Univariate Measures Research Articles

Neural Indicators Of Food Cue Reactivity, Regulation, And Valuation And Their Associations With Body Composition And Daily Eating Behavior.

Exposure to food cues activates the brain's reward system and undermines efforts to regulate impulses to eat. During explicit regulation, lateral prefrontal cortex activates and modulates activity in reward regions and decreases food cravings. However, it is unclear the extent to which between-person differences in recruitment of regions associated with reward processing, subjective valuation, and regulation during food cue exposure-absent instructions to regulate-predict body composition and daily eating behaviors. In this preregistered study, we pooled data from five fMRI samples (N = 262) to examine whether regions associated with reward, valuation, and regulation, as well as whole-brain pattern expression indexing these processes, were recruited during food cue exposure and associated with body composition and real-world eating behavior. Regression models for a single a priori analytic path indicated that univariate and multivariate measures of reward and valuation were associated with individual differences in BMI and enactment of daily food cravings. Specification curve analyses further revealed reliable associations between univariate and multivariate neural indicators of reactivity, regulation, and valuation, and all outcomes. These findings highlight the utility of these methods to elucidate brain-behavior associations and suggest that multiple processes are implicated in proximal and distal markers of eating behavior.

Association between C-reactive protein and polypoidal choroidal vasculopathy: a systematic review and meta-analysis.

Aetiological processes of polypoidal choroidal vasculopathy (PCV) remains poorly understood, but several studies indicate that immunity may play a role and report elevated levels of systemic C-reactive protein (CRP). In this systematic review and meta-analysis, we summarize available evidence in the field. We searched the databases PubMed/MEDLINE, EMBASE, Web of Science and the Cochrane Central on 19 March 2020. Two independent authors reviewed the studies and extracted data. Two independent authors reviewed the studies, extracted data and evaluated risk of bias within individual studies. Studies were reviewed in the text qualitatively and measures of association were included for quantitative analyses. Results from univariate analyses and multivariate-adjusted analyses were included for separate meta-analyses to evaluate whether the association was only due to factors associated with PCV. Four studies (246 patients with PCV and 2861 control individuals) were identified and included for a qualitative and quantitative analysis. Increased CRP was associated with PCV when pooling both univariate measures (OR 3.54, 95% CI: 2.13-5.89, p<0.0001) and multivariate-adjusted measures (OR 3.05, 95% CI: 1.56-5.98, p=0.0011). Sensitivity analyses confirmed robustness of the results. Increased CRP is associated to PCV, even after adjusting for demographics, lifestyle factors and co-morbidities. Clinical value of CRP in relation to PCV remains unclear, but the association gives much needed insight into the aetiology of a poorly understood disease.

Manutenção da amamentação da trabalhadora formal: fatores que influenciam e suas consequência

Objetivo: Caracterizar os fatores que interferem na manutenção da amamentação da mulher trabalhadora com ví­nculo empregatí­cio formal. Método: Pesquisa quantitativa observacional analí­tica, com 207 participantes, em Florianópolis, Santa Catarina, através de questionário online. Analisado por meio de medidas estatí­sticas univariadas. Resultados: Emergiram duas categorias: concepções relacionados í amamentação enquanto trabalhadora e consequências das interferências do trabalho materno sobre a amamentação. A primeira categoria subdividiu-se em 5 fatores: individuais, ambientais, organizacionais, culturais e aspectos legais. Conclusão: Fortalecer tecnologias de apoio í amamentação: creches no local de trabalho, implantação de salas de apoio í amamentação nas empresas e ampliação da licença-maternidade mostraram-se relevantes para a consolidação das polí­ticas de incentivo ao aleitamento materno no paí­s.

Factors associated with low contraceptive use amongst vulnerable mothers in South West State, Nigeria.

