# Presentation: 177796

## Developments in Avian Mortality Surveillance for WNV

**Millicent Eidson****, **
**MA, DVM, DACVPM (Epid.)**
**State Public Health Veterinarian and Director,**
**Zoonoses Program **
**New York State Department of Health**
**Associate Professor, Department of Epidemiology**
**University at Albany School of Public Health**

## AcknowledgementsInformation in this overview includes results based on the work of the following groups and individuals, as well as the authors of the scientific references listed on the slides.

**New York State Department of Health**
**Zoonoses Program: Y Hagiwara, A Ellis, G Johnson, K Schmit, et al.**
**Arthropod-borne Diseases Program: D White, B Backenson, A Anand, et al.**
**Wadsworth Center Arbovirus Laboratory: L Kramer, K Bernard, E Kauffman, et al.**
**HealthCom Network Systems Management: I Gotham, et al.**
**New York State Department of Environmental Conservation**
**Wildlife Pathology Unit: W Stone, J Okoniewski, J Therrien, et al.**
**New York Local Health Departments, including**
**New York City Department of Health: M Layton, A Fine et al.**
**Nassau County Department of Health: D Ackman et al.**
**Suffolk County Department of Health Services: B Harper et al.**
**United States Department of Agriculture Wildlife Services**
**New York Office: R Chipman et al.**
**CDC****,**** USGS ****(see slides)**
**Other States, including California, Florida, Illinois, Indiana, New Jersey, Texas, Virginia ****(see slides)**

## Why Real-Time Surveillance?

**WNV continues to cause morbidity and mortality in people, horses, and wildlife**
**Vector control and personal protection—when?**
**Vector control is expensive; there may be concerns about adverse consequences**
**Personal protection may be infrequently adopted; there may be concerns about adverse consequences**
**Need to identify areas of greater WNV risk to prioritize vector control and education **
**Need to appropriately warn when risk is high**
**Need to avoid ****false alarms**** when risk is not high**
**Timing is critical to interrupt transmission during the WNV season**

## Types of Bird Data *reviewed in this summary

**Avian mortality reports (not tested for WNV)***
**Laboratory-confirmed WNV + dead birds***
**Representing ~200 species ****(Komar. Advances in Virus Res. 2003;61:185-234)**
**Serosurveys of live birds**
**Christmas bird counts and other population-based studies of live birds**
**Periodic testing of sentinel flocks**
**Laboratory research (e.g., studies of transmission, clinical course, etc.)**

## Dead Bird Data is available from CDC 2004 WNV Activity in the United States(reported to CDC as of January 11, 2005)*

- Available at http://www.cdc.gov/ncidod/dvbid/westnile/surv&control04Maps.htm

## Dead bird data is available from USGS Cumulative 2004 Data as of 3 am, Jan 11, 2005 

**Available at http://westnilemaps.usgs.gov/us_bird.**
**html**

## State-specific dead bird data, USGS maps

| Alameda County | 23 |
| --- | --- |
| Alpine County | 3 |
| Amador County | 9 |
| Butte County | 118 |
| Calaveras County | 10 |
| Colusa County | 21 |
| Contra Costa County | 18 |
| Del Norte County | 3 |
| El Dorado County | 22 |
| Fresno County | 115 |
| Glenn County | 75 |
| Humboldt County | 17 |
| Imperial County | 1 |
| Inyo County | 12 |
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- Cumulative Dead Bird Infections by County - California, 2004

## Avian mortality results are associated with results from other surveillance systems

**2003, California:**
**MIRs increase in same time period as dead bird reports and positive birds**
- Reisen, et al. EID 2004;10:1369-78

