Meta-analysis of the association between gratitude and loneliness.

Gratitude is a positive social emotion that involves recognizing that others have brought benefits into one's life. Loneliness, on the other hand, is an unpleasant emotion resulting from a perceived lack of social connectedness. Although previous studies have reported an inverse association between gratitude and loneliness, these studies have not been systematically examined in a single review. To address this gap in the literature, we conducted a random-effects meta-analysis to examine the association between gratitude and loneliness. Analysis of 26 studies revealed a moderate sized effect (mean Fisher's z transformed correlation, zr = -.406, 95% confidence interval [CI] = -.463, -.349; mean back-transformed correlation, r = -.385, 95% CI = -.433, -.335). To complement these effect sizes, we calculated a probability-based common language effect size for correlations. Random-effects homogeneity testing suggested the presence of effect size heterogeneity. Analyses of both continuous and categorical moderators were non-significant, indicating that these variables did not influence effect size magnitude. Furthermore, publication bias tests suggested that our results were not influenced by unpublished studies. Finally, we proposed several statistical and clinical recommendations for future research. Regarding the latter, we offered suggestions for modifying gratitude enhancement programs with the aim of reducing loneliness.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections.

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Gratitude and social support mediate the association between mindfulness and mood: A cross-cultural replication study.

Swickert and colleagues surveyed young adults in the United States and found that gratitude and social support mediated the association between mindfulness and mood (both positive and negative affect). This study attempted to replicate Swickert et al.'s mediational findings using a young adult Hungarian sample. Results indicated that with one exception, the mediational findings were replicated. The exception was that for the Hungarians, gratitude did not mediate the association between mindfulness and negative affect. Overall, these findings indicate that the mediational effects of gratitude and social support are quite similar for individuals living in the United States and Hungary.

Test positivity - Evaluation of a new metric to assess epidemic dispersal mediated by non-symptomatic cases.

Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay. Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = -0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception -a country where isolation was not pursued-, all countries showed a negative correlation between TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.

Statistical methods for comparing test positivity rates between countries: Which method should be used and why?

The test positivity (TP) rate has emerged as an important metric for gauging the illness burden due to COVID-19. Given the importance of COVID-19 TP rates for understanding COVID-related morbidity, researchers and clinicians have become increasingly interested in comparing TP rates across countries. The statistical methods for performing such comparisons fall into two general categories: frequentist tests and Bayesian methods. Using data from Our World in Data (ourworldindata.org), we performed comparisons for two prototypical yet disparate pairs of countries: Bolivia versus the United States (large vs. small-to-moderate TP rates), and South Korea vs. Uruguay (two very small TP rates of similar magnitude). Three different statistical procedures were used: two frequentist tests (an asymptotic z-test and the 'N-1' chi-square test), and a Bayesian method for comparing two proportions (TP rates are proportions). Results indicated that for the case of large vs. small-to-moderate TP rates (Bolivia versus the United States), the frequentist and Bayesian approaches both indicated that the two rates were substantially different. When the TP rates were very small and of similar magnitude (values of 0.009 and 0.007 for South Korea and Uruguay, respectively), the frequentist tests indicated a highly significant contrast, despite the apparent trivial amount by which the two rates differ. The Bayesian method, in comparison, suggested that the TP rates were practically equivalent-a finding that seems more consistent with the observed data. When TP rates are highly similar in magnitude, frequentist tests can lead to erroneous interpretations. A Bayesian approach, on the other hand, can help ensure more accurate inferences and thereby avoid potential decision errors that could lead to costly public health and policy-related consequences.

Early network properties of the COVID-19 pandemic - The Chinese scenario.

OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.

Ezekiel's classic estimator of the population squared multiple correlation coefficient: Monte Carlo-based extensions and refinements.

