Detailed Inferences Research Articles

A new superfamily of freshwater mussels Deccanoidea nov. (Mollusca: Unionida) from an intertrappean deposit of Saurashtra province (Gujarat state, western India): An example of disjunct distribution

The Cretaceous-Palaeogene intertrappean deposits of the Deccan Volcanic Province of India comprise vertebrate, invertebrate and plant fossils of the dominantly continental realm. A 3–4 m thick shaly-sandstone lacustrine intertrappean sequence of Early Palaeogene (Palaeocene-Early Eocene) Bamanbor Formation, Chotila Basin (Saurashtra province, Gujarat State, western India) has yielded a collection of molluscans (bivalve and few gastropods) fauna. High-resolution digital photography and radiography of the recovered bivalves allowed us to gain detailed inferences on shell morphology. In a taxonomical context, we herein propose a new unionid molluscan superfamily Deccanoidea nov. represented by a single new family Deccanoidae nov. that includes two new subfamilies, that is, Deccanoinae nov. (represented by two new genera Deccanoida gen. nov. [including four new species: D. conrugis sp. nov., D. aleta sp. nov., and D. costaria sp. nov.] and Bamanboria gen. nov. [including one new species Bamanboria oblongis gen. et sp. nov.] and Chotiloinae nov. [represented by one new genus Chotilia gen. nov. and three new species: Chotilia trappeansis sp. nov., C. tuberculata sp. nov. and C. deccanensis sp. nov.]). Inorganic geochemistry of host (unionid-yielding) lithologies reveals oxic-freshwater conditions, humid-climate and moderate rainfall. The data on historical biogeography reveals that the Indian subcontinent hosted unionids during the late Cretaceous; however, the presence of new (at family/generic level) unionid fauna during the Early Palaeogene (Palaeocene-Early Eocene) in India indicates changes in the geographic position and ecologic condition linked to northward drift and lava outpouring, respectively. A disjunct distribution of unionid fauna in the erstwhile Gondwanan continents and insular India during the Cretaceous-Palaeogene interval can be explained in terms of both vicariance and dispersal.

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

A comparative analysis of several multivariate zero-inflated and zero-modified models with applications in insurance

. Given that insurance companies often operate across multiple lines of insurance business, where claim frequencies on different lines are often correlated, it often becomes advantageous to employ multivariate count modeling where dependence between lines of business can be included in the modeling. Due to the operation of bonus-malus systems where there is a reward to the insurance policyholder for not claiming, claims data in automobile insurance often exhibits an excess of common zeros, a characteristic known as multivariate zero-inflation. In this article, we propose two approaches to address this feature. The first approach involves utilizing a multivariate zero-inflated model, where we artificially enhance the probability of common zeros based on standard multivariate count distributions. The second approach applies a multivariate zero-modified model, which separately handles the common zeros and the number of claims incurred in each line given that at least one claim occurs. We present several models under these frameworks, along with detailed inference procedures. In the applications section, we conduct a comprehensive comparative analysis of these models using data from an automobile insurance portfolio.

PALYNOSTRATIGRAPHIC AND PALAEOENVIRONMENTAL INVESTIGATIONS OF THE MAASTRICHTIAN FROM OARDA DE JOS (SOUTHWESTERN TRANSYLVANIAN BASIN)

A palynological, palynofacies and organic geochemical investigation of the Upper Cretaceous continental vertebrate site of Oarda de Jos allowed us to constrain its age and to make more detailed inferences about its depositional environment. The recovered palynomorph assemblage is represented mainly by angiosperm pollen and fern spores, alongside rare taxa of gymnosperms. Biostratigraphically significant taxa identified in the assemblage restrict the age of the studies deposits to the later early Maastrichtian. The palynofloral evidence indicates plant communities that mainly preferred higher-altitude areas and cooler-wetter conditions. However, a spore and pollen assemblage which derives from vegetation typical of lacustrine areas or riverbanks also occurs in the studied deposits. The studied palynoflora shows quite significant differences compared to previously published palynological assemblages from the same geological unit (the Sebeș Formation), although from deposits located slightly to the north, at Pâclișa. Organic geochemical data show that certain organic biomarkers such as n-alkanes n-C16 to n-C18 reach the highest values, and indicate that they may derive from freshwater colonial green algae such as Scenedesmus. The palynofacies constituents used to reconstruct the depositional environment suggest an exclusive terrestrial organic matter deposited in a stagnant-water fluvial/lacustrine environment.

