This is the protocol for a Campbell systematic review. The main objective of this review is to answer the research question: What are the effects of organised sport on risk behaviour, personal, emotional and social skills of young people, who either have experienced or is at‐risk of experiencing an adverse outcome? Further, the review will attempt to answer if the effects differ between participants characteristics such as gender, age and risk indicator or between types of sport . We will use the following definition of organised sport: a structured activity through an organisation, requiring physical exertion and/or physical skill and is generally accepted as being a sport . The common meaning of the term ‘sport’ is very wide and includes more disciplines than for example the Olympic definition. In Olympic terminology ‘sport’ refers to all events sanctioned by an international sport federation, and may comprise several disciplines of which not all are necessary Olympic disciplines (the list is constantly changing). Likewise, not all international sport federations are part of the Olympic programme but are members of the General Association of International Sports Federations (GAISF) and are contested at the World Games. Currently, there are 97 member‐Federations where several includes multiple disciplines  which, of course, varies by sport, which can be individual or team oriented, contact sport, limited contact or no contact sport, and require different skills and competencies to perform effectively . The eligible setting is after‐school sport participation, that is, leisure activities, where social skills can be acquired through organised sport participation because of the unique demands of team sport such as the naturally afforded opportunities for youth to display skills such as co‐operation, compromise, teamwork, and leadership. But even participating in individual oriented sport, learning is an unavoidable part of the social life and participation in practice required when joining a sport club.Sport clubs may be school or non‐school sport clubs as long as the activity takes place after school hours. In most European countries, the main means of promotion of sport are non‐school sports clubs, while in the USA (and to some extent Canada) school‐based sport clubs are the main providers of sport Camiré, . The pop1.3Participation in organised sport have been shown to serve as important resources for reducing school failure and other problem/high‐risk behaviour Parker, . Youth rThere have been several strands of theories offered as a potential understanding of the theory of change behind sport participation  with its focus on the social‐contextual ingredients required for optimal growth and development  to need satisfaction, research has shown that in the context of physical education, perceptions of an autonomy‐supportive climate were strong positive predictors of students’ perceptions of autonomy  underlying competence judgements and ensuing feelings of success are more controllable and achievable compared to normative criteria  according to Duda .Finally, the study by Reinboth et al. ; tested Situated Learning  that did not restrict participants to be at‐risk youth.The review by Eime et al. ; searcheThe review by Spruit, Assink, et al. , includeThe  same author team performed a systematic review with meta‐analysis on all studies addressing the relationship between sports participation and delinquency in juveniles which were published before October 2015 .33.13.1.1The proposed project will follow standard procedures for conducting systematic reviews using meta‐analysis techniques.Randomised and quasi‐randomised) controlled trials will be included. To summarise what is known about the possible causal effects of sport programmes, we will include all study designs that use a control group, that is, a group of children/youth not participating in organised sport. The control group may be offered no treatment or an alternative treatment.1.Randomised and quasi‐randomised controlled trials .2.Non‐randomised studies .The study designs we will include in the review are:Studies using single group pre‐post comparisons will not be included. Non‐randomised studies using an instrumental variable approach will not be included—see Supporting Information: Appendix 3.1.2The review will include young people between 6 and 18 years of age, who either have experienced or is at‐risk of experiencing an adverse outcome such as school failure or drop‐out, substance and/or alcohol abuse, unemployment, long‐term poverty and delinquency/criminal behaviour.At‐risk may be based on such indicators as the young person's level of association with negative peers , hanging out on the streets or in gang neighbourhoods, poor academic history, coming from a highly distressed or crisis ridden, low income family in a racially/ethnically segregated neighbourhood, and prior involvement in illegal and delinquent activities.Studies where either the majority of participants are between 6 and 18 years of age or studies where a discrete age group within this range is provided will be included.3.1.3The intervention of interest is participation in leisure time organised sport. We will use the following definition of organised sport: a structured activity through an organisation, requiring physical exertion and/or physical skill and is generally accepted as being a sport. Generally there is a coach involved, from which participants follow directions and execute the skills taught. The organisation providing the activity may be school or non‐school sport clubs as long as the activity takes place after school hours. Leisure time physical activity defined as any unstructured physical activity outside of school hours is not eligible.necessary, but not sufficient condition for the achievement of certain outcomes  will be excluded.We will exclude studies that only address ‘exercise’, ‘physical activity’ or ‘physical education’, and not sport. In addition, we will exclude studies of yoga and studies of outdoor adventure programmes.The comparison population are young people at‐risk who do not attend organised sport programmes.3.1.4The primary focus is on measures of problem/high‐risk behaviour, such as delinquency, drug and alcohol use, high levels of externalizing problems, school failure, and in the longer run employment, education, training (NEET status). These outcomes may be measured by self‐reports or reports by authorities, administrative files, registers.A secondary focus is on measures of personal, social and emotional outcomes.Only valid and reliable outcomes that have been standardised on a different population  will be included. Examples of valid outcomes are measures from the Social Skills Rating System  1970–2023Academic Search (EBSCO) 1970–2023EconLit (EBSCO) 1970–2023PsycINFO (EBSCO) 1970–2023SocINDEX (EBSCO) 1970–2023International Bibliography of the Social Sciences (ProQuest) 1970–2023Sociological Abstracts (ProQuest) 1970–2023Science Citation Index Expanded (Web Of Science) 1990–2023Social Sciences Citation Index (Web Of Science) 1990–2023EMBASE (OVID) 1974–2023The following electronic bibliographic databases will be searched:3.2.2The search string is based on the PICO(s)‐model, and contains two concepts, of which we have developed two corresponding search facets: population characteristics and the intervention. The search string includes searches in title and abstract as well as subject terms and/or keywords for each facet. The subject terms in the facets will be selected according to the thesaurus or index of each database. Keywords will be supplied if the search technique provides additional results. Use of truncation and wildcards will be used to address English spelling variants.3.2.3Search 1–6 covers the interventionSearch 7–18 covers the population characteristicsSearch 19 combines the two search facetsBelow is an exemplified search string utilised to search the database SocINDEX through the EBSCO‐platform. The search string is structured in the following order:EBSCO SocINDEX 1970–25 August 2022. Search modes: Boolean/Phrase. Expanders: Apply equivalent subjects. Limiters: Date of Publication: 19700101–202212313.2.4Children and Youth ServicesThe Future of ChildrenResearch on Social Work PracticeJournal of Prevention and Intervention in the CommunityWe will conduct a hand search of specific journals, to make sure that all relevant articles are found. The hand search will focus on editions published between 2020 and 2022 to secure recently unpublished articles which have not yet been indexed in the bibliographic databases. We will decide upon which journals to hand search based on the identified records from the electronic searches. The following are examples of specific journals which we may decide to hand search:Most of the resources searched for unpublished literature include multiple types of references. As an example, the resources listed to identify reports from national bibliographical resources also include working papers and dissertations, as well as peer‐reviewed references and documents from preprint databases. In general, there is a great amount of overlap between the types of references in the chosen resources. The resources are listed once under the category of literature we expect to be most prevalent in the resource, even though multiple types of unpublished/published literature might be identified in the resource.Further resources for identifying dissertations might be added during the search process. A final list of resources will be included in the appendix of the review.EBSCO Open Dissertations (EBSCO‐host)OATD—Open Access Theses and Dissertations (oatd. org)We will search the following resources for dissertations:https://www.air.org/American Institutes for Research (AIR): Manpower Demonstration Research Corporation (MDRC): https://www.mdrc.org/https://www.urban.org/Urban Institute: https://scholar.google.comGoogle Scholar—https://google.comGoogle—We will search the following resources for working papers/conference proceedings:https://sportrxiv.org/SportRxiv—Open access subject repository of preprints, post prints within sport, exercise, performance, and health research—https://osf.io/preprints/socarxivSocArxiv—Open archive of the social sciences of working papers, preprints, and published papers—https://psyarxiv.comPsyArXiv—Open archive of preprints, working manuscripts, and post prints in the psychological sciences—https://osf.io/preprints/OSF Preprints—Open source multidisciplinary preprints as well as post prints and working papers—We will search the following resources for preprints, post prints, working papers and published papers:https://ppv.issuelab.orgPublic/Private Ventures (P/PV) publications  has ceased operations, its publications are archived with the Foundation Center's IssueLab: http://www.forskningsdatabasen.dk/enDanish National Research Database—http://swepub.kb.se/SwePub—Academic publications at Swedish universities—http://nora.openaccess.no/NORA—Norwegian Open Research Archives—https://core.ac.uk/CORE—research outputs from international repositories—https://www.skolporten.se/forskning/Skolporten—Swedish Dissertations—http://www.diva-portal.org/smash/DIVA—Digital Scientific Archives—We will search the following resources for non‐US literature:If we identify relevant systematic reviews during the search process, they will be used for citation‐tracking, to extract relevant references from the review.We will use citation‐tracking methods to identify more relevant literature. We will citation‐track forwards (by using Google Scholar and Web of Science) and backwards (by screening citations in the most relevant literature).We will contact international experts to identify unpublished and ongoing studiesStudies will not be excluded based on publication status or language . Studies authored before 1970 will not be included.3.33.3.1Randomised controlled trials are eligible, but we expect that a certain amount of studies will be conducted without randomisation of participants. Studies of the effect of participation in organised sport are required to have a control group for inclusion in the review. Participants may be allocated by, for example, time differences, location differences, decision makers, policy rules or participant preferences. We expect that the primary studies demonstrate pretreatment group equivalence via matching, statistical controls, or evidence of equivalence on key risk variables and participant characteristics as outlined in Section 3.3.2Under the supervision of review authors, two review team assistants will first independently screen titles and abstracts to exclude studies that are clearly irrelevant. Studies considered eligible by at least one assistant or studies were there is insufficient information in the title and abstract to judge eligibility, will be retrieved in full text. The full texts will then be screened independently by two review team assistants under the supervision of the review authors. Any disagreement of eligibility will be resolved by the review authors. Exclusion reasons for studies that otherwise might be expected to be eligible will be documented and presented in an appendix.The study inclusion criteria will be piloted by the review authors and team assistants bias arising from the randomisation process;(2)bias due to deviations from intended interventions ;(3)bias due to missing outcome data;(4)bias in measurement of the outcome;(5)bias in selection of the reported result.The five domains for individually