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 Furthermore, publication bias tests suggested that our results were not influenced by unpublished studies.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.

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.

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.

The current study aimed to investigate the validity and reliability of the Hungarian version of the brief Work-Family Conflict Questionnaire (Conflicto Trabajo – Familia, CCTF) using both homogeneous (social care workers, N = 206) and heterogeneous (N = 586) occupational samples. In order to examine construct validity, we explored both two-factor and bifactor models. Our findings provided greater support for the two-factor model (homogeneous sample: χ2 = 14.032, p = .379, df = 13; CFI = 0.999; NNFI = 0.998; RMSEA = 0.020 [0.000–0.051]; heterogeneous sample: χ2 = 40.213, p < .001 df = 13; CFI = 0.993; NNFI = 0.985; RMSEA = 0.060 [0.023–0.079]). Our results demonstrated good reliability (ω = 0.797–0.911) and predictive validity, which we tested by exploring the relation of the construct with burnout and psychosomatic symptoms. Our results suggest that the Hungarian version of the CCTF is a reliable and valid instrument for measuring both work-to-family and family-to-work conflict.

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.

Abstract. Although clinicians have a long history of using drawings for personality and emotional assessment, the empirical validation of the drawings has been inconsistent. The goal of this study was to examine the validity of the Sixty-Second Drawing Test (SSDT) in predicting close relationships and depression. The sample consisted of 2,883 Hungarian students. The SSDT required participants to draw a series of circles, where the circles represented the self, significant others, different moods, and God. Standardized questionnaires (the Experiences in Close Relationships–Revised and the Children’s Depression Inventory) were also administered. Generally speaking, small distances and relatively smaller self-circles were associated with better relationships. Depression was indicated by drawing large bad-mood circles that were close to one’s self-circle, along with small happiness-circles that were distant from one’s self-circle. The magnitudes of all associations were small to moderate, with explained variances ranging from 7.6% to 21.9%. The results suggest that using drawings of circles to represent important object-relations can, to some extent, predict interpersonal relations and depressive symptoms. Although we do not advocate using the SSDT as a clinical diagnostic measure, it can serve as a useful screening tool for identifying potential relational and affective difficulties.

The COVID-19 pandemic has wreaked havoc around the globe and caused significant disruptions across multiple domains [1] . Moreover, different countries have been differentially impacted by COVID-19—a phenomenon that is due to a multitude of complex and often interacting determinants [2] .

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.

AbstractCan the COVID-19 pandemic be stopped when the principal disseminators −asymptomatic cases− are not easily observable? This question was addressed exploring the cumulative epidemiologic data reported by 51 countries, up to October 2, 2020. In particular, the validity of test positivity and its inverse (the ratio of tests performed per case detected) to indicate whether asymptomatic cases are being detected and isolated –even when only a minor percentage of the population is tested− was evaluated. By linking test positivity data to the number of COVID-19 related deaths reported per million inhabitants, the research question was answered: countries that expressed a high percentage of test positivity (>5%) reported, on average, 15 times more deaths than countries that exhibited <1% test positivity. It is suggested that such a large difference in outcomes is due to the exponential growth that epidemics may experience when silent (asymptomatic) cases are not detected and, consequently, the disease disseminates. Because temporal and geo-referenced data on test positivity may facilitate cost-effective, site-specific testing policies, it is postulated that the risk of uncontrolled epidemics may be ameliorated when test positivity is investigated.

To optimize epidemiologic interventions, predictors of mortality should be identified. The US COVID-19 epidemic data, reported up to 31 March 2020, were analyzed using kernel regularized least squares regression. Six potential predictors of mortality were investigated: (i) the number of diagnostic tests performed in testing week I; (ii) the proportion of all tests conducted during week I of testing; (iii) the cumulative number of (test-positive) cases through 3-31-2020, (iv) the number of tests performed/million citizens; (v) the cumulative number of citizens tested; and (vi) the apparent prevalence rate, defined as the number of cases/million citizens. Two metrics estimated mortality: the number of deaths and the number of deaths/million citizens. While both expressions of mortality were predicted by the case count and the apparent prevalence rate, the number of deaths/million citizens was ≈3.5 times better predicted by the apparent prevalence rate than the number of cases. In eighteen states, early testing/million citizens/population density was inversely associated with the cumulative mortality reported by 31 March, 2020. Findings support the hypothesis that early and massive testing saves lives. Other factors —e.g., population density— may also influence outcomes. To optimize national and local policies, the creation and dissemination of high resolution geo-referenced, epidemic data is recommended.

ObjectivesTo control epidemics, sites more affected by mortality should be identified.MethodsDefining 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.ResultsTwenty 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.ConclusionsThe 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.

Many studies have examined psychological and substance use correlates of e-cigarette use. However, several potentially important personality and substance use variables have yet to be considered. In an effort to remedy this omission, we studied the associations among e-cigarette use, personality, and substance use in a sample of 380 college students. All associations were examined for both weekday and weekend e-cigarette use. The mean age of the sample was 19.1 with a standard deviation of 1.7. Regarding current e-cigarette use, 11.8% of participants reported using e-cigarettes on weekdays and 13.9% reported weekend use. The variables most strongly associated with being an e-cigarette user versus non-user were amount of tobacco cigarette smoking, being male, taking a prescribed stimulant medication for a diagnosed medical condition, and low levels of forgiveness toward others. Two of the variables (taking a prescribed stimulant medication and low forgiveness) are novel predictors that appear to have not been previously examined. Implications of the results for understanding e-cigarette use are discussed and directions for future research are considered.

Background: To control epidemics, decision-makers need information in real time and clinicians require data on locations more affected by mortality. To that end, an adaptation of Network Theory was applied to identify epidemic nodes, i.e., areas that included most fatalities per unit of time as well as explicit connections, such as highways.Methods: Geo-temporal Chinese data on the COVID-19 epidemic were investigated with six (linear, logarithmic, power, growth, exponential, and logistic) regression models. A z-test compared the slopes observed.Findings: This epidemic was located within a triple (road, railroad, and air) network of networks. Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed four network properties (synchronicity, long-distance connections, directionality and assortativity), which helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Later, rank II and III nodes reported fewer deaths, in that order. While the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The time series correlated with the mortality series. The power curve was the best fitting model for all slopes. Z-tests compared pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes, yielding z values ranging between 4.73 and 11.88. Because the z-critical value for alpha=0.001 in a two-tailed test was 3.30, all these comparisons were statistically significantly different. Therefore, rank I-III epidemic nodes – those reporting secondary deaths − were geo-temporally and statistically distinguishable.Interpretation: The geo-temporal progression of epidemics – not where and when they start−seems to be highly structured. Epidemic network properties can detect and distinguish regions that differ in mortality. Because geo-referenced analyses can be conducted in less than ten minutes, they may provide real time information to both decision-makers and clinicians.Funding Statement: FOF was funded through the support provided to the Food and Agriculture Organization of the United Nations by the United States Agency for International Development (USAID) – OSRO/GLO/507/USA on Global Health Security Agenda for the control of zoonosis in Africa.Declaration of Interests: All authors declare no competing interests. ALR is a co-inventor of US patent 10,429,389 and European Union patent 2,959 295, which are not related to this topic.

