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Abstract This study will seek to identify a measurable radiographic index, the Metaphyseal-Diaphyseal Index (MDI) score to determine whether intra-operative fracture in osteoporotic bone can be predicted. A 5 year prospective cohort of 560 consecutive patients, undergoing hemiarthroplasty (cemented or uncemented), was evaluated. A nested case-control study to determine risk factors affecting intra-operative fracture was carried out. The Vancouver Classification was used to classify periprosthetic fracture. The MDI score was calculated using radiographs from the uncemented group. As a control (gold standard), Yeung et al's Canal Bone Ratio (CBR) score was also calculated. From this, a receiver operating characteristic (ROC) curve was formulated for both scores and area under the curve (AUC) compared. Intra and inter-observer correlations were determined. Cost analysis was also worked out for adverse outcomes. Four hundred and seven uncemented and one hundred and fifty-three cemented stems were implanted. The use of uncemented implants was the main risk factor for intra-operative periprosthetic fracture. Sixty-two periprosthetic fractures occurred in the uncemented group (15.2%), nine occurred in the cemented group (5.9%), P < 0.001. The revision rate for sustaining a periprosthetic fracture (uncemented group) was 17.7%, P < 0.001 and 90 day mortality 19.7%, P < 0.03. MDI's AUC was 0.985 compared to CBR's 0.948, P < 0.001. The MDI score cut-off to predict fracture was 21, sensitivity 98.3%, specificity 99.8%, positive predictive value 90.5% and negative predictive value 98%. Multivariate regression analysis ruled out any other confounding factors as being significant. The intra and inter-observer Pearson correlation scores were r = 0.99, P < 0.001. JRI uncemented hemiarthroplasty has a significantly higher intra-operative fracture rate. We recommend cemented arthroplasty for hip fractures. We propose a radiographic system that may allow surgeons to select patients who are good candidates for uncemented arthroplasty, but it needs prospective validation.

ABSTRACT This study evaluates the capability of regional climate models ( RCMs) in simulating extreme rainfall events over Southern Africa, and in reproducing the characteristics of widespread extreme rainfall events ( WERE) in the Western Cape (South Africa). We obtained simulation datasets of nine RCMs from the Co-ordinated Regional Downscaling Experiment ( CORDEX) and compared them with observation datasets from the Global Precipitation Climatology Project ( GPCP) and Tropical Rainfall Measuring Mission ( TRMM) as well as with the reanalysis dataset ( ERAINT) that forced the simulations. A self-organising map was used to classify the WEREs into 12 nodes. The contribution of each RCM to each node was compared with the contributions of GPCP, TRMM and ERAINT. Using ERAINT dataset, we analysed the synoptic-scale atmospheric condition associated with each node. The results show that, in simulating the spatial distribution of the extreme rainfall event over Southern Africa, only four RCMs perform better than the forcing reanalysis ( ERAINT) while two RCMs perform worse than the reanalysis. All the RCMs underestimate the threshold of extreme rainfall over Western Cape, poorly simulate the inter-annual variability of the WEREs ( r ≤ 0.3), but correctly reproduce the maximum frequency of the WEREs in Autumn (March-May). The WEREs in the Western Cape may be broadly grouped into four synoptic rainfall patterns. The first pattern links WEREs with tropical rainfall activities (tropical-temperate troughs); the second pattern shows isolated WEREs; the third and fourth patterns link WEREs with rainfall activities in the mid-latitudes (frontal systems) and over the Agulhas Current, respectively. While most RCMs overestimate the frequency of the first pattern, they all underestimate the frequency of the second pattern but simulate the frequencies of the third and fourth patterns well. The results of this study should help in improving the RCM simulations over Southern Africa and in downscaling impacts of climate change on extreme rainfall events over the region. [ABSTRACT FROM AUTHOR]

Introduction: Globally, recognition is growing of the harmful impacts of high ambient temperatures (heat) on health in pregnant women and children. There remain, however, major evidence gaps on the extent to which heat increases the risks for adverse health outcomes, and how this varies between settings. Evidence gaps are especially large in Africa. We will conduct an individual participant data (IPD) meta-analysis to quantify the impacts of heat on maternal and child health in sub-Saharan Africa. A detailed understanding and quantification of linkages between heat, and maternal and child health is essential for developing solutions to this critical research and policy area., Methods and Analysis: We will use IPD from existing, large, longitudinal trial and cohort studies, on pregnant women and children from sub-Saharan Africa. We will systematically identify eligible studies through a mapping review, searching data repositories, and suggestions from experts. IPD will be acquired from data repositories, or through collaboration with data providers. Existing satellite imagery, climate reanalysis data, and station-based weather observations will be used to quantify weather and environmental exposures. IPD will be recoded and harmonised before being linked with climate, environmental, and socioeconomic data by location and time. Adopting a one-stage and two-stage meta-analysis method, analytical models such as time-to-event analysis, generalised additive models, and machine learning approaches will be employed to quantify associations between exposure to heat and adverse maternal and child health outcomes., Ethics and Dissemination: The study has been approved by ethics committees. There is minimal risk to study participants. Participant privacy is protected through the anonymisation of data for analysis, secure data transfer and restricted access. Findings will be disseminated through conferences, journal publications, related policy and research fora, and data may be shared in accordance with data sharing policies of the National Institutes of Health., Prospero Registration Number: CRD42022346068., Competing Interests: Competing interests: DPL MFC, GM, CP and ZM hold investments in the fossil fuel industry through their pension funds. The University of the Witwatersrand holds investments in the fossil fuel industry through their endowments and other financial reserves., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)