Emotions as infectious diseases in a large social network: the SISa model

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.     

Exploiting Social Networks to Mitigate the Obesity Epidemic

Despite significant efforts, obesity continues to be a major public health problem, and there are surprisingly few effective strategies for its prevention and treatment. We now realize that healthy diet and activity patterns are difficult to maintain in the current physical environment. Recently, it was suggested that the social environment also contributes to obesity. Therefore, using network‐based interaction models, we simulate how obesity spreads along social networks and predict the effectiveness of large‐scale weight management interventions. For a wide variety of conditions and networks, we show that individuals with similar BMIs will cluster together into groups, and if left unchecked, current social forces will drive these groups toward increasing obesity. Our simulations show that many traditional weight management interventions fail because they target overweight and obese individuals without consideration of their surrounding cluster and wider social network. The popular strategy for dieting with friends is shown to be an ineffective long‐term weight loss strategy, whereas dieting with friends of friends can be somewhat more effective by forcing a shift in cluster boundaries. Fortunately, our simulations also show that interventions targeting well‐connected and/or normal weight individuals at the edges of a cluster may quickly halt the spread of obesity. Furthermore, by changing social forces and altering the behavior of a small but random assortment of both obese and normal weight individuals, highly effective network‐driven strategies can reverse current trends and return large segments of the population to a healthier weight.     

The Effects of Media Reports on Disease Spread and Important Public Health Measurements

Controlling the spread of influenza to reduce the effects of infection on a population is an important mandate of public health. Mass media reports on an epidemic or pandemic can provide important information to the public, and in turn, can induce positive healthy behaviour practices (i.e., handwashing, social distancing) in the individuals, that will reduce the probability of contracting the disease. Mass media fatigue, however, can dampen these effects. Mathematical models can be used to study the effects of mass media reports on epidemic/pandemic outcomes. In this study we employ a stochastic agent based model to provide a quantification of mass media reports on the variability in important public health measurements. We also include mass media report data compiled by the Global Public Health Intelligence Network, to study the effects of mass media reports in the 2009 H1N1 pandemic. We find that the report rate and the rate at which individuals relax their healthy behaviours (media fatigue) greatly affect the variability in important public health measurements. When the mass media reporting data is included in the model, two peaks of infection result.     

Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans

Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of one's relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health.     

Postpartum depression: Etiology,treatment and consequences for maternal care

This article is part of a Special Issue “Parental Care”. Pregnancy and postpartum are associated with dramatic alterations in steroid and peptide hormones which alter the mothers' hypothalamic pituitary adrenal (HPA) and hypothalamic pituitary gonadal (HPG) axes. Dysregulations in these endocrine axes are related to mood disorders and as such it should not come as a major surprise that pregnancy and the postpartum period can have profound effects on maternal mood. Indeed, pregnancy and postpartum are associated with an increased risk for developing depressive symptoms in women. Postpartum depression affects approximately 10–15% of women and impairs mother–infant interactions that in turn are important for child development. Maternal attachment, sensitivity and parenting style are essential for a healthy maturation of an infant's social, cognitive and behavioral skills and depressed mothers often display less attachment, sensitivity and more harsh or disrupted parenting behaviors, which may contribute to reports of adverse child outcomes in children of depressed mothers. Here we review, in honor of the “father of motherhood”, Jay Rosenblatt, the literature on postnatal depression in the mother and its effect on mother–infant interactions. We will cover clinical and pre-clinical findings highlighting putative neurobiological mechanisms underlying postpartum depression and how they relate to maternal behaviors and infant outcome. We also review animal models that investigate the neurobiology of maternal mood and disrupted maternal care. In particular, we discuss the implications of endogenous and exogenous manipulations of glucocorticoids on maternal care and mood. Lastly we discuss interventions during gestation and postpartum that may improve maternal symptoms and behavior and thus may alter developmental outcome of the offspring.     

The influence of body weight on social network ties among adolescents

Evidence of negative stereotypes, prejudice and discrimination towards obese individuals has been widely documented. However, the effect of a larger body size on social network ties or friendship formations is less well understood. In this paper, we explore the extent to which higher body weight results in social marginalization of adolescents. Using data from a nationally representative sample of adolescents, we estimate endogeneity-corrected models including school-level fixed effects that account for bi-directionality and unobserved confounders to ascertain the effect of body weight on social network ties. We find that obese adolescents have fewer friends and are less socially integrated than their non-obese counterparts. We also find that such penalties in friendship networks are present among whites but not African-Americans or Hispanics, with the largest effect among white females. These results are robust to common environmental influences at the school-level and to controls for preferences, risk attitudes, low self-esteem and objective measures of physical attractiveness.     

