Table 5 - uploaded by Felix Elwert
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Increased mortality following the death of a spouse (the "widowhood effect") may be due to (1) causation, (2) bias from spousal similarity (homogamy), or (3) bias from shared environmental exposures. This article proposes new tests for bias in the widowhood effect by examining husbands, wives, and ex-wives in a longitudinal sample of over 1 million...
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Citations
... The fact that spouses often die close to each other in time-the widowhood effect-has been documented in a variety of settings and samples and among different age groups (e.g., Elwert and Christakis 2008;Gove 1973;Lillard and Waite 1995;Moon et al. 2011;Subramanian et al. 2008). Mortality is the most extreme of a broader set of consequences associated with the loss of a close partner. ...
... To summarize our results broadly, we find evidence that mortality rates increase after the death of a spouse. This is known as the widowhood effect (Dabergott 2022;Elwert and Christakis 2008;Ennis and Majid 2020;Gove 1973;Lillard and Waite 1995;Moon et al. 2011;Spreeuw and Owadally 2013;Subramanian et al. 2008). Moreover, we find that this effect is amplified among those whose spouses were less well connected to one's other social network contacts. ...
Research on "the widowhood effect" shows that mortality rates are greater among people who have recently lost a spouse. There are several medical and psychological explanations for this (e.g., "broken heart syndrome") and sociological explanations that focus on spouses' shared social-environmental exposures. We expand on sociological perspectives by arguing that couples' social connections to others play a role in this phenomenon. Using panel data on 1,169 older adults from the National Social Life, Health, and Aging Project, we find that mortality is associated with how well embedded one's spouse is in one's own social network. The widowhood effect is greater among those whose spouses were not well connected to one's other network members. We speculate that the loss of a less highly embedded spouse signals the loss of unique, valuable, nonredundant social resources from one's network. We discuss theoretical interpretations, alternative explanations, limitations, and directions for future research.
... Addressing collider bias in the causal modelling of social network data and learning-based hypotheses presents additional challenges (Lyons, 2011;Shalizi & Thomas, 2011). While some traditional approaches for addressing bias may assist with collider bias [i.e., counterfactual (Elwert & Christakis, 2008) or instrumental variable approaches (O'Malley et al., 2014)], several statistical models unique to network analysis have also been introduced as possible solutions. Shalizi and Thomas (2011) propose a model of tie formation and dissolution, aiming to identify instances where behaviors spread in response to tie formation and cease in response to tie dissolution, eliminating instances where an outcome is maintained by similar characteristics rather than social contagion (Krivitsky & Handcock, 2014). ...
Objectives We provide a brief overview of collider bias and its implications for criminological research.
Methods Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias.
Results We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology.
Conclusion We close by offering a general set of strategies for addressing the challenges
posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and it's attendant topics.
... In terms of the statistical methods for drawing causal inference, this work has two notable shortfalls. First, the application of mobility data as a real-time measure of contact between groups of people may introduce shared-exposure bias into the estimated effect [20]. Shared-exposure bias suggests causal peer effects may be a noncausal artifact of shared exposure to a common environment. ...
Recent research has attempted to document large-scale emotional contagion on online social networks. Despite emotional contagion being primarily driven by in-person mechanisms, less research has attempted to measure large-scale emotional contagion in in-person contexts. In this paper, I operationalize the temporal emotions associated with a particular city at particular points in time using sentiment analysis on Twitter data. Subsequently, I study how emotions converge between seven proximal cities in the state of Virginia, using two-way fixed effect models. I find that positive emotions tend to be synchronous between cities, but that effect is conditional on the level of contact between city residents at that period of time, as indicated by cell phone mobility data. I do not find any synchrony based on other types of emotions or general sentiment. I discourage drawing causal conclusions based on the presumed existence of several unmeasured sources of bias.
... There are, of course, a multitude of environmental conditions that may induce or prevent violent crime. These varying environmental conditions constitute shared-exposure bias, an important form of bias confounding results in causal peer effects analysis [24]. Shared-exposure bias is a main reason why neighborhoods connected through mobility patterns may serve as valuable sensors to predict future violence, as well as why results interpreted from analyses like this (that cannot control away shared-exposure bias) cannot make causal claims regarding the contagiousness of a phenomenon. ...
