Abstract

The risk of becoming a West Nile virus case in New York State, excluding New York City, was evaluated for persons whose town of residence was proximal to spatial clusters of dead American crows (Corvus brachyrhynchos). Weekly clusters were delineated for June–October 2002 by using both the binomial spatial scan statistic and kernel density smoothing. The relative risk of a human case was estimated for different spatial-temporal exposure definitions after adjusting for population density and age distribution using Poisson regression, adjusting for week and geographic region, and conducting Cox proportional hazards modeling, where the week that a human case was identified was treated as the failure time and baseline hazard was stratified by region. The risk of becoming a West Nile virus case was positively associated with living in towns proximal to dead crow clusters. The highest risk was consistently for towns associated with a cluster in the current or prior 1–2 weeks. Weaker, but positive associations were found for towns associated with a cluster in just the 1–2 prior weeks, indicating an ability to predict onset in a timely fashion.

Since it was first detected in New York City in 1999, West Nile virus (WNV) has become an expanding pandemic in the Western hemisphere (1), thus requiring active surveillance to identify geographic areas of high risk for human infection. This flavivirus is sustained in various avian species and is transmitted by mosquitoes among birds and from birds to mammals when the cycle amplifies (2). With WNV, human infection appears to be spatially-temporally associated with unusual bird die-offs, particularly among American crows (Corvus brachyrhynchos) (35).

New York State has responded by exploiting its Health Information Network, a secure, Web-based, enterprise-wide information infrastructure (6), to maintain real-time monitoring data related to WNV. Data include dead bird locations reported by the public, mandatory reporting of human cases, and laboratory tests for mosquitoes, birds, horses, and other animals.

Although laboratory results are necessary to confirm the presence of WNV, the average time of 23 days (based on the 2002 WNV season) for collection, shipment, processing, and testing of dead bird specimens reduces the real-time utility of these results for guiding prevention and control activities prior to human onset. In contrast, monitoring of crow deaths requires only a phone call and some data entry. Crows exhibit a high case-fatality rate, dying soon after infection, and they are both easily identified and fairly commonly distributed throughout the state (7). Simple maps of dead crow locations have proven valuable for identifying areas of high WNV activity (8), regardless of observation bias in such maps. Indeed, retrospective studies have revealed a positive association between county-level dead crow density (number per square mile) and the number of human cases for the years 2000 (9) and 2001 (10).

The initial studies cited above indicated that dead crow density may provide a critical real-time sentinel for WNV activity; however, geographic resolution finer than counties is required for community-level prevention and control activities (10). Furthermore, the surveillance system would benefit from a more objective method for identifying clusters of dead birds, or dead crows in particular.

To this end, two different methods were applied in New York City in the year 2001 to identify bird clusters that are statistically significant with respect to spatially random expectation (11, 12). Both approaches analyzed the locations of dead birds, excluding pigeons and unknown species, which were geocoded to census tracts. One approach (11) applied the spatial scan statistic (13) daily, using the Bernoulli model that compared dead bird counts after confirmed WNV activity (cases) with preoutbreak dead bird counts (controls) in each tract. The other approach (12), based on work by Knox (14), applied a contingency table analysis to detect significant space-time interaction of dead bird counts based on predefined definitions of “closeness” in both space and time. After calibration with previous data for defining “closeness,” the test was applied to each grid cell of a surface tessellation to estimate the probability of nonrandom space-time interaction of bird deaths. The result is a continuous surface of probability estimates, which helps focus areas of amplified WNV activity. Both the daily spatial scan statistic and the space-time Knox test identified significant dead bird clusters in similar locations that also captured the residences of five of seven diagnosed human cases in 2001.

Aside from New York City, the rest of New York State is analyzed for WNV activity each season by using data from the Health Information Network (6). Both the spatial scan statistic (13) and kernel density smoothing (15) are applied weekly to reported dead crow locations. Resulting maps are provided through the Health Information Network to help county health departments prioritize prevention and control efforts. These maps visually appear to effectively indicate WNV activity and even predict human onset; however, just like all studies discussed above, a formal statistical evaluation of the association between dead bird/crow clusters and human case onset has been lacking.

A recent retrospective study of WNV in Chicago, Illinois, for the year 2002 did show significant incidence ratios of 3.0 for WNV infection and 2.3 for WNV meningo-encephalitis for residents in high crow-mortality areas relative to those outside these areas (16). Areas whose kernel density estimates were in the top 90 percent (density >0.10) were chosen as “high,” and the whole season's data were pooled. Age was the only potential confounder evaluated and apparently did not yield a significant or confounding effect.