BackgroundYoung mothers tend to be more prone to high maternal and perinatal risks and are thus deemed vulnerable to adverse sexual and reproductive health rights (SRHR) in terms of their right to choose contraceptives of their choice to enhance their maternal well-being and childbirth spacing should be well discussed. Achieving sufficient SRHR may be averted if the use of family planning by disadvantaged groups is not given required attention.AimThis study aimed to identify and analyse the factors associated with the low use of contraceptives amongst vulnerable women in the South West region in Nigeria.SettingThe study area was purposively chosen to capture contraceptive use amongst vulnerable women in Osun State, Nigeria.MethodsA primary data collection was done in three senatorial districts of Osun State, Nigeria, with 140 respondents each to give a total of 420 respondents. Collected data were analysed using univariate, bivariate and multivariate measures.ResultsThe result showed a magnitude of association and relationship at both levels of analyses. Living arrangements and family types were 89% and 88.3%, respectively, associated with family planning use. In the same vein, living arrangement and family types were also statistically significant at p < 0.05with an odds ratio of 0.23 (95% CI: 0.1184–0.4583) and an odds ratio of 0.35 (95% CI: 0.1756–0.6970) with family planning use, respectively.ConclusionWe concluded that policies and interventions to accelerate and encourage contraceptives use amongst vulnerable mothers in South West, Nigeria should be targeted at those whose husbands lived elsewhere and those whose husbands have more than one wife.

Relationship Application Of Professional Nursing Practice Models With The Level Of Working Satisfaction Of Nurses In The MPKP Room Of Lasinrang Pinrang Hospital

Professional Nursing Practice Model (MPKP) is a method of comprehensive nursing care in providing quality nursing care. This study aims to investigate the correlation between the implementation of the professional nursing practice model and the work satisfaction rate of the working nurses at the MPKP room of the Regional Hospital of Lasinrang in Pinrang. The study is analytically observational with a cross-sectional design. The subject (n=124) are practicing nurses in the inpatient ward of Lasinrang Regional Hospital of Pinrang. The sample was selected employing a systematic random sampling technique. The data were obtained with a questionnaire distribution and observation checklist and the data were analyzed with univariate and bivariate measures. The chi-square test proves that the operand (p=0.000), pre-Competence (p=0.000), competence (p=0.000), competence case (p=0.164), and round (p=0.164) on the work satisfaction rate of the working nurses. This implies that there is a significant correlation the operand evaluation, pre-conference, post-conference, and the work satisfaction rate of the working nurses for the value of p&lt;0.05 but no significant correlation between case competency evaluation as well as round and the work satisfaction of the nurses with p&gt;0.05.

A resource conscious human action recognition framework using 26-layered deep convolutional neural network

Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions are mixed that degrade the recognition performance. In this article, we design a new 26-layered Convolutional Neural Network (CNN) architecture for accurate complex action recognition. The features are extracted from the global average pooling layer and fully connected (FC) layer, and fused by a proposed high entropy-based approach. Further, we propose a feature selection method name Poisson distribution along with Univariate Measures (PDaUM). Few of fused CNN features are irrelevant, and few of them are redundant that makes the incorrect prediction among complex human actions. Therefore, the proposed PDaUM based approach selects only the strongest features that later passed to the Extreme Learning Machine (ELM) and Softmax for final recognition. Four datasets are using for experimental analysis - HMDB51 (51 classes), UCF Sports (10 classes), KTH (6 classes), and Weizmann (10 classes). On these datasets, the ELM classifier gives an improved performance as compared to a Softmax classifier. The achieved accuracy on each dataset is 81.4%, 99.2%, 98.3%, and 98.7%, respectively. Comparison with existing techniques, it is shown that the proposed architecture gives better performance in terms of accuracy and testing time.

Does A Recumbent Lateral Stability Trainer Improve Balance Scores Among Older Adults Within 4 Weeks?

Falls are the leading cause of fatal and non-fatal injuries for older Americans. Modifying exercise programs to address balance and lower body strength would vastly improve stability and functional movement. Past literature has shown balance and strength are important in preventing falls, but few studies have focused on developing strength in a lateral plane compared to an anterior/posterior plane. PURPOSE: To determine if a lateral pedal recumbent training device that allows lower limb movement in a horizontal plane can improve balance scores among older adults in 4 weeks. METHODS: A two group experimental-control multivariate design was selected for the study. All subjects (n = 56) were between the ages of 59-80 years and without any physical limitations or medical issues. All participants were divided into 2 equal groups, pre-tested and post-tested on a computerized posturography plate (Bertec, Inc. Columbus, OH.) to determine Center of Pressure scores with eyes opened (EOSS), Center of Pressure scores with eyes closed (ECSS), Center of Pressure scores with eyes open perturbed surface (EOPS), and Center of Pressure scores with eyes closed perturbed surface (ECPS). The experimental group used the lateral trainer for 15 minutes, 3 times per week, for 4 consecutive weeks while the control group maintained a sedentary lifestyle. A repeated measures MANOVA was used to determine significance between the two groups within the 4 balance assessments. RESULTS: There were no significant main treatment effects for either group (p = .221). There were statistically significant differences over time for EOPS (p = .047) and ECPS (p = .047). Likewise, there were statistically significant differences for each univariate outcome with EOSS (p = .045) ECSS (p = .033), EOPS (p = .010), and ECPS (p = .026). Statistical power was achieved (> .98) for both univariate and multivariate measures. The multivariate outcome for group x time interaction accounted for 15.2% more variance than time alone (28.1%) for the experimental group alone. CONCLUSION: A recumbent lateral stability device can improve balance scores among older adults within 4 weeks of training, 3 x week for 15 minutes.

