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Years of life lost due to the psychosocial consequences of COVID19 mitigation strategies based on Swiss data

Authors:
  • Child and Adolescent Psychiatry CHUV

Abstract

Background: The pandemic caused by COVID-19 has forced governments to implement strict social mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies however carry a significant risk for mental health which can lead to increased short-term and long-term mortality and is currently not included in modelling the impact of the pandemic. Methods: We used years of life lost (YLL) as the main outcome measure as applied to Switzerland as an exemplar. We focused on suicide, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in marital status and social isolation as these are known to increase YLL in the context of imposed restriction in social contact and freedom of movement. We stipulated a minimum duration of mitigation of 3 months based on current public health plans. Results: The study projects that the average person would suffer 0.205 YLL due to psychosocial consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1% of the population, who will suffer an average 9.79 YLL. Conclusions: The results presented here are likely to underestimate the true impact of the mitigation strategies on YLL. However, they highlight the need for public health models to expand their scope in order to provide better estimates of the risks and benefits of mitigation.
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Years of life lost due to the psychosocial consequences of COVID19 mitigation strategies based on
Swiss data
Dominik A. Moser1,2, Jennifer Glaus2, Sophia Frangou3,4, Daniel S. Schechter2,5,6
1 University of Bern, Institute of Psychology, Bern, Switzerland
2 Lausanne University Hosptital, Child and Adolescent Psychiatry Service, Department of Psychiatry,
Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
3 Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, University of British
Columbia, Vancouver, Canada
4 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
5 Université de Genève Faculté de médecine, Department of Psychiatry, Geneva, Switzerland
6 New York University Grossman School of Medicine, Department of Child and Adolescent
Psychiatry, New York, NY USA
Corresponding Author: Dominik A. Moser email: dominik.moser@psy.unibe.ch
Short Title: COVID19: psychosocial stress and mortality
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Background: The pandemic caused by COVID-19 has forced governments to implement strict social
mitigation strategies to reduce the morbidity and mortality from acute infections. These strategies
however carry a significant risk for mental health which can lead to increased short-term and long-
term mortality and is currently not included in modelling the impact of the pandemic.
Methods: We used years of life lost (YLL) as the main outcome measure as applied to Switzerland as
an exemplar. We focused on suicide, depression, alcohol use disorder, childhood trauma due to
domestic violence, changes in marital status and social isolation as these are known to increase YLL
in the context of imposed restriction in social contact and freedom of movement. We stipulated a
minimum duration of mitigation of 3 months based on current public health plans.
Results: The study projects that the average person would suffer 0.205 YLL due to psychosocial
consequence of COVID-19 mitigation measures. However, this loss would be entirely borne by 2.1%
of the population, who will suffer an average 9.79 YLL.
Conclusions: The results presented here are likely to underestimate the true impact of the mitigation
strategies on YLL. However, they highlight the need for public health models to expand their scope in
order to provide better estimates of the risks and benefits of mitigation.
3
Introduction
Coronavirus disease 2019 (COVID-19) has led to the first truly global pandemic. At the time of writing
this paper, there were over two million reported cases worldwide and more than 130,000 deaths
attributed to COVID19 acute infection (1). Based on models of its spread, and potential for morbidity
and mortality, most governments worldwide have adopted mitigation strategies that essentially limit
social contacts (2, 3). The goal of these measures is to “flatten the curve” of acute presentations so
as to prevent widespread morbidity and the break-down of health care systems. Variants of these
social mitigation strategies range from “social distancing”, at-home-confinement- referred to as
“self-isolation”, to selective “quarantine”, and to population “lockdown” that includes restriction of
movement outdoors and closure of schools and all non-essential services and businesses.
None of the existing models have factored the possible adverse mental health effects of mitigation
at a population level. These adverse effects can be intuitively anticipated but have never been
rigorously modelled (4). Negative mental health outcomes can be attributed to the emotional and
physiological effects of the risk posed by the virus and by reduced physical activity, social interaction
and human physical contact (5-7). Studies on prior pandemics, such as the Severe Acute Respiratory
Syndrome (SARS)found that the length of quarantine was an important predictor of post-traumatic
stress disorder (PTSD), depression and anxiety with a cumulative prevalence exceeding 30% of the
population (8, 9). Psychosocial stressors within families and loneliness for those living alone are also
likely to spike in confinement and have adverse effects on mental and physical health (10-13).