## Correlations between Number of Human WNV Cases and Dead Bird Surveillance Factors by County, New York State, 2000

**Eidson. West Nile Virus: Detection, Surveillance, and Control. Annals NY Acad Sci 2001;951:38-53**

## Weekly Number of Human Cases versus Number of Dead Crow Sightings per Square Mile, New York State, 2000

**Weekly Number of Human Cases versus Number of ****Dead Crow Sightings per Square Mile, New York State, 2000**

**Eidson. West Nile Virus: Detection, Surveillance, and Control. Annals NY Acad Sci 2001; 951:38-53**

## Example of real-time automatically generated dead crow density curve on New York State’s Health Information Network

**Example of real-time automatically generated dead crow density curve on New York State’s Health Information Network**

**Notes:**
[camera on Dr. Eidson]
A specific graph showing exactly how dead crow density is changing is on HIN, as seen in this graph for Monroe County .[show this graph on the TV screen]
Line shows sharp increase in dead crow density in late July last year.
The two bars show represent the two human cases and the dates their symptoms started.
Viral activity was clearly spiking several weeks prior to the human cases, as indicated by the sharp rise in dead crow densities.
Monitoring such trends can allow a local jurisdiction to increase their education and prevention activities to help reduce risk of human cases
Not all human cases can be prevented, but such information can help minimize the number of cases.

## Summary of Dead Crow Densities on New York’s Public Website (www.health.state.ny.us)

**Summary of Dead Crow Densities on New York’s Public Website (www.health.state.ny.us)**

## Assessment of Dead Crow Density GraphsNew York State, 2001-2003

**Persons in counties with dead crow density **_**>**_** 0.1 per square mile had ~3 times the risk of WNV than persons in counties with lower dead crow densities**
**Risk was slightly decreased using only reports in database at the time—better early warning if data in system quickly **
**148/163 (91% ) of human cases preceded by county dead crow density **_**>**_** 0.1, 2000-2003**
**False + signal (elevated density with no human cases) in 6/58 counties (2000), 3/56 (2001), 2/45 (2002), 2/46 (2003)**
**This is a rapid, automated system that does not require lab testing, geocoding, or mapping.**
**Does not define focal areas of risk **_**within**_** a county.**

## Measures of early season crow WN activity* associated with human WN disease

| 2000 | RR | 95%CI | Sens | Spec | PPV |
| --- | --- | --- | --- | --- | --- |
| Crows/area | 7.0 | 2.1-23.2 | 0.7 | 0.86 | 0.54 |
| % + crows x human pop | 7.0 | 2.1-23.2 | 0.7 | 0.86 | 0.54 |
| 2001 |  |  |  |  |  |
| Crows/area | 3.9 | 1.8-8.2 | 0.4 | 0.94 | 0.67 |
| % + crows x human pop | 7.4 | 3.1-17.8 | 0.67 | 0.94 | 0.77 |

**Julian, et al. EID 2002;2:145-155**

***In 7 NE states, 6/17/00-7/28/00, comparing counties with high (75****th****%) vs. low activity**

## Indiana: increase in avian mortality prior to onset of human cases by at least a few weeks

**Dead bird data with MIRs provide the basis for public warnings, and local control including larviciding and adulticiding**

**M. Sinsko, Indiana State Dept. of Health**

## Association of dead crow reports and human cases, Chicago, 2002(Watson, et al. EID 2004;10:938-40)

**Spike in dead crow reports preceded spike in human cases**
**High crow-mortality areas (HCMAs) overlapped areas of human cases**
**311 city service calls of dead crow reports predicted human cases—now used for larviciding (*****Time*****, Feb. 7)**

