Detecting Spatial Autocorrelation for a Small Number of Areas: a practical example

Moran's I is commonly used to detect spatial autocorrelation in spatial data. However, Moran's I may lead to underestimating spatial dependence when used for a small number of areas. This led to the development of Modified Moran’s I, which is designed to work when there are few areas. In this paper, both methods will be presented. Many R programs enable calculating Moran's I, but to date, none have been available for calculating Modified Moran's I. This paper aims to present both methods and provide the R code for calculating Modified Moran's I, with an application to a case study of dengue fever across 14 regions in Makassar, Indonesia

Evaluating the impact of a small number of areas on spatial estimation

Background: There is an expanding literature on diferent representations of spatial random efects for diferent types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these diferent priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study. Methods: Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five diferent conditional autoregressive priors for a simple Bayesian Poisson model were considered: inde�pendent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight diferent sizes of areal grids, ranging from 4 to 2500 areas, and two diferent levels of both spatial autocorrelation and disease counts. Model goodness-of-ft measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered. Results: The simulation study showed that model performance varied under diferent scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised G = 2models performed similarly and better than the independent and localised G = 3 models. However, when the num�ber of areas were at least 100, all models performed diferently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G=3 was a better choice. Conclusion: Detecting spatial patterns can be difcult when there are very few areas. Understanding the character�istics of the data and the relative infuence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model

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Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling

A range of Bayesian models have been used to describe spatial and temporal patterns of disease in areal unit data. In this study, we applied two Bayesian spatio-temporal conditional autoregressive (ST CAR) models, one of which allows discontinuities in risk between neighbouring areas (creating ‘groups’), to examine dengue fever patterns. Data on annual (2002–2017) and monthly (January 2013 - December 2017) dengue cases and climatic factors over 14 geographic areas were obtained for Makassar, Indonesia. Combinations of covariates and model formulations were compared considering credible intervals, overall goodness of fit, and the grouping structure. For annual data, an ST CAR localised model incorporating av�erage humidity provided the best fit, while for monthly data, a single-group ST CAR autoregressive model incorporating rainfall and average humidity was preferred. Using appropriate Bayesian spatio-temporal models enables identification of different groups of areas and the impact of climatic covariates which may help inform policy decisions

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay

Identification of a new p53/MDM2 inhibitor motif inspired by studies of chlorofusin

Previous studies on the natural product chlorofusin have shown that the full peptide and azaphilone structure are required for inhibition of the interaction between MDM2 and p53. In the current work, we utilized the cyclic peptide as a template and introduced an azidonorvaline amino acid in place of the ornithine/azaphilone of the natural product and carried out click chemistry with the resulting peptide. From this small library the first ever non-azaphilone containing chlorofusin analogue with MDM2/p53 activity was identified. Further studies then suggested that the simple structure of the Fmoc-norvaline amino acid that had undergone a click reaction was also able to inhibit MDM2/p53 interaction. This is an example where studies of a natural product have led to the serendipitous identification of a new small molecule inhibitor of a protein-protein interaction

Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans

Background: Phytoplankton communities significantly contribute to global biogeochemical cycles of elements and underpin marine food webs. Although their uncultured genomic diversity has been estimated by planetary-scale metagenome sequencing and subsequent reconstruction of metagenome-assembled genomes (MAGs), this approach has yet to be applied for complex phytoplankton microbiomes from polar and non-polar oceans consisting of microbial eukaryotes and their associated prokaryotes. Results: Here, we have assembled MAGs from chlorophyll a maximum layers in the surface of the Arctic and Atlantic Oceans enriched for species associations (microbiomes) with a focus on pico- and nanophytoplankton and their associated heterotrophic prokaryotes. From 679 Gbp and estimated 50 million genes in total, we recovered 143 MAGs of medium to high quality. Although there was a strict demarcation between Arctic and Atlantic MAGs, adjacent sampling stations in each ocean had 51–88% MAGs in common with most species associations between Prasinophytes and Proteobacteria. Phylogenetic placement revealed eukaryotic MAGs to be more diverse in the Arctic whereas prokaryotic MAGs were more diverse in the Atlantic Ocean. Approximately 70% of protein families were shared between Arctic and Atlantic MAGs for both prokaryotes and eukaryotes. However, eukaryotic MAGs had more protein families unique to the Arctic whereas prokaryotic MAGs had more families unique to the Atlantic. Conclusion: Our study provides a genomic context to complex phytoplankton microbiomes to reveal that their community structure was likely driven by significant differences in environmental conditions between the polar Arctic and warm surface waters of the tropical and subtropical Atlantic Ocean. [MediaObject not available: see fulltext.

