We hope that the approach we have presented may help to guide future research to explain suicide clusters and social-media contagion. Posts pertaining to non-cluster controls were four times as receptive as those about cluster cases. 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. We found no “putatively harmful” or “putatively protective” content following any suicides. We also used concept mapping, word-emotion association, and sentiment analysis and gauged user reactions to posts using the reactions-to-posts ratio. We examined text segments for “putatively harmful” and “putatively protective” content (e.g., discussion of the suicide method vs. 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 used a scan statistic to identify suicide cluster cases occurring in spatiotemporal clusters and matched each case to 10 non-cluster control suicides. Our pilot case-control study presented a novel methodological approach to examining whether Facebook activity following cluster and non-cluster suicides differed. Social media may play a role in the “contagion” mechanism thought to underpin suicide clusters. SaTScan has no direct interface with any statistical, database, or GIS program, but it requires their use. Although analysis is easy to do and interpret, input and output are unnecessarily cumbersome. The end result is quite intuitive and includes the location of a cluster in space and time and the significance of the cluster based on a Monte Carlo simulation. Either the data may be aggregated to a geographic region or each case may have unique coordinates. Paraphrasing Kulldorff, SaTScan calculates a Poisson-based model according to a known population at risk, a Bernoulli model which allows for cases and controls, a space-time permutation model that needs only case data, an ordinal model, an exponential model for survival analysis, and a normal model for continuous data. The program offers a wide variety of scanning models. It places circles or ellipses of continuously varying size over a spatial study area and can add time as a continuously varying third-dimension scan. (Example: port~1 matches fort, post, or potr, and other instances where one correction leads to a match.SaTScan was developed by Martin Kulldorff to scan for temporal, spatial, and spatial temporal clusters. To use fuzzy searching to account for misspellings, follow the term with ~ and a positive number for the number of corrections to be made.(Example: shortcut^10 group gives shortcut 10 times the weight as group.) Follow the term with ^ and a positive number that indicates the weight given that term.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |