Error Variance Research Articles

Performance Assessment of a Coupled Circulation–Wave Modelling System for the Northwest Atlantic

We present a modified version of a coupled circulation–wave modelling system for the northwest Atlantic (CWMS-NWA) by including additional physics associated with wave–current interactions. The latest modifications include a parameterization of Langmuir turbulence and surface flux of turbulent kinetic energy from wave breaking in vertical mixing. The performance of the modified version of CWMS-NWA during Hurricane Arthur in 2014 is assessed using in situ measurements and satellite data. Several error statistics are used to evaluate the model performance, including correlation (R), root mean square error (RMSE), normalized model variance of model errors (γ2) and relative bias (RB). It is found that the simulated surface waves (R ≈ 94.0%, RMSE ≈ 27.5 cm, γ2≈ 0.16) and surface elevations (R ≈ 97.3%, RMSE ≈ 24.0 cm, γ2≈ 0.07) are in a good agreement with observations. The large-scale circulation, hydrography and associated storm-induced changes in the upper ocean during Arthur are reproduced satisfactorily by the modified version of CWMS-NWA. Relative to satellite observations of the daily averaged sea surface temperature (SST), the model reproduces large-scale features as demonstrated by the error metrics: R ≈ 97.8%, RMSE ≈ 1.6 ∘C and RB ≈ 8.6 ×10−3∘C.

Representational geometry explains puzzling error distributions in behavioral tasks

Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory. Here, we reassess this assumption and find that even in ideal observer models with Gaussian encoding noise, the error distribution is generally non-Gaussian, contrary to the commonly held belief. Critically, we find that the shape of the error distribution is determined by the geometrical structure of the encoding manifold via a simple rule. In the case of a high-dimensional geometry, the error distributions naturally exhibit flat tails. Using this insight, we apply our theory to visual short-term memory tasks, and find that it can account for a large array of experimental data with only two free parameters. Our results challenge the dominant view in the mechanisms and capacity constraints of working memory systems. They instead suggest that the Bayesian framework, which explains various aspects of perceptual behavior, also provides an excellent account of working memory. Overall, our results establish a direct connection between neural manifold geometry and behavior, and call attention to the geometry of the representation as a critically important, yet underappreciated factor in determining the character of errors in human behavior.

Assessing the transmission of global market volatility to Moroccan food prices: a VECM-based analysis with forecast error variance decomposition

PurposeThis paper examines the transmission of global food (GF) and global energy (GE) volatilities to the Moroccan food market, focusing on both aggregated and specific commodities such as dairy, meat and oils and fats.Design/methodology/approachUsing monthly data from September 2009 to June 2024, a vector error correction model (VECM) was estimated to capture short-run and long-run dynamics. The analysis is complemented by impulse response functions (IRFs) and forecast error variance decomposition (FEVD) to provide insights into the transmission mechanisms.FindingsThe results indicate an increased sensitivity of the Moroccan food market, particularly following the 2021 price spikes. The study identifies energy input costs, particularly related to fertilizers and transportation, as primary transmission mechanisms. Additionally, the real effective exchange rate (REER) also significantly influences the Moroccan food market.Research limitations/implicationsFurther research could explore the transmission from oil derivatives, such as gasoline and fuel, which are directly used in agricultural activities, to provide a more comprehensive understanding.Social implicationsUnderstanding the transmission of global shocks to domestic markets can help in developing strategies to enhance food security and protect consumers from price volatility.Originality/valueThis study contributes to the literature by comprehensively analyzing the direct and indirect transmission mechanisms from global food and energy markets to the Moroccan food market, with policy implications to enhance price stability.