## Other Avian Mortality Surveillance Findings

**U.S., 2000: in all 10 counties reporting human cases, a ****WNV+ bird was found an average of 44 days before human illness onset**
**Number of dead bird reports in each county increased many weeks before the first human cases (Marfin, et al. EID 2001;4:730-5)**
**U.S., 2001: ****counties that reported a WNV-infected dead bird before 8/5 were more than 6 times more likely than other counties to report a human case**** ****(Guptill et al. EID 2003;9:483-4)**
**New York, 2000: ****% positive crows associated with higher MIRs and human cases ****(Bernard et al. EID 2001;7:679-85).**
**Florida, 2001: ****corvid mortality most sensitive predictor of WNV activity**** ****(Blackmore et al., Am J Trop Med Hyg 2003;69:141-50)**
**Dead bird reports/100,000 persons correlated well with intensity of WNV activity in the county measured by other surveillance systems**
**Corvid mortality peaked on average 2.8 weeks prior to disease onset of their human case for some (not all) counties  **
**Illinois: ****Significant clustering of human cases in 2002 in areas with shorter distance to + dead bird**** (other factors also associated) ****(Ruiz, et al. Int J Health Geo 2004; 3:)**
**Harris County, Texas: most + birds were Blue Jays ****(Lillibridge, et al. Am J Trop Med Hyg 2004;70, 676-81)**
**New Jersey: mosquito control agencies use avian mortality reports in surveillance and control decisions**

## Spatial and temporal distribution of WNV positive  birds in Virginia in 2002; mid-August cut off date between early and late birds (similar pattern for 2003)

**Spatial and temporal distribution of WNV positive  birds in Virginia in 2002; mid-August cut off date between early and late birds (similar pattern for 2003)**
**(D. Gaines, Virginia Dept. of Health)**

**WNV Positive Birds as of **
**Dec. 31, 2002**

- WNV Positive Birds **(before 8/18/02)**

- WNV Positive Birds **(after 8/17/02)**

## Dead Crow Density: Interpolation Methods – Long Island, NY, 2001

**Simple Point Density**

**Advanced Kernel Density**

**Inverse Distance Weighting**

**Natural Neighbor**

**Legend - ****Crow reports**
**     ****1                   2                  3****	****/sqmi**

**Choosing an Interpolation Method for Estimating Dead Crow Density**

**Notes:**
April has already explained the general features of interpolation. I will demonstrate how some  of the methods available in Vertical Mapper represented the dead crow sighting data collected as part of WNV surveillance.
	These four maps display data from the same week, and all interpolations were based on a search area of 10 square miles. You can see that they all identified the hottest spots but there are several differences in how big the hot spots are and in how low and intermediate values are displayed. This information is important to us because counties would naturally want to locate their preventive efforts as precisely as possible.
	IDW, NN, AK, PD

## ADVANCED KERNEL

**ADVANCED KERNEL**
**DENSITY METHOD**

**< < <  DISPLAYED AS GRID**

**DISPLAYED AS CONTOUR  > > >**

**Notes:**
I just wanted to show you that the same grid values may be displayed in very different ways. In the map in the lower left, a color is assigned to each grid node (100 m by 100 m) according to its density function value. From this map April created a set of contour lines, which are lines that divide major subdivisions of grid values (here, at each 0.1 mark). Then she assigned colors to the area between the lines. Both maps portray the same grid information but look very different.

## (Week 31) 7/29 to 8/04

**(Week 31) 7/29 to 8/04**

**(Week 32) 8/05 to 8/11**

**(Week 33) 8/12 to 8/18**

**(Week 34) 8/19 to 8/25**

**Advanced Kernel Density for Identifying Clusters of Dead Crows, NYS, 2001**

**0****	****           1**

- Tiny dots=dead crow sightings, larger dots=+birds, largest circles=human cases, onset date

## (Week 31) 7/29 to 8/4

**(Week 31) 7/29 to 8/4**

**(Week 34) 8/19 to 8/25**

**(Week 33) 8/12 to 8/18**

**(Week 32) 8/5 to 8/11**

**SaTScan for Identifying Significant (p<.01) Clusters of Dead Crows, NYS, 2001**

- Tiny dots=dead crow sightings, green stars=+birds, yellow circles=human cases, onset dates

**Notes:**
From CDC week 31 to 34, August 2001, we observe increase in the
 viral activity expressed in WNV positive birds identified, the green 
stars.
The significant cluster on Suffolk county continues in the same area
 and became non significant in the CDC week 34, in that same week
 the Suffolk county human case onsets. Note the case is in the same
 area as I mentioned before.
Nassau County shows only non significant clusters from week 31 to 
week 32 and one significant cluster in week 34, in a different area 
than the former significant counties. In the week 33 Nassau county
, had a Human case in the area where a significant cluster was 
observed 6 weeks before, but during apparent reduced WNV 
activity by that time.