Ezekiel's adjusted R2 is widely used in linear regression analysis. The present study examined the statistical properties of Ezekiel's measure through a series of Monte Carlo simulations. Specifically, we examined the bias and root mean squared error (RMSE) of Ezekiel's adjusted R2 relative to (a) the sample R2 statistic, and (b) the sample R2 minus the expected value of R2. Simulation design factors consisted of sample sizes (N = 50, 100, 200, 400), number of predictors (2, 3, 4, 5, 6), and population squared multiple correlations (ρ2 = 0, .10, .25, .40, .60). Factorially crossing these design factors resulted in 100 simulation conditions. All populations were normal/Gaussian, and for each condition, we drew 10,000 Monte Carlo samples. Regarding systematic variation (bias), results indicated that with few exceptions, Ezekiel's adjusted R2 demonstrated the lowest bias. Regarding unsystematic variation (RMSE), the performance of Ezekiel's measure was comparable to the other statistics, suggesting that the bias-variance tradeoff is minimal for Ezekiel's adjusted R2. Additional findings indicated that sample size-to-predictor ratios of 66.67 and greater were associated with low bias and that ratios of this magnitude were accompanied by large sample sizes (N = 200 and 400), thus suggesting that researchers using Ezekiel's adjusted R2 should aim for sample sizes of 200 or greater in order to minimize bias when estimating the population squared multiple correlation coefficient. Overall, these findings indicate that Ezekiel's adjusted R2 has desirable properties and, in addition, these findings bring needed clarity to the statistical literature on Ezekiel's classic estimator.

Association of Infant Temperament With Subsequent Obesity in Young Children of Mothers With Gestational Diabetes Mellitus.

Importance: Infant temperament is associated with excess weight gain or childhood obesity risk in samples of healthy individuals, although the evidence has been inconsistent. To our knowledge, no prior research has examined this topic among children exposed to gestational diabetes mellitus (GDM) in utero. Objective: To prospectively evaluate infant temperament in association with overweight and obesity status at ages 2 to 5 years among children born to mothers who experienced GDM. Design, Setting, and Participants: This prospective cohort study took place at Kaiser Permanente Northern California medical centers. We studied singleton infants delivered at 35 weeks' gestational age or later to mothers who had been diagnosed with GDM. Data were collected from 2009 to 2016, and data analysis occurred from June 2017 to October 2018. Exposures: The primary exposures in the child's first year were soothability, distress to limitations, and activity aspects of temperament, as assessed by a validated questionnaire. Modifiable covariates in the child's first year included breastfeeding intensity and duration monthly ratio scores, along with the timing of the introduction of sugary beverages and complementary foods. Main Outcomes and Measures: The primary outcome was child overweight and obesity status, assessed at ages 2 to 5 years. Multinomial logistic regression models estimated adjusted odds ratios and 95% CIs for infants whose temperaments were measured at 6 to 9 weeks of age and categorized as elevated (≥75th percentile) or not elevated in the 3 domains. We controlled for nonmodifiable and modifiable covariates across models. Results: A total of 382 mother-infant pairs participted, including 130 infants (34.0%) who were non-Hispanic white, 126 infants (33.0%) who were Hispanic, 96 infants (25.1%) who were Asian, 26 infants (6.8%) who were non-Hispanic black, and 4 infants (1.1%) who were of other races/ethnicities. In descriptive analyses, elevated infant soothability and activity temperaments were associated with the early introduction of 100% fruit juice and/or sugar-sweetened beverages (at ages <6 months) and shorter breastfeeding duration (from 0 to <3 months), while elevated distress to limitations was associated with early introduction of complementary foods (at ages <4 months). Elevated soothability consistently was associated with a higher odds of later childhood obesity, with adjusted odds ratios across models ranging from 2.22 (95% CI, 1.04-4.73) to 2.54 (95% CI, 1.28-5.03). Greater breastfeeding intensity and duration (12-month combined) score was associated with lower odds of obesity, independent of infant temperament and other covariates. Conclusions and Relevance: Among this high-risk population of infants, elevated soothability was associated with early childhood obesity risk, perhaps in part because caregivers use sugary drinks to assuage infants. Soothability temperament may be a novel screening target for early obesity prevention interventions involving responsive feeding and emotion regulation.

