in Egypt (Wahdan et al., 1982), 77 percent in Chile (Levine et al., 1990), and Corresponding author. Tel.: +1 919 962 8901.
E-mail address: [email protected] (M. Emch). 1353-8292/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2006.01.005
53 percent in Indonesia (Simanjuntak et al., 1991). There are various factors that can result in different efficacies including different vaccine formulations, potencies, study designs, case definitions, strains of the wild-type agent, and the ecological (i.e., socioenvironmental) circumstances of the trial area. Conventional vaccine trial methods have an underlying assumption that the effect of the vaccine is the same throughout the trial area. This paper is a case study that tests whether this assumption is true for one vaccine trial. If the vaccine efficacy is spatially heterogeneous in a trial area then public health decisions based on global vaccine efficacy may not be realistic. This study uses three data sets, including a cholera vaccine trial database, a longitudinal
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demographic database of the rural population from which the vaccine trial participants were selected, and a household-level spatial database of the same study area. The cholera vaccine trial is one of the largest vaccine trials in history and the longitudinal demographic database of the study area is one of the most comprehensive in the developing world. All vital demographic events are noted through an extensive community-based data collection system. A corresponding household-level geographic information systems (GIS) database allows us to identify the household location of all individuals who took part in the trial as well as the household location of each person in the demographic surveillance system. Materials and methods Background In 1985, a community-based individually randomized oral cholera vaccine trial was conducted in Matlab, Bangladesh, the research site for the International Centre for Diarrhoeal Disease Research, Bangladesh-ICDDR,B (now called the Centre for Health and Population Research). This double-blind trial measured the efficacy of two vaccines, the B subunit-killed whole cell (BS–WC) and the killed whole cell (WC) only vaccines. The control agent was Escherichia coli K12 strain. Females aged 15 years and older and children aged 2–15 were the target groups in the trial (Clemens et al., 1990a). 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 in the study area. The surveillance took place at one hospital and two community-based treatment centers. During 3 years of follow-up, the cumulative protective efficacy was 50 percent for the BS–WC group (Po0:001) and 52 percent for the WC group (Po0:001). Protection was lower in children who were vaccinated between 2 and 5 years than in older persons. For children in this age group, protection waned after 4–6 months and was not evident during the third year. Persons older than 5 years of age who were vaccinated were protected even in the third year of follow-up (Clemens et al., 1990a). The study, however, did not indicate whether the level of protection of the vaccine is homogeneous throughout the trial area. We reanalyzed the same cholera vaccine trial data using spatial information to determine if there is any spatial pattern for vaccine efficacy in the study area,
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to identify if there are areas that have unusually high or low efficacies, and to determine why efficacy varies. This is accomplished by calculating neighborhood-level efficacy and by producing analytical maps 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, adjacent to where the Ganges River meets the Meghna River forming the Lower Meghna River. A health and demographic surveillance system (HDSS) has recorded all vital events of the study area population since 1963; the study area population has been approximately 200,000 since that time. The database is the most comprehensive longitudinal demographic database of a large population in the developing world. The people of the study area live in clusters of patrilineally related groups of households called baris. A vector GIS database of the Matlab field research area was created (Emch, 1999; Ali et al., 2001). Features in digital format include baris, rivers, and health facilities, and a floodcontrol embankment. Fig. 1 shows three features in the GIS database including the flood-control embankment, the Dhonagoda River, and baris. The three map views in Fig. 1 are displayed at different scales. The map view on the far right has the individual bari identification numbers visible. The baris are all identified by an ICDDR,B HDSS census number within the structure of the GIS database. This allows attribute data to be linked to the spatial database. In turn, demographic data, disease incidence, and vaccine status data can be linked to specific bari locations. The Matlab field research center has in- and out-patient services, a medical laboratory, and research facilities. One hundred and twenty community health workers (CHWs) visit each household area every 2 weeks to collect demographic, morbidity, and other data. The HDSS conducts periodic censuses and uses CHWs to update demographic data (e.g., births, deaths, and migrations). The study uses vaccine trial data collected in Matlab from 1985 to 1990 (Clemens et al., 1986a, b, 1987, 1988a–d, 1989a–c, 1990a, b, 1991; Durham et al., 1998; Sack et al., 1991; van Loon et al., 1996). The objective of the original randomized doubleblind, placebo-controlled trial was to determine whether three doses of BS–WC and WC vaccines
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Fig. 1. Study area GIS database.
reduces the incidence of laboratory confirmed cholera in children aged 2–5 years old and females over 15. The target group was individually randomized based on a simple random sampling scheme derived from HDSS records. The Matlab GIS database includes an accurate bari location for all individuals living in the study area including all vaccinees, controls, people who refused vaccines, and everyone else living in the study area who was not part of the study. The GIS database also includes the locations of the treatment facilities that were used in the passive surveillance system for the vaccine trial. Individual-level vaccine data were aggregated at the bari level so that they could be integrated with the GIS database. The original vaccine trial was conducted in 149 villages in the Matlab study area; in 1987, however, severe river erosion washed away seven villages, leaving only 142 villages that could be mapped during the 1994 GIS survey. There were 15 cholera cases in those seven villages during the three-year surveillance period that are not included in the efficacy calculation 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 neighborhood using different geographic scales. The vaccine trial data were linked to the GIS bari locations via the ICDDR,B HDSS identification number, a unique number assigned to all individuals in the study area. The GIS thus facilitates the identification of the dwelling locations of individuals who participated in the trial, as well as the entire population distribution of the Matlab study area. Efficacy was calculated by local neighborhood to determine how it varies in space. The two vaccine groups were aggregated because both of the vaccines were found to have similar efficacies (Clemens et al., 1990a). The protective efficacy of a neighborhood was calculated as: ai ¼ 1
Wi , li
where, ai is the protective efficacy in neighborhood i, Wi the vaccine incidence rate in neighborhood i, li the nonvaccinee incidence rate in neighborhood i. The incidence rates used to calculate the vaccineprotective efficacy are cumulative incidence rates (i.e., often called attack rates) during the six-months to two-year follow-up period. We removed cholera cases from the first 6 months of follow-up since the vaccine efficacy for the BS–WC was notably higher than that for WC-only vaccine during these initial
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months. The cases represent clinical and laboratory confirmed cholera cases using the same criteria described by Clemens et al. (1992). Fig. 2 shows how efficacy was measured for one spatial neighborhood. The black dots represent 12 different baris and the circle represents a 2000-m radius neighborhood around bari number 1. Table 1 lists the attribute data that are linked to the 10 baris within the neighborhood shown in Fig. 2; this attribute information was used to calculate neighborhoodlevel efficacy for the 1000-m radius area around this bari. The example shown in Fig. 2 yields an efficacy of 0.55 in the 1000-m radius area around bari number 1 for the follow-up period. There were 6423
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baris in the study area, thus the efficacy was computed for 6423 neighborhoods centering around each bari point. Different neighborhood sizes were explored ranging from 500 to 10,000 m, yielding different scale-dependent spatial patterns of vaccine efficacy (as reported in Table 2). Efficacy surface maps were created for the entire study area by interpolating the neighborhood-level values. In other words, the neighborhood-level data were used to create a continuous surface of protective efficacy. Several interpolation methods (i.e., ordinary kriging, local polynomials) were used with similar results, but those reported used a simple inverse distance weighted algorithm (10 nearest neighbors and a 2.8 inverse power function). The maps were created so that the spatial variation of efficacy within the study area can be visualized. Descriptive and analytical statistics are also reported. We wanted to see whether spatial patterns in the geographically weighted mean of the efficacies are due to genuine differences in local efficacy rather than the result of aspatial random variation. Therefore, we computed local z-scores (Fotheringham et al., 2002) of the ratio of incidences between vaccinees and placebo recipients. The z-score for neighborhood i (zi) is calculated as x¯ i m zi ¼ qffiffiffiffiffiffi Pffi s j wij2
s
7 8
where, x¯ i is the local (neighborhood i) mean of the log of ratio of incidences, m the global mean of the log of ratio of incidences, s the standard deviation
Fig. 2. One 2000-m neighborhood.