Much research has documented the contagiousness of violence. Some of this work has focused on contagiousness as operationalized by the spread across geographical space, while other work has examined the spread within social networks. While the latter body of work struggles with incomplete network data, the former constitutes a theoretical mismatch with how violence should spread. Theory instead strongly suggests that violence contagion should diffuse through everyday mobility networks rather than just adjacently through geographical space. Beyond contagion itself, I argue that neighborhoods connected through mobility networks should serve as useful short-term sensors in predicting imminent violence because these sets of residents tend to experience shared environmental exposures, which may induce synchrony in the likelihood of violence. I explore this topic and these relationships using violent crime data from the three largest U.S. cities: New York City, Los Angeles, and Chicago. Using two-way fixed effects models, I test whether or not violence in mobility-connected alter neighborhoods in the preceding hour predicts violence in an ego neighborhood in the next hour. Across all three jurisdictions, I find that recent violence in the neighborhoods to which a neighborhood is connected through mobility ties can strongly predict that neighborhood’s odds of subsequent violence. Furthermore, spatial proximity has no significant effect on the likelihood of violent crime after controlling for mobility ties. I conclude by arguing that mobility patterns are an important pathway in the prediction of violence.
... Bereavement is the -the situation of having recently lost a significant person through death‖ and is a known health risk that contributes to health disparities through biopsychosocial mechanisms (Richardson et al., 2015;Stroebe et al., 2007;Umberson, 2017). Among bereavement types, recent spousal loss and -widowhood effects‖ present some of the most severe consequences, including mental health problems, physical health declines, and elevated mortality (Domingue et al., 2021;Elwert & Christakis, 2008a, 2008bEnnis & Majid, 2019;Wörn et al., 2020), which compound other bereavement-related challenges, such as financial insecurity (Umberson, 2017). ...
... For one, high-quality, population-based data from the crisis period is only beginning to emerge. It is not yet possible, for instance, to examine whether those bereaved by COVID-19 suffer from elevated mortality compared to pre-pandemic studies of elevated mortality following recent bereavement (Elwert & Christakis, 2008a, 2008b. However, contemporary scholarship on widowhood effects finds that the manifestation of short-term mental health problems following bereavement is highly predictive of subsequent physical health declines and elevated mortality (Domingue et al., A c c e p t e d M a n u s c r i p t 2021). ...
... Although we can assess the differential impact hypothesis, the data we use are not well-suited to examine subsidiary hypotheses such as whether bereavement from deaths by causes other than COVID-19 during the pandemic has differential associations with mental health (either compared to pre-pandemic bereavement or to COVID-19 bereavement). Our focal population is older adults, who are at the highest risks of losing a spouse to COVID-19 A c c e p t e d M a n u s c r i p t Wang et al., 2021) and are the population most often studied with respect to widowhood effects (Elwert & Christakis, 2008a, 2008bUmberson, 2017). Data, 2021Data, , 2022, meaning it is nearly certain that no sample participants lost a spouse to COVID-19 during this Wave of data collection (less than 8% of Wave 8 participants were interviewed in March 2020). ...
Objectives
The death of a spouse is an established predictor of mental health decline that foreshadows worsening physical health and elevated mortality. The millions widowed by COVID-19 worldwide may experience even worse health outcomes than comparable pre-pandemic widows given the particularities of dying, mourning, and grieving during a pandemic defined by protracted social isolation, economic precarity, and general uncertainty. If COVID-19 pandemic bereavement is more strongly associated with mental health challenges than pre-pandemic bereavement, the large new cohort of COVID-19 widow(er)s may be at substantial risk of downstream health problems long after the pandemic abates.
Methods
We pooled population-based Survey of Health, Ageing and Retirement in Europe data from 27 countries for two distinct periods: (1) pre-pandemic (Wave 8, fielded October 2019 to March 2020; N = 46,266) and (2) early-pandemic (COVID Supplement, fielded June to August 2020; N = 55,796). The analysis used a difference-in-difference design to assess whether a spouse dying from COVID-19 presents unique mental health risks (self-reported depression, loneliness, and trouble sleeping), compared to pre-pandemic recent spousal deaths.
Results
We find strong associations between recent spousal death and poor mental health before and during the pandemic. However, our difference-in-difference estimates indicate those whose spouses died of COVID-19 have higher risks of self-reported depression and loneliness, but not trouble sleeping, than expected based on pre-pandemic associations.
Discussion
These results highlight that the millions of COVID-19 widow(er)s face extreme mental health risks, eclipsing those experienced by surviving spouses pre-pandemic, furthering concerns about the pandemic’s lasting impacts on health.
... Elwert and Christakis (2008) use graphical causal models to show that the widowhood effect is real without killing anyone. ...
Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead use temporal and probabilistic information to infer exactly when X is causally relevant to Y in each of the senses captured by Woodward’s interventionist treatment. Then, I consider the upshot of these findings for causal decision theory, and argue that the commonsense assumption is especially powerful when an agent seeks to determine whether so-called “dominance reasoning” is applicable.