In this paper, we quantify the association between dead crow clusters and human onset of WNV in New York State, outside of New York City, for the year 2002, while also considering the potential confounders or effect modifiers of human population density and age distribution. The effects of time are evaluated two ways: as an implicit covariate in a Poisson regression and as the “waiting time” until onset in a Cox proportional hazards model.

MATERIALS AND METHODS

Data

Dead crow locations.

Weekly reports of dead crow sightings were downloaded from the dead bird surveillance database on New York State's Health Information Network (6) for June 2–October 12, 2002, corresponding to weeks 23–41, as recognized by the US Centers for Disease Control and Prevention. Dead bird locations are reported by the public through either their county health departments or a dead bird hotline maintained by the New York office of the US Department of Agriculture Wildlife Service. Counties outside of New York City were assigned to six regions, as indicated in figure 1, for managing geocoding activity and performing region-specific cluster analysis. Most dead bird locations were geocoded to an actual street address by using MapMarker (17) supplemented by manual geocoding based on investigative information for those reports that could not automatically be geocoded to a street address. Only those dead crow reports that were available on the Health Information Network in real time and were used for mapping in the subsequent week were utilized for this analysis. Dead crow reports entered into the Health Information Network at later dates were not available for mapping and were not used.

FIGURE 1.

The six regions of New York State used for separate spatial analyses of dead crow clusters during the week of September 1–7, 2002.

FIGURE 1.

The six regions of New York State used for separate spatial analyses of dead crow clusters during the week of September 1–7, 2002.

Human cases.

Human cases were those who met the Centers for Disease Control and Prevention's case definition of laboratory-confirmed or probable WNV-associated fever or neurologic disease (18). Of 53 cases reported in New York State outside of New York City for 2002, we analyzed data for 50 cases whose reported onset date was within 2 weeks after dead crow mapping ended for the season.

Human population characteristics.

Data from the year 2000 US Census were used for population counts by age in county subdivisions, which in New York State are called townships. Our study included 1,006 subdivisions outside of New York City.

Cluster detection

Spatial cluster analysis was performed weekly in each of the six regions shown in figure 1 for the dead crow reports of the particular week. Two methods were used for identifying clusters—the spatial scan statistic (13) and kernel density smoothing (15). An example of results for these two methods is shown for the Long Island region in figure 2, and a detailed discussion follows.

FIGURE 2.

Dead crow locations and cluster analyses for Long Island, New York, during the week of September 1–7, 2002: kernel density smoothing (A) and spatial scan statistic clusters (B) (note that all clusters significant at the p < 0.05 level were also significant at p < 0.01).

FIGURE 2.

Dead crow locations and cluster analyses for Long Island, New York, during the week of September 1–7, 2002: kernel density smoothing (A) and spatial scan statistic clusters (B) (note that all clusters significant at the p < 0.05 level were also significant at p < 0.01).

Spatial scan statistic.

Using SaTScan 3.0 software (19), we applied the binomial model of the purely spatial scan statistic (11) to each of the six regions separately. With dead crow locations assigned to census tracts, circular search windows started with individual tracts and expanded to include neighboring tracts until a maximum of 15 percent of all crows, including the current week's cases and baseline controls, within the region was reached. This “15 percent” criterion is based on previous research (20). Given the dead birds reported within a monitoring week (cases) and during a control period prior to the WNV season (controls), the scan statistic calculates a likelihood ratio for each search window, which is proportional to 

\begin{eqnarray*}&&\left(\frac{c}{n}\right)^{c}\left(\frac{n{-}c}{n}\right)^{n{-}c}\left(\frac{C{-}c}{N{-}n}\right)^{C{-}c}\\&&{\times}\left(\frac{(N{-}n){-}(C{-}c)}{N{-}n}\right)^{(N{-}n){-}(C{-}c)}\end{eqnarray*}
for c cases and n total cases and controls inside the search window, and C cases and N total cases and controls throughout the region, including within the search window. Each observed likelihood ratio was then compared with 999 Monte Carlo simulations from the null model to evaluate significance. Two separate sets of spatial scan statistic clusters were then retained: those significant at the p < 0.05 level and those significant at the p < 0.01 level. The control period was from January 1 until 3 weeks prior to the week of analysis, allowing at least a 2-week buffer between crow deaths in the absence of viral activity (controls) and those in its presence (cases). When the first laboratory-positive bird was reported in a region, the control period ended with that bird's found date for all subsequent analyses.