Table 1 Neighborhood efficacy data Identification number
Vaccinee population
Placebo population
1 2 3 4 5 6 7 8 9 10 Total Vaccinee incidence Placebo incidence Efficacy
12 2 23 24 25 12 25 22 34 25 204
7 6 25 22 32 25 45 23 25 20 230
Vaccinee cholera cases 0 0 0 1 0 1 0 0 0 0 2 0.0098 0.022 0.55
Placebo cholera cases 1 0 0 2 0 1 0 0 1 0 5
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Table 2 Neighborhood efficacy summary statistics (N ¼ 6423) Neighborhood Size (m)
250 500 750 1000 1500 2000 2500 3000 4000 5000 6000 7000 8000 9000 10,000
Efficacy Mean
Variance
Minimum
Maximum
0.48 0.34 0.50 0.36 0.45 0.47 0.48 0.48 0.49 0.50 0.50 0.49 0.49 0.49 0.49
1.3456 0.1936 0.1024 0.2025 0.0676 0.0324 0.0196 0.0144 0.0121 0.0081 0.0064 0.0064 0.0049 0.0036 0.0036
1.24 1.65 2.17 3.2 1.86 0.49 0.01 0.02 0.22 0.33 0.38 0.35 0.37 0.39 0.41
0.99 0.92 0.95 0.95 0.96 0.94 0.92 0.91 0.91 0.86 0.77 0.75 0.72 0.69 0.70
of the log of ratio of incidences, and wij the weight assigned to bari j. The sum of the weight is 1 and since the z-scores were based on the ratio of the incidence between vaccinees and placebo recipients, a value of |zi|X1.65 was used to accept our hypothesis of a significantly extreme value of efficacy at the 95 percent confidence level (one sided). A negative zscore indicates higher protective efficacy and a positive value lower efficacy. In an effort to determine why the vaccine protective efficacy varied in space we measured whether several ecological variables were related to efficacy levels within different spatial subsets of the trial area. We used a 2000-m neighborhood for the analyses because it retained local-level protective efficacy variation and the distribution was normal thus allowing us to conduct multiple regression analysis. Results Table 2 summarizes the efficacy values for the 6423 local neighborhoods around each bari point using 15 different neighborhood sizes. While using the smallest neighborhood of 250 m describes locallevel variation of efficacy, the summary statistics show that using a smaller neighborhood does not provide a complete account of the spatial variation. Many of the local areas did not have laboratory confirmed cases of cholera in the placebo group, vaccine group, or both during the two-year surveil-
No placebo cases
No vaccine and placebo cases
No vaccine cases
1165 942 543 182 9 0 0 0 0 0 0 0 0 0 0
2651 942 309 150 12 3 0 0 0 0 0 0 0 0 0
1220 927 442 170 17 0 0 0 0 0 0 0 0 0 0
lance period. The number of local areas without laboratory confirmed cholera cases in each group is shown in the last three columns of the table. For instance, a 250-m filter size resulted in 1165 (18 percent) neighborhoods without placebo cases, 1220 (19 percent) without vaccine cases, and 2651 (41 percent) without both placebo and vaccine cases. Thus, only 22 percent of the neighborhoods had cases. Efficacy in 250-m neighborhoods ranged from 1.24 to 0.99 with a mean of 0.48; these summary statistics were calculated using only those neighborhoods with cases. A negative efficacy value means that cholera incidence was higher in the vaccine group than the placebo group. This likely happened when there were very few observations in a local area. A 10,000-m neighborhood results in efficacy values ranging from 0.41 to 0.70 with a mean of 0.49 and a variance near zero. This mean value is the same as the global efficacy values reported in the original trial, and the low variance suggests that local variation in efficacy is obscured using such a large neighborhood. The descriptive efficacy statistics suggest that a 2000-m neighborhood is the best scale for measuring local efficacy variation in this study area, since it produced a large range of efficacy (0.49–0.94) with moderate degree of variation (0.03) in the data, and virtually all of the neighborhoods have cases. Fig. 3 displays the local efficacy surface maps that were created using neighborhood sizes ranging from 500 to 5000 m. The lighter tones are mostly areas with no cases and a few areas with higher incidence
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500 Meter Filter
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1000 Meter Filter
0
5
2000 Meter Filter
10 Kilometer
5000 Meter Filter
Efficacy <0 0 to 0.1 0.1 to 0.3 0.3 to 0.5 0.5 to 0.6 0.6 to 0.8 >0.8
Local Vaccine Efficacy Neighborhoods After 3-Year Followup N
Fig. 3. Local efficacy maps using 500–5000 m neighborhoods.