randomised trials are:For cluster‐randomised trials, an additional domain is included ((1b) Bias arising from identification or recruitment of individual participants within clusters). We will use the latest template for completion . In the cluster randomised template however, only the risk of bias due to deviation from the intended intervention  is present and the signalling question concerning the appropriateness of the analysis used to estimate the effect is missing. Therefore, for cluster randomised trials we will only use the signalling questions concerning the bias arising from identification or recruitment of individual participants within clusters from the template for cluster randomised parallel‐group trials; otherwise we will use the template and signalling questions for individually randomised parallel‐group trials.We will assess the risk of bias in non‐randomised studies, using the model ROBINS –I, developed by members of the Cochrane Bias Methods Group and the Cochrane Non‐Randomised Studies Methods Group bias due to confounding(2)bias in selection of participants(3)bias in classification of interventions(4)bias due to deviations from intended interventions;(5)bias due to missing outcome data;(6)bias in measurement of the outcome;(7)bias in selection of the reported result.The ROBINS‐I tool covers seven domains  through which bias might be introduced into non‐randomised studies:The first two domains address issues before the start of the interventions and the third domain addresses classification of the interventions themselves. The last four domains address issues after the start of interventions and there is substantial overlap for these four domains between bias in randomised studies and bias in non‐randomised studies trials  is too problematic in this domain to provide any useful evidence on the effects of intervention, and it is excluded from the data synthesis. The same critical level of risk of bias (excluding the result from the data synthesis) is not directly present in the RoB 2 tool, according to the guidance to the tool  and unobservable . There is no single non‐randomised study design that always solves the selection problem. Different designs represent different approaches to dealing with selection problems under different assumptions, and consequently require different types of data. There can be particularly great variations in how different designs deal with selection on unobservables. The ‘adequate’ method depends on the model generating participation, that is, assumptions about the nature of the process by which participants are selected into a programme.A major difficulty in estimating causal effects of sport participation is the potential endogeneity of the young individual's life circumstance and developmental state that leads to the decision of participating in organised sport and if not accounted for it will yield biased estimates.As there is no universal correct way to construct counterfactuals for non‐randomised designs, we will look for evidence that identification is achieved, and that the authors of the primary studies justify their choice of method in a convincing manner by discussing the assumption(s) leading to identification (the assumption(s) that make it possible to identify the counterfactual). Preferably the authors should make an effort to justify their choice of method and convince the reader that the only difference between a treated individual and a non‐treated individual is the treatment. The judgement is reflected in the assessment of the confounder unobservables in the list of confounders considered important at the outset  effect. The risk of bias assessments will therefore be in relation to this specific effect.The risk of bias assessments of both randomised trials and non‐randomised studies will consider adherence and differences in additional interventions (‘co‐interventions’) between intervention groups. Relevant co‐interventions are those that individuals might receive with or after starting the intervention of interest and that are both related to the intervention received and prognostic for the outcome of interest. Important co‐interventions we will consider are interventions delivered as part of sport‐based youth development programmes. These programmes may be explicitly teaching personal and social responsibility or other life skills. Although these types of programmes are not eligible  from d Wilson . If not For dichotomous outcomes, we will calculate odds ratios with 95% confidence intervals. Delinquency and school failure, are examples of relevant dichotomous outcomes in this review.There are statistical approaches available to re‐express dichotomous and continuous data to be pooled together , or when participants are randomised by treatment locality. Non‐randomised studies may also include clustered assignment of treatment. Effect sizes and standard errors from such studies may be biassed if the unit‐of‐analysis is the individual and an appropriate cluster adjustment is not used  trials  to avoid dependence between effect sizes. This method provides an unbiased estimate of the mean effect size parameter but overestimates the standard error. Random effects models applied when synthetic effect sizes are involved actually perform better in terms of standard errors than do fixed effects models Hedges, . HoweverIf pooling is not appropriate , only one intervention group will be coded and compared to the control group to avoid overlapping samples. The choice of which estimate to include will be based on our risk of bias assessment. We will choose the estimate that we judge to have the least risk of bias .In some cases, several studies may have used the same sample of data or some studies may have used only a subset of a sample used in another study. We will review all such studies, but in the meta‐analysis we will only include one estimate of the effect from each sample of data. This will be done to avoid dependencies between the ‘observations’  in the meta‐analysis. The choice of which estimate to include will be based on our risk of bias assessment of the studies. We will choose the estimate from the study that we judge to have the least risk of bias . If two (or more) studies are judges to have the same risk of bias and one of the studies (or more) uses a subset of a sample used in another study (or studies) we will include the study using the full set of participants.When the results are measured at multiple time points, each outcome at each time point will be analysed in a separate meta‐analysis with other comparable studies taking measurements at a similar time point. As a general guideline, these will be grouped together as follows: (1) post‐intervention, (2) less than a year follow up, (3) 1–2 year follow up and (4) More than 2 year follow up. However, should the studies provide viable reasons for an adjusted choice of relevant and meaningful duration intervals for the analysis of outcomes, we will adjust the grouping.Meta‐analysis of outcomes will be conducted on each metric  test, and the I2, and τ2 statistics  and relative effects  will be conducted separately.As different computational methods may produce effect sizes that are not comparable, we will be transparent about all methods used in the primary studies  and use caution when synthesising effect sizes. Special caution will be taken concerning studies using regression discontinuity designs (RDD) to estimate the treatment effect. In sharp RDDs, a threshold of a (non‐manipulable) forcing/running variable determines which students receive a treatment and which do not, that is, the design is similar to a RCT in the sense that the random sequence determining treatment assignment can be seen as a running variable  test, and the I2, and τ2 statistics , and we therefore expect heterogeneity amongst primary study outcomes, all analyses of the overall effect will be inverse variance weighted using random effects statistical models that incorporate both the sampling variance and between study variance components into the study level weights. Random effects weighted mean effect sizes will be calculated using 95% confidence intervals, and we will provide a graphical display (forest plot) of effect sizes. Graphical displays for meta‐analysis performed on ratio scales sometimes use a log scale, as the confidence intervals then appear symmetric. This is however not the case for the software Revman 5 which we plan to use in this review . The graphical displays using odds ratios and the mean effect size will be reported as an odds ratio. Heterogeneity amongst primary outcome studies will be assessed with 95% prediction intervals will be reported.For subsequent analyses of moderator variables that may contribute to systematic variations, we will use the mixed‐effects regression model if there are a sufficient number of studies. This model is appropriate if a predictor explaining some between‐studies variation is available, but there is a need to account for the remaining uncertainty  in the meta‐analysis. The choice of which estimate to include will be based on our quality assessment of the studies. We will choose the estimate from the study that we judge to have the least risk of bias, with particular attention paid to Confounding bias.τ2) and the effect sizes within each study are equi correlated (ρ). The method is approximately efficient, since it uses approximate inverse‐variance weights: they are approximate given that ρ is, in fact, unknown and the correlation structure may be more complex. We will calculate weights using estimates of τ2, setting ρ = 0.80 and conduct sensitivity tests using a variety of ρ values; to assess if the general results and estimates of the heterogeneity is robust to the choice of ρ. We will use the small sample adjustment to the residuals used in RVE as proposed by Bell and McCaffrey  approach  to avoid dependence between effect sizes.3.3.12We will investigate the following factors with the aim of explaining potential observed heterogeneity: study‐level summaries of participant characteristics  are available) and risk indicator . In addition, we will investigate programme characteristics such as team/individual, contact/no contact, intensity and duration.If the number of included studies is sufficient and given there is variation in the covariates , we will perform moderator analyses (multiple meta‐regression using the mixed model) to explore how observed variables are related to heterogeneity.τ2, setting ρ = 0.80 and conduct sensitivity tests using a variety of ρ values; to assess if the general results are robust to the choice of ρ. We will use the small sample adjustment to the residuals used in RVE and the Satterthwaite degrees of freedom (Satterthwaite, If there are a sufficient number of studies, we will apply the RVE approach and use approximately inverse variance weights calculated using a method proposed by Hedges et al. . This teWe will report 95% confidence intervals for regression parameters. We will estimate the correlations between the covariates and consider the possibility of confounding. Conclusions from meta‐regression analysis will be cautiously drawn and will not solely be based on significance tests. The magnitude of the coefficients and width of the confidence intervals will be taken into account as well. Otherwise, single factor subgroup analysis will be performed. The assessment of any difference between subgroups will be based on 95% confidence intervals. Interpretation of relationships will be cautious, as they are based on subdivision of studies and indirect comparisons.In general, the strength of inference regarding differences in treatment effects amongst subgroups is controversial. However, making inferences about different effect sizes amongst subgroups on the basis of between‐study differences entails a higher risk compared to inferences made on the basis of within study differences; see Schandelmaier et al. . We willWe will also consider the degree of consistence of differences, as making inferences about different effect sizes amongst subgroups entails a higher risk when the difference is not consistent within the studies; see Schandelmaier et al. .3.3.13Sensitivity analysis will be carried out by restricting the meta‐analysis to a subset of all studies included in the original meta‐analysis and will be used to evaluate whether the pooled effect sizes are robust across components of risk of bias. We will consider sensitivity analysis for each domain of the risk of bias checklists and restrict the analysis to studies with a low risk of bias.Sensitivity analyses with regard to research design and statistical analysis strategies in the primary studies will be an important element of the analysis to ensure that different methods produce consistent results.3.3.14We do not plan to include qualitative research.3.3.15We do not plan to include Summary of findings and assessment of the certainty of the evidence.Approximate date for submission of the systematic review will be no longer than 2 years after protocol approval.Trine Filges will be responsible for updating the review and updates can be expected each second year.Content: Else Ladekjær, Trine FilgesSystematic review methods: Trine FilgesStatistical analysis: Trine Filges, Mette VernerInformation retrieval: Elizabeth BengtsenThere are no potential conflicts of interest.Internal sourcesNo sources of support providedExternal sourcesNo sources of support providedSupporting information.Click here for additional data file.


