A Pilot Case-Control Study of the Social Media Activity Following Cluster and Non-Cluster Suicides in Australia

Social media may play a role in the “contagion” mechanism thought to underpin suicide clusters. Our pilot case-control study presented a novel methodological approach to examining whether Facebook activity following cluster and non-cluster suicides differed. We used a scan statistic to identify suicide cluster cases occurring in spatiotemporal clusters and matched each case to 10 non-cluster control suicides. We identified the Facebook accounts of 3/48 cluster cases and 20/480 non-cluster controls and their respective friends-lists and retrieved 48 posthumous posts and replies (text segments) referring to the deceased for the former and 606 for the latter. We examined text segments for “putatively harmful” and “putatively protective” content (e.g., discussion of the suicide method vs. messages discouraging suicidal acts). We also used concept mapping, word-emotion association, and sentiment analysis and gauged user reactions to posts using the reactions-to-posts ratio. We found no “putatively harmful” or “putatively protective” content following any suicides. However, “family” and “son” concepts were more common for cluster cases and “xx”, “sorry” and “loss” concepts were more common for non-cluster controls, and there were twice as many surprise- and disgust-associated words for cluster cases. Posts pertaining to non-cluster controls were four times as receptive as those about cluster cases. We hope that the approach we have presented may help to guide future research to explain suicide clusters and social-media contagion.

Temporal Study of Salmonella enterica Serovars Isolated from Fluff Samples from Ontario Poultry Hatcheries between 2009 and 2018

The objectives of this study were to determine the prevalence, temporal trends, seasonal patterns, and temporal clustering of Salmonella enterica isolated from fluff samples from poultry hatcheries in Ontario between 2009 and 2018. A scan statistic was used to identify clusters of common serovars and those of human health concern. A multi-level logistic regression model was used to identify factors (poultry commodity, year, season) associated with S. enterica presence. The period prevalence of S. enterica was 7.5% in broiler hatcheries, 1.6% in layer hatcheries, 7.6% in turkey hatcheries, 29.7% in waterfowl hatcheries, and 13.8% in game-bird hatcheries. An overall increasing trend in S. enterica prevalence was identified in waterfowl and game-bird hatcheries, while a decreasing trend was identified in broiler and turkey hatcheries. Overall, the most common S. enterica serovars were Kentucky, Enteritidis, Heidelberg, and Senftenberg. Salmonella enterica ser. Enteritidis was the most common serovar in waterfowl hatcheries. Temporal clusters were identified for all poultry commodities. Seasonal effects varied by commodity, with the highest odds of S. enterica occurring in the summer and fall. Our study offers information on the prevalence and temporality of S. enterica serovars that might guide prevention and control programs at the hatchery level.

INCIDENCE AND LETHALITY OF COVID-19 CLUSTERS IN BRAZIL VIA CIRCULAR SCAN METHOD

The COVID-19 pandemic has spread rapidly around the world in a frightening way. In Brazil, the third country with the highest number of infected and deaths from the disease, it is important for government health authorities to identify the federation units that stand out in cases and deaths from this disease to target resources. The circular scan statistic proposed by Martin Kulldorff allows to identify with some statistical significance the units of the federation that stand out in relation to the number of cases and deaths of COVID-19 in Brazil. Such units of federation are known as clusters. Once these clusters were identified, we used the coefficients of incidence and lethality to better describe the behavior of these clusters during three phases of the pandemic: the initial phase, the peak phase, and also the stability and fall phase. We observed changes in the location of the clusters identified in these three phases and used the R software and also the SaTScan software to obtain the maps and results, which were consistent with what was reported by the Brazilian media.

Spatially varying effects of measured confounding variables on disease risk

Background The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence. Methods We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina. Results The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender. Conclusion The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Study the Effects of COVID-19 in Punjab, Pakistan using Space-time Scan Statistic for Policy Measures in Regional Agriculture and Food Supply Chain

Food service and retailing sectors play a vital role in economics of Punjab, Pakistan. Pakistan is included in top 50 countries which are estimated to face serious agriculture and food deficiency related challenges due to the world-wide pandemic coronavirus 2019 (COVID-19). The aim of this study was to study the effects of COVID-19 on food security and agriculture in Punjab, Pakistan using space-time scan statistic (STSS). A survey was conducted at 720 points in different districts of the province. The STSS detected “active” and emerging clusters that are current at the end of our study aera – particularly 17 clusters were formed while adding the updated case data. ArcGIS 10.3 software was used to find relative risk (RR) values; the maximum RR value was found to be 42.19 and maximum observed cases 53265 during June 15th – July 1st. Due to the highest number of cases of COVID-19 and RR vales during July, mostly farmers faced many difficulties during the cultivation of cotton and rice. Mostly farmers (72%) observed increase in prices of inputs (fertilizers and pesticides) during lockdown. The timely results (attained through STSS and RR) can inform decision makers and public health officials about where to improve the allocation of resources (including those for farming community), also, where to apply stricter quarantines and travel bans. If the supply chain of agriculture related inputs is disturbed, farmers may find it quite difficult to access markets, which could result in a decline in production and sales of crops and livestock in study area. It is suggested that to protection of food security and to decrease the effect of the lockdown, Punjab government needs to review food policy as well as analyze how market forces will respond to the imbalanced storage facilities and capacity, supply and demand, and price control of products. The findings of this study can also help policy-makers to formulate an effective food security and agriculture adaptation strategy.

Epidemiological Factors Associated With Caligus rogercresseyi Infection, Abundance, and Spatial Distribution in Southern Chile

Sea lice (Caligus rogercresseyi) are external parasites that affect farmed salmonids in Chile, and the scale of their sanitary and economic impact cannot be overstated. Even though space–time patterns suppose parasite aggregation, specific locations related to different infestation levels, as well as their associated factors across the geographic range involved, had not been investigated as of the writing of the present article. The understanding of the effects and factors entailed by the presence of C. rogercresseyi may be deemed a key element of Integrated Pest Management (IPM). In the present study, the multivariate spatial scan statistic was used to identify geographic areas and times of C. rogercresseyi infestation and to estimate the factors associated with such patterns. We used official C. rogercresseyi monitoring data at the farm level, with a set of 13 covariates, to provide adjustment within the analyses. The analyses were carried out for a period of 5 years (2012–2016), and they included three fish species (Salmo salar, Oncorhynchus mykiss, and Oncorhynchus kisutch) in order to assess the consistency of the identified clusters. A retrospective multinomial, spatial, and temporal scan test was implemented to identify farm clusters of either of the different categories of C. rogercresseyi infested farms: baseline, medium, and high, based on the control chemical threshold established by the health authority. The baseline represents adequate farm performance against C. rogercresseyi infestation. Then, production and environmental factors of the medium and high infestation farms were compared with the baseline using regression techniques. The results revealed a total of 26 clusters (p < 0.001), of which 12 correspond to baseline, 1 to medium, and the remaining 13 to high infestation clusters. In general, baseline clusters are detected in a latitudinal gradient on estuarine areas, with increasing relative risks to complex island water systems. There is a spatial structure in specific sites, north of Los Lagos Region and central Aysén Region, with high infestation clusters and epidemic peaks during 2013. In addition, average weight, salmon species, chemotherapeutants, latitude, temperature, salinity, and year category are factors associated with these C. rogercresseyi patterns. Recommendations for an IPM plan are provided, along with a discussion that considers the involvement of stock density thresholds by salmon species and the spatial structure of the efficacy of chemical control, both intended to avoid the advance of resistance and to minimize environmental residues.