Confidence intervals for correlations when data are not normal.

With nonnormal data, the typical confidence interval of the correlation (Fisher z') may be inaccurate. The literature has been unclear as to which of several alternative methods should be used instead, and how extreme a violation of normality is needed to justify an alternative. Through Monte Carlo simulation, 11 confidence interval methods were compared, including Fisher z', two Spearman rank-order methods, the Box-Cox transformation, rank-based inverse normal (RIN) transformation, and various bootstrap methods. Nonnormality often distorted the Fisher z' confidence interval-for example, leading to a 95 % confidence interval that had actual coverage as low as 68 %. Increasing the sample size sometimes worsened this problem. Inaccurate Fisher z' intervals could be predicted by a sample kurtosis of at least 2, an absolute sample skewness of at least 1, or significant violations of normality hypothesis tests. Only the Spearman rank-order and RIN transformation methods were universally robust to nonnormality. Among the bootstrap methods, an observed imposed bootstrap came closest to accurate coverage, though it often resulted in an overly long interval. The results suggest that sample nonnormality can justify avoidance of the Fisher z' interval in favor of a more robust alternative. R code for the relevant methods is provided in supplementary materials.

Reduced Parasite Burden in Children with Falciparum Malaria and Bacteremia Coinfections: Role of Mediators of Inflammation.

Bacteremia and malaria coinfection is a common and life-threatening condition in children residing in sub-Saharan Africa. We previously showed that coinfection with Gram negative (G[-]) enteric Bacilli and Plasmodium falciparum (Pf[+]) was associated with reduced high-density parasitemia (HDP, >10,000 parasites/µL), enhanced respiratory distress, and severe anemia. Since inflammatory mediators are largely unexplored in such coinfections, circulating cytokines were determined in four groups of children (n = 206, aged <3 yrs): healthy; Pf[+] alone; G[-] coinfected; and G[+] coinfected. Staphylococcus aureus and non-Typhi Salmonella were the most frequently isolated G[+] and G[-] organisms, respectively. Coinfected children, particularly those with G[-] pathogens, had lower parasite burden (peripheral and geometric mean parasitemia and HDP). In addition, both coinfected groups had increased IL-4, IL-5, IL-7, IL-12, IL-15, IL-17, IFN-γ, and IFN-α and decreased TNF-α relative to malaria alone. Children with G[-] coinfection had higher IL-1ß and IL-1Ra and lower IL-10 than the Pf[+] group and higher IFN-γ than the G[+] group. To determine how the immune response to malaria regulates parasitemia, cytokine production was investigated with a multiple mediation model. Cytokines with the greatest mediational impact on parasitemia were IL-4, IL-10, IL-12, and IFN-γ. Results here suggest that enhanced immune activation, especially in G[-] coinfected children, acts to reduce malaria parasite burden.

Meta-analysis of the association between methamphetamine use and high-risk sexual behavior among heterosexuals.

A large body of research has found that nonheterosexual methamphetamine users engage in substantially higher levels of risky sex compared to nonusers. Considerably fewer studies have examined methamphetamine use and high-risk sex among heterosexuals. The present study is a meta-analysis of the empirical literature on methamphetamine use and high-risk sexual behavior among heterosexual individuals. Four risky sex outcomes were examined: unprotected vaginal intercourse, unprotected anal sex, inconsistent condom use, and sex with multiple partners. Analysis of 24 studies (26 independent samples) including 286,781 individuals found that the pooled mean weighted odds ratios ranged from 1.37 (unprotected vaginal intercourse) to 1.72 (inconsistent condom use), indicating that the odds of engaging in risky sex for heterosexual methamphetamine users is, on average, between 37% and 72% greater than for nonmethamphetamine users. Date of publication, percentage of White Caucasian respondents, and sample size were significant moderators of effect size magnitude. Moreover, symmetry plots revealed little direct evidence for publication bias. It is recommended that future research explore additional categorical and continuous variables as potential moderators of effect size strength. (PsycINFO Database Record

Infant emotional distress, maternal restriction at a home meal, and child BMI gain through age 6years in the Colorado Adoption Project.