Research on a Density-Based Clustering Method for Eliminating Inter-Frame Feature Mismatches in Visual SLAM Under Dynamic Scenes

Visual SLAM relies on the motion information of static feature points in keyframes for both localization and map construction. Dynamic feature points interfere with inter-frame motion pose estimation, thereby affecting the accuracy of map construction and the overall robustness of the visual SLAM system. To address this issue, this paper proposes a method for eliminating feature mismatches between frames in visual SLAM under dynamic scenes. First, a spatial clustering-based RANSAC method is introduced. This method eliminates mismatches by leveraging the distribution of dynamic and static feature points, clustering the points, and separating dynamic from static clusters, retaining only the static clusters to generate a high-quality dataset. Next, the RANSAC method is introduced to fit the geometric model of feature matches, eliminating local mismatches in the high-quality dataset with fewer iterations. The accuracy of the DSSAC-RANSAC method in eliminating feature mismatches between frames is then tested on both indoor and outdoor dynamic datasets, and the robustness of the proposed algorithm is further verified on self-collected outdoor datasets. Experimental results demonstrate that the proposed algorithm reduces the average reprojection error by 58.5% and 49.2%, respectively, when compared to traditional RANSAC and GMS-RANSAC methods. The reprojection error variance is reduced by 65.2% and 63.0%, while the processing time is reduced by 69.4% and 31.5%, respectively. Finally, the proposed algorithm is integrated into the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 to validate its effectiveness in eliminating feature mismatches between frames in visual SLAM.

Use of patient portals to disclose STI test histories to sex partners among U.S. men who have sex with men: A call for research and guidance.

This study investigates the use of patient portals for disclosing sexually transmitted infection (STI) test result histories to sexual partners among men who have sex with men (MSM) in the United States. Using data from the 2022-2023 American Men's Internet Survey, this cross-sectional analysis examined demographic, behavioral, and healthcare-related factors associated with MSM's utilization of patient portals for sharing STI test results. Participants (N = 2601) were surveyed on portal use, STI testing frequency, and partner disclosure practices. 18% of survey participants used portals to disclose a STI test result to parrtners, with higher prevalences among individuals aged 16-37 years old versus 56 years and older, black non-Hispanic (26.4%) versus white non-Hispanic (16.8%), and those reporting nine or more times being tested for STIs (30.1%) versus 1 to 3 times (7.5%). Participants reporting sex work (33.3% vs 17.7%) and HIV pre-exposure prophylaxis (PrEP) use (24.2% vs 13.0%) also had a higher prevalence of portal use for disclosure. Multivariable Poisson regression analysis with robust error variance showed that sexual behavioral stigma from family and friends [adjusted prevalence ratio (aPR) = 1.11; p = .04] also, being tested for STIs four to 8 (aPR = 2.21; p < .001) and nine or more times aPR = 3.42; p < .001) were significantly associated with prevalence of portal-based disclosure. Findings suggest patient portals may be viable tools for STI prevention in MSM communities, especially among individuals with increased vulnerability to STI acquisition. Addressing disparities in portal access and explaining patient portal capabilities may enhance the role of digital health in reducing STI transmission among MSM communities.

Increasing certainty in projected local extreme precipitation change

The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world. These improved projections could benefit nearly 90% of the world’s population by permitting better impact assessment and adaptation planning at local levels. Our physically motivated strategy, which considers the thermodynamic and dynamic components of projected extreme precipitation change, exploits the link between global warming and the thermodynamic component of extreme precipitation. Rigorous cross-validation provides strong evidence of its reliability in constraining local extreme precipitation projections.

Validation of the Maslach burnout inventory-general survey 9-item short version (MBI-GS9) among care aides in Canadian nursing homes

BackgroundThe Maslach Burnout Inventory-General Survey (MBI-GS) is the leading measure of burnout for all occupations. The MBI-GS9, the 9-item version of the MBI-GS, was formulated based on the MBI-GS and has been used for several years. However, very few studies have systematically tested its psychometric properties, and none have focused on care aides working in nursing homes who are susceptible to burnout.MethodsFollowing the Standards for Educational and Psychological Testing, this study validated the MBI-GS9 among 3,765 care aides from 91 Canadian nursing homes, using data collected between September 2019 and February 2020 by the Translating Research in Elder Care (TREC) program.ResultsThe Exhaustion subscale had good reliability with coefficients around 0.66–0.74. The Cynicism subscale had medium reliability with coefficients around 0.60–0.66, and the Efficacy subscale also had medium reliability with coefficients around 0.51–0.58. The MBI-GS9 was significantly correlated with various conceptually related constructs, such as health status, working environment, job satisfaction, psychological empowerment, work engagement, and organizational citizenship behaviors. The MBI-GS9 had a three-factor structure in the full sample and showed equivalent factor structure, factor loadings, latent values, factor variance and error variance across different sex and age groups. Care aides with English as their first language showed higher latent values of the Exhaustion subscale compared to those with English as a second language.ConclusionOverall, the MBI-GS9 exhibited acceptable psychometric properties, but medium reliability of cynicism and efficacy subscales, for measuring burnout among care aides in nursing homes, demonstrating equivalence across sex and gender groups. When comparing across different languages or racial or ethnic groups among care aides, it is important to consider inequivalent latent values on Exhaustion before comparing scores on the measure.