The interaction between environmental stressors may be a greater threat to biota than any individual ecological threat on its own. Land‐use change and inappropriate fire regimes are known to pose great challenges to biodiversity conservation worldwide. Despite much research being conducted into their singular impacts on ecosystems, very few have investigated how their interaction may be affecting the biota of a region. We used data from surveys in 1998/2000 and 2019/2020 to compare the feeding guild assemblages of bird communities in different habitats within the greater Darwin region. By compiling two sets of spatial data, land‐use change, and fire history mapping, we were able to investigate their interaction and impact on the avian assemblages in the Darwin urban area. Using Generalized Linear Mixed Models (GLMM) we found that an increase in urbanization significantly affected fire occurrence across study sites. Furthermore, we found that the interaction between land‐use change and fire regimes had a significant effect on species that primarily feed on fruit. We conclude that while an increase in urbanization did not directly affect the avian assemblages, the impact of land‐use change on the fire regimes indirectly impacted urban bird community structures. Using spatial data and historical and recent bird surveys, we investigated the impact of land‐use change and fire on the Darwin bird assemblages. We determined that the interaction between land‐use change and fire regimes directly affected frugivores. Furthermore, we found that the effect of land‐use change on fire regimes indirectly impacted avian assemblages. The climate is that of the monsoonal tropics comprising distinct dry and wet seasons  with transitional periods in between  and dry, large areas of mangrove vegetation and tracts of green space containing native and introduced tropical plant species supporting an array of fauna  and walking pre‐determined transect lines. Novel surveying techniques involved bioacoustic recording of birds, both stationary (point‐counts) and moving (line‐transect) via the use of a drone. Four distinct habitat types, eight sites of each, were surveyed  and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery for the years 1998 to 2012, and Landsat 8 Operational Land Imager (OLI) for the years 2013 to 2018 (ground sample distance [GSD] 30 m); fire mapping employed the same imagery as well as Sentinel‐2 imagery for the years 2016–2018 (GSD 20 m).2.3.12) of each category within each study site zone was calculated in ArcGIS. The categories of suburban, periurban, buildings and roads were combined in the category “urbanization” and grass, woodland, and forest combined for “greenspace.” The differences in urbanization and greenspace were measured by comparing relevant land class area data from the maps between the various periods. Accuracy assessments of the classified images were undertaken as per Fischer et al.  and overall (from 1998 to 2018). The Landsat imagery covering the study area was sourced via the United States Geological Survey's Global Visualization Viewer (GloVis) and processed using the methods as described in Fischer et al. , in ArcGr et al. , using sr et al. . Random 2 and expressed as a proportion of the 1 km buffer surrounding the study site coordinates.Land class areas between the time periods were calculated in m2.3.2A fire history of the study sites indicating the intra‐seasonality of fires during the fire season , along with the proportion of the study site burnt, was determined by calculating the Mid‐Infrared Burn Index (MIRBI) and the differenced Mid‐Infrared Burn Index (dMIRBI) between satellite image dates Table .2 and expressed as a proportion of the 1 km buffer around the study site coordinates.MIRBI was chosen as it is a spectral index that incorporates short‐wave infrared reflectance (SWIR) wavelengths. Compared with those indices that use only visible or near‐infrared (NIR) reflectance, such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index  was used to access and process images from its extensive publicly available catalog of Landsat and Sentinel 2 imagery. Using GEE code provided .2.4Land‐use change was recorded as either an increase, decrease, or no change in the amount of land categorized as urbanized  or greenspace ; changes were recorded for each time period  and overall (1998–2018). To determine the effect of changing land use on fire regimes, the total number of years that fire occurred at a site was recorded for each land‐use change time period (“fire occurrence”) and expressed as a proportion; the average proportion of the total land area of the site burned (‘mean % burnt’) for each land‐use change time period was also calculated. Fire data were then separated into the early, mid, or late dry season, to encompass the annual fire season, and the fire occurrence and the area burnt were again calculated with regard to the seasonality of fire. As with land‐use changes, changes to the fire occurrence and mean % burnt were noted as an increase, decrease or no change. Changes in the incidence and timing of fire were compared using a Generalized Linear Mixed Model (GLMM) where we included urban and greenspace change , and interactions between urban and greenspace change and habitat type , as fixed effects.Bird species were categorized by their primary food source, that is, the singular food source predominating the diet, similar to Fischer et al. . PrimaryWe again employed a GLMM to compare the change in guild member proportions by primary food source. As with the fire regime analysis, urban change and greenspace change were fixed effects but we also included the overall change in the fire occurrence and the mean % burnt ; interactions between land‐use change and fire regimes and land‐use change and habitat type were again examined.p > .05).DHARMa residual diagnostics was used to assess the suitability of the models; additionally, residuals were checked for autocorrelation and found to be independent . Greenspace increased in 11 sites, the highest being in an area classed as Native Vegetation (26%), but some considerable increases were recorded in two of the New Streetscape sites (16% and 20%). The greatest decrease in greenspace was also found in New Streetscape sites (42%). No change to urban land use was found in five sites, and one site showed no change to the amount of greenspace.3.1.3All sites experienced fire at least once between 1998 and 2018, except for one Parkland and one Established Streetscape site. Two sites, both in the Native Vegetation class, were recorded as having been burnt every year. The majority of fires occurred in the mid‐dry season when conditions are optimum for fuel loads from fallen leaf litter and flammability is at a maximum.Combining data for all habitat types, the highest average proportion of sites burnt was that of the Native Vegetation with very little fire occurring in areas classified as Established Streetscapes Figure .3.2p = .038, n = 128), with 66.7% of sites showing a decrease, but had no statistically significant effect on the mean % burnt. The interaction between greenspace change and habitat indicated that the effects on fire occurrence were habitat dependent with an effect on fire occurrence found to be statistically significant only in Parkland habitats .Results of the GLMM indicated that an increase in urbanization significantly affected the fire occurrence . The interaction between an increase in urban change and New Streetscape habitats in the early dry season was found to be statistically significant with regard to fire occurrence, as was the interaction between an increase in urban change and Native Vegetation habitats in the early and late dry seasons. A statistically significant interaction between Parkland habitats and greenspace with respect to fire occurrence was observed in the mid dry season period.When we analyzed the effect of land‐use change on the timing of fire, urban change had an effect during each seasonal period on fire occurrence Table . No chan3.3Fire regimes had a statistically significant effect on five of the eight feeding guild categories Table . The pro4Land‐use change and inappropriate fire regimes are two of the greatest threats to global biodiversity  and Whistling Kites  are often seen in large numbers during and after fire events, as they prey on species escaping the fire, or feed on carrion left behind , Magpie‐larks  and Black‐faced Cuckoo‐shrikes (Coracina novaehollandiae melanops), have also been known to exploit resources exposed at fire sites, hours or even a month later  and, ergo, had to trust the accuracy and veracity of unknown data collectors. Subsequent research would benefit from having consecutive bird surveys undertaken by the same observer or group of surveyors. Furthermore, the quality of satellite imagery has vastly improved over time; earlier imagery lacks the clarity and resolution of later imagery. Satellite visit times have also increased in frequency allowing for more images to be available in later years, thus increasing the chances of cloud‐free, or minimal cloud, images. As technology improves, the quality of data for comparison across time will improve further. While every effort was made to confirm fire occurrence and the size of burnt areas, it must be acknowledged that smaller fires may have gone undetected in the earlier images.Despite being able to contribute to the comparatively meager store of knowledge regarding land‐use change and fire regime interaction and their influence on vertebrate communities, our study prompts further research. Examining invertebrate‐preferring species more closely may explain why they appear sensitive to changes to their primary food source despite potentially having other sources of food; this would be greatly aided by research into the combined effects of land‐use change and fire regimes on native insect communities of the Darwin region. Consideration must be given, too, as to why vertebrate and vegetation feeders were unaffected by either land‐use change or fire regimes.5Our study aimed to investigate the effect of changing land use and associated fire regime change on the guild structures of the Darwin avian assemblages. While land‐use change is causing little change to the avian assemblage, we determined that altered land use on fire regimes is having an indirect influence on urban bird community structures. We have also demonstrated the interactive effects of land‐use change, particularly an increase in urbanization, and fire regimes can impact specific feeding guilds. Overall, a deeper analysis of specific species within feeding guilds and individual species reactions to land‐use change and fire regimes is necessary to be able to best formulate conservation strategies and management plans for ecosystems within the urban environment.Sarah Fischer: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Andrew C. Edwards: Conceptualization (supporting); methodology (supporting); writing – original draft (supporting); writing – review and editing . Stephen T. Garnett: Methodology (supporting); writing – original draft (supporting); writing – review and editing . Timothy G. Whiteside: Methodology (supporting); writing – original draft (supporting); writing – review and editing . Patrice Weber: Methodology (supporting); writing – original draft (supporting); writing – review and editing (supporting).The authors declare no competing interests.


JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.Targeted fracture prevention strategies among late‐life adults should balance fracture risk versus competing mortality risk. Models have previously been constructed using Fine‐Gray subdistribution methods. We used a machine learning method adapted for competing risk survival time to evaluate candidate risk factors and create models for hip fractures and competing mortality among men and women aged 80 years and older using data from three prospective cohorts . Random forest competing risk models were used to estimate absolute 5‐year risk of hip fracture and absolute 5‐year risk of competing mortality (excluding post–hip fracture deaths). Models were constructed for both outcomes simultaneously; minimal depth was used to rank and select variables for smaller models. Outcome specific models were constructed; variable importance was used to rank and select variables for inclusion in smaller random forest models. Random forest models were compared to simple Fine‐Gray models with six variables selected a priori. Top variables for competing risk random forests were frailty and related components in men while top variables were age, bone mineral density (BMD) , and frailty components in women. In both men and women, outcome specific rankings strongly favored BMD variables for hip fracture prediction while frailty and components were strongly associated with competing mortality. Model discrimination for random forest models varied from 0.65 for mortality in women to 0.81 for hip fracture in men and depended on model choice and variables included. Random models performed slightly better than simple Fine‐Gray model for prediction of competing mortality, but similarly for prediction of hip fractures. Random forests can be used to estimate risk of hip fracture and competing mortality among the oldest old. Modest gains in performance for mortality without hip fracture compared to Fine‐Gray models must be weighed against increased complexity. © 2023 The Authors.  Although there has been substantial research investigating the risk factors for hip fracture and for mortality post–hip fracture, very little research has been devoted to estimating the varying magnitude of hip fracture risk versus the competing risk of mortality without hip fracture among the oldest old. Using a simple five‐variable model estimated using the Fine‐Gray approach, a method derived from survival time which accounts for the competing risk, we have shown that fracture and competing mortality have different risk factors in older men leading to different risk profiles. We sought to extend and improve this prior work by considering additional risk factors in a larger and more diverse cohort of late‐life adults.Targeted fracture prevention strategies among late‐life adults should take into account the competing risk of mortality as it is materially related to both absolute and relative fracture risks.2, 3 In contrast, there are only a few machine learning methods that have been developed for use with survival time data with competing risks. Random forests were first introduced by Breiman as an extension of classification and regression trees and have subsequently been adapted for use with survival time data and predictions in the presence of competing risks. Random forests are by their nature nonparametric. Thus, they are extremely flexible and make few assumptions about the relationships between variables.The choice of the modeling approach represents one potential area of improvement in methodology used to address fracture prediction in late‐life. Many studies have considered machine learning methods applied to fracture risk prediction by considering fracture as a binary outcome, which presupposes either cross‐sectional data or complete and fixed follow‐up.17 In Our primary aim was to use random forest models to select important risk factors for the 5‐year absolute risk of hip fracture accounting for the competing risk of mortality and the 5‐year absolute risk of mortality (excluding post hip fracture death) among men and women age 80 years and older. We evaluated model performance and created simple Fine Gray models for the purpose of comparison. Our secondary aim was to assess the advantages and disadvantages of a random forest models compared to Fine Gray models to generate risk predictions for fracture and competing mortality. Osteoporotic Fracture in Men study (MrOS), and Health Aging and Body Composition study (HABC). Further details on study site and eligibility are shown in Table SWe studied community‐dwelling participants enrolled in three prospective cohort studies of older adults: Study of Osteoporotic Fractures (SOF),4 OsteSOF and MrOS participants were queried every 4 months and HABC participants every 6 months to ascertain vital status and incident fractures, including hip fractures. The response rate for these contacts was over 95% for active surviving participants in all cohorts. Self‐reported hip fractures in all cohorts were confirmed by radiographic reports. Deaths were verified with death certificates. Participant follow‐up was included until event  or censoring (maximum 5 years after the index examination). The mean ± SD follow‐up time to the first occurrence of hip fracture, mortality or censoring was 4.4 ± 1.2 years for both men and women.Age, self‐reported race/ethnicity, recalled height and weight at age 25 years, and history of fracture after age 50 years were collected at baseline in each cohort. Height, weight, health status, and smoking status were recorded at each examination. Long‐term height loss was calculated as the difference between measured height at the index examination and height at age 25 years, whereas current measured weight at index examination expressed as percentage of self‐reported weight at age 25 years indicated direction and magnitude of long‐term weight change. History of fracture at the index examination was determined based on history of fracture since age 50 years at the baseline exam and confirmed fractures between the baseline and index examination and categorized as follows: recent (within last 5 years), not recent but after age 50 years, and no fracture after age 50 years. Fall history in the previous year was assessed using triannual postcards. Usual walk speed was measured over a 6‐m course in SOF and MrOS and a 4‐m course in HABC with the latter recalibrated to match 6‐m gait speed and grip strength was measured using a hand‐held dynamometer. Those unable to perform the walk speed or grip strength tests were assigned a value of 0 for the continuous measure. Shrinking was defined as recent weight loss >10 lbs or >5% of baseline weight or current weight <18.5 kg/m2. Participants also self‐reported if they had poor energy and low physical activity . Health status at the index examination was self‐reported as excellent, good, fair, poor, or very poor. Time required to stand from a chair five times without using the arms was measured and converted to a ratio of stands per second; weakness (chair stands) was indicated by inability to perform the test. At the index examination, participants self‐reported their ability to walk two to three blocks on level ground or climb 10 stairs without difficulty (scored 0), with some difficulty (scored 1), with a lot of difficulty (score 2), or unable (score 3). The scores for these two mobility tasks were combined into one variable, ranging from 0 to 6.Clinic measures at the index examination included components of phenotypic frailty; slowness , weakness , poor energy, low physical activity, and shrinking.22 Usu We considered each medication class as a candidate predictor variable of incident hip fracture and mortality without hip fracture based on prior medical literature review and a biologically plausible mechanism by which each medication class may be associated with risk of hip fracture and/or mortality. Candidate medications included: antidepressants, medications for type 2 diabetes, anti‐epileptic medications, angiotensin converting enzyme (ACE) inhibitors, benzodiazepines, beta blockers, angiotensin receptor blockers (ARB), systemic corticosteroids, calcium channel blockers, H1 receptor antagonists, H2 receptor antagonists, loop diuretics, potassium sparing diuretics, thiazide diuretics, narcotics, proton pump inhibitors, thyroid hormone replacement therapy, selective serotonin receptor inhibitors (SSRIs), and warfarin.Participants were asked to bring all current prescription medication containers with them to the clinic. Medications were recorded in an electronic medication inventory database and matched to its ingredients(s) based on the Iowa Drug information Service drug vocabulary .24 We Participants were asked if they had been diagnosed by a health care provider as having arthritis, breast cancer (women), prostate cancer (men), cancer, coronary heart disease , stroke, heart failure, diabetes, dementia, depression, and Parkinson's disease. Participants were considered to have diabetes, Parkinson's disease, depression, or dementia if they either self‐reported a physician diagnosis or were treated with medication used to treat that diagnosis. Extensive quality control procedures were carried out at all examinations including centralized training and certification of DXA technicians and scanning of a central hip phantom at each clinical center at regular intervals.BMD at the total hip was measured at the index examination with dual‐energy X‐ray absorptiometry  using standardized protocols as described.27 Ext RSF does not assume linearity or functional form and allows for complex interactions between variables. RSF has several component steps: Step 1: draw bootstrap samples, Step 2: construct tree for each bootstrap sample using recursive partitioning based on randomly selected variables and cut‐points, Step 3: average model estimates over all trees to obtain (forest) estimate for full data. There were two types of forests depending on the splitting rule in Step 2. To construct a single forest for BOTH outcomes , the splitting rule was based on a modified log‐rank test adapted for competing risks. To construct outcome specific forests, the splitting rule was based on a log‐rank test for the chosen outcome . Missing values were present for some variables. In order to keep all variables under consideration without limiting the sample to those participants with complete data for all candidate risk factors, we used random forest imputation prior to model construction.All analyses were stratified by sex. The survival data was truncated at 5 years with prediction and outcome calculated at that time‐point. We used random survival forest models (RSFs), a machine learning approach that uses tree‐based models to create survival‐time predictions accounting for competing risk outcomes .19 RSF because this measure is both stable and takes both outcomes into account, while selection for outcome specific forests was based on variable importance (VIMP) to optimize outcome specific discrimination. Minimal depth for a variable is found by taking distance from the root of a tree to the maximal subtree with the given variable as the base node. Variables with low minimal depth are those that better predict outcome status . Permutation VIMP compares the estimated error from sample data to that where the given variable has been replaced by a new variable which is a random permutation of the original variable. By construction, higher VIMP indicates greater impact of the given variable on estimated model error compared to replacement with randomly permuted (noninformative) variable, and thus greater variable importance.Variable selection for random forests modeling both outcomes was based on minimal depth (MD)30 becModel performance was assessed using out‐of‐bootstrap sample estimates, ie, model performance corrected for optimism. Each random forest depends on the bootstrap sample selection and estimated VIMP depends on permutations. Thus, the set of variables chosen for the final model