Pelagic-benthic resource polymorphism in Schizopygopsis thermalis Herzenstein 1891 (Pisces, Cyprinidae) in a headwater lake in the Salween River system on the Tibetan Plateau.

Resource polymorphism is a ubiquitous phenomenon in vertebrates and may represent a critical intermediate stage in speciation. Freshwater lakes in high‐altitude areas represent a natural system for understanding resource polymorphism in fishes benefiting from diverse lacustrine environments and species‐poor fish assemblages. We report resource polymorphism in a cyprinid fish, Schizopygopsis thermalis, in Lake Amdo Tsonak Co, a headwater lake in the upper Salween River system. Two discrete intraspecific morphs, planktivorous and benthivorous, were identified according to geometric morphometrics and traditional univariate linear measures. The planktivorous morph exhibits a longer head, longer upper and lower jaw, larger asymptotic standard length (L∞), lower growth rate (k), and higher growth performance index (φ) than the benthivorous morph. With respect to descriptive traits, the planktivorous morph possesses a large, terminal mouth and obvious mucus pores on the cheek and chin, while the benthivorous morph is characterized by a more inferior mouth with a sharpen horny edge on the lower jaw and unconspicuous mucus pores. The discrete pelagic–benthic resources and low interspecific competition in the lake system might drive the initial differentiation of the two morphs, and partial spatial reproductive isolation in breeding further maintains and reinforces the differences between them.

Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks.

Electroencephalogram (EEG) microstates that represent quasi‐stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non‐Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long‐short‐term‐memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM‐based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200–2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long‐range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short‐term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.

The effects of phylogeny, body size, and locomotor behavior on the three-dimensional shape of the pelvis in extant carnivorans.

The mammalian pelvis is thought to exhibit adaptations to the functional demands of locomotor behaviors. Previous work in primates has identified form-function relationships between pelvic shape and locomotor behavior; few studies have documented such relationships in carnivorans, instead focusing on long bones. Most work on the functional morphology of the carnivoran pelvis, in particular, has used univariate measures, with only a few previous studies incorporating a three-dimensional (3D) analysis. Here we test the hypothesis that carnivoran taxa that are characterized by different locomotor modes also differ in 3D shape of the os coxae. Using 3D geometric morphometrics and phylogenetic comparative methods, we evaluate the phylogenetic, functional, and size-related effects on 3D pelvis shape in a sample of 33 species of carnivorans. Using surface models derived from laser scans, we collected a suite of landmarks (N = 24) and curve semilandmarks (N = 147). Principal component analysis on Procrustes coordinates demonstrates patterns of shape change in the ischiopubis and ilium likely related to allometry. Phylogenetic generalized least squares analysis on principal component scores demonstrates that phylogeny and body size have greater effects on pelvic shape than locomotor function. Our results corroborate recent research finding little evidence of locomotor specialization in the pelvis of carnivorans. More research on pelvic morphological integration and evolvability is necessary to understand the factors driving pelvic evolution in carnivorans.

Early deviation from normal structural connectivity: A novel intrinsic severity score for mild TBI.