Error analysis for the implicit boundary integral method

The implicit boundary integral method (IBIM) provides a framework to construct quadrature rules on regular lattices for integrals over irregular domain boundaries. This work provides a systematic error analysis for IBIMs on uniform Cartesian grids for boundaries with different degrees of regularity. First, it is shown that the quadrature error gains an additional order of d-12 from the curvature for a strongly convex smooth boundary due to the “randomness” in the signed distances. This gain is discounted for degenerated convex surfaces. Then the extension of error estimate to general boundaries under some special circumstances is considered, including how quadrature error depends on the boundary’s local geometry relative to the underlying grid. Bounds on the variance of the quadrature error under random shifts and rotations of the lattices are also derived.

Immediate skin-to-skin contact and postpartum hemorrhagic morbidity.

To examine rates of postpartum hemorrhagic (PPH) morbidity among patients who did and did not have immediate skin-to-skin contact (SSC). This study was a retrospective cohort of all non-anomalous, term singleton vaginal births at a Level IV center over 2 years. Exclusion criteria included COVID-19. Immediate SSC was defined as at least 60 min of direct contact initiated between parturient and neonate within 10 min of birth. The primary outcome was a composite of maternal morbidity related to PPH compared among those with and without immediate SSC. We used multivariable Poisson regression adjusted for possible confounders with robust error variance to determine the strength of the association. Of 8623 deliveries during the study period, 3520 (40.8%) deliveries were included; of which 2428 (55.5%) had immediate SSC and 1028 (31.0%) did not. Immediate SSC reduced the overall rate of composite morbidity (adjusted relative risk 0.78, 95% confidence interval 0.65-0.92), and rate of blood loss 1000 mL or greater, use of additional uterotonics, and use of mechanical tamponade. Blood loss and third stage of labor duration were significantly less for immediate SSC. Transfusion rate and hematocrit change after delivery, did not differ. Immediate SSC among term vaginal deliveries was associated with a significant reduction in PPH. Emphasis on early contact for maternal benefit may increase uptake of SSC and provide an accessible intervention for PPH in high- and low-resource settings.

Observer-based adaptive control for fractional-order strict-feedback nonlinear systems with state quantisation and input quantisation

This paper investigates the adaptive control problems of fractional-order strict-feedback nonlinear systems with state quantisation and input quantisation. Both Caputo and Riemann–Liouville derivative definitions are considered. Based on these two definitions, two fractional-order state observers are constructed to smooth the discontinuous quantised states, respectively. Then, new error variables and the dynamic filters are introduced to facilitate the design of the adaptive quantised controller. It is shown that the designed controller is valid for fractional-order strict-feedback nonlinear systems under both Caputo derivative definition and Riemann–Liouville derivative definition. Finally, three examples are given to demonstrate the effectiveness of the proposed method.

What explains monetary policy rate uncertainty? Evidence from the Americas

ABSTRACT Using Consensus Forecasts for 8 American countries, we compute uncertainty regarding outcomes based on the sum of disagreement across forecasters and the error variance of the mean forecaster. Uncertainty for inflation and monetary policy tend to move together. This is consistent with economic forecasters using a Taylor rule to form their expectations and believing that central bankers put a high weight on inflation deviations. We find that monetary policy rate uncertainty in the short term (12 months ahead) is strongly associated with higher uncertainty regarding the inflation rate in the next year. The result is significant across all countries in the medium term. Inflation uncertainty is a much more powerful predictor of monetary policy uncertainty than GDP growth uncertainty in the medium term. Furthermore, the post-pandemic years are the first period in the twenty-first century in which uncertainty for monetary policy and inflation surged across all countries. The post-pandemic period saw a significant reduction in GDP growth uncertainty across all countries (except Argentina), but this was not enough to attenuate the increase in inflation and monetary policy uncertainty.