Bayesian Semiparametric Local Clustering of Multiple Time Series Data

In multiple time series data, clustering the component profiles can identify meaningful latent groups while also detecting interesting change points in their trajectories. Conventional time series clustering methods, however, suffer the drawback of requiring the co-clustered units to have the same cluster membership throughout the entire time domain. In contrast to these “global” clustering methods, we develop a Bayesian “local” clustering method that allows the functions to flexibly change their cluster memberships over time. We design a Markov chain Monte Carlo algorithm to implement our method. We illustrate the method in several real-world datasets, where time-varying cluster memberships provide meaningful inferences about the underlying processes. These include a public health dataset to showcase the more detailed inference our method can provide over global clustering alternatives, and a temperature dataset to demonstrate our method’s utility as a flexible change point detection method. Supplemental materials for this article, including R codes implementing the method, are available online.

Characterization of Achromadora porosa sp. n. (Chromadorida: Achromadoridae) from the coal mines, with a detailed inference on the phylogeny of the group and the relationships between congeners

Characterization of Achromadora porosa sp. n. (Chromadorida: Achromadoridae) from the coal mines, with a detailed inference on the phylogeny of the group and the relationships between congeners

Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l₁-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.

Integrating spaceborne estimates of structural diversity of habitat into wildlife occupancy models

Vegetation structure is a crucial dimension of wildlife habitat, responsive to global changes in human activities and ecosystem processes. NASA’s recent Global Ecosystem Dynamics Investigation (GEDI) provides an exciting opportunity to explore how spaceborne waveform observations can improve our ability to measure wildlife habitat and advance animal ecology in the Anthropocene. We tested the utility of GEDI data in univariate occupancy models to estimate habitat use in a remote mountain system in central Idaho, USA. We collected data from 49 camera trap stations from two surveys in 2018–2019 and modeled the occupancy for each of seven mammal species representing different trophic levels and feeding strategies: American black bear (Ursus americanus), deer (Odocoileus hemionus), elk (Cervus canadensis), moose (Alces alces), coyote (Canis latrans), wolf (Canis lupus), and mountain lion (Puma concolor). We first derived structural diversity indices (richness, evenness, and divergence) of GEDI-derived canopy height, plant area index, and foliage height diversity to represent different dimensions of vegetation structure. This spatial aggregation is necessary due to gaps in GEDI footprints and parallels commonly used functional diversity metrics applied to biological communities that are calculated using trait probability densities. We measured these indices across three spatial scales that reflect different species movement and habitat selection patterns. We found the structural diversity indices of canopy height, foliage height diversity, and plant area index had the strongest effects on the occupancy of most mammals compared to two-dimensional (2D) variables (e.g. tree cover, normalized difference vegetation index). The spatial extent of these indices also influenced the strength of response, highlighting the importance of selecting a scale large enough to capture sufficient GEDI footprints but small enough to reflect site-level variance. Compared to 2D covariates, our results suggest that GEDI variables allow researchers to generate more detailed inference on the forms of habitat that wildlife use. We discuss the implications of these findings for habitat management and future wildlife research from local to global scales.

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.

Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values.

A Generalized Deep Learning Clustering Algorithm Based on Non-Negative Matrix Factorization

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.