Osteo-cise: Strong Bones for Life: protocol for a community-based randomised controlled trial of a multi-modal exercise and osteoporosis education program for older adults at risk of falls and fractures

Background : Osteoporosis affects over 220 million people worldwide, and currently there is no \u27cure\u27 for the disease. Thus, there is a need to develop evidence-based, safe and acceptable prevention strategies at the population level that target multiple risk factors for fragility fractures to reduce the health and economic burden of the condition. Methods : The \u27Osteo-cise: Strong Bones for Life\u27 study will investigate the effectiveness and feasibility of a multi-component targeted exercise, osteoporosis education/awareness and behavioural change program for improving bone health and muscle function, and reducing falls risk in community-dwelling older adults at an increased risk of fracture. Men and women aged 60 years or above will participate in an 18-month randomised controlled trial comprising a 12-month structured and supervised community-based program and a 6-month \u27research to practise\u27 translational phase. Participants will be randomly assigned to either the \u27Osteo-cise\u27 intervention or a self-management control group. The intervention will comprise a multi-modal exercise program incorporating high velocity progressive resistance training, moderate impact weight-bearing exercise and high challenging balance exercises performed three times weekly at local community-based fitness centres. A behavioural change program will be used to enhance exercise adoption and adherence to the program. Community-based osteoporosis education seminars will be conducted to improve participant knowledge and understanding of the risk factors and preventative measures for osteoporosis, falls and fractures. The primary outcomes measures, to be collected at baseline, 6, 12, and 18 months, will include DXA-derived hip and spine bone mineral density measurements and functional muscle power (timed stair-climb test). Secondary outcomes measures include: MRI-assessed distal femur and proximal tibia trabecular bone micro-architecture, lower limb and back maximal muscle strength, balance and function (four square step test, functional reach test, timed up-and-go test and 30-second sit-to-stand), falls incidence and health-related quality of life. Cost-effectiveness will also be assessed. Discussion : The findings from the Osteo-cise: Strong Bones for Life study will provide new information on the efficacy of a targeted multi-modal community-based exercise program incorporating high velocity resistance training, together with an osteoporosis education and behavioural change program for improving multiple risk factors for falls and fracture in older adults at risk of fragility fracture.<br /

Hearing loss prevalence and years lived with disability, 1990–2019: findings from the Global Burden of Disease Study 2019

Background Hearing loss affects access to spoken language, which can affect cognition and development, and can negatively affect social wellbeing. We present updated estimates from the Global Burden of Disease (GBD) study on the prevalence of hearing loss in 2019, as well as the condition's associated disability. Methods We did systematic reviews of population-representative surveys on hearing loss prevalence from 1990 to 2019. We fitted nested meta-regression models for severity-specific prevalence, accounting for hearing aid coverage, cause, and the presence of tinnitus. We also forecasted the prevalence of hearing loss until 2050. Findings An estimated 1·57 billion (95% uncertainty interval 1·51–1·64) people globally had hearing loss in 2019, accounting for one in five people (20·3% [19·5–21·1]). Of these, 403·3 million (357·3–449·5) people had hearing loss that was moderate or higher in severity after adjusting for hearing aid use, and 430·4 million (381·7–479·6) without adjustment. The largest number of people with moderate-to-complete hearing loss resided in the Western Pacific region (127·1 million people [112·3–142·6]). Of all people with a hearing impairment, 62·1% (60·2–63·9) were older than 50 years. The Healthcare Access and Quality (HAQ) Index explained 65·8% of the variation in national age-standardised rates of years lived with disability, because countries with a low HAQ Index had higher rates of years lived with disability. By 2050, a projected 2·45 billion (2·35–2·56) people will have hearing loss, a 56·1% (47·3–65·2) increase from 2019, despite stable age-standardised prevalence. Interpretation As populations age, the number of people with hearing loss will increase. Interventions such as childhood screening, hearing aids, effective management of otitis media and meningitis, and cochlear implants have the potential to ameliorate this burden. Because the burden of moderate-to-complete hearing loss is concentrated in countries with low health-care quality and access, stronger health-care provision mechanisms are needed to reduce the burden of unaddressed hearing loss in these settings