Kernel density smoothing.

Using Vertical Mapper (21) within MapInfo's geographic information system software (22), we estimated dead crow density for cells of a raster grid based on the sum of kernel functions within each cell, standardized to the real unit interval from 0 to 1. The Epanechnikov kernel estimate was chosen to weight neighbors as a parabolically decreasing function of distance until the edge of the search radius was reached. An adaptive bandwidth was allowed to change for each grid cell to include a specified number of closest points, which were in turn chosen each week according to an adaptive kernel smoothing key we created. This key related the overall number of dead crow locations to an associated number of closest points. Areas whose cells had densities above 0.5 were transformed by a contouring procedure into polygons to delineate clusters. Experience shows that clusters become too diffuse and noninformative as densities drop below 0.5.

Exposure definition

Exposure was defined as being close in space and time to a dead crow cluster. Spatial association with dead crow clusters was determined by using a geographic information system (22) according to the following protocol. Census tracts within clusters were selected, and a 1-mile buffer (1 mile = 1.609 km) was created around the common outer boundary of these tracts, since the principal mosquito carrier in the eastern United States, Culex pipiens, is known to fly up to 1 mile (23). Each of the 1,006 towns outside of New York City was then identified as being “in or adjacent to” a cluster in a particular week if the town boundary intersected a cluster or a buffer area for that week.

Temporal associations were designed to address particular questions. To evaluate whether there is a spatial relation between dead crows and human cases, we considered towns exposed during the “current plus any prior week” of the season, and we conducted the more focused evaluation of exposure during the “current plus prior 1–2 weeks” based on the incubation period of approximately 3–14 days in humans (24). To evaluate the ability of recent dead crow clusters to predict human onset, we considered towns exposed during only the prior 1–2 weeks.

Estimating relative risk

The period used for estimating risk was the week of July 28, when the first human case was recorded, to the week of October 5, when the last human case was recorded for which crow surveillance data were also available within the previous 2 weeks. Risk estimates therefore represent the 2002 WNV season in New York, conditional on occurrence of the first human case. Poisson regression and Cox proportional hazards modeling were applied separately to estimate the relative risk of a human case during the 10-week season using different, but related methods.

Using Poisson regression (25), we regressed the number of cases in each of n = 10,060 unique combinations of the 1,006 towns and 10 weeks on exposure after offsetting the total population and adjusting for possible confounders and other covariates that may explain geographic variability of human onset. Exposure was defined by a binary indicator variable (exposed/unexposed). Specific covariates were 1) region, as indicated in figure 1; 2) Centers for Disease Control and Prevention week (category valued); 3) different transformations of town population density (people per square mile); and 4) proportion of the town population over age 50 years, since persons older than age 50 years are at higher risk of symptomatic meningo-encephalitis (26). Both the population density and the age distribution were evaluated as potential confounders or effect modifiers with respect to exposure. Poisson modeling was performed with SAS 8.02 software (SAS Institute, Inc., Cary, North Carolina) by using the GENMOD procedure.

Realizing that the time until human onset of WNV is essentially a “waiting time” situation, we turned to the theory of Cox proportional hazards modeling (27). The instantaneous disease rate for individual i at any observed time t, defined as the hazard function hi(t), is modeled as hi(t) = exp(βx)h0(t), where x and β are vectors of explanatory covariates and their coefficients, respectively, and h0(t) is the hazard corresponding to “baseline” values of the covariates x. For a binary exposure variable (xE = 1 if exposed and 0 otherwise), the estimated hazard ratio, exp(β̂E), provides an estimate of relative risk after adjusting for the other covariates.

Exposure was modeled as a time-dependent variable that could arise and go away repeatedly throughout the 10-week study period. The effect of geographic region, as depicted in figure 1, was evaluated separately as both an implicit covariate and a stratifier, where the baseline hazard, h0(t), was allowed to be unique for each region. The other covariates mentioned in the Poisson regression discussion were also examined to help identify potential confounders and effect modifiers when estimating hazard ratios.

Each of the 1,006 towns in this study was weighted by its total population, representing the approximately 11 million people not reported as being infected with WNV. They were treated as censored observations because they were not infected/diagnosed within the observation period. Each of the 50 cases was treated as a town with a population of size one. Town-level demographic information was then associated with cases and noncases according to their town of residence.