differ slightly across different bootstrap samples. Sensitivity analyses were performed to assess dependence of variable selection and model performance on random seeds. We created parsimonious outcome specific models by dropping all variables with VIMP <0.003 in the given model run. for the outcomes of hip fracture and mortality without hip fracture based on six selected predictors. For simplicity, we started with our previously published simple model based on five independent variables: age, femoral neck BMD, prior fracture, fall history, and number of chronic medical conditions. We added one additional independent variable to this model, the number of frailty phenotype components, because it was a single variable summary of many important predictors in the random forest models. To keep the Fine‐Gray models parsimonious, we did not consider higher order terms or interactions. We also assumed constant subdistribution hazard ratios for ordinal variables.Finally, we created simple Fine‐Gray models12 for (version 3.1.1) and ggRandomForest, version 2.2.1. Analytic details and sample code provided in the Supporting Information.Analysis was performed using Stata 17 and R (v 4.2.2) using the R‐packages randomForestSRC31 . Of these men, 46.5% had at least two chronic conditions, 16.4% reported two or more falls in the last year, 8% experienced a fracture within the past 5 years, and 18.1% had long‐term height loss of at least 8 cm. Among the women, the average age was 82.6 years; mean weight was 65.4 kg and mean femoral neck BMD T‐score was −1.0. Of these women, 41.1% had at least two chronic conditions, 33.7% reported two or more falls in the last year, 15.1% experienced a fracture within the past 5 years, and 21% had long‐term height loss of at least 8 cm. During the 5‐year follow‐up, 123 (3.1%) men and 325 (6.6%) women had an incident hip fracture; 922 (23.1%) men and 824 (16.6%) women died without experiencing an incident hip fracture.The characteristics at the index examination of the 3989 men and 4953 women included in the analyses are shown in Table Figures Among both men and women, gait speed and weight change since age 25 years were included in the list of the five variables with lowest MD based on a random forest designed to predict both hip fracture and mortality. Among men, the three additional variables with low minimal depth were number of frailty components, grip strength, and chair stand speed. In contrast, the three additional variables with the lowest MD in women were total hip BMD, femoral neck BMD and age.Among both men and women, total hip BMD and femoral neck BMD were each included in the list of five variables with highest VIMP based on a random forest designed to predict hip fracture. Among men, the three additional variables with the highest VIMP for hip fracture prediction included inability to perform chair stand test, chair stand speed, and number of medications, while in women the three additional variables with the highest VIMP for hip fracture prediction included weight change since age 25 years, gait speed, and weight.Among both men and women, gait speed and inability to do chair stands were included in the list of the five variables with highest VIMP based on a random forest designed to predict mortality before hip fracture. Among men, the three additional variables with the highest VIMP for mortality prediction were number of frailty components, grip strength, and slowness, whereas in women the three additional variables with the highest VIMP were age, weight change since age 25 years and difficulty walking three blocks.The overall performance of the random forest models  is shown in Table The first set of random forest models was designed to predict both hip fracture and mortality without hip fracture using modified log‐rank test to split nodes. For the models including nearly 60 variables (57 in men and 58 in women), the discrimination as determined by the C‐statistic was higher for hip fracture outcomes  than for competing mortality . Using the same criteria to split nodes, models with fewer predictors were obtained by dropping variables with high minimal depth; discrimination of these models with fewer independent variables  was similar to the larger models.The next set of random forest models focused on predicting incident hip fracture. The model with the full set of predictors had similar discrimination for both hip fracture and mortality before hip fracture outcomes to the first series of models predicting both outcomes. Selecting only variables with fracture VIMP >0.003, it was possible to achieve nearly the same discrimination for hip fracture with only 10 variables in men and seven variables in women, although the discrimination for mortality without fracture was 0.03–0.04 lower for the smaller models than it was for the larger models.The final set of random forest models focused on predicting mortality without hip fracture. The model with the full set of predictors had similar discrimination for both hip fracture and competing mortality outcomes to the first series of models predicting both outcomes.By selecting only variables with mortality VIMP >0.003, it was possible to obtain nearly the same discrimination for competing mortality with only 16 variables in men and 12 variables in women. The fracture discrimination was substantially lower (by 0.09) for the parsimonious model compared to the larger model in men, but remained similar in women. We note that BMD variables were not included in the smaller model for men, but total hip BMD remained in the smaller model for women.T‐score), and a higher number of falls in the past year  were associated with increased risk of hip fracture. Among women, lower femoral neck BMD  and recent fracture  were associated with increased risk of hip fracture. Among both men and women, older age, greater number of chronic conditions, greater number of frailty components were associated with increased mortality without hip fracture, and among women lower BMD was also associated with increased mortality without hip fracture.Table The random forest models with all predictors based on predicting both outcomes offered a slight improvement in the point estimate for the discrimination of mortality before hip fracture for both men (C‐statistic 0.69 versus 0.67) and women (C‐statistic 0.68 versus 0.65) compared with the simple Fine‐Gray models. In contrast, the random forest models offered no clear advantage for the discrimination of hip fracture compared with the simple Fine‐Gray models. In fact, the performance of the Fine‐Gray models for the prediction of hip fracture was comparable to the most parsimonious random forest models in both men and women albeit with even fewer total predictors (6 versus 7–10).Random forest models with competing risks can be used to predict hip fracture and mortality without hip fracture among the oldest old. The main findings of the variable selection did not yield novel strong predictors for these outcomes, but rather reinforced the notion that hip fracture risk models are driven in large part by information from BMD variables, while competing mortality models are driven in large part by information related to markers or components of phenotypic frailty. Importantly, our findings indicate that most clinical risk factors aside from these main constructs have a modest impact on model performance (VIMP <0.005). While one might presume that there is a best model, results from our analyses suggest that models with very different sets of variables, including those with fewer variables, have very similar performance. Finally, while the point estimate of performance of the random forest models for mortality without hip fracture was in some cases better than the simple Fine‐Gray model, a salient question is whether possible improvement in model performance outweighs increase in model complexity. In the case of hip fracture accounting for competing risk of mortality, the Fine‐Gray model has similar performance to the random forest model while being a more parsimonious model. Parsimony enables better validation and testing of the model using external data and enhances feasibility of model for clinical use.T‐score = −2.5).BMD is known to be a strong and consistent predictor of fracture. In our analysis, both total hip and femoral neck BMD are high‐ranking variables as determined by minimal depth and hip fracture VIMP criteria in men and women. However, in both men and women, total hip BMD is ranked higher by minimal depth criterion while femoral neck is ranked higher by hip fracture VIMP. This suggests that total hip BMD is favored when a simple binary predictor is needed because lower minimal depth reflects higher likelihood of variable with randomly selected binary cut‐point to be selected at a node, whereas femoral neck BMD is favored for overall model performance of the continuous variable, because VIMP measures error introduced by permutation of continuous variables. The clinical relevance of this finding is uncertain as it applies to randomly selected cut‐points, while clinical cut‐points tend to be fixed  had high variable importance for mortality without hip fracture among women, while the total number of frailty components had high variable importance for both hip fracture and mortality without hip fracture.Frailty as defined by phenotypic criteria is known to be a risk factor for both all‐cause32 and or Garvan fracture risk calculators, previous research has noted the relationship between poor physical performance and higher risk of hip fracture in both men and women. In contrast, we note that race/ethnicity (non‐Hispanic white versus black/other) was not chosen in any of our random forest models. Large population‐based samples have shown variation in hip fracture risk by race/ethnicity. Our analysis suggests that while there may be some association between this variable and fracture risk it has limited impact on overall model discrimination because other variables were consistently chosen in preference to race/ethnicity. Likewise, few medication classes or specific diseases were included as risk factors for hip fracture in our models. This finding may reflect that some of the fracture risk attributable to these medications is related to falls and fall history. We note that variable selection based on lower MD favors variables with more cut‐points while variable selection based on higher VIMP measures overall change in permutation model error which again may be very small due to prevalence of the risk factor. We also note that variable ranking does not reflect formal statistical tests and thus interpretation should be made accordingly.Very few binary variables were rated among the top predictors by any of the rankings. The notable exceptions were inability to perform chairs stands (men) and slowness defined by gait speed <0.6 m/s (women). Although neither measure is part of FRAX5 or G used classification and regression tree analysis in the MrOS cohort and found that specific age and BMD cutoffs were associated with high hip fracture risk. Because our focus was strictly on adults age of 80 years and older, it is not surprising that our results suggest that femoral neck BMD is the dominant risk factor in this patient population with its restricted age range. Ioannidis and colleagues used the same tree‐based approach to predict 1‐year fracture risk in a large Canadian long‐term care cohort. Due to the size of the cohort, the terminal nodes for the resulting tree revealed a wide spectrum of short‐term hip fracture risk ranging from <1% to greater than 12.6%. BMD was not measured in the cohort and the major risk factor  is likely to be specific to individuals residing in the long‐term care setting. The fracture discrimination in this cohort (C = 0.67) was lower than that in many population‐based cohorts, likely attributable to the lower variation in risk, ie, lower risk gradient and therefore discrimination. Ho‐Le and colleagues considered the use of artificial neural nets (ANNs) in