values in the vaccine group compared with the placebo group. The darker tones reveal areas with the highest incidence values. Each of the maps shows that efficacy varies in space. As expected the larger the neighborhood, the closer the mean local efficacy value is to the global efficacy value of approximately 0.48 found in the original vaccine trial. While the smaller neighborhood sizes reveal pockets of local variation in efficacy, the larger sizes reveal a trend of higher efficacy values in the northern region of the study area. Fig. 4 shows the z-score maps for the same four neighborhood sizes that were used to make the continuous surface maps
shown in Fig. 3. The areas with negative z-scores indicate areas with significantly higher protectivevaccine efficacy (in red). The 2000 m neighborhood, which we have said is the best size for this study, reveals that the northern part of the study area had areas with significantly high protective efficacy values. Table 3 shows the results of the regression analysis that measures relationships between efficacy and the ecological variables listed. The variables included in the model explain 26% of the variation of efficacy suggesting that there are other reasons for the variation not included (R2 ¼ 0.26).
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Neighborhood size: 500m z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64 > = 1.65
Neighborhood size: 2000m
Neighborhood size: 1000m
z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64 > = 1.65
Neighborhood size: 5000m z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64
z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64
Fig. 4. Local z-score maps.
Variables that were inversely related to neighborhood vaccine efficacy include vaccine coverage rate, population size, literacy, net migration into neighborhoods, and distance from the passive surveillance facilities. Those that were positively related to neighborhood efficacy include the average age of vaccine and placebo recipients, percentage of Hindus, percentage of farmers and distance from the river. Discussion This study illustrates that protective efficacy of the oral cholera vaccines vary in space within the trial
area. Scale-dependent patterns of efficacy are shown using different neighborhood sizes. The spatial variation of efficacy may be attributed to variation in the disease burden due to ecological differences and/or spatial variation of vaccinated individuals in the study area. Several studies have shown that cholera incidence is spatially heterogeneous in the Matlab 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 and socioeconomic status, were all determinants of cholera. Ali et al. (2002c) found that proximity to
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Table 3 Regression results of efficacy versus neighborhood variables Factor
Vaccine coverage (%) Neighborhood population size Average age of vaccine or placebo recipients % of Hindus in the neighborhood % literate people in the neighborhood % farming households in the neighborhood % net migration in the neighborhood Distance from the treatment center (km) Distance from the river
Coefficients B
1.705 .00085 2.338 .729 .908 1.356 12.261 6.594 8.121
Std. error
.062 .000 .606 .034 .112 .073 .302 .262 .403
t
27.692 8.145 3.858 21.139 8.124 18.499 40.590 25.123 20.159
Sig.
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
95% Confidence interval for B Lower bound
Upper bound
1.825 0.001 1.150 0.661 1.127 1.212 12.853 7.108 7.331
1.584 0.001 3.526 0.796 0.689 1.499 11.669 6.079 8.911
R2: 26.26%.
surface water and high population density are determinants of cholera incidence. All of these studies either explicitly or implicitly conclude that risk of cholera varies spatially within the Matlab study area. Since risk influences disease transmission it might therefore impact vaccine efficacy. This paper identifies several areas in the northern part of study area that have significantly higher efficacy (shown in red in the z-score map). An investigation of why the vaccine performed so well in these areas will show the circumstances in which the vaccine works best. In contrast, the lower efficacy values in the south-central part of the study area might help us understand why the vaccine might fail. Several ecological factors explain the efficacy variation. Efficacy is higher when the average age within a neighborhood is higher. This is consistent with individual-level analysis that showed that the vaccine worked better for adults than children. Efficacy is lower in neighborhoods with a larger percentage of vaccinated people. When more people have been vaccinated in a neighborhood there is herd protection of non-vaccinated people (Ali et al., 2005). In other words, there is an indirect benefit because cholera transmission is reduced in higher coverage neighborhoods. We have thus provided a theoretical basis for why neighborhood age and vaccine coverage percentage are related to efficacy. Age is related because the high-efficacy neighborhoods are composed of an older population; it is simply a biological compositional effect. Vaccine coverage rate is related because people in low-coverage neighborhoods have a higher probability of being infected. The other neighborhood-level variables that are related to cholera vaccine efficacy include popula-
tion density, percentage of Hindus, literacy rates, percentage of farming households, net migration into the area, distance to the river, and treatment center distance. Treatment center distance was included in the model because people are less likely to report to passive surveillance facilities if they live further away; it is essentially a variable to control for this bias. The underlying biological mechanisms behind why the other neighborhood-level variables affect efficacy are not well understood. In other words, we do not have a clear theory of why these contexts might be related to efficacy. Efficacy is higher in neighborhoods where a larger percentage of people are illiterate, have more Hindus, have a large percentage of farmers, have high rates of outmigration, a lower population density, and are far from the main river. These neighborhood-level variables were chosen for this analysis because they were found to be related to spatial variation of cholera incidence and they were available for the same time period that the trial was conducted. Literacy is one measure of socio-economic status (SES) and probably a better proxy than assets and income in this community. The study area is comprised of mostly very poor people and there is not much variability to SES here. However, there was a SES gradient to efficacy in which the vaccine did not work as well in poorer communities. There is no plausible biological basis for why the vaccine would work better in predominantly Hindu communities compared to Muslim communities. However, it is possible that there could be some unknown behavioral variable that is related to religious persuasion. While this study did not test the exact mechanism behind why all of these neighborhood-level vari-
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ables might be related to efficacy, it is still important to understand the context in which a vaccine works. If all vaccine trials collected neighborhood-level information about socio-environmental variation in the population then we might know why there is such large trial-to-trial variation in efficacy for the same vaccine. If we find consistency in how well the vaccine works in populations with similar characteristics then we will at least know the context in which the vaccine will most likely work at the population level. While neighborhood-level variables clearly affect the efficacy of the oral cholera vaccine, they only explain 26% of the variation. One reason is that we did not have data for all neighborhood-level variables including some we know are related to disease burden such as neighborhood-level sanitation. Also, neighborhood-level studies do not typically explain a large amount of variation compared with individual-level studies (Mayer and Jencks, 1989; Macintyre et al., 1993; Diez Roux, 2001). One reason for this is that there might also be unknown individual-level effects that are not specified. Inclusion of spatial information into a vaccine trial can provide new valuable insights into the effectiveness of a vaccine. Since individually randomized phase III vaccine trials are already expensive, adding a spatial component would not be cost prohibitive. Inexpensive global positioning system receivers can be used to collect household-level geographic data during the initial census for the trial. Alternatively, satellite imagery can be used to develop household geographic databases in urban areas (Ali et al., 2004). Deciding whether or not to vaccinate diverse populations is a difficult decision for developing world public health advisors with limited resources. In this study, we observed wide variation in efficacy between neighborhoods. Vaccine trials must give public health practitioners detailed information concerning the levels of efficacy that a particular vaccine might achieve in different settings. Measuring neighborhood variation in efficacy is as important as understanding individual-level efficacy variation, which traditional phase III trials have always calculated (Wahdan et al., 1982; Levine et al., 1990; Simanjuntak et al., 1991; Clemens et al., 1990a). Developing country health policy makers must carefully evaluate trial results because of limited resources. This study suggests that vaccinating all people in a population may not be worthwhile because disease incidence and ecological factors
vary in space. Policy makers should critically examine local efficacy variation and identify target areas to introduce vaccines so that programs are cost effective. This study shows that efficacy varies in space, and therefore the global efficacy estimate may not be realistic. The issues addressed in this study do not only pertain to vaccine trials. Any individually randomized trial assumes that effect of the trial is the same throughout the area. If local variation of the outcome exists then the two reasons identified in this study that explain the variation, namely indirect protection and differential socioenvironmental circumstances risk, are likely to be important. Therefore, the need for local-level efficacy measures shown in this study also exists for other health interventions. Acknowledgments Funding for this study was provided by Grant No. 1R03AI53214-01 National Institute of Allergies and Infectious Disease, National Institutes of Health and Grant No. 0323131 Geography and Regional Science Program, National Science Foundation.