Proportional hazards modeling was performed with the PHREG procedure in SAS 8.02 software, with population weights specified by the FREQ statement, and Efron's approximation to the partial likelihood (28) was chosen for handling tied onset times. Although observations were made in discrete time (weekly), the process of human infection and disease development occurs in real time. For the methods available for tied “failure” times of a real-time process, Efron's method was chosen because it provides the closest approximation to the actual likelihood (29).

RESULTS

The distributions of dead crows and human WNV cases are presented in table 1 by region and week. This table shows the rise and fall of WNV activity statewide, where the dead crow reports peak 1 week prior to the peak in human cases. The number of human cases and the population size exposed to dead crow clusters are reported in table 2 for the different exposure definitions. Data from tables 1 and 2 can be used to calculate crude relative risks. Estimates of the relative risk for human WNV cases associated with the various spatial-temporal exposure definitions are presented in tables 3 and 4 for Poisson and proportional hazards regression, respectively.

TABLE 1.

Distribution of dead crow reports (no.) and of human cases of West Nile virus (no.), by week in 2002 and region, New York State (excluding New York City)*


 

Control period crow data
 
 
Week of the West Nile virus season
 
           
Total
 

 
Period end date
 
No. of crows
 
7/14–7/20
 
7/21–7/27
 
7/28–8/3
 
8/4–8/10
 
8/11–8/17
 
8/18–8/24
 
8/25–8/31
 
9/1–9/7
 
9/8–9/14
 
9/15–9/21
 
9/22–9/28
 
9/29–10/5
 
 
Crow reports                
    North July 9 167 14 31 16 33 53 26 47 67 43 39 24 19 412 
    West June 15 202 61 81 73 130 349 518 582 483 461 252 204 155 3,349 
    Central June 21 168 23 33 32 71 115 221 447 535 329 275 176 103 2,360 
    Capital May 1 119 10 10 24 52 52 73 89 72 48 28 28 24 510 
    South April 25 118 84 77 86 123 160 282 238 227 166 103 103 55 1,704 
    Long Island May 27 15 61 53 102 71 126 136 172 166 88 58 34 1,075 
        Statewide  789 253 285 333 480 855 1,256 1,575 1,550 1,135 755 569 364 9,410 
Human cases                
    North     
    West     12 
    Central     13 
    Capital     
    South     
    Long Island     18 
        Statewide
 

 

 

 

 
1
 
2
 
4
 
4
 
7
 
13
 
7
 
5
 
6
 
1
 
50
 

 

Control period crow data
 
 
Week of the West Nile virus season
 
           
Total
 

 
Period end date
 
No. of crows
 
7/14–7/20
 
7/21–7/27
 
7/28–8/3
 
8/4–8/10
 
8/11–8/17
 
8/18–8/24
 
8/25–8/31
 
9/1–9/7
 
9/8–9/14
 
9/15–9/21
 
9/22–9/28
 
9/29–10/5
 
 
Crow reports                
    North July 9 167 14 31 16 33 53 26 47 67 43 39 24 19 412 
    West June 15 202 61 81 73 130 349 518 582 483 461 252 204 155 3,349 
    Central June 21 168 23 33 32 71 115 221 447 535 329 275 176 103 2,360 
    Capital May 1 119 10 10 24 52 52 73 89 72 48 28 28 24 510 
    South April 25 118 84 77 86 123 160 282 238 227 166 103 103 55 1,704 
    Long Island May 27 15 61 53 102 71 126 136 172 166 88 58 34 1,075 
        Statewide  789 253 285 333 480 855 1,256 1,575 1,550 1,135 755 569 364 9,410 
Human cases                
    North     
    West     12 
    Central     13 
    Capital     
    South     
    Long Island     18 
        Statewide
 

 

 

 

 
1
 
2
 
4
 
4
 
7
 
13
 
7
 
5
 
6
 
1
 
50
 
*

The number of dead crows serving as controls for the binomial spatial scan statistic is shown, along with the final end date of the control period. For the West Nile virus season, dead crow reports are for each week starting with 2 weeks prior to the first human case, and the 50 human cases are reported for the week of disease onset.

Weeks are formatted as follows, for example: 7/14–7/20, July 14 to July 20.

TABLE 2.