ObjectiveStudies of outcome after traumatic brain injury (TBI) are hampered by the lack of robust injury severity measures that can accommodate spatial-anatomical and mechanistic heterogeneity. In this study we introduce a Mahalanobis distance measure (M) as an intrinsic injury severity measure that combines in a single score the many ways a given injured brain's connectivity can vary from that of healthy controls. Our objective is to test the hypotheses that M is superior to univariate measures in (1) discriminating patients and controls and (2) correlating with cognitive assessment.MethodsSixty-five participants (34 with mild TBI, 31 controls) underwent diffusion tensor MRI and extensive neuropsychological testing. Structural connectivity was inferred for all participants for 22 major white matter connections. Twenty-two univariate measures (1 per connection) and 1 multivariate measure (M), capturing and summarizing all connectivity change in a single score, were computed.ResultsOur multivariate measure (M) was able to better discriminate between patients and controls (area under the curve 0.81) than any individual univariate measure. M significantly correlated with cognitive outcome (Spearman ρ = 0.31; p < 0.05). No univariate measure showed significant correlation after correction for multiple comparisons.ConclusionsHeterogeneity in the severity and distribution of injuries after TBI has traditionally complicated the understanding of outcomes after TBI. Our approach provides a single, continuous variable that can fully capture individual heterogeneity. M's ability to distinguish even mildly injured patients from controls and its correlation with cognitive assessment suggest utility as an imaging-based marker of intrinsic injury severity.

Meta-analysis on pulse disturbances reveals differences in functional and compositional recovery across ecosystems.

Most ecosystems are affected by anthropogenic or natural pulse disturbances, which alter the community composition and functioning for a limited period of time. Whether and how quickly communities recover from such pulses is central to our understanding of biodiversity dynamics and ecosystem organisation, but also to nature conservation and management. Here, we present a meta-analysis of 508 (semi-)natural field experiments globally distributed across marine, terrestrial and freshwater ecosystems. We found recovery to be significant yet incomplete. At the end of the experiments, disturbed treatments resembled controls again when considering abundance (94%), biomass (82%), and univariate diversity measures (88%). Most disturbed treatments did not further depart from control after the pulse, indicating that few studies showed novel trajectories induced by the pulse. Only multivariate community composition on average showed little recovery: disturbed species composition remained dissimilar to the control throughout mostexperiments. Still, when experiments revealeda higher compositional stability, they tended to also show higher functional stability. Recovery was more complete when systems had high resistance, whereas resilience and resistance were negatively correlated. The overall results were highly consistent across studies, but significant differences between ecosystems and organism groups appeared. Future research on disturbances should aim to understand these differences, but also fill obvious gaps in the empirical assessments for regions (especially the tropics), ecosystems and organisms. In summary, we provide general evidence that (semi-)natural communities can recover from pulse disturbances, but compositional aspects are more vulnerable to long-lasting effects of pulse disturbance than the emergent functions associated to them.

Interpretable Machine Learning for Diversified Portfolio Construction

In this paper, the authors construct a pipeline to benchmark Hierarchical Risk Parity (HRP) relative to Equal Risk Contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage (volatility target). The authors use interpretable machine learning concepts (explainable AI) to compare the robustness of the strategies and to back out implicit rules for decision making. The empirical dataset consists of 17 equity index, government bond and commodity futures markets across 20 years. The two strategies are backtested for the empirical dataset and for about 100’000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes.

A Multiplex Connectivity Map of Valence-Arousal Emotional Model

high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users’ emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants’ ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes.

Evaluation of economic forecasts for Austria

In this paper, we evaluate macroeconomic forecasts for Austria and analyze the effects of external assumptions on forecast errors. We consider the growth rates of real GDP and the demand components as well as the inflation rate and the unemployment rate. The analyses are based on univariate measures like RMSE and Theil’s inequality coefficient and also on the Mahalanobis distance, a multivariate measure that takes the variances of and the correlations between the variables into account. We compare forecasts generated by the two leading Austrian economic research institutes, the Institute for Advanced Studies (IHS) and the Austrian Institute of Economic Research (WIFO), and additionally consider the forecasts produced by the European Commission. The results indicate that there are no systematic differences between the forecasts of the two Austrian institutes, neither for the traditional measures nor for the Mahalanobis distance. Generally, forecasts become more accurate with a decreasing forecast horizon, as expected; they are unbiased for forecast horizons of less than a year considering traditional measures and for the shortest forecast horizon considering the Mahalanobis distance. Finally, we find that mistakes in external assumptions, in particular regarding EU GDP and the oil price, translate into forecast errors for GDP and inflation.