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ARTICLE IN PRESS M. Emch et al. / Health & Place 13 (2007) 238–248 Clemens, J.D., Sack, D.A., Harris, J.R., Chakraborty, J., Khan, M.R., Stanton, B.F., Kay, B.A., Khan, M.U., Yunus, M., Atkinson, W., 1986b. Field trial of oral cholera vaccines in Bangladesh. Lancet 2 (8499), 124–127. Clemens, J.D., Stanton, B.F., Chakraborty, J., Sack, D.A., Khan, M.R., Huda, S., Ahmed, F., Harris, J.R., Yunus, M., Khan, M.U., 1987. B subunit-whole cell and whole cell-only oral vaccines against cholera: Studies on reactogenicity and immunogenicity. Journal of Infectious Diseases 155 (1), 79–85. Clemens, J.D., Sack, D.A., Harris, J.R., Chakraborty, J., Khan, M.R., Stanton, B.F., Ali, M., Ahmed, F., Yunus, M., Kay, B.A., 1988a. Impact of B subunit killed whole-cell and killed whole-cell-only oral vaccines against cholera upon treated diarrhoeal illness and mortality in an area endemic for cholera. Lancet 1 (8599), 1375–1379. Clemens, J.D., Harris, J.R., Sack, D.A., Chakraborty, J., Ahmed, F., Stanton, B.F., Khan, M.U., Kay, B.A., Huda, N., Khan, M.R., 1988b. Field trial of oral cholera vaccines in Bangladesh: results of one year of follow-up. Journal of Infectious Diseases 158 (1), 60–69. Clemens, J.D., Sack, D.A., Harris, J.R., Chakraborty, J., Neogy, P.K., Stanton, B., Huda, N., Khan, M.U., Kay, B.A., Khan, M.R., 1988c. Cross-protection by B subunit-whole cell cholera vaccine against diarrhea associated with heat-labile toxin-producing enterotoxigenic E. coli: results of a largescale field trial. Journal of Infectious Diseases 158 (2), 372–377. Clemens, J.D., Harris, J.R., Sack, D.A., Chakraborty, J., Ahmed, F., Stanton, B.F., Huda, N., Khan, M.R., Khan, M.U., Kay, B.A., 1988d. Field trial of oral cholera vaccines in Bangladesh. Southeast Asian Journal of Tropical Medicine and Public Health 19 (3), 417–422. Clemens, J.D., Sack, D.A., Harris, J.R., Chakraborty, J., Khan, M.R., Huda, S., Ahmed, F., Gomes, J., Rao, M.R., Svennerholm, A.M., 1989a. ABO blood groups and cholera: new observations on specificity of risk and modification of vaccine efficacy. Journal of Infectious Diseases 159 (4), 770–773. Clemens, J.D., Harris, J.R., Kay, B.A., Chakraborty, J., Sack, D.A., Ansaruzzaman, M., Rahman, R., Stanton, B.F., Khan, M.U., Khan, M.R., 1989b. Oral cholera vaccines containing B-subunit-killed whole cells and killed whole cells only. II. Field evaluation of cross-protection against other members of the Vibrionaceae family. Vaccine 7 (2), 117–120. Clemens, J.D., Stanton, B.F., Harris, J.R., Chakraborty, J., Sack, D.A., Rao, M.R., Ahmed, F., Ansaruzzaman, M., Yunus, M.D., Svennerholm, A.M., 1989c. Exclusion of clinically atypical or microbiologically mixed diarrhoeal episodes from outcome events in a field trial of oral cholera vaccines. International Journal of Epidemiology 18 (2), 440–445. Clemens, J.D., Sack, D.A., Harris, J.R., van Loon, F., Chakraborty, J., Ahmed, F., Rao, M.R., Khan, M.R., Yunus, M., Huda, N., 1990a. Field trial of oral cholera vaccines in Bangladesh: Results from three-year follow-up. Lancet 335 (8684), 270–273. Clemens, J.D., Sack, D.A., Chakraborty, J., Rao, M.R., Ahmed, F., Harris, J.R., van Loon, F., Khan, M.R., Yunis, M., Huda, S., 1990b. Field trial of oral cholera vaccines in Bangladesh: evaluation of anti-bacterial and anti-toxic breast-milk immunity in response to ingestion of the vaccines. Vaccine 8 (5), 469–472.