Infant temperament and parental feeding practices may be risk factors for childhood obesity, however most studies have relied upon parent-report assessments. We tested whether infant emotional distress and maternal restrictive feeding at 12-months of age, assessed observationally at a home feeding interaction, predicted child BMI through age 6years. We conducted a prospective observational study of 86 children (34 girls and 52 boys, from 55 adoptive and 31 non-adoptive families) enrolled in the Colorado Adoption Project. Mother-infant feeding interactions were video-recorded during a home snack or meal at year 1, and child anthropometrics (length or height, and weight) were assessed at years 1 through 6. The main outcome measures were child weight-for-length at year 1 and body mass index (BMI: kg/m(2)) at years 2-6. Results of generalized linear models indicated that greater infant emotional distress at 12-months predicted greater increases in child weight status through age 6years, B=0.62 and odds ratio (OR)=1.87. In separate analyses, restrictive feeding interacted with child sex in predicting weight status trajectories (p=.012). Male infants whose mothers displayed any compared to no restriction at year 1 showed a downward BMI trajectory from 2 to 6years; for female infants, exposure to any compared to no restriction prompts predicted increasing BMI from 4 to 6years. In sum, early obesity prevention strategies should pay greater attention to infant temperament, especially distress and negative affect, and how parents respond to such cues. Additionally, 'responsive feeding' strategies that provide an alternative to restriction warrant greater research during infancy.

Sensation seeking indirectly affects perceptions of risk for co-occurrent substance use.

High sensation seekers engage in more frequent substance use and perceive a host of potentially dangerous activities as less risky than do low sensation seekers. However, despite a plethora of research on these topics, no study has examined the extent to which personal substance use mediates the association between sensation seeking and perceived risk of substance use. To address this question, we recruited a sample of 79 young adults (mean age=19.1 years, standard deviation=1.4). Participants completed questionnaire measures of sensation seeking, substance use, and perceived risk of co-occurrent substance use. Results from path-analytic modeling indicated that both alcohol use and marijuana use mediated the influence of sensation seeking on perceptions of risk for moderately risky, but not highly risky, pairs of substances. Strengths and limitations of the present study were discussed and directions for future research were suggested.

Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality.

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

Perceived risk of co-occurrent substance abuse.

A strong negative correlation between perceptions of risk for co-occurrent substance abuse and the variability associated with those risk perceptions was found in two independent samples.

Suppressed circulating bicyclo-PGE2 levels and leukocyte COX-2 transcripts in children co-infected with P. falciparum malaria and HIV-1 or bacteremia.

In holoendemic Plasmodium falciparum transmission regions, malarial anemia is a leading cause of childhood morbidity and mortality. Identifying biomarkers of malaria disease severity is important for identifying at-risk groups and for improved understanding of the molecular pathways that influence clinical outcomes. We have previously shown that decreased cyclooxygenase (COX)-2-derived prostaglandin E2 (PGE2) levels are associated with enhanced clinical severity in cerebral malaria, malarial anemia, and malaria during pregnancy. Since children with malaria often have increased incidence of additional infections, such as bacteremia and HIV-1, we extend our previous findings by investigating COX-2 and PGE2 in children with falciparum malaria and co-infection with either bacteremia or HIV-1. Plasma bicyclo-PGE2/creatinine levels and peripheral blood COX-2 transcripts were significantly reduced in co-infected children relative to those with malaria mono-infection. Furthermore, suppression of circulating bicyclo-PGE2 was significantly associated with reduced hemoglobin levels in both mono- and co-infected children with malaria, suggesting that bicyclo-PGE2 may represent both a marker and mediator of malaria pathogenesis.

Feedback-based, system-level properties of vertebrate-microbial interactions.

BACKGROUND: Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. METHODS: To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. RESULTS: In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D-, or microbial-negative) groups: D+ and D- data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D- data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. CONCLUSIONS: More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.