Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case–control study

Background Suicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention. Objectives Data linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance. Methods We will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case–control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths. Discussion We will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

Sylvatic trichinellosis in Texas

There are no published reports of domestic or sylvatic trichinellosis in Texas. The aim of the present survey was to determine the presence of Trichinella species in selected representative species of potential wildlife reservoirs in southern Texas. In 1998-99, tongues of 211 wild mammals were collected in southern Texas: 154 coyotes (Canis latrans), three bobcats (Lynx rufus), 32 racoons (Procyon lotor), 1 3 opossum (Didelphis marsupialis), four ocelots (Leopardus pardalis) and five wild boars (Sus scrofa). Presence of Trichinella sp. larvae was investigated by artificial digestion and larvae of positive samples were identified at the species level by a multiple-polymerase chain reaction analysis. Nine (5.8 %) coyotes had trichinellosis ; in the muscles of seven of these coyotes, the larvae were identified as Trichinella murrelli. This is the first report of sylvatic trichinellosis in Texas

Sylvatic trichinellosis in Texas

There are no published reports of domestic or sylvatic trichinellosis in Texas. The aim of the present survey was to determine the presence of Trichinella species in selected representative species of potential wildlife reservoirs in southern Texas. In 1998-99, tongues of 211 wild mammals were collected in southern Texas: 154 coyotes (Canis latrans), three bobcats (Lynx rufus), 32 racoons (Procyon lotor), 1 3 opossum (Didelphis marsupialis), four ocelots (Leopardus pardalis) and five wild boars (Sus scrofa). Presence of Trichinella sp. larvae was investigated by artificial digestion and larvae of positive samples were identified at the species level by a multiple-polymerase chain reaction analysis. Nine (5.8 %) coyotes had trichinellosis ; in the muscles of seven of these coyotes, the larvae were identified as Trichinella murrelli. This is the first report of sylvatic trichinellosis in Texas