Determinants of Severe Hypocalcemia After Parathyroidectomy in Patients with End-Stage Kidney Disease and Renal Hyperparathyroidism: A Retrospective Cohort Study.

Background: Parathyroidectomy (PTX) is generally curative in renal hyperparathyroidism (RHPT) that is refractory to medical treatment in end-stage kidney disease (ESKD) patients. Severe hypocalcemia is a common complication of PTX and results in increased monitoring, interventions, lengths of stay, and costs of care. This study aimed to find the determinants and cutoff values of the biochemical determinants, if any, for severe post-operative hypocalcemia after PTX in adult patients with ESKD. Methods: Severe post-operative hypocalcemia was defined as a lowest adjusted serum calcium level < 2 mmol/L during a hospitalization stay following PTX. Receiver operating curves (ROCs) with area under the curve (AUC) values for pre-operative intact parathyroid hormone (iPTH) and pre-operative alkaline phosphatase (ALP) levels against hypocalcemia were used to determine cutoffs. Generalized linear models using Poisson regression with robust error variance were used to estimate the relative risk of severe post-operative hypocalcemia. Results: In total, 75 patients (38 women, 50.7%) with a mean age of 53.8 ± 11.4 years were enrolled; 43 (57%) patients developed severe hypocalcemia post-PTX and had higher pre-operative serum iPTH and ALP levels, as well as a significantly longer hospitalization post-operation (10.5 vs. 4.3 days, p =< 0.001). A pre-operative iPTH level of >166 pmol/L had an AUC-ROC of 0.73 and 72% sensitivity and 73% specificity, respectively, in predicting severe post-operative hypocalcemia with a relative risk of 2.00 [95% CI 1.27-3.33, p = 0.003]. Conclusions: A pre-operative iPTH level > 166 pmol/L is a strong risk predictor for post-operative severe hypocalcemia. Pre-emptive interventions in this high-risk group could potentially result in a reduced length of stay and lower acuity of care.

Character Association and Path Analysis of Garlic (Allium sativum L.) for Yield and Its Attributes

This research sought to determine the associations between bulb yield and other traits and to measure their effects of yield attributes on bulb yield of garlic genotypes. The experiment was laid out in randomized complete block design (RCBD) with three replications using twenty garlic genotypes with one standard check variety (HL) at Kulumsa Agricultural Research center, during 2020-2021. Yield and agronomic traits were determined. The data from two years were analyzed separately because the homogeneity of error variances of both seasons were not similar. In both seasons the association analyses indicated that the bulb yield was positively and significantly associated with plant vigor (0.9762), plant height (0.9057), neck thickness (0.5918), leaf width (0.7527), number of clove per bulb (0.766), cloves weight (0.9621), bulb polar diameter (0.9247), pseudo stem length (0.7885), clove height (0.9161), clove diameter (0.6412). Days to maturity and leaf length were negatively correlated with bulb yield at genotypic and phenotypic level. Consequences of path analyses indicated that bulb polar diameter (1.2478), clove height (1.0911), clove weight (0.9621), plant height (0.9057) and plant vigor (0.9762) had the highest positive direct effect on bulb yield at genotype level in first season and number of clove per bulb (0.9971), leaf width (0.9126), neck thickness (0.9912) in the 20221 season. On the other hand, most of the indirect effects of yield components on bulb yield were significant and positive. Because of the significant direct effects and positive correlations of those traits on bulb yield at phenotypic and genotypic levels, may be observed as important traits for garlic genotypes selection programs.

