Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.We performed a hierarchical Bayesian meta-analysis of 65 observational studies published between 2000 and 2024 to quantify the strength of association between candidate risk factors and brucellosis infection. The model—run separately for animal- and herd-level outcomes—identified abortion history as the most influential predictor (pooled odds ratio ≈ 5 at both scales). Other factors that elevated risk included retained placenta, multiparity and co-housing with sheep or goats. Vaccination reduced the odds of infection in individual cattle (~0.7) but, paradoxically, was linked to higher odds at herd level (~1.5), suggesting diagnostic interference or management bias. Producer knowledge and routine veterinary oversight each halved the likelihood of herd-level infection, underscoring the value of sound biosecurity culture.These findings consolidate previously fragmented evidence by clarifying the critical risk factors for bovine brucellosis and, by closing this knowledge gap, guide more effective control measures while highlighting the need for additional high quality studies to further refine prevention strategies.Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.We performed a hierarchical Bayesian meta-analysis of 65 observational studies published between 2000 and 2024 to quantify the strength of association between candidate risk factors and brucellosis infection. The model—run separately for animal- and herd-level outcomes—identified abortion history as the most influential predictor (pooled odds ratio ≈ 5 at both scales). Other factors that elevated risk included retained placenta, multiparity and co-housing with sheep or goats. Vaccination reduced the odds of infection in individual cattle (~0.7) but, paradoxically, was linked to higher odds at herd level (~1.5), suggesting diagnostic interference or management bias. Producer knowledge and routine veterinary oversight each halved the likelihood of herd-level infection, underscoring the value of sound biosecurity culture.These findings consolidate previously fragmented evidence by clarifying the critical risk factors for bovine brucellosis and, by closing this knowledge gap, guide more effective control measures while highlighting the need for additional high quality studies to further refine prevention strategies.

Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.We performed a hierarchical Bayesian meta-analysis of 65 observational studies published between 2000 and 2024 to quantify the strength of association between candidate risk factors and brucellosis infection. The model—run separately for animal- and herd-level outcomes—identified abortion history as the most influential predictor (pooled odds ratio ≈ 5 at both scales). Other factors that elevated risk included retained placenta, multiparity and co-housing with sheep or goats. Vaccination reduced the odds of infection in individual cattle (~0.7) but, paradoxically, was linked to higher odds at herd level (~1.5), suggesting diagnostic interference or management bias. Producer knowledge and routine veterinary oversight each halved the likelihood of herd-level infection, underscoring the value of sound biosecurity culture.These findings consolidate previously fragmented evidence by clarifying the critical risk factors for bovine brucellosis and, by closing this knowledge gap, guide more effective control measures while highlighting the need for additional high quality studies to further refine prevention strategies.Bovine brucellosis, primarily caused by Brucella abortus, is a widespread zoonotic disease that threatens cattle health and productivity and poses public health risks, leading to major economic losses. Although numerous risk factors—from husbandry practices to animal health conditions—have been implicated, findings across individual studies remain inconsistent, leaving a critical knowledge gap about the primary drivers of infection.We performed a hierarchical Bayesian meta-analysis of 65 observational studies published between 2000 and 2024 to quantify the strength of association between candidate risk factors and brucellosis infection. The model—run separately for animal- and herd-level outcomes—identified abortion history as the most influential predictor (pooled odds ratio ≈ 5 at both scales). Other factors that elevated risk included retained placenta, multiparity and co-housing with sheep or goats. Vaccination reduced the odds of infection in individual cattle (~0.7) but, paradoxically, was linked to higher odds at herd level (~1.5), suggesting diagnostic interference or management bias. Producer knowledge and routine veterinary oversight each halved the likelihood of herd-level infection, underscoring the value of sound biosecurity culture.These findings consolidate previously fragmented evidence by clarifying the critical risk factors for bovine brucellosis and, by closing this knowledge gap, guide more effective control measures while highlighting the need for additional high quality studies to further refine prevention strategies.

The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by using any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estimand that incorporates the standard fuzzy RD estimand as a special case. Applying the proposed approach, we estimate the impacts of bank capital on bank failure in the pre-Great Depression era in the United States. Our RD design takes advantage of the minimum capital requirements, which change discontinuously with town size.

The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This paper establishes identification and robust bias-corrected inference for such RD designs. Causal identification is achieved by utilizing any changes in the distribution of the continuous treatment at the RD threshold (including the usual mean change as a special case). We discuss a double-robust identification approach and propose an estimand that incorporates the standard fuzzy RD estimand as a special case. Applying the proposed approach, we estimate the impacts of bank capital on bank failure in the pre-Great Depression era in the United States. Our RD design takes advantage of the minimum capital requirements, which change discontinuously with town size.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.