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Clemens, J.D., van Loon, F., Sack, D.A., Chakraborty, J., Rao, M.R., Ahmed, F., Harris, J.R., Khan, M.R., Yunus, M., Huda, S., 1991. Field trial of oral cholera vaccines in Bangladesh: serum vibriocidal and antitoxic antibodies as markers of the risk of cholera. Journal of Infectious Diseases 163 (6), 1235–1242. Clemens, J.D., Sack, D.A., Rao, M.R., Chakraborty, J., Khan, M.R., Kay, B., Ahmed, F., Banik, A.K., van Loon, F.P., Yunus, M., 1992. Evidence that inactivated oral cholera vaccines both prevent and mitigate Vibrio cholerae O1 infections in a cholera-endemic area. Journal of Infectious Diseases 166 (5), 1029–1034. Clemens, J., Brenner, R., Rao, M., Tafari, N., Lowe, C., 1996. Evaluating new vaccines for developing countries: efficacy or effectiveness? Journal of the American Medical Association 275 (5), 390–397. Diez Roux, A.V., 2001. Investigating Neighborhood and Area Effects on Health. American Journal of Public Health 91 (11), 1783–1789. Durham, L.K., Longini Jr., I.M., Halloran, M.E., Clemens, J.D., Nizam, A., Rao, M., 1998. Estimation of vaccine efficacy in the presence of waning: application to cholera vaccines. American Journal of Epidemiology 147 (10), 948–959. Emch, M.E., 1999. Diarrheal disease risk in Matlab, Bangladesh. Social Science and Medicine 49, 519–530. Emch, M.E., Ali, M., 2001. Spatial and temporal patterns of diarrheal disease in Matlab, Bangladesh. Environment and Planning A 33 (2), 339–350. Emch, M.E., Ali, M., 2003. Spatial cluster analysis for etiological research and identification of socio-environmental risk factors. In: Khan, O., Skinner, R. (Eds.), Geographic Information Systems and Health Applications. Idea Group Publishing, Hershey, Pennsylvania, pp. 172–187. Fotheringham, A.S., Brunsdon, C., Charlton, M.E., 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley Europe, Chichester. Glass, R.I., Becker, S., Huq, M.I., Stoll, B.J., Khan, M.U., Merson, M.H., Lee, J.V., Black, R.E., 1982. Endemic cholera in rural Bangladesh, 1966–1980. American Journal of Epidemiology 116 (6), 959–970. Hughes, J.M., Boyce, J.M., Levine, R.J., Khan, M., Aziz, K.M.A., Huq, M.I., Curlin, G.T., 1982. Epidemiology of eltor cholera in rural Bangladesh: Importance of surface water in transmission. Bulletin of the World Health Organization 60 (3), 395–404. Khan, M.U., 1981. Role of Water Supply and Sanitation in the Incidence of Cholera in Refugee Camps. Scientific Report 45. International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh. Levine, M., Ferreccio, C., Black, R., 1990. Large-scale field trial of Ty21a live oral typhoid vaccine in enteric-coated capsule formulation. Lancet 336, 891–894. Macintyre, S., Maciver, S., Soomans, A., 1993. Area, class and health: should we be focusing on places or people? Journal of Social Policy 22 (2), 213–234. Mayer, S.E., Jencks, C., 1989. Growing up in poor neighborhoods: how much does it matter? Science 243, 1441–1445. Myaux, J., Ali, M., Felsestein, A., Chakraborty, J., de Francisco, A., 1997. Spatial distribution of watery diarrhoea in children: Identification of risk areas in a rural community of Bangladesh. Health and Place 3 (3), 181–186.
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Tor/Ogawa cholera in rural East Bengal. The Lancet 2, 985–987. van Loon, F.P., Clemens, J.D., Chakraborty, J., Rao, M.R., Kay, B.A., Sack, D.A., Yunus, M., Ali, M., Svennerholm, A.M., Holmgren, J., 1996. Field trial of inactivated oral cholera vaccines in Bangladesh: results from 5 years of followup. Vaccine 14 (2), 162–166. Wahdan, M., Sarie, C., Derisier, Y., 1982. A controlled field trial of live Salmonella typhii strain Ty21a oral vaccine against typhoid. Journal of Infectious Diseases 145, 292–295.
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 a
Department of Geography, Saunders Hall, University of North Carolina, Chapel Hill, NC 27599-3220 USA b International Vaccine Institute, Seoul, Korea c ICDDR, 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 of conventional vaccine trial methods. In particular, they suggested that public health practitioners cannot use traditional protective efficacy measures to make decisions about whether or not to vaccinate diverse populations. Many trials, including those measuring the efficacy of the Ty21a typhoid fever vaccine, have produced conflicting results in different settings. The Ty21a vaccine, for example, had an efficacy of 96 percent in Egypt (Wahdan et al., 1982), 77 percent in Chile (Levine et al., 1990), and Corresponding author. Tel.: +1 919 962 8901.