... While there is widespread agreement over the widow(er)hood effect's confirmed presence in the literature (Elwert & Christakis, 2008b;Ennis & Majid, 2021;Lichtenstein et al., 1998;Martikainen & Valkonen, 1996a;Stroebe, 1994;Sullivan & Fenelon, 2014;Vable et al., 2015), not all widowed individuals seem equally affected by the phenomenon (Manzoli et al., 2007;Moon et al., 2011;Shor et al., 2012). Thus, the effect's strength determined by studies differs (Moon et al., 2011;Shor et al., 2012)-indicating special importance of the moderating factors gender (e.g., Gove, 1973;Helsing & Szklo, 1981), age (e.g., Kraus & Lilienfeld, 1959;Martikainen & Valkonen, 1996b;Smith & Zick, 1996), and time (e.g., Schaefer et al., 1995;Wright et al., 2015). ...
... Stroebe et al. (2006) provide an integrative risk factor framework to understand bereavement adjustments and outcomes. This framework posits that various stressors stemming from bereavement correspond with a range of poor health outcomes, from mental health problems to elevated mortality risk (Elwert & Christakis, 2008a, 2008bStroebe et al., 2007). In the short term, the health challenges associated with bereavement can manifest as mental health problems, which often foreshadow physical health declines (Domingue et al., 2020). ...
Objectives
The COVID-19 pandemic has left older adults around the world bereaved by the sudden death of relatives and friends. We examine if COVID-19 bereavement corresponds with older adults’ reporting depression in 27 countries, and test for variation by gender and country context.
Methods
We analyze SHARE COVID-19 data collected between June-August 2020 from N=51,383 older adults (age 50–104) living in 27 countries, of whom 1,363 reported the death of a relative or friend from COVID-19. We estimate pooled-multilevel logit regression models to examine if COVID-19 bereavement was associated with self-reported depression and worsening depression, and we test whether national COVID-19 mortality rates moderate these assocations.
Results
COVID-19 bereavement is associated with significantly higher probabilities of both reporting depression and reporting worsened depression among older adults. Net of one’s own personal loss, living in a country with the highest COVID-19 mortality rate is associated with women’s reports of worsened depression but not men’s. However, the country’s COVID-19 mortality rate does not moderate associations between COVID-19 bereavement and depression.
Discussion
COVID-19 deaths have lingering mental health implications for surviving older adults. Even as the collective toll of the crisis is apparent, bereaved older adults are in particular need of mental health support.
... Widowhood would then correlate with mortality risk, but possibly without any causality. However, even though arguments like these may be valid to some extent, only a limited selection effect has been found in studies that have investigated causality by, for example, comparing the mortality risk after spousal loss with the risk after the death of an ex-spouse (Elwert and Christakis 2008a), examining the cause-of-death type for both spouses (Elwert and Christakis 2008b;Boyle et al. 2011), or using fixed effects models to account for stable attributes of couples (Christakis and Allison 2006). ...
With few exceptions, greater disparities in mortality risk by socio-economic status (SES) have been found among men than among women. Most research has also shown that the higher mortality risk after widowhood (the widowhood effect) is greater for men. However, a different picture appears when examining these associations jointly. Based on Swedish register data, this study shows that widowhood weakens, or even reverses, the sex differences in socio-economic disparities in mortality. The overall findings also indicate that higher SES elevates the widowhood effect for men but diminishes it for women, and that the widowhood effect is greater for women than men in the lowest SES categories. These results imply that men with higher SES are more vulnerable after widowhood, perhaps because of their previous relatively privileged situation. The disadvantage of widows in lower SES categories may reflect exposure to financial strains after spousal loss and inequalities in the healthcare system.
... Studies have indicated that partners in romantic relationships tend to be similar on a variety of different factors, including educational levels, health, and health behaviors. If spouses select into a relationship based on similar health or health behaviors, a correlation in the timing of the death of a husband and wife could be attributable to sharing similar characteristics and health behaviors rather than any direct effect of partner loss (Goldman 1993;Smith and Zick 1994), although research that has attempted to tackle this issue directly suggests that homophily bias is not the key driver of the observed widowhood mortality effect (Elwert and Christakis 2008b). ...
Although the associations among marital status, fertility, bereavement, and adult mortality have been widely studied, much less is known about these associations in polygamous households, which remain prevalent across much of the world. We use data from the Utah Population Database on 110,890 women and 106,979 men born up to 1900, with mortality follow-up into the twentieth century. We examine how the number of wife deaths affects male mortality in polygamous marriages, how sister wife deaths affect female mortality in polygamous marriages relative to the death of a husband, and how marriage order affects the mortality of women in polygamous marriages. We also examine how the number of children ever born and child deaths affect the mortality of men and women as well as variation across monogamous and polygamous unions. Our analyses of women show that the death of a husband and the death of a sister wife have similar effects on mortality. Marriage order does not play a role in the mortality of women in polygamous marriages. For men, the death of one wife in a polygamous marriage increases mortality to a lesser extent than it does for men in monogamous marriages. For polygamous men, losing additional wives has a dose-response effect. Both child deaths and lower fertility are associated with higher mortality. We consistently find that the presence of other kin in the household-whether a second wife, a sister wife, or children-mitigates the negative effects of bereavement.