Number of human cases of West Nile virus and population size (in millions) exposed to clusters of dead crows, according to different exposure definitions, during Centers for Disease Control and Prevention weeks 31–40 (July 28–October 5) in 2002, New York State (excluding New York City)*


 

Week
 
         

 
7/28–8/3
 
8/4–8/10
 
8/11–8/17
 
8/18–8/24
 
8/25–8/31
 
9/1–9/7
 
9/8–9/14
 
9/15–9/21
 
9/22–9/28
 
9/29–10/5
 
SaTScanp < 0.05           
    1 or 2 weeks prior           
        Cases 
        Population 2.7 5.1 5.6 3.8 2.3 4.0 4.8 4.9 3.6 4.1 
    Current or 1–2 weeks prior           
        Cases 
        Population 6.0 5.7 5.6 4.6 4.0 5.1 5.1 5.2 4.2 4.8 
    Any prior week           
        Cases 10 
        Population 6.4 7.2 7.6 7.6 7.8 8.1 8.1 8.1 8.1 8.1 
SaTScan p < 0.01           
    1 or 2 weeks prior           
        Cases 
        Population 1.1 4.1 4.2 3.1 1.3 3.0 3.4 3.3 3.1 3.0 
    Current or 1–2 weeks prior           
        Cases 
        Population 4.7 4.3 4.3 3.5 3.0 4.1 3.6 4.2 3.4 3.4 
    Any prior week           
        Cases 
        Population 4.2 6.1 6.1 6.1 6.1 6.4 6.4 6.4 6.4 6.7 
Kernel density           
    1 or 2 weeks prior           
        Cases 
        Population 1.9 2.6 2.7 2.3 2.8 4.0 4.4 3.2 4.1 4.4 
    Current or 1–2 weeks prior           
        Cases 
        Population 2.8 2.8 2.7 3.2 4.4 4.5 4.4 4.6 4.4 3.3 
    Any prior week           
        Cases 11 
        Population
 
5.9
 
6.0
 
6.0
 
6.0
 
6.0
 
6.2
 
6.3
 
6.3
 
6.7
 
6.8
 

 

Week
 
         

 
7/28–8/3
 
8/4–8/10
 
8/11–8/17
 
8/18–8/24
 
8/25–8/31
 
9/1–9/7
 
9/8–9/14
 
9/15–9/21
 
9/22–9/28
 
9/29–10/5
 
SaTScanp < 0.05           
    1 or 2 weeks prior           
        Cases 
        Population 2.7 5.1 5.6 3.8 2.3 4.0 4.8 4.9 3.6 4.1 
    Current or 1–2 weeks prior           
        Cases 
        Population 6.0 5.7 5.6 4.6 4.0 5.1 5.1 5.2 4.2 4.8 
    Any prior week           
        Cases 10 
        Population 6.4 7.2 7.6 7.6 7.8 8.1 8.1 8.1 8.1 8.1 
SaTScan p < 0.01           
    1 or 2 weeks prior           
        Cases 
        Population 1.1 4.1 4.2 3.1 1.3 3.0 3.4 3.3 3.1 3.0 
    Current or 1–2 weeks prior           
        Cases 
        Population 4.7 4.3 4.3 3.5 3.0 4.1 3.6 4.2 3.4 3.4 
    Any prior week           
        Cases 
        Population 4.2 6.1 6.1 6.1 6.1 6.4 6.4 6.4 6.4 6.7 
Kernel density           
    1 or 2 weeks prior           
        Cases 
        Population 1.9 2.6 2.7 2.3 2.8 4.0 4.4 3.2 4.1 4.4 
    Current or 1–2 weeks prior           
        Cases 
        Population 2.8 2.8 2.7 3.2 4.4 4.5 4.4 4.6 4.4 3.3 
    Any prior week           
        Cases 11 
        Population
 
5.9
 
6.0
 
6.0
 
6.0
 
6.0
 
6.2
 
6.3
 
6.3
 
6.7
 
6.8
 
*

The number of unexposed cases each week can be obtained by subtracting from the total cases provided in table 1. The unexposed population can be obtained by subtracting from the total population outside of New York City, which equals 10,968,179.

Weeks are formatted as follows, for example: 7/28–8/3, July 28 to August 3.

Refer to reference 19 for more information about this software.

TABLE 3.