a cohort of 1187 older women in the Dubbo cohort of older community‐living Australian adults. They found that ANNs had better sensitivity and specificity at selected cut‐offs for the prediction of hip fracture within 10‐years than other approaches, including logistic regression. Classical approaches rely on statistical testing for the presence of interactions, but it is likely such testing was underpowered with only 54 hip fractures in the derivation cohort. The selected cohort was not limited to the oldest old, and hence age versus BMD interactions may have been important in hip fracture prediction in this population. Thus, in cases where interactions are present in the underlying data a nonparametric approach might be more efficient.Our approach to the machine learning was largely driven by the study question, in particular survival time and the relevance of the competing risk of mortality among the oldest old. Others have used a wide spectrum of machine learning approaches that consider hip fracture risk a classification problem, ie, they aim to create a classifier that best separates those who have hip fracture versus those who do not, a slightly different question amenable to several machine learning approaches. Su and colleagues39 use used a collection of supervised classification machine learning algorithms  as well as generalized linear models to determine hip fracture risk from administrative claims data that included BMD testing. Unfortunately, interpretation of the results of this study is challenging. Hip fracture cases and non‐cases had very different mean BMD in unadjusted analysis, but other variables that were similar in cases and controls had higher variable importance. The exceptional performance of the associated model (test area under the curve [AUC] ~0.90) is difficult to explain unless the chosen variables  were in some way a proxy of clinical diagnosis or treatment of osteoporosis. The black‐box nature of the models in this study limits assessment and interpretation of its findings.Kruse and colleagues15 use used a super‐learner algorithm based on combination of machine learning and regression analysis to assess hip fracture risks factors from claims data. The super‐learner model had acceptable performance, but was slightly inferior to that of logistic regression and other component algorithms in the validation set. Thus, although nonparametric approaches may in general be useful, they add a layer of complexity that may not improve model performance. The dominant risk factor for hip fracture is hip BMD, and this risk factor is a robust predictor of hip fracture in parametric and semiparametric models. Hence, the additional flexibility of other modeling approaches may be of limited advantage in hip fracture prediction.Engels and colleagues14 useThere are many potential advantages to using competing risk random forest models. These advantages include that models are tolerant of highly correlated predictors and there is no need to specify functional form and possible interactions. Although there are algorithms to automate assessment of functional form in classical regression models, these algorithms become challenging when the number of potential predictors increase. The assessment of pairwise interactions is also challenging with a large number of candidate predictors. Random forests leverage the underlying flexibility of tree‐based models and can model nonlinear relationships as well as pairwise and higher‐order interactions. The downside of this flexibility is that there are no equations linking the variables with the estimated risk. Our results show that despite these theoretical benefits, the use of these models for the prediction of hip fracture and mortality before hip fracture does not exceed a simple model that lacks the model flexibility of random forests.One of the practical implications of the random forest model construction is that there is no way to replicate predictions without an actual forest. Future predictions thus require the original forest  or a new forest that replicates the predictions with synthetic data. Model development is also more complex as each data set would generate a different model and there is no easy way to compare model parameters. Hence, validation of prediction models in separate population cohorts is likely to be challenging. It is possible for models with entirely different component variables to result in nearly identical predictions. It is also possible for models with exactly the same component variables to end up with different predictions due to differences due to construction . Further research is needed to develop appropriate decision rules regarding inclusion of predictors in different contexts. Finally, models created above are not designed to assess causal relationships, ie, the fact that corticosteroids were not selected as an important risk factor for hip fracture risk prediction is not related to causal risk, which should be assessed by different models. but incorporating such measures is beyond the scope of the present analysis.A key limitation of risk prediction or prognostic models is that model performance can never be perfect as they are designed for prediction of future events rather than diagnosis or classification of disease. Results of our random forest competing risk analyses suggest that addition of clinical risk factors yields only a modest improvement for discrimination of future hip fracture events in late‐life adults over that obtained with a models based primarily on total hip and femoral neck BMD. Previous studies in younger participants suggest that including hip structure characteristics that determine bone strength may further improve hip fracture prediction beyond that provided by models based on standard DXA  BMD,41 butIn this study of late‐life adults, we used random forest models accounting for the competing mortality risk to construct prediction models for hip fracture and competing mortality before hip fracture. Our results indicate that although there are overlapping risk factors for hip fracture and competing mortality, hip BMD dominates models predicting hip fracture, while components of phenotypic frailty dominate models predicting death without hip fracture. Advantages of this method included model flexibility and seamless imputation of missing data, but these were counterbalanced by limitations including increasing model complexity and difficulties of model validation. In summary, we found that a standard parsimonious Fine‐Gray model based on major clinical risk factors for hip fracture and mortality may be most appropriate for shared clinical decision‐making regarding whether or not to initiate and continue osteoporosis drug treatment in late‐life adults.Lisa Langsetmo: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; writing – original draft; writing – review and editing. John T Schousboe: Conceptualization; data curation; funding acquisition; investigation; methodology; writing – original draft; writing – review and editing. Brent C Taylor: Investigation; methodology; project administration; writing – review and editing. Jane A. Cauley: Funding acquisition; investigation; methodology; writing – review and editing. Howard A. Fink: Investigation; methodology; writing – review and editing. Peggy M. Cawthon: Data curation; funding acquisition; investigation; methodology; project administration; writing – review and editing. Deborah M. Kado: Investigation; methodology; writing – review and editing. Kristine Ensrud: Conceptualization; funding acquisition; investigation; methodology; project administration; writing – original draft; writing – review and editing.DK reports receiving royalties from UpToDate. The other authors have nothing to disclose.https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/jbm4.10757.The peer review history for this article is available at Data S1. Supporting Information.Click here for additional data file.


To the editor,Over 13 billion Coronavirus disease 2019 (COVID‐19) vaccines have been administered worldwide. However, there is a lack of evidence‐based guidance on the management of children with reported allergic reactions to the COVID‐19 vaccine or those with pre‐existing conditions such as mast cell disorders.To date, only one paper by Rosa Duque et al (2022),Given the high degrees of parental anxiety around childhood immunisation and the likelihood of seasonal vaccine boosters against emerging strains of COVID‐19, there is a need for more research on reported adverse reactions in children together with guidance on testing, management and public health messaging.We present a series of paediatric cases who underwent testing and challenged currently available paediatric COVID‐19 vaccines to determine future safety of administration  and intradermal testing (IDT) followed by a graded vaccine challenge as is standard protocol in our centre for suspected IgE‐mediated reactions. The SPT used sodium chloride 0.9% as a negative control and histamine as a positive control with standard EAACI definitions used to determine positive  and negative results.After negative skin testing, a graded vaccine challenge at 30‐min intervals was performed starting with 10% of the vaccine dose followed by the remaining 90%. Patients were monitored for 1 hour with regular observations and then followed up by telephone on days 2 and 7 to ensure no delayed reactions. The four patients with pre‐existing conditions were directly challenged under medical supervision without skin testing.Of the six children who were skin tested, four had negative skin testing; two of these four had a successful challenge to the culprit vaccine. Two were not challenged as they were already fully vaccinated according to current guidelines. One of the six had an equivocal skin test for Comirnaty but was negative for Spikevax and underwent a successful Spikevax challenge without adverse events. The remaining child had a positive skin test for both Comirnaty and Spikevax; he had received two Comirnaty vaccines in the community and had similar mild localised cutaneous reactions on both occasions. He was negative for Novavax (NVX‐CoV2373) on skin testing. He was not challenged as had completed his primary vaccine course but was advised to avoid mRNA vaccines with the potential to have Novavax in the future. The four children with pre‐existing conditions had successful challenges with no adverse events. No one was premedicated prior to challenge and there were no delayed reactions.Our data show that 9 of 10 children studied would be safely able to have at least one brand of mRNA COVID‐19 vaccine in the future. Patients were not challenged with non‐mRNA vaccines as they were not licensed at the time, but as of July 2022, Novavax gained approval in children over 12 years.Results from this paediatric cohort are consistent with studies in adults who tolerated second doses of the vaccines despite adverse reactions to the first.Despite the low incidence rate of severe allergic reactions, there is significant anxiety within the general population. Twelve percent of parents who chose not to vaccinate their children cited fears of an allergic reaction.More global paediatric data on allergic and adverse reactions to COVID‐19 vaccines and trials on optimal management might help improve immunisation rates in children and alleviate parental fears.Rishabh Kulkarni: Formal analysis ; Writing – original draft (Lead); Writing – review & editing (Lead). Annabelle Arnold: Conceptualization ; Resources ; Writing – original draft (Supporting); Writing – review & editing (Supporting). Adrian Buzynski: Conceptualization ; Formal analysis ; Methodology ; Resources ; Writing – original draft (Supporting); Writing – review & editing (Supporting). Michaela Lucas: Conceptualization ; Formal analysis ; Resources (Lead); Supervision (Lead); Writing – original draft ; Writing – review & editing .There are no funding sources to declare and the authors declare that they have no relevant conflicts of interest.