Test for Change in Error Variance of Multiple Time Series

Changes in levels of multiple time series based on their common components helps characterize the shared behavior of the data generating process with known events causing perturbations in their movements. However, changes in variance of the error structure leads to misspecification of models that complicates statistical inference. Volatility structures may incorporate variance into the time series models, but these can easily lead to overparameterization in multiple time series. A model with inherent changes in variance structure is used to develop a bootstrap-based test for presence of changes in variance of multiple time series. Simulation studies shows that the test is correctly-sized and powerful compared to CUSUM-based test in a wide range of scenarios. The test is advantageous specially in the presence of strong autocorrelations and even in unbalanced data. Using global stock prices with the onset of lockdowns against the COVID19 pandemic as the stimulus of the changepoint, the test was able to detect significant changes in error variance before and during the pandemic. While CUSUM-based test also recognized the significant changes in multiple time series, linear trend was misconstrued as evidence of the presence of change in variance in stock prices.

A recursive filter for a class of two-dimensional nonlinear stochastic systems

&lt;p&gt;A recursive filtering problem on minimum variance is investigated for a type of two-dimensional systems incorporating noise and a random parameter matrix in the measurement equation, along with random nonlinearity. It methodically describes random variables using statistical characteristics, placing a strong emphasis on the application of random multivariate analysis and computational techniques. A bidirectional time-sequence recursive filter is designed to achieve unbiasedness and reduce error variance effectively. This involves deriving the gain matrix through a completion of squares method and solving a complex difference equation with two independent variances. To facilitate the online implementation of this filter, various formulations and an algorithm are proposed. A numerical study demonstrates the effectiveness of the design in practical applications.&lt;/p&gt;

SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering.

A Directed Acyclic Graph (DAG) offers an easy approach to define causal structures among gathered nodes: causal linkages are represented by arrows between the variables, leading from cause to effect. Recently, industry and academics have paid close attention to DAG structure learning from observable data, and many techniques have been put out to address the problem. We provide a two-step approach, named SEMdag(), that can be used to quickly learn high-dimensional linear SEMs. It is included in the R package SEMgraph and employs a two-stage order-based search using previous knowledge (Knowledge-based, KB) or data-driven method (Bottom-up, BU), under the premise that a linear SEM with equal variance error terms is assumed. We evaluated our framework's for finding plausible DAGs against six well-known causal discovery techniques (ARGES, GES, PC, LiNGAM, CAM, NOTEARS). We conducted a series of experiments using observed expression (or RNA-seq) data, taking into account a pair of training and testing datasets for four distinct diseases: Amyotrophic Lateral Sclerosis (ALS), Breast cancer (BRCA), Coronavirus disease (COVID-19) and ST-elevation myocardial infarction (STEMI). The results show that the SEMdag() procedure can recover a graph structure with good disease prediction performance evaluated by a conventional supervised learning algorithm (RF): in the scenario where the initial graph is sparse, the BU approach may be a better choice than the KB one; in the case where the graph is denser, both BU an KB report high performance, with highest score for KB approach based on topological layers. Besides its superior disease predictive performance compared to previous research, SEMdag() offers the user the flexibility to define distinct structure learning algorithms and can handle high dimensional issues with less computing load. SEMdag() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

EXPLORING THE INFLATIONARY AND ECONOMIC EFFECT OF THE ABSENCE OF COINS IN TRADE (PMS) TRANSACTIONS IN NIGERIA: A DATA-DRIVEN APPROACH

Coins are still widely used in financial transactions in various countries, including Nigeria, despite their economic importance. However, in Nigeria, people often do not realize how their attitudes and tastes toward coins' rejection contribute to inflation. This study employed the ARIMA model, the autoregressive (VAR) model, and the random forest machine learning method to analyze the impact of differentials from pump premium motor spirit (PMS) price changes on economic uncertainty and the inflation rate. The ARIMA model forecast showed that the absence of coins increases inflation for ahead periods. The impulse response from the VAR model shows a significant positive effect of the variables that can cause a significant response from the economic policy uncertainty, though with a zero value at the end period. The forecast variance error decomposition of the VAR model shows that the NPP variable, SPP variable, and ZPP variable increasingly contributed to the variations of economic uncertainty, more specifically in the long term. The machine learning method revealed that the ZPP and NPP variables have more importance and contribution in estimating economic uncertainty among study variables in Nigeria, with a small contribution from the SPP variable using the random forest method. The mean square error and node impurity increased the most when the ZPP variable was randomly permuted. The study emphasizes the adverse effect of coins' rejection or absence in the economy and argues that a coin's return can have a significant positive impact on the Nigerian economy by reducing inflation and facilitating financial inclusion.