Available data suggest that stress associated with population-wide disasters increases the level of
violence, including domestic violence and child abuse (14, 15). These are recognised risk factors for
mental health and substance abuse problems (16) as well as suicide (17).
The anticipated impact of the COVID19 pandemic on mental health is expected to be significant but
has not been considered in formulation current public policies. To address this gap, the present
study makes a rapid model-projection concerning the years of life lost (YLL) if restrictive social
mitigation measures are implemented for a period of 3 months. This duration was chosen as it aligns
with the expected duration of social mitigation in many countries. We use data from Switzerland as
an example. The model focuses on what we consider to be the major contributors to YLL affecting
the majority of the population, namely: suicide, emergence or increase in psychopathology,
childhood physical abuse and continued restriction of movement and at home confinement.
To be clear, this model focuses on changes to psychosocial risk factors. The COVID-19 crisis may also
have other adverse consequences that may impact on longevity such as economic adversity, changes
to activities of daily living such as eating, sleeping, smoking and ordinary alcohol consumption or
decrease in medical provision to those who have health problems unrelated to COVID-19. Such
additional factors are however beyond the scope of the present study. A more precise estimation of
the mental health impact of the pandemic will be possible as relevant data become available.
Methods:
Model: We conducted a literature review focusing on studies reporting on YLL in connection to
situations conceptually similar to the current pandemic. These included data from studies on
confinement in different contexts and from previous disasters including pandemics. We focused on
studies from developed countries, primarily Switzerland, and when not available, from Europe
followed by the United States, based on the United Nations Development Programme- Country
Classification System. Switzerland has a population of 8.57 million (18) and introduced an
“extraordinary situation” on March 16 2020. All boarders were closed to travel, all schools, markets,
restaurants, non-essential shops, bars and entertainment and leisure facilities were closed, and all
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public and private events and gatherings were prohibited (19). Several regions had already taken a
number of these measures in the preceding days and weeks. The Federal Council called on members
of the public to avoid all unnecessary contact, maintain physical distance from others and stay
whenever possible at home. According to government announcements these measures are expected
to continue until at least April 27 2020 while a number of measures aimed at “social distancing”,
including prohibition of gatherings, are expected to last until at least June 8 (20) .
The following risk factors were considered based on their importance and data availability:
Suicidality, depression, alcohol use disorder, childhood trauma due to domestic violence, changes in
marital status, and social isolation. The projection of YLL for each of these factors is further
described below. Data concerning the incidence of the risk factors as well as their impact on YLL
were then applied to a model that assumes population-wide severe social mitigation policies (stay-
at-home and restriction of outdoors movement) for a duration of 3 months. As a general rule, the
present model erred on the conservative side when choosing YLL. For purposes of illustration only,
we also present projected YLL for countries other than Switzerland based on their population size
(21) assuming similar prevalence of risk factors.
The model involved a six step process.
For each factor:
1. Estimation of baseline risk of outcome i (BRi) based on the literature
2. Estimation of YLL per incident of outcome i (YLLi) from the literature
3. Estimation of increased risk factor during the pandemic for outome i (PRi), where possible
based on literature
4. Estimation of the increased incident cases relating to the pandemic outcome i (PICi)
PICi=(PRi-1)*BRi*0.25
Where PRi is the estimate of the increased risk of outcome i relating to the pandemic, D is the
duration of the social mitigation measures, which is fixed 0.25 years (3 months)i
5. Estimation of YLL for incidencei due to the pandemic (PYYLi)
PYLLi= PICi *YLLi
6. Calculation of summary statistics
PICs is the sum of all PICi; PYLLs is the sum of all PYLLi
Average YLL per impacted Person: PICs / PYLLs
Percentage of Persons impacted: PICs/100* Population of Switzerland (8.57 million)
Average PYLL per person of the general population: PYLLs/ Population of Switzerland (8.57
million)
To align with current models that focus on acute mortality, we focus on the 3-month period which
represents an underestimate of the overall impact of the pandemic.