Visiting parents in times of COVID-19: The impact of parent-adult child contacts on the psychological health of the elderly

Using the 8th wave of the SHARE and the SHARE Corona Survey, we investigated whether the disruption of parent–adult child contacts due to social distancing restrictions increased the symptoms of depression among old age individuals during the first wave of the COVID-19 pandemic. We model the relationship between the disruption of parent–adult child contacts and the mental health of the elderly using a recursive simultaneous equation model for binary variables. Our findings show that the likelihood of disruption of parent–adult child contacts was higher with adult children who do not live with or close to their parents (i.e., in the same household or in the same building) for whom contact disruption increases about 15 %. The duration of restrictions to movement and lockdowns also has a positive and significant effect on parent-child contact disruption: an additional week of lockdown significantly increases the probability of parent-child contact disruption, by about 1.5 %. The interventions deemed essential to reduce the spread of the pandemic, such as the “stay-at-home” order, necessarily disrupted personal parent–child contacts and the social processes that facilitate psychological well-being, increasing the probability of suffering from a deepening depressed mood by about 17 % for elderly parents.     

Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies

We address estimation of the marginal effect of a time‐varying binary treatment on a continuous longitudinal outcome in the context of observational studies using electronic health records, when the relationship of interest is confounded, mediated, and further distorted by an informative visit process. We allow the longitudinal outcome to be recorded only sporadically and assume that its monitoring timing is informed by patients' characteristics. We propose two novel estimators based on linear models for the mean outcome that incorporate an adjustment for confounding and informative monitoring process through generalized inverse probability of treatment weights and a proportional intensity model, respectively. We allow for a flexible modeling of the intercept function as a function of time. Our estimators have closed‐form solutions, and their asymptotic distributions can be derived. Extensive simulation studies show that both estimators outperform standard methods such as the ordinary least squares estimator or estimators that only account for informative monitoring or confounders. We illustrate our methods using data from the Add Health study, assessing the effect of depressive mood on weight in adolescents.     

An individual-based model of hepatitis A transmission

Viral hepatitis A, as other endemic diseases, represents a public health priority worldwide. To study long-time scale human pathogens through individual-based simulations, the development of a dynamic network of contacts is required. In this work, we introduce an individual-based model accounting for the birth and death of the individuals, the generation of new households, and the educational career of the individuals, in order to investigate viral hepatitis A dynamics in the most affected Italian areas. Intervention options such as targeted vaccination, social distancing measures (e.g., closure of day care centers and kindergartens) and improvements in standards of living and hygiene are evaluated. Results show that a very low vaccination coverage is sufficient to control hepatitis A in Italy, while its elimination is not possible since new cases are continuously imported from high endemicity areas outside the country. Finally, the considered social distancing measures can be counterproductive since the fraction of recovered individuals does not decline while the age at infection increases, thus augmenting the probability of developing acute symptoms.     

Depression scores associate with chronotype and social jetlag in a rural population

In public health, mood disorders are among the most important mental impairments. Patients with depressive episodes exhibit daily mood variations, abnormal patterns in sleep-wake behavior, and in the daily rhythms of several endocrine-metabolic parameters. Although the relationship between the sleep/circadian processes and mood disorders is poorly understood, clock-related therapies, such as light therapy, sleep deprivation, and rigid sleep schedules, have been shown to be effective treatments. Several studies investigated the relationship between circadian phenotype (chronotype) and depression. These focused mainly on urban populations and assessed diurnal preferences (Morningness-Eveningness score) rather than the actual timing of sleep and activity. Here, we used the Beck Depression Inventory (BDI) in an essentially rural population (N?=?4051), and investigated its relation to circadian phenotype (chronotype and social jetlag), assessed with the Munich Chronotype Questionnaire (MCTQ). In our study design, we (i) normalized both chronotype and BDI scores for age and sex (MSF(sas) and BDI(as), respectively); (ii) calculated individual social jetlag (misalignment of the biological and social time); and (iii) investigated the relationship between circadian phenotypes and BDI scores in a population homogeneous in respect to culture, socioeconomic factors, and daily light exposure. A 15.65% (N?=?634) of the participants showed mild to severe depressive BDI scores. Late chronotypes had a higher BDI(as) than intermediate and early types, which was independent of whether or not the participants were smokers. Both chronotype and BDI(as) correlated positively with social jetlag. BDI(as) was significantly higher in subjects with >2?h of social jetlag than in the rest of the population?again independent of smoking status. We also compared chronotype and social jetlag distributions between BDI categories (no symptoms, minimal symptoms, and mild to severe symptoms of depression) separately for men and women and for four age groups; specifically in the age group 31?40 yrs, subjects with mild to severe BDI scores were significantly later chronotypes and suffered from higher social jetlag. Our results indicate that misalignment of circadian and social time may be a risk factor for developing depression, especially in 31- to 40-yr-olds. These relationships should be further investigated in longitudinal studies to reveal if reduction of social jetlag should be part of prevention strategies. (Author correspondence: karla.allebrandt@med.uni-muenchen.de ).     