E-mail address: [email protected] (M. Emch). 1353-8292/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2006.01.005
53 percent in Indonesia (Simanjuntak et al., 1991). There are various factors that can result in different efficacies including different vaccine formulations, potencies, study designs, case definitions, strains of the wild-type agent, and the ecological (i.e., socioenvironmental) circumstances of the trial area. Conventional vaccine trial methods have an underlying assumption that the effect of the vaccine is the same throughout the trial area. This paper is a case study that tests whether this assumption is true for one vaccine trial. If the vaccine efficacy is spatially heterogeneous in a trial area then public health decisions based on global vaccine efficacy may not be realistic. This study uses three data sets, including a cholera vaccine trial database, a longitudinal
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demographic database of the rural population from which the vaccine trial participants were selected, and a household-level spatial database of the same study area. The cholera vaccine trial is one of the largest vaccine trials in history and the longitudinal demographic database of the study area is one of the most comprehensive in the developing world. All vital demographic events are noted through an extensive community-based data collection system. A corresponding household-level geographic information systems (GIS) database allows us to identify the household location of all individuals who took part in the trial as well as the household location of each person in the demographic surveillance system. Materials and methods Background In 1985, a community-based individually randomized oral cholera vaccine trial was conducted in Matlab, Bangladesh, the research site for the International Centre for Diarrhoeal Disease Research, Bangladesh-ICDDR,B (now called the Centre for Health and Population Research). This double-blind trial measured the efficacy of two vaccines, the B subunit-killed whole cell (BS–WC) and the killed whole cell (WC) only vaccines. The control agent was Escherichia coli K12 strain. Females aged 15 years and older and children aged 2–15 were the target groups in the trial (Clemens et al., 1990a). 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 in the study area. The surveillance took place at one hospital and two community-based treatment centers. During 3 years of follow-up, the cumulative protective efficacy was 50 percent for the BS–WC group (Po0:001) and 52 percent for the WC group (Po0:001). Protection was lower in children who were vaccinated between 2 and 5 years than in older persons. For children in this age group, protection waned after 4–6 months and was not evident during the third year. Persons older than 5 years of age who were vaccinated were protected even in the third year of follow-up (Clemens et al., 1990a). The study, however, did not indicate whether the level of protection of the vaccine is homogeneous throughout the trial area. We reanalyzed the same cholera vaccine trial data using spatial information to determine if there is any spatial pattern for vaccine efficacy in the study area,
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to identify if there are areas that have unusually high or low efficacies, and to determine why efficacy varies. This is accomplished by calculating neighborhood-level efficacy and by producing analytical maps 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, adjacent to where the Ganges River meets the Meghna River forming the Lower Meghna River. A health and demographic surveillance system (HDSS) has recorded all vital events of the study area population since 1963; the study area population has been approximately 200,000 since that time. The database is the most comprehensive longitudinal demographic database of a large population in the developing world. The people of the study area live in clusters of patrilineally related groups of households called baris. A vector GIS database of the Matlab field research area was created (Emch, 1999; Ali et al., 2001). Features in digital format include baris, rivers, and health facilities, and a floodcontrol embankment. Fig. 1 shows three features in the GIS database including the flood-control embankment, the Dhonagoda River, and baris. The three map views in Fig. 1 are displayed at different scales. The map view on the far right has the individual bari identification numbers visible. The baris are all identified by an ICDDR,B HDSS census number within the structure of the GIS database. This allows attribute data to be linked to the spatial database. In turn, demographic data, disease incidence, and vaccine status data can be linked to specific bari locations. The Matlab field research center has in- and out-patient services, a medical laboratory, and research facilities. One hundred and twenty community health workers (CHWs) visit each household area every 2 weeks to collect demographic, morbidity, and other data. The HDSS conducts periodic censuses and uses CHWs to update demographic data (e.g., births, deaths, and migrations). The study uses vaccine trial data collected in Matlab from 1985 to 1990 (Clemens et al., 1986a, b, 1987, 1988a–d, 1989a–c, 1990a, b, 1991; Durham et al., 1998; Sack et al., 1991; van Loon et al., 1996). The objective of the original randomized doubleblind, placebo-controlled trial was to determine whether three doses of BS–WC and WC vaccines
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Fig. 1. Study area GIS database.
reduces the incidence of laboratory confirmed cholera in children aged 2–5 years old and females over 15. The target group was individually randomized based on a simple random sampling scheme derived from HDSS records. The Matlab GIS database includes an accurate bari location for all individuals living in the study area including all vaccinees, controls, people who refused vaccines, and everyone else living in the study area who was not part of the study. The GIS database also includes the locations of the treatment facilities that were used in the passive surveillance system for the vaccine trial. Individual-level vaccine data were aggregated at the bari level so that they could be integrated with the GIS database. The original vaccine trial was conducted in 149 villages in the Matlab study area; in 1987, however, severe river erosion washed away seven villages, leaving only 142 villages that could be mapped during the 1994 GIS survey. There were 15 cholera cases in those seven villages during the three-year surveillance period that are not included in the efficacy calculation 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 neighborhood using different geographic scales. The vaccine trial data were linked to the GIS bari locations via the ICDDR,B HDSS identification number, a unique number assigned to all individuals in the study area. The GIS thus facilitates the identification of the dwelling locations of individuals who participated in the trial, as well as the entire population distribution of the Matlab study area. Efficacy was calculated by local neighborhood to determine how it varies in space. The two vaccine groups were aggregated because both of the vaccines were found to have similar efficacies (Clemens et al., 1990a). The protective efficacy of a neighborhood was calculated as: ai ¼ 1
Wi , li
where, ai is the protective efficacy in neighborhood i, Wi the vaccine incidence rate in neighborhood i, li the nonvaccinee incidence rate in neighborhood i. The incidence rates used to calculate the vaccineprotective efficacy are cumulative incidence rates (i.e., often called attack rates) during the six-months to two-year follow-up period. We removed cholera cases from the first 6 months of follow-up since the vaccine efficacy for the BS–WC was notably higher than that for WC-only vaccine during these initial
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months. The cases represent clinical and laboratory confirmed cholera cases using the same criteria described by Clemens et al. (1992). Fig. 2 shows how efficacy was measured for one spatial neighborhood. The black dots represent 12 different baris and the circle represents a 2000-m radius neighborhood around bari number 1. Table 1 lists the attribute data that are linked to the 10 baris within the neighborhood shown in Fig. 2; this attribute information was used to calculate neighborhoodlevel efficacy for the 1000-m radius area around this bari. The example shown in Fig. 2 yields an efficacy of 0.55 in the 1000-m radius area around bari number 1 for the follow-up period. There were 6423
11 2 10
00
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baris in the study area, thus the efficacy was computed for 6423 neighborhoods centering around each bari point. Different neighborhood sizes were explored ranging from 500 to 10,000 m, yielding different scale-dependent spatial patterns of vaccine efficacy (as reported in Table 2). Efficacy surface maps were created for the entire study area by interpolating the neighborhood-level values. In other words, the neighborhood-level data were used to create a continuous surface of protective efficacy. Several interpolation methods (i.e., ordinary kriging, local polynomials) were used with similar results, but those reported used a simple inverse distance weighted algorithm (10 nearest neighbors and a 2.8 inverse power function). The maps were created so that the spatial variation of efficacy within the study area can be visualized. Descriptive and analytical statistics are also reported. We wanted to see whether spatial patterns in the geographically weighted mean of the efficacies are due to genuine differences in local efficacy rather than the result of aspatial random variation. Therefore, we computed local z-scores (Fotheringham et al., 2002) of the ratio of incidences between vaccinees and placebo recipients. The z-score for neighborhood i (zi) is calculated as x¯ i m zi ¼ qffiffiffiffiffiffi Pffi s j wij2
s
7 8
where, x¯ i is the local (neighborhood i) mean of the log of ratio of incidences, m the global mean of the log of ratio of incidences, s the standard deviation
Fig. 2. One 2000-m neighborhood.