## Assoc. of Dead Bird Clusters with Human Cases, New York State, 2002

| Analysis | SatScan clusters, p<0.05 | SatScan clusters, p<0.01 | Kernel density hotspots |
| --- | --- | --- | --- |
| Crude (no adj) CMH chi-sq Poisson 
PH model | 
2.02(1.14,3.57)
2.49(1.38,4.52)
2.33(1.24,4.35) |  
2.41(1.39,4.18)
3.02(1.71,5.35)
2.70(1.48,4.94) |  
3.49(1.94,6.28)
3.95(2.18,7.16)
3.70(1.95,7.02) |
| Adj region, wk Poisson 
PH model | 
2.63(1.37,5.05)
2.18(1.07,4.44) | 
3.32(1.69,6.50)
2.25(1.04,4.89) | 
3.61(1.91,6.81)
3.65(1.81,7.33) |
| Reg,wk,density Poisson 
PH model | 
2.24(1.17,4.26)
2.08(1.04,4.16) | 
2.95(1.56,5.59)
2.12(1.00,4.48) | 
2.83(1.45,5.53)
3.27(1.56,6.89) |

**(Used Cochran-Mantel-Haenszel test, Poisson regression, & proportional hazards model)**

## New York State Geographic Information System (GIS) Analyses, Conclusions

**Risk of WNV 2-3 times higher (for SaTScan) or 3-4 times higher (for kernel density) among populations in towns in/near areas of high dead crow density than among populations in towns not near areas**
**Other factors that might account for higher risk (age distribution, population density of towns) were included in models, yet exposure to areas of high dead crow density still at least doubled the risk of WNV to human populations**

## GIS Analyses, New York City, 2001

**DYCAST system identifying nonrandom space-time interaction of dead birds successfully identified areas of high risk for human infection for 5 of 7 human cases at least 13 days prior to illness onset ****(Theophilides et al. AJE 2003;157:843-54)**
**Spatial scan statistic: dead bird clusters--median 12 days before human onset, median 17 days before human diagnosis.  In most cases, dead bird clusters also preceded time of collection of WNV-positive mosquitoes and birds ****(Mostashari, EID 2003;9:641-6)**
**Areas with clustering receive increased vector surveillance--additional mosquito trapping**
**Dead bird trends help to confirm control efficacy**

## Dead crow densities and adulticide spray dates, 4 areas of New York State

- Eidson. West Nile Virus: Detection, Surveillance, and Control. Annals NY Acad Sci, 2001;951:38-53

**Dead crow densities and adulticide spray dates, 4 areas of New York State**

## Approach summarized in Rogers et al. Photogrammetric Eng & RS. 2002;68:109-10.

**Use of Remote Sensing to Predict Risk**

## Potential Limitations, GIS Approaches

**Purpose of cluster detection: inform county health departments where risk is high**
**if mosquito control measures are used in high-risk areas, risk might decrease; need to add in control data to assess impact**
**Analyses use home address, and often can’t judge whether that was location of infection (missing work history, travel history)**
**Cost/resources of GIS system: in 1997-1998 national survey, only about 7% of governmental agencies that participated (mostly counties) indicated that a dept. concerned with health and human services within their organization produced or used geographic information **
**BT funds don’t automatically improve this situation**
**Ruiz et al. J Med Systems 2004;28:385-95.**

## Bird collection, shipment, necropsy, and testing requires resources and time: Mean number of days between steps, NYS, 2002

**Bird collection, shipment, necropsy, and testing requires resources and time: Mean number of days between steps, NYS, 2002**

**Person finds a dead bird**
**Person reports bird to county/hotline**
** **
**Record for bird added to HIN**
**Bird received at WPU for necropsy**
**WPU ships specimen to lab**
**Lab posts result on HIN**

