Association of Antenatal Care Visit with Utilization of Institutional Delivery Care Services in Afghanistan: Intersections Between Education, Wealth, and Household Decision-Making Autonomy

Background: The importance of antenatal visits in safe motherhood and childbirth is well documented. However, less is known how social determinants of health interact with antenatal care (ANC) visits in shaping the uptake of professional delivery care services in low-income countries. This study examines the association of ANC visits with institutional delivery care utilization outcomes in Afghanistan. Further, we assess the extent to which ANC visits intersect with education, wealth, and household decision-making autonomy in predicting two outcomes of delivery care utilization- delivery at a health facility and delivery assisted by a skilled birth attendant.Methods: We used data from the Afghanistan Demographic and Health Survey (AfDHS) 2015. The analytic sample included 15,581 women of reproductive age (15-49). We assessed the associations using logistic regression models, estimated the predicted probability of delivery care outcomes using statistical interactions, and presented estimates in margins plot. Results: Regression analyses adjusted for socioeconomic and demographic covariates suggest that women who had 4 or more ANC visits were 5.7 times (95% CI= 4.78, 7.11, P<0.001) more likely to use delivery care at a health facility and 6.5 times (95% CI= 5.23, 8.03; P<0.001) more likely to have a delivery assisted by a skilled birth attendant compared to women who had no ANC visit. Estimates from models with statistical interactions between ANC, education, wealth, and decision-making autonomy suggest that women with higher social status were more advantageous in utilizing institutional delivery care services compared to women with lower levels of social status. Conclusion: Our findings suggest that the association of ANC visit with institutional delivery care services is stronger among women with higher social status. The results have implications for promoting safe motherhood and childbirth through improving women’s social status.

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

Lessons and tips for designing a machine learning study using EHR data

Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.

Psychopathy as an Emergent Interpersonal Syndrome: What Is the Function of Fearlessness?

Lilienfeld and colleagues (this issue) propose that some personality disorders can be conceptualized as emergent interpersonal syndromes (EIS). An EIS elicits negative interpersonal reactions in others. Further, an EIS results from statistical interactions between symptom dimensions that are uncorrelated. As a prototypical EIS, psychopathy is an interaction between boldness (or fearlessness) and interpersonal antagonism. The authors marshal many threads of research to develop an intriguing idea that suggests the “whole” of psychopathy is more than the sum of its parts. Unfortunately, the authors focus primarily on psychopathy, and fail to provide convincing quantitative data for the statistical interaction that forms the basis for their theory. Also missing from this model of personality pathology is a consideration of what function boldness serves; viewing boldness as a means to accomplish the (maladaptive) rewarding goals that motivate the individual high in antagonism and disinhibition may serve to flesh out this theory and our conceptualization of personality pathology more broadly.

Personality Disorders as Emergent Interpersonal Syndromes: Psychopathic Personality as a Case Example

Personality disorders have long been bedeviled by a host of conceptual and methodological quandaries. Starting from the assumption that personality disorders are inherently interpersonal conditions that reflect folk concepts of social impairment, the authors contend that a subset of personality disorders, rather than traditional syndromes, are emergent interpersonal syndromes (EISs): interpersonally malignant configurations (statistical interactions) of distinct personality dimensions that may be only modestly, weakly, or even negatively correlated. Preliminary support for this perspective derives from a surprising source, namely, largely forgotten research on the intercorrelations among the subscales of select MMPI/MMPI-2 clinical scales. Using psychopathic personality as a case example, the authors offer provisional evidence for the EIS hypothesis from four lines of research and delineate its implications for personality disorder theory, research, and classification. Conceptualizing some personality disorders as EISs elucidates long-standing quandaries and controversies in the psychopathology literature and affords fruitful avenues for future investigation.

Topological Information Data Analysis

This paper presents methods that quantify the structure of statistical interactions within a given data set, and were applied in a previous article. It establishes new results on the k-multivariate mutual-information ( I k ) inspired by the topological formulation of Information introduced in a serie of studies. In particular, we show that the vanishing of all I k for 2 ≤ k ≤ n of n random variables is equivalent to their statistical independence. Pursuing the work of Hu Kuo Ting and Te Sun Han, we show that information functions provide co-ordinates for binary variables, and that they are analytically independent from the probability simplex for any set of finite variables. The maximal positive I k identifies the variables that co-vary the most in the population, whereas the minimal negative I k identifies synergistic clusters and the variables that differentiate–segregate the most in the population. Finite data size effects and estimation biases severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and the k-dependences. We give an example of application of these methods to genetic expression and unsupervised cell-type classification. The methods unravel biologically relevant subtypes, with a sample size of 41 genes and with few errors. It establishes generic basic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism. We propose that higher-order statistical interactions and non-identically distributed variables are constitutive characteristics of biological systems that should be estimated in order to unravel their significant statistical structure and diversity. The topological information data analysis presented here allows for precisely estimating this higher-order structure characteristic of biological systems.

Specks of Dirt and Tons of Pain: Dosage Distinguishes Impurity From Harm

Levels of moral condemnation often vary with outcome severity (e.g., extreme destruction is morally worse than moderate damage), but this is not always true. We investigated whether judgments of purity transgressions are more or less sensitive to variation in dosage than judgments of harm transgressions. In three studies, adults ( N = 426) made moral evaluations of harm and purity transgressions that systematically varied in dosage (frequency or magnitude). Pairs of low-dosage and high-dosage transgressions were presented such that the same sets of modifiers (e.g., “occasionally” vs. “regularly,” “small” vs. “large”) or amounts (e.g., “millimeter” vs. “centimeter”) were reused across moral domains. Statistical interactions between domain and dosage indicated robust distinctions between the perceived wrongness of high-dosage and low-dosage harms, whereas moral evaluations of impure acts were considerably less influenced by dosage. Our findings support the existence of a cognitive distinction between purity-based and harm-based morals and challenge current wisdom regarding relationships between intentions and outcomes in moral judgment.

Do Psychopathic Traits Statistically Protect Against PTSD? A Retrospective Study of Vietnam Veterans

Although psychopathy traits are traditionally associated with maladaptivity, certain traits may statistically buffer against risk for posttraumatic stress disorder (PTSD). Research suggests that psychopathy traits are differentially associated with PTSD, as boldness traits are negatively related to PTSD whereas disinhibition features are positively related. The authors sought to clarify the relations between psychopathy and PTSD in a large sample of Vietnam veterans (N = 2,598) and to examine the statistical interactions among (a) psychopathy traits and (b) combat exposure and psychopathy traits in predicting PTSD. Results indicate that psychopathy traits are differentially associated with PTSD in combat-exposed veterans, although the authors found little evidence that boldness was protective against PTSD. Nonetheless, meanness was significantly, albeit weakly, protective against PTSD in the presence of combat exposure. The authors consider the implications of these findings for future research, including the need to consider fearlessness as a heterogeneous construct, and they examine whether the findings generalize to PTSD in DSM-5.