Table 1 Neighborhood efficacy data Identification number
Vaccinee population
Placebo population
1 2 3 4 5 6 7 8 9 10 Total Vaccinee incidence Placebo incidence Efficacy
12 2 23 24 25 12 25 22 34 25 204
7 6 25 22 32 25 45 23 25 20 230
Vaccinee cholera cases 0 0 0 1 0 1 0 0 0 0 2 0.0098 0.022 0.55
Placebo cholera cases 1 0 0 2 0 1 0 0 1 0 5
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Table 2 Neighborhood efficacy summary statistics (N ¼ 6423) Neighborhood Size (m)
250 500 750 1000 1500 2000 2500 3000 4000 5000 6000 7000 8000 9000 10,000
Efficacy Mean
Variance
Minimum
Maximum
0.48 0.34 0.50 0.36 0.45 0.47 0.48 0.48 0.49 0.50 0.50 0.49 0.49 0.49 0.49
1.3456 0.1936 0.1024 0.2025 0.0676 0.0324 0.0196 0.0144 0.0121 0.0081 0.0064 0.0064 0.0049 0.0036 0.0036
1.24 1.65 2.17 3.2 1.86 0.49 0.01 0.02 0.22 0.33 0.38 0.35 0.37 0.39 0.41
0.99 0.92 0.95 0.95 0.96 0.94 0.92 0.91 0.91 0.86 0.77 0.75 0.72 0.69 0.70
of the log of ratio of incidences, and wij the weight assigned to bari j. The sum of the weight is 1 and since the z-scores were based on the ratio of the incidence between vaccinees and placebo recipients, a value of |zi|X1.65 was used to accept our hypothesis of a significantly extreme value of efficacy at the 95 percent confidence level (one sided). A negative zscore indicates higher protective efficacy and a positive value lower efficacy. In an effort to determine why the vaccine protective efficacy varied in space we measured whether several ecological variables were related to efficacy levels within different spatial subsets of the trial area. We used a 2000-m neighborhood for the analyses because it retained local-level protective efficacy variation and the distribution was normal thus allowing us to conduct multiple regression analysis. Results Table 2 summarizes the efficacy values for the 6423 local neighborhoods around each bari point using 15 different neighborhood sizes. While using the smallest neighborhood of 250 m describes locallevel variation of efficacy, the summary statistics show that using a smaller neighborhood does not provide a complete account of the spatial variation. Many of the local areas did not have laboratory confirmed cases of cholera in the placebo group, vaccine group, or both during the two-year surveil-
No placebo cases
No vaccine and placebo cases
No vaccine cases
1165 942 543 182 9 0 0 0 0 0 0 0 0 0 0
2651 942 309 150 12 3 0 0 0 0 0 0 0 0 0
1220 927 442 170 17 0 0 0 0 0 0 0 0 0 0
lance period. The number of local areas without laboratory confirmed cholera cases in each group is shown in the last three columns of the table. For instance, a 250-m filter size resulted in 1165 (18 percent) neighborhoods without placebo cases, 1220 (19 percent) without vaccine cases, and 2651 (41 percent) without both placebo and vaccine cases. Thus, only 22 percent of the neighborhoods had cases. Efficacy in 250-m neighborhoods ranged from 1.24 to 0.99 with a mean of 0.48; these summary statistics were calculated using only those neighborhoods with cases. A negative efficacy value means that cholera incidence was higher in the vaccine group than the placebo group. This likely happened when there were very few observations in a local area. A 10,000-m neighborhood results in efficacy values ranging from 0.41 to 0.70 with a mean of 0.49 and a variance near zero. This mean value is the same as the global efficacy values reported in the original trial, and the low variance suggests that local variation in efficacy is obscured using such a large neighborhood. The descriptive efficacy statistics suggest that a 2000-m neighborhood is the best scale for measuring local efficacy variation in this study area, since it produced a large range of efficacy (0.49–0.94) with moderate degree of variation (0.03) in the data, and virtually all of the neighborhoods have cases. Fig. 3 displays the local efficacy surface maps that were created using neighborhood sizes ranging from 500 to 5000 m. The lighter tones are mostly areas with no cases and a few areas with higher incidence
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500 Meter Filter
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1000 Meter Filter
0
5
2000 Meter Filter
10 Kilometer
5000 Meter Filter
Efficacy <0 0 to 0.1 0.1 to 0.3 0.3 to 0.5 0.5 to 0.6 0.6 to 0.8 >0.8
Local Vaccine Efficacy Neighborhoods After 3-Year Followup N
Fig. 3. Local efficacy maps using 500–5000 m neighborhoods.
values in the vaccine group compared with the placebo group. The darker tones reveal areas with the highest incidence values. Each of the maps shows that efficacy varies in space. As expected the larger the neighborhood, the closer the mean local efficacy value is to the global efficacy value of approximately 0.48 found in the original vaccine trial. While the smaller neighborhood sizes reveal pockets of local variation in efficacy, the larger sizes reveal a trend of higher efficacy values in the northern region of the study area. Fig. 4 shows the z-score maps for the same four neighborhood sizes that were used to make the continuous surface maps
shown in Fig. 3. The areas with negative z-scores indicate areas with significantly higher protectivevaccine efficacy (in red). The 2000 m neighborhood, which we have said is the best size for this study, reveals that the northern part of the study area had areas with significantly high protective efficacy values. Table 3 shows the results of the regression analysis that measures relationships between efficacy and the ecological variables listed. The variables included in the model explain 26% of the variation of efficacy suggesting that there are other reasons for the variation not included (R2 ¼ 0.26).
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Neighborhood size: 500m z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64 > = 1.65
Neighborhood size: 2000m
Neighborhood size: 1000m
z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64 > = 1.65
Neighborhood size: 5000m z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64
z-value <=-1.65 -0.01 to -1.64 0.00 to 1.64
Fig. 4. Local z-score maps.
