The Influence of Sociodemographic Factors on the Engagement of Citizens in the Detection of Dead Corvids During the Emergence of West Nile Virus in Ontario, Canada.

West Nile virus (WNv) was introduced into North America in 1999, and by 2002 was identified in most regions of Ontario, Canada. Surveillance of WNv included testing of corvids found dead and reported by citizens across Ontario, which at the time was a novel citizen science application for disease surveillance. While this surveillance program was successful for timely identification of WNv as it emerged and spread across the province, it is important to consider the influence of non-disease factors on surveillance data collected by the public. The objective of this study was to examine associations between rates of citizen phone reports of dead corvids and sociodemographic factors within the geographic areas where the reports were obtained. The data were grouped by forward sortation area (FSA), a geographical area based upon postal codes, which was linked with census data. Associations between the weekly rate of citizen reports and FSA-level sociodemographic factors were measured using multilevel negative binomial models. There were 12,295 phone call reports of dead corvids made by citizens in 83.3% of Ontario FSAs. Factors associated with the weekly rate of phone reports included the proportion of high-rise housing, the proportion of households with children, the proportion of seniors in the population, the proportion of citizens with no knowledge of either official language and the latitude of the FSA. There were higher rates of citizen phone reports in FSAs with <80% high-rise housing and greater proportions of households with children. A positive and negative association in the rate of calls with the proportion of seniors and latitude of the FSA, respectively, were moderated by the proportion of the population with knowledge of official language(s). Understanding the sociodemographic characteristics associated with citizen reporting rates of sentinels for disease surveillance can be used to inform advanced cluster detection methods such as applying the spatial scan test with normal distribution on residuals from a regression model to reduce confounding. In citizen-derived data collected for disease surveillance, this type of approach can be helpful to improve the interpretation of cluster detection results beyond what is expected.

A comparison of West Nile virus surveillance using survival analyses of dead corvid and mosquito pool data in Ontario, 2002-2008.

The aim of this study was to improve understanding of the relative performance of the use of dead wild corvids and mosquito pools infected with West Nile virus (WNv) in surveillance for WNv activity in the environment. To this end, all records on dead corvid submissions and mosquito pools tested in Public Health Units (PHUs) in Ontario, from 2002 to 2008, were explored. Survival analyses were employed using the first-WNv-positive cases detected each year for each PHU, and censored observations for PHUs which did not detect WNv during a given year using each data source (504 observations). Survival analyses were employed to compare the number of surveillance weeks before WNv was detected by either data source, and the influence of temporal, geographic and sociodemographic factors on these data. The outcome measurement for the final accelerated failure time (AFT) model with log-logistic distribution was a time ratio, which represents the ratio of the survival time of one group relative to another. Dead corvid surveillance was faster at detecting WNv than testing mosquito pools during the early years of WNv incursion into Ontario, while mosquito testing found WNv more quickly later in the study period. There was also regional variation in time-to-detection of WNv, by modality, as well as for various types of urban/rural settings. In comparison to mosquito surveillance, West Nile virus was detected more quickly using dead corvid surveillance in sparsely populated regions. These areas may benefit from collection of dead corvids to optimize detection and direct early surveillance efforts. When we compared the time-to-detection of WNv using dead corvids and the onset of human cases in PHUs, we found that dead corvid surveillance was predictive of West Nile activity in health units that reported human cases during the first 3 years of the incursion into Ontario.

Exploring relationships between whole carcass condemnation abattoir data, non-disease factors and disease outbreaks in swine herds in Ontario (2001-2007).

BACKGROUND: Improving upon traditional animal disease surveillance systems may allow more rapid detection of disease outbreaks in animal populations. In Ontario, between the years 2001 - 2007, widespread outbreaks of several diseases caused major impacts to the swine industry. This study was undertaken to investigate whether whole carcass condemnation data of market pigs from provincial abattoirs from 2001 - 2007 could have provided useful information for disease surveillance of Ontario swine. The objective was to examine the suitability of these data for detection of disease outbreaks using multi-level models and spatial scan statistics. We investigated the ability of these data to provide spatially-relevant surveillance information by determining the approximate distance pigs are shipped from farm to provincial abattoirs in the province, and explored potentially biasing non-disease factors within these data. RESULTS: Provincially-inspected abattoirs in Ontario were found to be located in close proximity to the hog farms of origin. The fall season and increasing abattoir capacity were associated with a decrease in condemnation rates. Condemnation rates varied across agricultural regions by year, and some regions showed yearly trends consistent with the timing of emergence of new disease strains that affected the Ontario swine population. Scan statistics identified stable clusters of condemnations in space that may have represented stable underlying factors influencing condemnations. The temporal scans detected the most likely cluster of high condemnations during the timeframe in which widespread disease events were documented. One space-time cluster took place during the beginning of the historical disease outbreaks and may have provided an early warning signal within a syndromic surveillance system. CONCLUSIONS: Spatial disease surveillance methods may be applicable to whole carcass condemnation data collected at provincially-inspected abattoirs in Ontario for disease detection on a local scale. These data could provide useful information within a syndromic disease surveillance system for protecting swine herd health within the province. However, non-disease factors including region, season and abattoir size need to be considered when applying quantitative methods to abattoir data for disease surveillance.

Suitability and limitations of portion-specific abattoir data as part of an early warning system for emerging diseases of swine in Ontario.

BACKGROUND: Abattoir data have the potential to provide information for geospatial disease surveillance applications, but the quality of the data and utility for detecting disease outbreaks is not well understood. The objectives of this study were to 1) identify non-disease factors that may bias these data for disease surveillance and 2) determine if major disease events that took place during the study period would be captured using multi-level modelling and scan statistics. We analyzed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007. During these years there were outbreaks of porcine circovirus-associated disease (PCVAD), porcine reproductive and respiratory syndrome (PRRS) and swine influenza that produced widespread disease within the province. Negative binomial models with random intercepts for abattoir, to account for repeated measurements within abattoirs, were created. The relationships between partial carcass condemnation rates for pneumonia and nephritis with year, season, agricultural region, stock price, and abattoir processing capacity were explored. The utility of the spatial scan statistic for detecting clusters of high partial carcass condemnation rates in space, time, and space-time was investigated. RESULTS: Non-disease factors that were found to be associated with lung and kidney condemnation rates included abattoir processing capacity, agricultural region and season. Yearly trends in predicted condemnation rates varied by agricultural region, and temporal patterns were different for both types of condemnations. Some clusters of high condemnation rates of kidneys with nephritis in time and space-time preceded the timeframe during which case clusters were detected using traditional laboratory data. Yearly kidney condemnation rates related to nephritis lesions in eastern Ontario were most consistent with the trends that were expected in relation to the documented disease outbreaks. Yearly lung condemnation rates did not correspond with the timeframes during which major respiratory disease outbreaks took place. CONCLUSIONS: This study demonstrated that a number of abattoir-related factors require consideration when using abattoir data for quantitative disease surveillance. Data pertaining to lungs condemned for pneumonia did not provide useful information for predicting disease events, while partial carcass condemnations of nephritis were most consistent with expected trends. Techniques that adjust for non-disease factors should be considered when applying cluster detection methods to abattoir data.