Association between interpersonal trust, reciprocity, and depression in South Korea: a prospective analysis

Background

A growing body of empirical evidence indicates that low-level social capital is related to poor mental health outcomes. However, the prospective association between social capital and depression remains unclear, and no published studies have investigated the association with longitudinal data in East-Asian countries.

Methods

We analyzed data from the ongoing Korean Welfare Panel Study to prospectively investigate association between social capital and depression. Social capital was measured at the individual level by two items specific to interpersonal trust and reciprocity. Depression was annually assessed as a dichotomous variable using the Center for Epidemiologic Studies Depression Scale. After excluding participants who had depression in 2006, logistic regression models were applied to estimate the association between each social capital indicator and new-onset depression developed in 2007 or long-term depression in both 2007 and 2008. We also examined the association in a subpopulation restricted to healthy participants after excluding individuals with any pre-existing disability, chronic disease, or poor self-rated health condition.

Results

Compared to the high interpersonal trust group, the odds ratios of developing new-onset and long-term depression among the low interpersonal trust group were 1.22 (95% CI: 1.08∼1.38) and 1.23 (95% CI: 1.03∼1.50), respectively, and increased to 1.32 (95% CI: 1.10∼1.57) and 1.47 (95% CI: 1.05∼2.08) in the subpopulation analyses restricted to healthy individuals. Although the low and intermediate reciprocity group also had significantly higher odds of developing new-onset depression compared to the high reciprocity group, the effects were attenuated and statistically non-significant in the subpopulation analyses.

Conclusion

Low interpersonal trust appears to be an independent risk factor for new-onset and long-term depression in South Korea.     

Relationships between depression,lifestyle and quality of life in the community dwelling elderly: a comparison between gender and age groups

This study aimed to comprehensively investigate the comprehensive relationships between depression and the characteristics of lifestyle and quality of life (QOL) of healthy, community dwelling elderly, and compare them according to gender and age groups. 1302 subjects (657 males and 645 females) were used for analysis. The investigators in this study were researchers working at universities in each prefecture. Data collection was conducted in a general delivery survey and interview setting or an education class setting. The geriatric depression scale (GDS) consisting of 15 items with a dichotomous scale was used to assess depression symptoms in the elderly. In addition, 16 items selected from the four factors of economic situation, physical health, social activity, and personal status were used to assess lifestyle. Furthermore, this study investigated life satisfaction, morale, and physical function with the LSI scale, PGC morale scale and the ADL scale of the Ministry of Education, Science and Culture, respectively. According to our results, depression characteristics of the elderly differ between gender and age groups. Depression increases in the old-old elderly rather than in the young-old elderly and is highest in old-old females. The factors significantly related to depression in community dwelling elderly were the number of friends and morale. In particular, an increase in the number of friends was related to a decrease in depression. Depression in the old-old elderly was more significantly related to many lifestyle items compared with the young-old elderly, and especially in the old-old elderly, the extent of social activities related to a decrease in depression.     

Psychosocial and Environmental Correlates of Walking,Cycling, Public Transport and Passive Transport to Various Destinations in Flemish Older Adolescents

Background

Active transport is a convenient way to incorporate physical activity in adolescents’ daily life. The present study aimed to investigate which psychosocial and environmental factors are associated with walking, cycling, public transport (train, tram, bus, metro) and passive transport (car, motorcycle, moped) over short distances (maximum eight kilometres) among older adolescents (17–18 years), to school and to other destinations.

Methods

562 older adolescents completed an online questionnaire assessing socio-demographic variables, psychosocial variables, environmental variables and transport to school/other destinations. Zero-inflated negative binomial regression models were performed.

Results

More social modelling and a higher residential density were positively associated with walking to school and walking to other destinations, respectively. Regarding cycling, higher self-efficacy and a higher social norm were positively associated with cycling to school and to other destinations. Regarding public transport, a higher social norm, more social modelling of siblings and/or friends, more social support and a higher land use mix access were positively related to public transport to school and to other destinations, whereas a greater distance to school only related positively to public transport to school. Regarding passive transport, more social support and more perceived benefits were positively associated with passive transport to school and to other destinations. Perceiving less walking and cycling facilities at school was positively related to passive transport to school only, and more social modelling was positively related to passive transport to other destinations.

Conclusions

Overall, psychosocial variables seemed to be more important than environmental variables across the four transport modes. Social norm, social modelling and social support were the most consistent psychosocial factors which indicates that it is important to target both older adolescents and their social environment in interventions promoting active transport. Walking or cycling together with siblings or friends has the potential to increase social norm, social modelling and social support towards active transport.     

Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.     