Acoustic Complexity Index to assess benthic biodiversity of a partially protected area in the southwest of the UK

Abstract The soundscape of the marine environment is a relatively understudied area of ecology that has the potential to provide large amounts of information on biodiversity, reproductive behaviour, habitat selection, spawning and predator–prey interactions. Biodiversity is often visually assessed and used as a proxy for ecosystem health. Visual assessment using divers or remote video methods can be expensive, and limited to times of good weather and water visibility. Previous studies have concluded that acoustic measures, such as the Acoustic Complexity Index (ACI), correlate with visual biodiversity estimates and offer an alternative to assess ecosystem health. Here, the ACI measured over 5 years in a Marine Protected Area (MPA) in the UK, Lyme Bay, was analysed alongside another monitoring method, Baited Remote Underwater Video Systems (BRUVs). Two treatments were sampled annually in the summer from 2014 until 2018 with sites inside the MPA, as well as Open Control sites outside of the MPA. Year by year correlations, which have been used elsewhere to test ACI, showed significant correlations with Number of Species and ACI. However, the sign of these correlations changed almost yearly, showing that more in-depth analyses are needed. Multivariate analysis of the benthic assemblage composition (from BRUVs) was carried out by Permutational Multivariate Analysis of Variance (PERMANOVA) using Distance Matrices. Although not consistently correlating with univariate measures, the ACI was significantly interacting with the changing benthic assemblage composition, as it changed over time and protection (Inside vs Outside the MPA). ACI showed potential to allude to shifting benthic communities, yet with no consistency when used alongside univariate measures of diversity. Although it is not without its own disadvantages, and thus should be developed further before implementation, the ACI could potentially reflect more complex changes to the benthos than simply the overall diversity.

Influence diagnostics and outlier detection for meta-analysis of diagnostic test accuracy.

Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta-analyses, some studies may have markedly different characteristics from the others and potentially be inappropriate to include. The inclusion of these "outlying" studies might lead to biases, yielding misleading results. In addition, there might be influential studies that have notable impacts on the results. In this article, we propose Bayesian methods for detecting outlying studies and their influence diagnostics in DTA meta-analyses. Synthetic influence measures based on the bivariate hierarchical Bayesian random effects models are developed because the overall influences of individual studies should be simultaneously assessed by the two outcome variables and their correlation information. We propose four synthetic measures for influence analyses: (a) relative distance, (b) standardized residual, (c) Bayesian p-value, and (d) influence statistic on the area under the summary receiver operating characteristic curve. We also show that conventional univariate Bayesian influential measures can be applied to the bivariate random effects models, which can be used as marginal influential measures. Most of these methods can be similarly applied to the frequentist framework. We illustrate the effectiveness of the proposed methods by applying them to a DTA meta-analysis of ultrasound in screening for vesicoureteral reflux among children with urinary tract infections.

Application of a kNN-based similarity method to biopharmaceutical manufacturing.

Machine learning-based similarity analysis is commonly found in many artificial intelligence applications like the one utilized in e-commerce and digital marketing. In this study, a kNN-based (k-nearest neighbors) similarity method is proposed for rapid biopharmaceutical process diagnosis and process performance monitoring. Our proposed application measures the spatial distance between batches, identifies the most similar historical batches, and ranks them in order of similarity. The proposed method considers the similarity in both multivariate and univariate feature spaces and measures batch deviations to a benchmarking batch. The feasibility and effectiveness of the proposed method are tested on a drug manufacturing process at Biogen.

Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

Assessing joint spatial autocorrelations between mortality rates due to cardiovascular conditions in South Africa.

South Africa is experiencing an increasing burden of noncommunicable diseases (NCDs). There is evidence of co-morbidity of several NCDs at small geographical areas in the country. However, the extent to which this applies to joint spatial autocorrections of NCDs is not known. The objective of this study was to derive and quantify multivariate spatial autocorrections for NCDrelated mortality in South Africa. The study used mortality attributable to cerebrovascular, ischaemic heart failure and hypertension captured by the country's Department of Home Affairs for the years 2001, 2007 and 2011. Both univariate and pairwise spatial clustering measures were derived using observed, empirical Bayes smoothed and age-adjusted standardised mortality rates. Cerebrovascular and ischaemic heart co-clustering was significant for the years 2001 and 2011. Cerebrovascular and hypertension co-clustering was significant for the years 2007 and 2011, while hypertension and ischaemic heart co-clustering was significant for the year 2011. Co-clusters of cerebrovascular-ischaemic heart disease are the most profound and located in the south-western part of the country. It was successfully demonstrated that bivariate spatial autocorrelations can be derived for spatially dependent mortality rates as exemplified by mortality rates attributed to three cardiovascular conditions. The identified co-clusters of spatially dependent health outcomes may be targeted for an integrated intervention and monitoring programme.