Distributed strategies for pursuit-evasion of high-order integrators

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

Analyzing the impact of the shadow economy on economic growth in Cambodia

A model of equations known as the VAR model has been employed to investigate the relationships among four endogenous variables: the growth rate of GDP, the shadow economy, the inflation rate, and final consumption expenditure. The initial response of GDP to shocks from the shadow economy is negative for the first three quarters, with the effect gradually decreasing from the fifth to the tenth quarter. In the first and second quarters, there is no observable impact on GDP due to shocks from the inflation rate; however, this shock becomes harmful between the third and fifth quarters before stabilizing and dissipating. The findings from the impulse response function suggest that GDP responds positively to changes in final consumption expenditure during the first two quarters, with the influence waning from the third quarter onward. Over the forecast horizon, the effect of the shadow economy on GDP variation is estimated to range from 3.38% to 6.11%, while the inflation rate's impact fluctuates between 0.06% and 5.05%, and final consumption expenditure contributes to changes between 0.31% and 0.34%. Furthermore, the analysis of forecast error variance decomposition throughout the projected period indicates that GDP variation is considerably influenced by the dynamics of the shadow economy, inflation rate, and consumption expenditure.

Associations between mental health conditions in pregnancy and maternal socioeconomic status: a population-based retrospective cohort study in Ontario, Canada

BackgroundThe World Health Organization has recognized maternal mental illness as an emerging issue. Previous studies have indicated that maternal mental illness is associated with socioeconomic status (SES). However, there is a lack of research concerning the mental health of pregnant people with low SES in Ontario, Canada. In this study, we examined associations between mental health conditions during pregnancy and two SES indicators: the pregnant person’s residential neighbourhood income and education level.MethodsA population-based retrospective cohort study was conducted, consisting of all singleton pregnancies resulting in stillbirths or live births in Ontario hospitals from April 1, 2012, to March 31, 2021. Data were linked from the BORN Information System database, Canadian Institute for Health Information Discharge Database, and Canadian Census. Poisson regression with robust error variance models was performed to estimate the relative risks of anxiety, depression, anxiety and/or depression, or any mental health condition during pregnancy, by SES indicator. We adjusted for maternal age, obesity status in pre-pregnancy, certain pre-existing maternal health conditions, substance use during pregnancy, race, and rural or urban residence.ResultsWithin the cohort (n = 1,202,292), 10.5% (126,076) and 8.1% (97,135) of pregnant individuals experienced anxiety and depression, respectively, and 15.8% (189,616) had at least one mental health condition during pregnancy. The trend test (p < 0.0001) showed a significant downward trend in the total rates of mental health conditions by increasing SES quintiles. Pregnant individuals in the lowest neighbourhood income quintile tended to have a higher risk of anxiety (aRR: 1.24, 95%CI: 1.22–1.27), depression (aRR: 1.56, 95%CI: 1.52–1.59), anxiety and/or depression (aRR: 1.13, 95%CI: 1.11–1.15), or any mental health condition (aRR: 1.18, 95%CI: 1.16–1.19). Similarly, pregnant people living in the lowest education level neighbourhoods had higher likelihoods of anxiety (aRR: 1.66, 95%CI: 1.62–1.69), depression (aRR: 2.09, 95%CI: 2.04–2.14), anxiety and/or depression (aRR: 1.42, 95%CI: 1.39–1.44), and any mental health condition (aRR: 1.41, 95%CI: 1.38–1.43).ConclusionsDespite a universal healthcare system, the variations in mental health prevalence and risk during pregnancy based on SES suggest health inequity in Ontario, Canada. Future studies are needed to examine the mechanisms of this health inequity to guide policy makers in reducing disparities in Ontario.