Mood and anxiety disorders in women with PCOS

Women with polycystic ovary syndrome have gynecologic, reproductive and metabolic co-morbidities that span their entire lifespan. More recently a higher risk of mood and anxiety disorders has been reported in women with PCOS. Women with PCOS have higher depression scores and a higher risk of depression independent of BMI. Although clinical features of hyperandrogenism affect health related quality of life, the association between hirsutism, acne, body image and depression is currently unclear. Similarly there is limited data on the association between variables such as biochemical hyperandrogenism or infertility and depression. Women with PCOS are also at risk for symptoms of generalized anxiety disorder. There is insufficient data examining the risk of other anxiety disorders such as social phobia, obsessive compulsive disorders and panic disorder. In a number of patients some of these disorders coexist increasing the health burden. These data underscore the need to screen all women with PCOS for mood and anxiety disorders and adequately treat women who are diagnosed with these conditions.     

Depressive symptoms in adolescents with early and continuously treated phenylketonuria: associations with phenylalanine and tyrosine levels

Background

Previous research has suggested an increased risk for individuals with phenylketonuria (PKU) of developing depression and other mood disorders. As PKU can disrupt neurotransmitter synthesis via biochemical mechanisms, depressive symptoms are hypothesised to result from neurotransmitter dysregulation. Whilst adherence (or return) to the phenylalanine-restricted diet may resolve or improve symptoms of depression, data to demonstrate a direct relationship between biochemistry and mood in this population are lacking.

Methods

Thirteen adolescents with early and continuously treated PKU and eight sibling controls were compared in their total reported depressive symptoms. A general executive function assessment was also undertaken in the PKU group. Correlations between depressive symptoms and biochemical markers were examined within the PKU group only.

Results

Correlational analyses within the PKU group demonstrated strong and significant associations between depressive symptoms and long term exposure to either a high phenylalanine:tyrosine ratio, or low tyrosine. Increasing symptoms of depression were also found to be associated with poorer executive function in the PKU sample. However, both groups of adolescents scored within the normal range in symptoms of depression (p > 0.05).

Conclusions

Significant associations were observed between biochemical markers indicating poorer dietary control and increasing depressive symptoms in a sample of adolescents with early and continuously treated PKU, although symptoms of depression remained within the normal range. An association between depressive symptoms and poorer EF was also demonstrated. Further research is needed to establish whether the depressive symptoms observed in this young population represent an emerging (subclinical) risk for major depressive disorder as they age.     

Disease Surveillance on Complex Social Networks

As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors—sampling the most connected, random, and friends of random individuals—in three complex social networks—a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals—early and accurate detection of epidemic emergence and peak, and general situational awareness—we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R₀). For diseases with a low R₀, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R₀ is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.     

The Spread of Sleep Loss Influences Drug Use in Adolescent Social Networks

Troubled sleep is a commonly cited consequence of adolescent drug use, but it has rarely been studied as a cause. Nor have there been any studies of the extent to which sleep behavior can spread in social networks from person to person to person. Here we map the social networks of 8,349 adolescents in order to study how sleep behavior spreads, how drug use behavior spreads, and how a friend''s sleep behavior influences one''s own drug use. We find clusters of poor sleep behavior and drug use that extend up to four degrees of separation (to one''s friends'' friends'' friends'' friends) in the social network. Prospective regression models show that being central in the network negatively influences future sleep outcomes, but not vice versa. Moreover, if a friend sleeps ≤7 hours, it increases the likelihood a person sleeps ≤7 hours by 11%. If a friend uses marijuana, it increases the likelihood of marijuana use by 110%. Finally, the likelihood that an individual uses drugs increases by 19% when a friend sleeps ≤7 hours, and a mediation analysis shows that 20% of this effect results from the spread of sleep behavior from one person to another. This is the first study to suggest that the spread of one behavior in social networks influences the spread of another. The results indicate that interventions should focus on healthy sleep to prevent drug use and targeting specific individuals may improve outcomes across the entire social network.     

Social Network Analysis Predicts Health Behaviours and Self-Reported Health in African Villages

The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.     

Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox

The friendship paradox is the phenomenon that in social networks, people on average have fewer friends than their friends do. The generalized friendship paradox is an extension to attributes other than the number of friends. The friendship paradox and its generalized version have gathered recent attention due to the information they provide about network structure and local inequalities. In this paper, we propose several measures of nodal qualities which capture different aspects of their activities and influence in online social networks. Using these measures we analyse the prevalence of the generalized friendship paradox over Twitter and we report high levels of prevalence (up to over 90% of nodes). We contend that this prevalence of the friendship paradox and its generalized version arise because of the hierarchical nature of the connections in the network. This hierarchy is nested as opposed to being star-like. We conclude that these paradoxes are collective phenomena not created merely by a minority of well-connected or high-attribute nodes. Moreover, our results show that a large fraction of individuals can experience the generalized friendship paradox even in the absence of a significant correlation between degrees and attributes.