Risk, estimated by Poisson regression modeling, of human cases of West Nile virus in 2002 in New York State (excluding New York City) for various definitions of exposure to dead crow clusters relative to being unexposed


Temporal criteria and adjustments
 

Clustering method
 
     
 Spatial scan statistic
 
   Kernel density
 
 
 p < 0.05
 
 p < 0.01
 
   
 RR*
 
95% CI*
 
RR
 
95% CI
 
RR
 
95% CI
 
1–2 weeks prior       
    None 1.97 1.13, 3.44 2.29 1.31, 4.00 2.59 1.49, 4.50 
    Region, week 1.99 1.09, 3.62 2.14 1.13, 4.04 2.22 1.22, 4.06 
    Full 1.94 1.06, 3.53 2.11 1.15, 3.87 1.25 0.65, 2.42 
Current week or 1–2 weeks prior       
    None 2.50 1.38, 4.52 3.02 1.71, 5.35 3.95 2.18, 7.16 
    Region, week 2.63 1.37, 5.05 3.32 1.70, 6.50 3.61 1.91, 6.81 
    Full 2.41 1.26, 4.63 2.91 1.52, 5.57 2.15 1.03, 4.48 
Any prior week       
    None 1.50 0.77, 2.94 1.70 0.94, 3.07 4.00 1.88, 8.52 
    Region, week 1.13 0.55, 2.31 1.40 0.68, 2.87 3.85 1.75, 8.48 
    Full
 
1.41
 
0.67, 2.94
 
1.37
 
0.68, 2.76
 
2.23
 
0.91, 5.49
 

Temporal criteria and adjustments
 

Clustering method
 
     
 Spatial scan statistic
 
   Kernel density
 
 
 p < 0.05
 
 p < 0.01
 
   
 RR*
 
95% CI*
 
RR
 
95% CI
 
RR
 
95% CI
 
1–2 weeks prior       
    None 1.97 1.13, 3.44 2.29 1.31, 4.00 2.59 1.49, 4.50 
    Region, week 1.99 1.09, 3.62 2.14 1.13, 4.04 2.22 1.22, 4.06 
    Full 1.94 1.06, 3.53 2.11 1.15, 3.87 1.25 0.65, 2.42 
Current week or 1–2 weeks prior       
    None 2.50 1.38, 4.52 3.02 1.71, 5.35 3.95 2.18, 7.16 
    Region, week 2.63 1.37, 5.05 3.32 1.70, 6.50 3.61 1.91, 6.81 
    Full 2.41 1.26, 4.63 2.91 1.52, 5.57 2.15 1.03, 4.48 
Any prior week       
    None 1.50 0.77, 2.94 1.70 0.94, 3.07 4.00 1.88, 8.52 
    Region, week 1.13 0.55, 2.31 1.40 0.68, 2.87 3.85 1.75, 8.48 
    Full
 
1.41
 
0.67, 2.94
 
1.37
 
0.68, 2.76
 
2.23
 
0.91, 5.49
 
*

RR, relative risk; CI, confidence interval.

Adjusted for region, week, town population density, density squared, and proportion of town population aged >50 years.

TABLE 4.

Risk, estimated as a hazard ratio by Cox proportional hazards modeling, of human cases of West Nile virus in 2002 in New York State (excluding New York City) for various definitions of exposure to dead crow clusters relative to being unexposed


Temporal criteria and adjustments
 

Clustering method
 
     
 Spatial scan statistic
 
   Kernel density
 
 
 p < 0.05
 
 p < 0.01
 
   
 RR*
 
95% CI*
 
RR
 
95% CI
 
RR
 
95% CI
 
1–2 weeks prior       
    None 1.70 0.93, 3.11 1.91 1.03, 3.53 2.49 1.37, 4.53 
    Stratified 1.65 0.82, 3.33 1.40 0.65, 3.03 2.51 1.28, 4.92 
    Stratified-adjusted 1.47 0.74, 2.93 1.35 0.64, 2.84 1.61 0.79, 3.29 
Current week or 1–2 weeks prior       
    None 2.33 1.24, 4.35 2.70 1.48, 4.94 3.70 1.95, 7.02 
    Stratified 2.18 1.07, 4.44 2.25 1.04, 4.89 3.65 1.81, 7.33 
    Stratified-adjusted 1.87 0.93, 3.76 1.97 0.93, 4.19 2.34 1.09, 5.02 
Any prior week       
    None 1.27 0.63, 2.57 2.14 1.15, 4.00 3.38 1.57, 7.27 
    Stratified 0.95 0.44, 2.03 1.87 0.88, 3.97 2.95 1.31, 6.64 
    Stratified-adjusted
 