Results
A summary of the results is provided in Table 1 and details of the estimation of the increased YLL
linked to the pandemic are presented below.
Suicidality:
BRi: In 2017, Switzerland counted 1043 suicides excluding assisted suicide or euthanasia. Non
euthanasia-related suicide was the cause of death in 16 out of 1000 deaths (22).
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YLLi: US data from 2016, showed 1.542 million years were lost due to suicide (23) across 44965
suicides (24) leading to 34.3 YLL per death (1.542 million/44965).
PRi: We extrapolated on the relationship between confinement and suicidality from data from the
penitentiary system. Community cell-confined prisoners already have an increased suicide rate by
factors between 3.5 and 21 compared to the general population (25, 26). However, the risk of
suicide for prisoners in single cells is further increased approximately 9 to 15 times (27).
Extrapolating from these data, we assume that confinement in the household increases their
likelihood of suicide by a factor of 3 in multi-person households and a factor of 27 in single-person
households (3*9). We assume this increase is stable for the entire 3-month duration. In Switzerland,
16% of the population live in single-households (28). These calculations result in a population-wide
PRi of 6.84 (0.16*27 + 0.84*3).
PICi: 5.84* 1043*0.25= 1523 additional suicides.
PYLLi: = 1523*34.3 = 52239.
Depression:
General: As mood and anxiety disorders are comorbid, we used data on depression in our model as
it is likely to capture much of the distress related psychopathology; additionally, depression has the
most convincing link to YLL which is our outcome of interest (29).
BRi: We used population prevalence data for depression as they capture both incident and pre-
existing conditions(30, 31). Accordingly, we estimated that the pre-pandemic risk of depression for
the Swiss population in a 3-month period is 3.45% (i.e. 8.57 million*0.0345= 295’665) with 64.7% of
affected individuals being women. Because BRi is already adjusted for the 3-month period, no
further correction for PICi was undertaken.
YLLi: Using data from a prior study on depression (32), we assumed loss of 7.91 years of life for men
and 6.22 for women. Given the male-female ratio for depression (64.7% women) this results in YLLi =
6.82.
PRi: Three years after the SARS epidemic, the proportion of persons with symptoms related to higher
stress was still increased by a factor of 3.47 among those who had been in quarantine, thus
demonstrating the long-term implications of the phenomenon (33). A study of an Australian
population quarantined due to equine flu suggested a similar 3-fold increase in depression (34). We
used the latter estimate as it was the most conservative. We also factored in that -given therapy-
84% of individuals with depression are likely to remit within 3 years (31). To be conservative and to
capture cases most likely associated with mortality, we adjusted the model accordingly leading to a
PRi of 1.32.
PICi: 295665*0.32=94613.
PYLLi: 94613*6.82= 645’2260 YLL.
Alcohol use disorder:
General: Distress under any circumstances is a known risk factor for alcohol abuse disorder (AUD).
There is abundant evidence of increased alcohol consumption during the current pandemic (35).
Although the use of other substances seems to be increased as well, our model focuses on AUD as it
is the most prevalent substance abuse disorder and the major contributor to mortality worldwide
(36). Substance use disorders are significantly associated with increased mortality due to increased
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accidents, impulse-dyscontrol leading to violence and suicide, as well as increased physical morbidity
(i.e. cardiovascular, gastro-intestinal, hepatic, and other somatic conditions) (37-39).
BRi: In Switzerland, 16.1 of men and 3.2% of women were estimated to suffer from AUD, meaning
that 83.5% of cases are men and 16.5% are women (36).