Investigation of Egg External Quality Characteristics of Linda Geese with Data Mining Methods

In this study, the external quality characteristics of eggs belonging to Linda accidents, a poultry species, were investigated with a data mining approach. The 288 Linda goose eggs used in the study were 36 weeks old; Their width was 52.34 mm, egg length was 76.8 mm, egg weight was 120.43 g, and shape index value was 68.26. Eggs were clustered according to shape index and weight measurements using the K-means clustering algorithm, a data mining approach. Statistically significant differences were found between the clusters in width, height, shape index, and weight (p<0.05). The findings of our study showed that eggs with a low index had high- weight; eggs with a high index were low-weight eggs. According to the results of this study, it was concluded that the shape index value might be related to egg weight. More detailed inferences can be made using data mining algorithms for different poultry species.

Testing anxiety's effect on movement planning and correction: Online upper-limb corrections are not completely automatic

Via three experiments, we investigated heightened anxiety's effect on the offline planning and online correction of upper-limb target-directed aiming movements. In Experiment 1, the majority of task trials allowed for the voluntary distribution of offline planning and online correction to achieve task success, while a subset of cursor jump trials necessitated the use of online correction to achieve task success. Experiments 2 and 3 replicated and elaborated Experiment 1 by assessing movement-specific reinvestment propensity and manipulating the self-control resources of participants. This allowed more detailed inference of cognitive resource utilisation to tease apart the effects of conscious processing and distraction-based anxiety mechanisms. For the first time, we demonstrate that: anxiety-induced online-to-offline motor control shifts can be overridden when the need for online correction is necessitated (i.e., in jump trials); anxiety-induced online-to-offline shifts seem to be positively predicted by conscious processing propensity; and optimal spatial efficacy of limb information-based online correction seems to require cognitive resources. We conclude that long-standing definitions of limb information-based online correction require revision, and that both conscious processing and distraction theories appear to play a role in determining the control strategies of anxiety induced upper limb target directed aiming movements.

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient’s survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient’s likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.

Multistep Super Resolution Double-U-net (SRDUN) for enhancing the resolution of Berea sandstone images

Digital Rock Physics (DRP) has opened a new avenue through the microscale world of rocks leading to more detailed inferences. However, High Resolution (HR) images are problematic from several aspects such as low field of view, huge memory and long time and high cost of image acquisition. Recent advances in Machine Learning (ML) and especially Convolutional Neural Networks (CNN) highly improved the ability of producing HR images from their Low Resolution (LR) counterparts known as Super Resolution (SR) images. In this procedure, the scale-difference between LR and HR images is a key parameter, while a high scale-difference leads to unreliable results regardless of the method. To overcome such a problem, we aim to both propose a multiscale procedure and introduce an efficient network namely Super Resolution Double-U-net (SRDUN). To this end, unlike conventional studies where LR images are synthetically produced from HR images, we capture these images from a Berea sandstone individually in five different scales (16.263, 12.922, 9.499, 5.775 and 3.501 μm for samples with a side of 10, 8, 6, 4 and 2-mm). Then, the SRDUN is introduced, where a first U-net is used to store feature maps of HR images and are imported in a second U-net as informative skip connections. Results show SRDUN could perform more accurate than common networks for small scale-differences. However, when it comes to high scale-differences it becomes more complicated. We propose a multistep procedure, where several mid-step SRDUNs are used to fill the gap of information. Results of this procedure reveal that the multistep procedure could perform much better than the direct approach. These results are then demonstrated by some computations of reservoir parameters such as porosity, pore equivalent diameter distribution, morphometric properties and permeability. Porosity and permeability computations showed that the error of computed values from SR images is increased by increasing scale-difference, however, the multistep procedure leads to more reliable results. The multistep procedure could preserve better the pore size distribution, however, in morphometric properties, the direct approach performs better since the multistep procedure suffers from a smoothing effect.