1.11
 
0.51, 2.42
 
1.61
 
0.77, 3.39
 
1.61
 
0.67, 3.90
 

Temporal criteria and adjustments
 

Clustering method
 
     
 Spatial scan statistic
 
   Kernel density
 
 
 p < 0.05
 
 p < 0.01
 
   
 RR*
 
95% CI*
 
RR
 
95% CI
 
RR
 
95% CI
 
1–2 weeks prior       
    None 1.70 0.93, 3.11 1.91 1.03, 3.53 2.49 1.37, 4.53 
    Stratified 1.65 0.82, 3.33 1.40 0.65, 3.03 2.51 1.28, 4.92 
    Stratified-adjusted 1.47 0.74, 2.93 1.35 0.64, 2.84 1.61 0.79, 3.29 
Current week or 1–2 weeks prior       
    None 2.33 1.24, 4.35 2.70 1.48, 4.94 3.70 1.95, 7.02 
    Stratified 2.18 1.07, 4.44 2.25 1.04, 4.89 3.65 1.81, 7.33 
    Stratified-adjusted 1.87 0.93, 3.76 1.97 0.93, 4.19 2.34 1.09, 5.02 
Any prior week       
    None 1.27 0.63, 2.57 2.14 1.15, 4.00 3.38 1.57, 7.27 
    Stratified 0.95 0.44, 2.03 1.87 0.88, 3.97 2.95 1.31, 6.64 
    Stratified-adjusted
 
1.11
 
0.51, 2.42
 
1.61
 
0.77, 3.39
 
1.61
 
0.67, 3.90
 
*

RR, relative risk; CI, confidence interval.

Stratified by region and adjusted for town population density, density squared, and proportion of town population aged >50 years.

For Poisson regression, results are presented for the unadjusted model to show crude relative risks, along with the model that adjusts for region and week only and the full model including adjustments for human demographics. Note that interaction terms were never significant, indicating a lack of effect modification by any covariates. For the full models reported in table 3 that evaluate spatial scan statistic clusters, each covariate was significant (p < 0.01), after we adjusted for all other covariates, for all three temporal definitions of exposure. When kernel density clusters were evaluated, results were similarly significant for exposure 1–2 weeks prior. However, for the other two temporal definitions, significance of the human demographic covariates was reduced (0.01 < p < 0.05), and population density squared was rendered insignificant.

For proportional hazards modeling, week is no longer a covariate because it becomes the response variable. Region was used as a stratifier, thus allowing the baseline hazard to vary among regions. The importance of stratifying by region was illustrated by unstratified models in which region was treated as an implicit covariate, resulting in much lower and less significant relative risks of exposure. The remaining covariates for population density and age distribution had the same effects as with Poisson regression; therefore, table 4 includes the full model with all covariates, along with the crude model (no stratification or covariate adjustment) and the crude-stratified model (no covariate adjustment).

DISCUSSION

Our findings indicate that the risk of human WNV-associated fever or neurologic disease is higher for persons living in towns in or adjacent to clusters of dead crows than for persons not living adjacent to such clusters. Most importantly, there is an increased risk with exposure during the 1–2 weeks prior, providing evidence that dead crow clusters can predict human onset in a timely fashion. The predictive ability was observed even after we adjusted for human population density, and clusters delineated by the scan statistic according to criteria of p < 0.05 and p < 0.01 had a positive association that was significant at the levels of p = 0.03 and p = 0.02, respectively, when evaluated by Poisson regression. This finding indicates that clusters of dead crow reports may be used to predict human WNV in areas outside of New York State, where mosquito-infested and WNV-infected areas may be more rural.

Of the two methods used for delineating spatial crow clusters, the scan statistic has the distinct advantage of providing statistical significance, thus allowing a more objective basis for identifying clusters. Furthermore, clusters delineated by the scan statistic are much less sensitive to confounding by human population density when compared with kernel density clusters, as shown in tables 3 and 4. A trade-off of such a parametric test is that it requires distributional assumptions, although these assumptions are reasonable. A distinct disadvantage of the binomial model of the scan statistic, as applied in this paper, is that it depends on a control population of dead crows. The number of controls is determined by factors that vary both within and between regions, such as surveillance effort, public interest in reporting, and the location of susceptible birds. For example, Long Island had a substantially smaller control group than the other regions (table 1); therefore, clusters may depend more on the location of control birds than on the location of case birds in this region. However, it is worthwhile to note that, of the 18 cases on Long Island, the scan statistic did predict (1–2-week prior analyses) 10 of them, compared with nine predicted by kernel density. Another potential disadvantage of the scan statistic in general is that it evaluates artificial circular clusters. Therefore, this method could prove to be more valuable if the potential cluster boundaries were allowed to be more flexible, driven by the data, reflecting more natural cluster shapes (3032).