Transperineal (TP) biopsy has recently replaced the transrectal ultrasound (TRUS) approach as the ideal method of biopsy in the United Kingdom with growing trends to adopt. To minimise transmission of COVID‐19 during the first wave of the pandemic, the British Association of Urological Surgeons Section of Oncology issued guidelines reducing general anaesthesia (GA) procedures and initiate COVID‐secure ‘green’ site diagnostics. As a result of these guidelines and reduction in clinical diagnostics trust‐wide, we ceased all TRUS diagnostics and implemented a centralised, nurse‐led LA TP biopsy service.A waiting list was developed for those awaiting prostate cancer diagnostics across the network. A COVID‐secure ‘green’ site was quickly identified with TP biopsies starting soon after. Quality improvement methodology was utilised and a run chart was used to show if changes were sustainable.Successful implementation and centralisation of a TP biopsy service occurred with TRUS guided biopsies ceasing across all sites on 12 May 2020. The procedures were carried out by urology advanced nurse practitioners under local anaesthesia with a select few occurring under GA. Centralising the service in a COVID‐secure manner freed up dedicated theatre sessions and personal leading to increased efficiency elsewhere. The service was robust and was maintained upon lifting of COVID restrictions.A centralised, nurse led LA TP biopsy service in a procedural unit was implemented successfully. The service has remained resilient upon lifting of restrictions and return to business as usual. This led to improved performance across trust by freeing up valuable resources and staff to undertake more duties. The service remains highly valued trust‐wide. We aimed to look at data in real time to ensure that changes could be made so that this process can be both maintained after COVID restrictions are lifted and could be replicated amongst other trusts.2TP biopsies had been carried out at a tertiary centre at the Royal London Hospital (RLH), part of Barts NHS Hospitals Trust since 2008. Referrals from across the trust were made to the RLH in the event of previous negative TRUS biopsy, and these were carried out under general anaesthetic.2.1As part of Barts Health NHS trust's strategy to decrease the reliance on TRUS biopsy in August 2018, equipment for TP biopsies was moved to Whipps Cross University Hospital as part of the centralisation process and biopsies commenced under general anaesthetic in theatres in August 2018. This was done with a view to eventually phasing out TRUS biopsy as a first‐line diagnostic test, working closely with Guys a St Thomas' NHS Foundation trust2.2In March 23, the United Kingdom went into a national lockdown due to the COVID‐19 pandemic. Subsequent BAUS guidance was released to minimise the risk of spread, and prostate biopsies were put on hold. In March 2020, the decision was made to cease routine TRUS biopsies. GA TP biopsies performed in theatres by a urology consultant were also ceased at this point, and it was planned to expedite a centralised TP biopsy service as the first‐line standard in Barts Health NHS Trust at Newham University Hospital. This was carried out by trust‐wide consultant urologists and a lead urology specialty nurse with experience in project management and implementation of complex projects (PA) using quality improvement (QI) and implementation of science framework. Weekly meetings were held to ensure smooth transition and allowed for fast‐paced problem solving that may have arisen because of the pandemic.A virtual waiting list was developed with the most urgent cases being identified for biopsy once service resumed. The patients on the waiting list near the time of lockdown were risk‐stratified as per the BAUS section of Oncology Covid‐19 strategy.2.3The biopsies were to take place in a COVID‐secure manner, with all members adhering to Barts Health NHS Trust PPE guidance, with members of staff wearing full PPE; gloves, aprons and visors and patients having a negative COVID swab 3 days prior to the procedure as well as self‐isolating.There were 56 patients waiting for a TP biopsy prior to the services start date consisting of 13 patients under routine active surveillance for previously diagnosed prostate cancer, 14 patients who had delays to an earlier biopsy, 26 patients of a 2‐week wait suspected cancer pathway and 3 patients who cancelled the procedure altogether. The waiting list consisted of patients being referred as early as the 8th of October 2019, and the last case we observed was referred on the 5th of November 2020. Once an efficient service was built with standard operating procedures in place, the TP biopsy service was relocated in January 2021 within the trust to a purpose built ‘green’ urology diagnostic and treatment centre at Whipps Cross University Hospital to streamline the service further. A small number of patients meeting certain criteria such as being unable to tolerate a GA procedure or those requiring repeat biopsies were still offered a GA procedure by a consultant urologist albeit at a significantly lower number.The time between referral date and procedure completion was calculated using an Excel Spreadsheet, and waiting times were monitored in real time using a run chart to monitor service efficiency in real time so that changes could be made if required. Finally, quantitative analysis was used to calculate to see if there was a statistically significant reduction in the waiting times for prostate biopsy in roughly six monthly timeframes; pre‐lockdown (26 September 2019–23 March 2020), during strict lockdown measures (12 May 2020–5 November 2020) and upon easing of lockdown (1 June 2021–29 November 2021)  at Barts Health NHS Trust for all activity both TRUS and TP biopsies across all sites from January 2018 to November 2021. A search was carried out using the procedural codes a list of all procedures was sent to us. Each case was then validated by two doctors with experience in urology  for quality control and to ensure each case was coded correctly.3The age range of patients undergoing biopsy where available was 40–85 years. Biopsies were requested for patients with mpMRI scans showing PIRADS 3 lesions or greater or those whose PSA density did not correlate with mpMRI prostate volumes.3.1U test. The results showed that there was a significant difference between the average waiting time pre‐ and post‐lockdown . The mean waiting time for those in pre‐lockdown was 145 days whereas in post‐lockdown was 23 days. These results are illustrated in Figure A Shapiro–Wilk test showed that the data were not normally distributed when looking at pre‐ and post‐lockdown groups as a whole subjected to extreme outliers. We subsequently ran a Mann–Whitney 3.2U test. The results showed that the average waiting time for a TP biopsy during lockdown restrictions was 23 days (n = 144) and post‐lockdown and subsequent increase in clinical activity 30 days (n = 271). This difference was not statistically significant (U = 18461.5 p = 0.36812).A Schapiro–Wilk test showed that this comparative data were not normally distributed, and we subsequently ran a Mann–Whitney 4When looking at Figure The positive impact on service waiting times is clearly visualised in the Figure Run charts are a useful tool in quality improvement as we are clearly able to visualise the impact before and after the implementation of a new service, monitor the process over time to detect shifts and trends in data in real time.It is possible that due to the unprecedented nature of this pandemic, there were less patients attending their primary healthcare provider for routine PSA tests and subsequently less target referrals being made to the service.Less patients' histology being discussed at MDT, streamlining the MDT,No document cases trust wide of admissions for post‐biopsy sepsis.Use of antibiotics used in this procedure was reduced to less than 3% of cases.Two hundred eighty additional GA operations were achieved due to extra theatre and staff capacity.The results have shown that there was a statistically significant improvement in the number of days waiting for a TP biopsy pre‐lockdown when compared with lockdown . The mean waiting time was reduced from 145 to 23 days post‐introduction of the gateway pathway; however, this could be explained by long wait active surveillance cases. Interestingly, when observing the significant increase in clinical activity between 1 June 2021 and 29 November 2021, we can see that despite the increase in clinical activity in trust when comparing 12 May 2020–5 November 20 (n = 144) to 1 June 2021–29 November 2021 (n = 271), there was no statistically significant increase in the mean waiting time for a biopsy . This shows that a nurse‐led TP biopsy service under LA is a resilient service able to withstand pressures of increased clinical activity and the transition to BAU. There is current an ongoing drive within the trust to train a second ANP to perform TP biopsy, which may lead to a further efficiency and durability and possible further reductions in patient wait times, which has been identified as a further limitation and an area for further improvement.Other additional benefits included the following:This study may be of interest to a number of trusts in the UK as converting a TP biopsy service. Previous limitations from a large cohort study and cost analysis by Roberts et al. have suggested that there are significant savings when transferring from a TRUS to a consultant led, GA, TP approach.5In conclusion, it is clear that regardless of the pandemic urology departments nationally should continue their efforts towards an LA nurse‐led TP biopsy service. The implementation of this QI project shows that a nurse‐led LA TP biopsy service is not only something achievable but also sustainable regardless of the disruption caused by the current international circumstances. By centralising the biopsy service, we have improved business performance across the trust, and sessions that were often time consuming were freed up across three other sites, releasing consultant urology staff  to undertake more complex duties and release on average >1 sessions a week of theatre time. It has eliminated post‐biopsy sepsis and as a result saved inpatient beds in our trust as well as high‐dependency care unit/intensive care unit beds.Not only may this service maximise use of resources available to urology departments but it may make further sense economically in the long term. The reduction in waiting lists and efficiency of our service has shown that nothing can stop a well‐planned and well‐managed quality improvement project while the team behind it remain determined to improve patient outcomes.David Winters is the lead author who wrote paper, collected data and performed the analysis. Ashley Mehmi collected data. Amar Odedera collected data and aided in article writing. Lydia Wilson collected data. Joey Ancheta performed the biopsies and implemented the service. Sally Buttleman performed the biopsy and implemented the service. Paula Allchorne implemented the service. Prabhakar Rahman implemented service. Shahid Khan is one of the study leads, who helped in the project implementation and data capture. James S. A. Green is one of the study leads, who helped develop the original idea and QI study design.None.