Variables that were inversely related to neighborhood vaccine efficacy include vaccine coverage rate, population size, literacy, net migration into neighborhoods, and distance from the passive surveillance facilities. Those that were positively related to neighborhood efficacy include the average age of vaccine and placebo recipients, percentage of Hindus, percentage of farmers and distance from the river. Discussion This study illustrates that protective efficacy of the oral cholera vaccines vary in space within the trial
area. Scale-dependent patterns of efficacy are shown using different neighborhood sizes. The spatial variation of efficacy may be attributed to variation in the disease burden due to ecological differences and/or spatial variation of vaccinated individuals in the study area. Several studies have shown that cholera incidence is spatially heterogeneous in the Matlab 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 and socioeconomic status, were all determinants of cholera. Ali et al. (2002c) found that proximity to
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Table 3 Regression results of efficacy versus neighborhood variables Factor
Vaccine coverage (%) Neighborhood population size Average age of vaccine or placebo recipients % of Hindus in the neighborhood % literate people in the neighborhood % farming households in the neighborhood % net migration in the neighborhood Distance from the treatment center (km) Distance from the river
Coefficients B
1.705 .00085 2.338 .729 .908 1.356 12.261 6.594 8.121
Std. error
.062 .000 .606 .034 .112 .073 .302 .262 .403
t
27.692 8.145 3.858 21.139 8.124 18.499 40.590 25.123 20.159
Sig.
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
95% Confidence interval for B Lower bound
Upper bound
1.825 0.001 1.150 0.661 1.127 1.212 12.853 7.108 7.331
1.584 0.001 3.526 0.796 0.689 1.499 11.669 6.079 8.911
R2: 26.26%.
surface water and high population density are determinants of cholera incidence. All of these studies either explicitly or implicitly conclude that risk of cholera varies spatially within the Matlab study area. Since risk influences disease transmission it might therefore impact vaccine efficacy. This paper identifies several areas in the northern part of study area that have significantly higher efficacy (shown in red in the z-score map). An investigation of why the vaccine performed so well in these areas will show the circumstances in which the vaccine works best. In contrast, the lower efficacy values in the south-central part of the study area might help us understand why the vaccine might fail. Several ecological factors explain the efficacy variation. Efficacy is higher when the average age within a neighborhood is higher. This is consistent with individual-level analysis that showed that the vaccine worked better for adults than children. Efficacy is lower in neighborhoods with a larger percentage of vaccinated people. When more people have been vaccinated in a neighborhood there is herd protection of non-vaccinated people (Ali et al., 2005). In other words, there is an indirect benefit because cholera transmission is reduced in higher coverage neighborhoods. We have thus provided a theoretical basis for why neighborhood age and vaccine coverage percentage are related to efficacy. Age is related because the high-efficacy neighborhoods are composed of an older population; it is simply a biological compositional effect. Vaccine coverage rate is related because people in low-coverage neighborhoods have a higher probability of being infected. The other neighborhood-level variables that are related to cholera vaccine efficacy include popula-
tion density, percentage of Hindus, literacy rates, percentage of farming households, net migration into the area, distance to the river, and treatment center distance. Treatment center distance was included in the model because people are less likely to report to passive surveillance facilities if they live further away; it is essentially a variable to control for this bias. The underlying biological mechanisms behind why the other neighborhood-level variables affect efficacy are not well understood. In other words, we do not have a clear theory of why these contexts might be related to efficacy. Efficacy is higher in neighborhoods where a larger percentage of people are illiterate, have more Hindus, have a large percentage of farmers, have high rates of outmigration, a lower population density, and are far from the main river. These neighborhood-level variables were chosen for this analysis because they were found to be related to spatial variation of cholera incidence and they were available for the same time period that the trial was conducted. Literacy is one measure of socio-economic status (SES) and probably a better proxy than assets and income in this community. The study area is comprised of mostly very poor people and there is not much variability to SES here. However, there was a SES gradient to efficacy in which the vaccine did not work as well in poorer communities. There is no plausible biological basis for why the vaccine would work better in predominantly Hindu communities compared to Muslim communities. However, it is possible that there could be some unknown behavioral variable that is related to religious persuasion. While this study did not test the exact mechanism behind why all of these neighborhood-level vari-
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ables might be related to efficacy, it is still important to understand the context in which a vaccine works. If all vaccine trials collected neighborhood-level information about socio-environmental variation in the population then we might know why there is such large trial-to-trial variation in efficacy for the same vaccine. If we find consistency in how well the vaccine works in populations with similar characteristics then we will at least know the context in which the vaccine will most likely work at the population level. While neighborhood-level variables clearly affect the efficacy of the oral cholera vaccine, they only explain 26% of the variation. One reason is that we did not have data for all neighborhood-level variables including some we know are related to disease burden such as neighborhood-level sanitation. Also, neighborhood-level studies do not typically explain a large amount of variation compared with individual-level studies (Mayer and Jencks, 1989; Macintyre et al., 1993; Diez Roux, 2001). One reason for this is that there might also be unknown individual-level effects that are not specified. Inclusion of spatial information into a vaccine trial can provide new valuable insights into the effectiveness of a vaccine. Since individually randomized phase III vaccine trials are already expensive, adding a spatial component would not be cost prohibitive. Inexpensive global positioning system receivers can be used to collect household-level geographic data during the initial census for the trial. Alternatively, satellite imagery can be used to develop household geographic databases in urban areas (Ali et al., 2004). Deciding whether or not to vaccinate diverse populations is a difficult decision for developing world public health advisors with limited resources. In this study, we observed wide variation in efficacy between neighborhoods. Vaccine trials must give public health practitioners detailed information concerning the levels of efficacy that a particular vaccine might achieve in different settings. Measuring neighborhood variation in efficacy is as important as understanding individual-level efficacy variation, which traditional phase III trials have always calculated (Wahdan et al., 1982; Levine et al., 1990; Simanjuntak et al., 1991; Clemens et al., 1990a). Developing country health policy makers must carefully evaluate trial results because of limited resources. This study suggests that vaccinating all people in a population may not be worthwhile because disease incidence and ecological factors
vary in space. Policy makers should critically examine local efficacy variation and identify target areas to introduce vaccines so that programs are cost effective. This study shows that efficacy varies in space, and therefore the global efficacy estimate may not be realistic. The issues addressed in this study do not only pertain to vaccine trials. Any individually randomized trial assumes that effect of the trial is the same throughout the area. If local variation of the outcome exists then the two reasons identified in this study that explain the variation, namely indirect protection and differential socioenvironmental circumstances risk, are likely to be important. Therefore, the need for local-level efficacy measures shown in this study also exists for other health interventions. Acknowledgments Funding for this study was provided by Grant No. 1R03AI53214-01 National Institute of Allergies and Infectious Disease, National Institutes of Health and Grant No. 0323131 Geography and Regional Science Program, National Science Foundation.
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