Summary Background To stop pandemics, such as COVID-19, infected individuals should be detected, treated if needed, and –to prevent contacts with susceptible individuals-isolated. Because most infected individuals may be asymptomatic, when testing misses such cases, epidemics may growth exponentially, inducing a high number of deaths. In contrast, a relatively low number of COVID-19 related deaths may occur when both symptomatic and asymptomatic cases are tested. Methods To evaluate these hypotheses, a method composed of three elements was evaluated, which included: (i) county- and country-level geo-referenced data, (ii) cost-benefit related considerations, and (iii) temporal data on mortality or test positivity (TP). TP is the percentage of infections found among tested individuals. Temporal TP data were compared to the tests/case ratio (T/C ratio) as well as the number of tests performed/million inhabitants (tests/mi) and COVID-19 related deaths/million inhabitants (deaths/mi). Findings Two temporal TP profiles were distinguished, which, early, displayed low (∼ 1 %) and/or decreasing TP percentages or the opposite pattern, respectively. Countries that exhibited >10 TP % expressed at least ten times more COVID-19 related deaths/mi than low TP countries. An intermediate pattern was identified when the T/C ratio was explored. Geo-referenced, TP-based analysis discovered municipalities where selective testing would be more cost-effective than alternatives. Interpretations When TP is low and/or the T/C ratio is high, testing detects asymptomatic cases and the number of COVID-19 related deaths/mi is low. Geo-referenced TP data can support cost-effective, site-specific policies. TP promotes the prompt cessation of epidemics and fosters science-based testing policies. Funding None Research in context Evidence before this study To map this field, bibliographic searches were conducted in the Web of Science , which included the following results: (i) COVID-19 (95,133 hits), (ii) SARS COV-2 (33,680 hits), (iii) testing policy and COVID-19 (939 hits), (iv) testing policy and SARS COV-2 (340 hits), (v) testing policy and COVID-19 and asymptomatic (80 hits), (vi) testing policy and SARS COV-2 and asymptomatic (54 hits); (vii) test positivity and COVID-19 and validation (7 hits), and (viii) test positivity and SARS CoV-2 and validation (5 hits). Therefore, before this study, testing policy in relation to asymptomatic cases as well as test positivity represented a very low proportion (between ∼1 thousandth to ∼ 1 ten thousandth) of all publications. While many articles distinguished between diagnostic and screening tests, no paper was found in which testing policy is mentioned as part of a process ultimately designed to isolate all infected individuals. The few articles that mentioned test positivity only investigated symptomatic cases. These quanti/qualitative assessments led the authors to infer that neither testing policy nor test positivity had been adequately validated and/or investigated. Added value of this study We provide the first validation of test positivity as an estimate of disease prevalence under rapidly changing conditions: in pandemics, disease prevalence may vary markedly within short periods of time. We also address a double limitation of control campaigns against COVID-19, namely: it is unknown who and where to test. Asymptomatic cases are not likely to seek medical assistance: while they feel well, they silently spread this pandemic. Because they represent approximately half of all infected individuals, they are a large, moving, and invisible target. Where to find them is also unknown because (i) randomized testing is likely to fail and (ii) testing is very limited. Usually, the locations where infected people reside are not randomly distributed but geographically clustered, and, up to now less than four persons per thousand inhabitants are tested on a given day. However, by combining geo-referenced test positivity data with cost-benefit considerations, we generate approaches not only likely to induce high benefits without increasing costs but also free of assumptions: we measure bio-geography as it is. Implications of all the available evidence The fact that asymptomatic cases were not tested in many countries may explain the exponential growth and much higher number of deaths observed in those countries. Ineffective testing (and, therefore, ineffective isolation) can also result from the absence of geo-referenced data analysis. Because the geographical location where people reside, work, study, or shop is not a random event, the analysis of small greographical areas is essential. Only when actual geographical relationships are observed, optimal (cost-benefit oriented) testing policies can be devised.

Ezekiel's adjusted R-2 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 R-2 relative to (a) the sample R-2 statistic, and (b) the sample R-2 minus the expected value of R-2. 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 (rho(2) = 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 R-2 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 R-2. 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 R-2 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 R-2 has desirable properties and, in addition, these findings bring needed clarity to the statistical literature on Ezekiel's classic estimator.

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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.

Mindfulness allows an individual to reside in a state of nonjudgmental conscious awareness. In this state, individuals are able to make deliberate choices about their thoughts and emotions and in doing so, select more optimal experiences for themselves. In the present study, we predicted individuals who are more mindful are able to purposely attend to the environment, and as a result, would be more likely to notice and be grateful for the positive aspects of life that might otherwise go unappreciated. Given the strong links in the literature between mindfulness and well-being, we also examined whether gratitude might serve as a mediator in the relationship between mindfulness and mood. Additionally, because gratitude is believed to strengthen ties to others, we also tested whether gratitude and perceived social support might serve as mediators in a multi-mediated model. Specifically, it was predicted that mindfulness would contribute to the expression of heightened gratitude which, in turn, would influence a heightened sense of perceived support. This heightened sense of support from others was then predicted to enhance feelings of positive mood states and decrease feelings of negative mood states. Participants (N=700) completed a survey that assessed mindfulness, gratitude, perceived support, and mood. Findings showed a significant association between mindfulness and gratitude. Mediational analyses also showed that both gratitude and perceived support served to mediate the relationship between mindfulness and positive and negative mood. However, in the case of negative mood, our hypothesized model did not provide the best fit to the data. The implications of these findings are discussed with regard to the role mindfulness, gratitude and perceived support play in the promotion of positive and negative mood states.