Evolution and dispersal of mitochondrial DNA haplogroup U5 in Northern Europe: insights from an unsupervised learning approach to phylogeography

BackgroundWe combined an unsupervised learning methodology for analyzing mitogenome sequences with maximum likelihood (ML) phylogenetics to make detailed inferences about the evolution and diversification of mitochondrial DNA (mtDNA) haplogroup U5, which appears at high frequencies in northern Europe.MethodsHaplogroup U5 mitogenome sequences were gathered from GenBank. The hierarchal Bayesian Analysis of Population Structure (hierBAPS) method was used to generate groups of sequences that were then projected onto a rooted maximum likelihood (ML) phylogenetic tree to visualize the pattern of clustering. The haplogroup statuses of the individual sequences were assessed using Haplogrep2.ResultsA total of 23 hierBAPS groups were identified, all of which corresponded to subclades defined in Phylotree, v.17. The hierBAPS groups projected onto the ML phylogeny accurately clustered all haplotypes belonging to a specific haplogroup in accordance with Haplogrep2. By incorporating the geographic source of each sequence and subclade age estimates into this framework, inferences about the diversification of U5 mtDNAs were made. Haplogroup U5 has been present in northern Europe since the Mesolithic, and spread in both eastern and western directions, undergoing significant diversification within Scandinavia. A review of historical and archeological evidence attests to some of the population interactions contributing to this pattern.ConclusionsThe hierBAPS algorithm accurately grouped mitogenome sequences into subclades in a phylogenetically robust manner. This analysis provided new insights into the phylogeographic structure of haplogroup U5 diversity in northern Europe, revealing a detailed perspective on the diversity of subclades in this region and their distribution in Scandinavian populations.

Socio-Demographic Variations on Age-Sex Mortality in Namibia: An Analysis of the 2016 Civil Registration and Vital Statistics Data

Mortality studies are important for the effectiveness of subsystem of health services in a country. Before the Coronavirus pandemic outbreak, there has been a gradual decline in the global mortality indicators, which can be linked to the improving economic development and social wellbeing of the global population, especially, in developing regions such as East Asia and the Pacific, Latin America and the Caribbean, Eastern Europe and the Middle East. This decline in mortality and the high fertility in developing countries are the contributing factors to the increase on global population. Apart from the aggregated frequencies of deaths and crude death rates reported in population census reports, little to no attention has been paid to detailed inference mortality analysis with respect to the age-sex variation perspective in Namibia. Thus, this paper used the negative binomial regression modelling technique to perform an inference mortality analysis across all ages and both sexes in the country as well as across regions and marital status using the 2016 Civil Registration Vital systems data from the Ministry of Home Affairs and Immigration. Results showed that there was a significant relationship between mortality and theindividuals' age, sex, marital status and region. In addition, Oshana, Kavango East, Khomas, Hardap and Omaheke regions had high mortality rates, while infants and elderly individuals had a high probability of dying. Furthermore, the study revealed that individuals who were single and aged 15-59 and 5-14 years had less expected death count. Hence, it is recommended that interventions (such as affordable and proper health care and well-being services) targeted at the (most) vulnerable age groups, marital group and regions be made a priority, in order to meet Sustainable Development Goal 3.

Antarctic upper mantle rheology

Abstract The Antarctic mantle and lithosphere are known to have large lateral contrasts in seismic velocity and tectonic history. These contrasts suggest differences in the response timescale of mantle flow across the continent, similar to those documented between the northeastern and southwestern upper mantle of North America. Glacial isostatic adjustment and geodynamical modelling rely on independent estimates of lateral variability in effective viscosity. Recent improvements in imaging techniques and the distribution of seismic stations now allow resolution of both lateral and vertical variability of seismic velocity, making detailed inferences about lateral viscosity variations possible. Geodetic and palaeosea-level investigations of Antarctica provide quantitative ways of independently assessing the 3D mantle viscosity structure. While observational and causal connections between inferred lateral viscosity variability and seismic velocity changes are qualitatively reconciled, significant improvements in the quantitative relations between effective viscosity anomalies and those imaged by P- and S-wave tomography have remained elusive. Here we describe several methods for estimating effective viscosity from S-wave velocity. We then present and compare maps of the viscosity variability beneath Antarctica based on the recent S-wave velocity model ANT-20 using three different approaches.

