A meta-analysis of previous falls and subsequent fracture risk in cohort studies

NC Harvey acknowledges funding from the UK Medical Research Council (MC_PC_21003; MC_PC_21001). The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, and 75N92021D00005. Funding for the MrOS USA study comes from the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. Funding for the SOF study comes from the National Institute on Aging (NIA), and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), supported by grants (AG05407, AR35582, AG05394, AR35584, and AR35583). Funding for the Health ABC study was from the Intramural research program at the National Institute on Aging under the following contract numbers: NO1-AG-6–2101, NO1-AG-6–2103, and NO1-AG-6–2106.Peer reviewedPostprin

Partners No More: Relational Transformation and the Turn to Litigation in Two Conservationist Organizations

The rise in litigation against administrative bodies by environmental and other political interest groups worldwide has been explained predominantly through the liberalization of standing doctrines. Under this explanation, termed here the floodgate model, restrictive standing rules have dammed the flow of suits that groups were otherwise ready and eager to pursue. I examine this hypothesis by analyzing processes of institutional transformation in two conservationist organizations: the Sierra Club in the United States and the Society for the Protection of Nature in Israel (SPNI). Rather than an eagerness to embrace newly available litigation opportunities, as the floodgate model would predict, the groups\u27 history reveals a gradual process of transformation marked by internal, largely intergenerational divisions between those who abhorred conflict with state institutions and those who saw such conflict as not only appropriate but necessary to the mission of the group. Furthermore, in contrast to the pluralist interactions that the floodgate model imagines, both groups\u27 relations with pertinent agencies in earlier eras better accorded with the partnership-based corporatist paradigm. Sociolegal research has long indicated the importance of relational distance to the transformation of interpersonal disputes. I argue that, at the group level as well, the presence or absence of a (national) partnership-centered relationship determines propensities to bring political issues to court. As such, well beyond change in groups\u27 legal capacity and resources, current increases in levels of political litigation suggest more fundamental transformations in the structure and meaning of relations between citizen groups and the state

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation

<div><p>Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis—where individuals are sampled sooner post-infection—rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to robustly and accurately identify clusters for the most cost-effective deployment of outbreak management and prevention resources.</p></div

Computing time required by six clustering methods to process five different trees each relating approximately 1000 simulated sequences.

<p>Computing time required by six clustering methods to process five different trees each relating approximately 1000 simulated sequences.</p

Schematic diagram of a model-based genetic clustering method.

<p>The rate of branching events (approximating transmission events) evolves according to a Markov-modulated Poisson process (MMPP). (A) A lineage switches between two states (fast and slow) that control the rates that branching events occur. (B) When rate shifts are mapped to the tree, we can identify clusters of high transmission rates (red).</p

Performance of MMPP and five nonparametric clustering methods on simulated data.

<p>Sequence data were simulated under three scenarios where the minority subpopulation had (1) a faster transmission rate (left); (2) a faster sampling rate (centre), or; (3) both faster rates of transmission and sampling (right). The <i>x</i>- and <i>y</i>-axes correspond to the false and true positive rates of classifying individuals into the minority subpopulation, respectively. Each point represents the outcome when the MMPP model was applied to one of 100 replicate simulations. Each line represents the receiver-operator characteristic curve for one of the five nonparametric clustering methods (see figure legend), where different false and true positive rates were obtained by varying a threshold parameter of the method.</p

Comparison of predicted clusters and actual clusters of faster transmission.

<p>We mapped the clustering predictions from three different methods (Cluster Picker, subtree clustering and MMPP) onto one of the neighbor-joining trees reconstructed from sequence data simulated under the faster-transmission scenario. Branches are coloured light red if the method assigns that branch to a cluster, and dark blue otherwise. The correct assignments are indicated by labeling tips with filled circles if they belong in a cluster. Cluster Picker (version 1.2.4) was run with default initial and main support thresholds (0.9) and a genetic distance threshold of 0.025. Subtree clusters were extracted from the tree with a bootstrap threshold of 90% and mean branch length threshold of 0.0065.</p

Comparison of three different clustering methods applied to a phylogeny reconstructed from real HIV data.

<p>The phylogeny was reconstructed by maximum likelihood from a published data set of HIV-1 subtype B <i>pol</i> sequences from a recent study of genetic clusters in Seattle, U.S. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005868#pcbi.1005868.ref038" target="_blank">38</a>]. We adjusted the ClusterPicker and subtree clustering method parameters until the number of individuals assigned to clusters was similar to the number reported in the original study. For instance, the subtree method was used with a mean branch length cutoff of 0.65% and a bootstrap cutoff of 90%. Branches assigned to clusters are highlighted in dark red.</p

Ancestral Reconstruction - Fig 5

<p><b>Plots of 200 trajectories of each of: Brownian motion with drift 0 and <i>σ</i>² = 1 (black); Ornstein–Uhlenbeck with <i>σ</i>² = 1 and <i>α</i> = −4 (green); and Ornstein–Uhlenbeck with <i>σ</i>² = 1 and <i>α</i> = −40 (orange)</b>.</p