This article examines the impact of working‐hour restrictions on the well‐being, performance, and education of medical residents in Japan. Despite Japan's plan to introduce new regulations for resident working hours by 2024, there is still an ongoing debate regarding their appropriateness. This review provides a comprehensive overview of the current regulations of resident working hours worldwide, with a specific focus on weekly hours. The varying regulations are highlighted, including the 80‐hour‐per‐week regulation in the United States and the 48‐h‐per‐week regulation in the European Union influencing other regions. The article also discusses the effectiveness of working‐hour restrictions on residents' mental health, with shorter working hours having potentially greater benefits. However, the impacts on medical safety and resident education are mixed, and further reduction in working hours must be carefully considered to avoid adverse effects. The planned changes to working‐hour limits for residents in Japan offer a unique opportunity to gain new evidence on the impact of such regulations, which will be of interest to policymakers and researchers worldwide. In Japan, all medical school graduates who wish to become clinicians, regardless of their desired specialty, must undergo 2 years of clinical training to acquire basic clinical skills.Although Japan is lagging in implementing such measures, resident work‐hour restrictions are expected to be introduced in 2024. The traditional medical education system in Japan emphasizes a culture of hard work and dedication. Remarkably, residents were not legally recognized as workers until 2005, over 30 years after the commencement of the residency training system in Japan in 1968.The impact of work‐hour restrictions is complex and depends on various factors. A large body of literature evaluates the impact of resident work‐hour restrictions (WHR), primarily in terms of resident well‐being, patient safety, and resident education. Most research investigating the consequences of weekly work‐hour limitations involves observational studies conducted before and after the imposition of work‐hour limits. The weekly work‐hour limit varies among countries, with the United States (U.S.) and Europe having set limits between 80 and 48 h weekly, respectively.Given the ongoing controversy surrounding resident work hours, it is important to understand the current evidence on this topic. This comprehensive review provides an overview of the literature on resident working hours, concentrating primarily on their global regulatory landscape and the influence of their limitation, with a specific emphasis on the available Japanese literature. This review aims to help Japanese and international readers consider the optimal balance of resident working hours.2The regulation of work hours for residents has been predominantly enforced in the U.S., Europe, and Canada, with other nations, particularly in Asia, more recently adopting similar measures Table .8, 102.1In 2018, the Japanese government implemented a series of measures aimed at promoting reforms in the workforce.Notably, the enforcement of the working‐hour reform for physicians (including residents) was postponed until 2024 because of the unique nature of their profession. To ensure the effectiveness of the healthcare system, the MHLW's Study Group on Working Hour Reform for Doctors has suggested setting a standard limit of 960 overtime hours per year for physicians, with the option for longer working hours in specific cases and proposing extended working hours for physicians involved in community medicine, emergency medicine, and those undergoing training.It is important to note that Level C is not mandatory for all residents but is only applicable to those working in medical institutions designated by the prefecture based on actual work conditions. The proportion of residents who will take Level C has not yet been disclosed and it is feasible that only a limited number of training hospitals will be classified as Level C . Additionally, by 2035, Level B will be eliminated and integrated into Level A, whereas Level C will be gradually phased out. The details of the execution of this reduction remain undetermined.2.2In the U.S., a medical incident that occurred in New York in 1984, known as the Libby Zion case, triggered the implementation of regulations on resident work hours.2.3The European Working Time Directive (EWTD) has been in place since 1998, restricting working hours to ensure the health and safety of workers across all occupations in the European Union (EU). The law limits working hours to 48 h weekly, with a minimum of 11 h of rest within a 24‐h period.2.4There are no nationwide regulations or laws governing resident working hours in Canada.2.5Concerns about extended working hours for resident physicians in South Korea led to the implementation of a law in 2015 aimed at improving their working conditions and status. The legislation mandates a maximum of 80 working hours weekly and 36 h continuously.The Ministry of Health and Welfare in Taiwan introduced guidelines for resident working conditions in 2013.3Residents' working hours have decreased in the past two decades, primarily in Europe and the U.S., with other countries following suit with the primary objective of safeguarding medical safety and resident well‐being3.1Extended work hours and excessive workload have been linked to adverse health effects, including mental disorders , sleep disorders, cardiovascular diseases, and suicide.The 80‐h limit on workweeks, primarily in the U.S., is likely to have a positive effect on mental health by mitigating burnout. In 2011, a systematic review showed that five out of eight studies examining the impact of the WHR introduced in the US in 2003, resulted in decreased burnout levels, particularly emotional exhaustion.Two key points warrant attention regarding the impact of weekly work hours on mental health. First, the widely implemented 80‐h‐per‐week limit may not represent the optimal threshold. Although several studies have established an association between exceeding this benchmark and compromised psychological well‐being, other investigations have revealed a dose–response relationship between work hours and depressive symptoms, as exemplified by Ogawa et al.'s study. A study published in 2022, analyzed 17000 U.S. interns from 2009 to 2020,3.2Inadequate sleep and fatigue from long work hours have been associated with decreased attention and cognitive performance.The lack of published studies examining the association between residents' work hours and medical errors was unexpected. Landrigan et al. (2004) conducted a randomized controlled trial that compared 30‐ and 16‐h shifts for intensive care unit interns.Multiple systematic reviews have shown that implementing work hour restrictions for residents (2003 WHR) is associated with a decrease in patient mortality.Investigations have also been undertaken regarding the effect of WHRs on complications, with systematic reviews presenting inconclusive findings.3.3The primary objective of implementing WHRs is to enhance the working conditions of residents and improve patient safety. However, supervisors and residents have raised concerns regarding the potentially negative implications of these measures on educational aspects. The impact on education has been objectively measured through various parameters such as training opportunities, number of case experiences, and examination performance.A substantial body of research has scrutinized the impact of WHRs on the number of case experiences, with a specific focus on the surgical experience of surgical residents. Several studies have established that resident caseloads either decrease or remain unchanged. A systematic review conducted in 2011 examined the impact of work‐hour restrictions on caseloads and found that out of 37 studies, 11 (30%) revealed a reduction in surgical experience, while 25 showed no significant change.The effect of the 80‐h work‐week restriction on exam scores has been primarily studied in the U.S., and it appears that it does not necessarily worsen scores. According to a 2011 systematic review, out of 10 studies that evaluated in‐training exam scores, two reported improvement, two reported worsening, and the remaining six found no significant differences.–65 h per week, and there was no significant increase in self‐study time for those working over 65 h per week.The influence of WHRs on other educational outcomes has also been examined. Investigations on the effects of the 2003 WHRs on U.S. internal medicine residents have revealed limited engagement in educational activities.4According to Level C‐1 regulations in Japan, the maximum weekly working hours for residents will be limited to 80 h weekly starting in April 2024. This is in line with the work‐hour limit observed in the U.S., and findings from the U.S. context may be applicable to Japan. The implementation of this regulation is anticipated to have a favorable impact on the well‐being of residents; however, it may have adverse consequences for education  when evaluating the effects of resident work‐hour limitations in Japan. The introduction of a new physician specialty training system in 2018 mandated a minimum of 3 years of specialized training for physicians seeking to acquire a subspecialty.5The Japanese government announced its intention to implement weekly work‐hour restrictions for residents in 2024. The restrictions involve a combination of 80‐ and 60‐h weekly limits for each training hospital. While these measures are anticipated to enhance residents' well‐being and work‐life balance, there is apprehension that strict work‐hour limitations may compromise the quality of resident education and patient care. Despite the global acceptance of the 80‐h‐per‐week threshold following its introduction in the U.S., the incorporation of dual work‐hour restrictions in Japan is expected to represent a significant social demonstration experiment. Comprehensive studies examining the impact of work‐hour limitations in Japan could provide pivotal evidence for other countries to devise appropriate working‐hour limits.The authors declare that no conflicts of interest exist.None.