Revisiting the demographic history of Central African populations from a genetic perspective

Africa is the continent of our species’ origin and the deep history of humans is represented by African genetic variation. Through genetic studies, it has become evident that deep African population history is captured by relationships among African hunter-gatherers and that the world’s deepest population divergences occur among these groups. In this review, we look back at a study published by Cavalli-Sforza and colleagues in 1969 entitled “Studies on African Pygmies. I. A pilot investigation of Babinga Pygmies in the Central African Republic (with an analysis of genetic distances)”. The study analyzed 19 classical polymorphisms and found that the deepest divergences in African populations were represented by hunter-gatherer groups such as the southern African San and the central African rainforest hunter-gatherers. We repeated the original analyses from Cavalli-Sforza et al. [1] with about 22 thousand times more genome-wide genetic markers in populations similar to those included in the original study. Our high-resolution analyses gave similar results regarding the relationships of early-diverging African populations compared to the classical polymorphism analyses. This finding, however, does not imply that research has stagnated and that developments in technology and genetic methods over the last fifty years delivered no additional information regarding African history and adaptation. We review how technology and population genetic methods have advanced to give more detailed inferences about population structure, migrations, admixture patterns, timing of admixture, sex-biased admixture, and inferences of selection and adaptive introgression in rainforest hunter-gatherers and other African populations. We also comment on how sequencing of ancient DNA has influenced findings and deliberate on the progress and development of more complex models of African history, including alternatives to tree-models and the inference of possible archaic admixture in African populations. We review the growing complexity of our picture of population history in central Africa and Africa as a whole, emerging from genomic studies and other disciplines investigating human population history and adaptation. While data and knowledge are accumulating, certain populations and areas remain underrepresented in genomic research. Their inclusion, possibly also through ancient DNA studies, together with new methods of analysis and the testing of representative models of deep population history in Africa, will help to build a more complete picture of past population history in Africa. Based on “Cavalli-Sforza LL, Zonta LA, Nuzzo F, Bernini L, de Jong WW, Meera Khan P, Ray AK, Went LN, Siniscalco M, Nijenhuis LE, van Loghem E, Modiano G. Studies on African Pygmies. I. A pilot investigation of Babinga Pygmies in the Central African Republic (with an analysis of genetic distances). Am J Hum Genet. 1969 May;21(3):252-274”.

The association between opioids, environmental, demographic, and socioeconomic indicators and COVID-19 mortality rates in the United States: an ecological study at the county level

BackgroundThe spread of the COVID-19 pandemic throughout the world presents an unprecedented challenge to public health inequities. People who use opioids may be a vulnerable group disproportionately impacted by the current pandemic, however, the limited prior research in this area makes it unclear whether COVID-19 and opioid use outcomes may be related, and whether other environmental and socioeconomic factors might play a role in explaining COVID-19 mortality. The objective of this study is to evaluate the association between opioid-related mortality and COVID-19 mortality across U.S. counties.MethodsData from 3142 counties across the U.S. were used to model the cumulative count of deaths due to COVID-19 up to June 2, 2020. A multivariable negative-binomial regression model was employed to evaluate the adjusted COVID-19 mortality rate ratios (aMRR).ResultsAfter controlling for covariates, counties with higher rates of opioid-related mortality per 100,000 persons were found to be significantly associated with higher rates of COVID-19 mortality (aMRR: 1.0134; 95% CI [1.0054, 1.0214]; P = 0.001). Counties with higher average daily Particulate Matter (PM2.5) exposure also saw significantly higher rates of COVID-19 mortality. Analyses revealed rural counties, counties with higher percentages of non-Hispanic whites, and counties with increased average maximum temperatures are significantly associated with lower mortality rates from COVID-19.ConclusionsThis study indicates need for public health efforts in hard hit COVID-19 regions to also focus prevention efforts on overdose risk among people who use opioids. Future studies using individual-level data are needed to allow for detailed inferences.