**< 1 day (n=43,830)**

**7 days (n=4,057)**

**10 days (n=4,060)**

**8 days (n=41,843)**

**< 1 day (n=6,176)**

**23 days**
**(n=4,129)**

## Rapid Field Test, VecTest: $8 per dipstick

**Sensitivity****, oral specimens, Am. Crows (unless otherwise noted)**
**87%,**** ****(>80% some other species), ****NY: ****Stone et al., EID 2004;10:2175-81 **
**84%,**** Canada: ****Lindsay et al., EID 2003;9:1406-10**
**100%,**** IL****, Yaremych et al., EID 2003;9:1319-21**
**40%**** TX,LA oral; ****46%**** brain ****(all species), Siirin et al., VBZD 2004;4:204-9**
**100%**** ****MS (corvids, cloacal swabs),**** Henson et al. Clin Lab Sci 2004;17:218**
**>93.5%,**** ****Ontario, owls; <42.9% other species (oral,cloacal)**** ****Gancz et al. EID. 2004;10:2204-6.**
**Specificity****, oral specimens**
**98%,**** NY (all birds)**
**79%**** (Ontario); 94% (Manitoba, Am. Crows) ****(Lindsay)**
**25%,**** IL (mixed fecal, saliva, and tissue samples) ****(Yaremych)**
**100%,**** Ontario, owls; 85.7%, raptors ****(Gancz)**
**Sensitivity can be lower with lower prevalence**
**April–June, NY: ****17%, 2003****; 82%, 2004**

## Rapid Test, RAMP (reader $3500, cartridge $15)New York State Wildlife Pathology Unit, 2004, compared to RT-PCR, Preliminary Results*

|  | Sensitivity |  |
| --- | --- | --- |
|  | RAMP | VecTest |
| All birds | 79.8% | 69.1% |
| Corvids | 90.8% | 80.9% |
| Am. crows | 91.2% | 83.3% |
| Blue Jays | 82.8% | 65.5% |

| Specificity |  |
| --- | --- |
| RAMP | VecTest |
| 97.3% | 99.2% |
| 94.4% | 98.6% |
| 96.1% | 100% |
| 89.5% | 94.7% |

***Preliminary results provided on 2/3/05 by WPU; not yet reviewed by NYSDOH Wadsworth Center Arbovirus Laboratory or Zoonoses Program**

## Many States Have Maps of Dead Bird Surveillance on Public Web

## Some states have online dead bird report forms

**N.C.**

**MICHIGAN**

**MISSISSIPPI**

**CALIF.**

**PENN.**

## Tollfree dead bird reporting hotline

**USDA Wildlife Services supports tollfree dead bird reporting in several jurisdictions, including NYS**

|  | NYS* |  | Hotline |  | % |
| --- | --- | --- | --- | --- | --- |
|  | All | Crow | All | Crow | hotline |
| 2001 | 19,675 | 7,600 | 1,167 | 407 | 5.9% |
| 2002 | 36,824 | 18,021 | 5,472 | 1,916 | 14.9% |
| 2003 | 20,578 | 8,325 | 3,600 | 1,186 | 17.5% |
| 2004 | 9,585 | 1,908 | 1,932 | 365 | 20.2% |

***excluding New York City**

## Dead bird identification tips

**UTAH**

**OK**

**IDAHO**

**ILLINOIS**

**WYOMING**

**NEW HAMPSHIRE**

**MAINE**

**ALABAMA**

## Conclusions, Avian Mortality Surveillancein Real Time

**In different years and areas, dead bird reports have offered value for identifying areas of higher risk, and are widely used**
**Dead crow reports are often a good, quick indicator (especially dead crow density)**
**New rapid testing systems may improve timeliness and use of WNV+ data**
**GIS/mapping approaches have value for focal identification of risk, but require resources**
**Specific bird species and levels used for decision-making may vary**