Kernel density delineation of clusters has the advantage of being a rather simple, nonparametric smoothing method that does not depend on a control population. However, as noted above, it is very sensitive to confounding by human population density since it uses the size of the geographic area, rather than a set of control birds, in the denominator. Of course, a major disadvantage is that choosing a critical density for delineating clusters is arbitrary. Kernel density smoothing will always help to visualize spatial point densities, but it should be supplemented by quantitative methods such as the scan statistic when objective cluster delineation is necessary.

The two evaluation tools produced similar relative risk point estimates and associated confidence intervals, which is reassuring because it is generally expected (3335); yet, we are presenting a rather novel application of both methods. Note, however, that the proportional hazards estimates had consistently larger confidence intervals and are therefore more conservative. Either method may be appropriate, although proportional hazards modeling has been treated as the “gold standard” for comparing other methods of estimating relative risk, primarily because it requires the fewest assumptions (33).

Aside from human population density, we cannot exclude the possibility of other confounders we are unaware of and did not adjust for. Covariates that are unaccounted for, whether or not they are confounders, may also lead to residual spatial autocorrelation since we are not analyzing a random sample of individuals but a population distributed over space. For example, two friends from adjacent towns may be bitten by infected mosquitoes while walking together in a local park. By simple geographic proximity, they are not behaving independently of each other. Future research could more formally evaluate the presence of residual spatial autocorrelation; however, this is not expected to be a limiting factor in our conclusions, and, therefore, we did not complicate our models by adding a random effect for spatial location.

Surveillance for dead crow clusters is limited by observation bias since it depends on public reporting. Although we adjusted for human population density, other factors, such as differences in public awareness among the different regions and/or weeks, may still influence observation bias. Future research should aim to reduce observation bias and improve the geospatial estimation of WNV activity by incorporating covariate information about human demographics (36) along with climatic (37) and environmental (36, 38) data. For example, Poisson regression may be used to model counts of dead crows, or laboratory-confirmed WNV infection in any species, by using predictor variables derived from maps of land cover/vegetation, land use, wetlands, water bodies, temperature, previous year's WNV activity, and so forth. Both environmental and human demographic covariates have been significantly associated with human WNV infection in the Chicago, Illinois, area (36). With 6 years of data now in hand for New York State, models could possibly be developed and calibrated for predicting WNV activity with greater accuracy than can be obtained by using just the current year's dead crow clusters.

WNV continues to spread across the United States at an epizootic level. It is the leading mosquito-borne disease in the United States in the past century in terms of morbidity and mortality, and it has spread northward to Canada and southward to the Caribbean, Mexico, Central America, and South America (39, 40). Confirmation of viral activity by testing dead birds continues to be an excellent method of verifying annual recurrence of risk in most areas of the United States (41). However, with the disease capable of rapid amplification in its bird-mosquito cycle, basing prevention and control decisions on laboratory results can be difficult for several reasons. Unless rapid field testing kits are used, there can be delays between identifying a dead bird and obtaining definitive laboratory test results. Taking action once humans have been exposed may still reduce the total number of human infections but will be less effective than using earlier signals of increasing viral activity. Dead crow sightings have been demonstrated to be a valuable crude indicator of viral activity, and the statewide evaluation reported in this paper provides quantitative evidence of increased risk for humans living near dead crow clusters. Thus, jurisdictions responsible for WNV surveillance should consider cluster detection methods to identify focal areas of viral activity sufficiently early enough to provide personal protection warnings or to conduct mosquito control activities.

Work on this study was partially supported by federal funds from National Science Foundation grant 9983304 entitled, “Developing a National Infectious Disease Information Infrastructure: An Experiment in West Nile Virus and Botulism”; from the National Institute of Allergy and Infectious Disease, National Institutes of Health, under contract N01-A1-25490; and from the Centers for Disease Control and Prevention under cooperative agreement U50/CCU223671.

The authors thank the local health departments and the US Department of Agriculture's Wildlife Services New York office for WNV surveillance reports; the New York State Department of Environmental Conservation's Wildlife Pathology Unit and the New York State Department of Health (NYSDOH) Wadsworth Center's Arbovirus Laboratory for processing and testing dead birds; the NYSDOH Arthropod-borne Disease Program and the Wadsworth Center's Encephalitis PCR and Diagnostic Immunology Laboratories for human WNV surveillance; Yoichiro Hagiwara for managing the New York State dead bird surveillance system; the NYSDOH Healthcom network for system development and support; and staff and students of the NYSDOH Zoonoses Program and Bureau of Communicable Disease Control for their contribution to the WNV surveillance system and the dead crow maps.

The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the supporting agencies.

Conflict of interest: none declared.

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