YLLi: A cross-national Scandinavian study indicated that life expectancy among inpatients with AUD
was reduced by 24-28 years compared to the general population (39). When only deaths from
natural causes are considered, then life expectancy is reduced by 18.1 years in men and 16.5 years in
women (38). To be conservative, and to account for the preponderance of men with AUD we
assumed a reduction of life expectancy by 18 years in men (83.5% of cases) and 16 years in women
(16.5% of cases) resulting in a total YLLi of 17.67 years.
PRi: We assume a population-level increase in AUD of 0.15% per month, with the first month leading
to a higher incidence (0.3%). Therefore, countermeasures lasting 3 months would increase incidence
by approximately 0.6%.
PICi: 0.6% of the population (8.57 million) = 51’000.
PYLLi: 51000*17.67 = 901’170.
Marital Status:
General: Recent media reports indicate that divorce rates have increased since the instigation of
COVID-19 mitigation policies (11, 40) In individual cases, divorces and separations can be beneficial
to individual health and stress levels (for example in situations of abuse). Overall, however, even
after taking into account risk factors that contribute to divorce and separation (i.e. financial
stressors, mental and physical illness and substance abuse), divorce and separation given the
experience of relational and economic stress, loss, and greater likelihood for social isolation, have
been shown to have a negative impact on longevity (10). Causes for this may also include
detrimental habits that individuals may adopt to cope with the stress, loss, and isolation (such as
increased smoking (41)). Additionally having parents who divorce during childhood has been
estimated to increase mortality by 44% and reduce life expectancy by an average of 4 years (42).
BRi: In 2018, there were 16542 divorces in Switzerland leading to a BRi=33084. Additionally 12212
minors were affected by the breakdown of marital relationships (43).
YLLi: A German study estimated that YLL atteibutable to divorce range between 3-8 years for
women and 4-9 years for men (44). For this projection, 3.5 YLLi were modeled per couple (4 years
for men, 3 years for women) and 4 years for each affected minor (42).
PRi: We based our calculation on the increase in divorce rate for the year following the Hurricane
Hugo disaster (45) (wherefore factor D is omitted in the calculation of PICi). PRi was modelled as
1.63.
PICi: for adults: 33084 *0.63 = 20842; for affected minors: 12212*0.63 = 7694.
PYLLi: for adults: 20842*3.5= 72947; for affected minors: 7694*4=30776.
Childhood trauma due to domestic violence:
General: Although family violence is commonly targeted towards both women and children, we
focus specifically on the effects on children as specific impact on women was hard to quantify.
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BRi: Even when not directly being the victim themselves, children being witnesses to violence can be
an adverse childhood event.
In 2013, 9381 victims of domestic violence registered by the Swiss police. However, a survey
indicated that that this latter number of victims would only represent 22% of the actual number,
which would increase the number to 42641 victims per year (9381/22*100). 64.5% of domestic
violence referred to violent interactions between either parents and their children or current
romantic partners (46) (42641*0.645=27’503). Of Swiss multi-person-households 46% include
children (28) and on average there are 1.76 children living in each of these households (47). BRi is
therefore assumed to be 22’266 (27503*0.46*1.76).
YLLi: Experiencing 3 or more adverse childhood events (ACEs) is associated with 9.5 years of reduced
expected quality longevity (48). Among adults, 25% report having experienced multiple averse
childhood events (49). Because of this we conservatively project that only about every fourth of
these events will lead to the full loss of 9.5 years; we therefore adjusted the YLLi to 2.37 years.
PRi: According to the World Health Organization (WHO), there has been a threefold increase in
family violence since the start of the pandemic (50). However, additional events are not likely to be
normally distributed across victims (46) and the measures to which these numbers refer may have
been stricter than the one in Switzerland; accordingly we adopted a conservative PRi = 2.
PICi: 22266*1*0.25=5567.
PYLLi: 5567*2.37=13194.
Social isolation and reduced social connectedness:
General: No studies were found that indicated the cost of social isolation or reduced social
connectivity in YLL in a way directly adaptable to the present study. Moreover, the entire population
is somewhere on a spectrum from socially hyperconnected to socially isolated. However, studies
concerning risk ratios do exist.
BRi: The entire Swiss population of 8.57 million is on a spectrum from socially connected to socially
isolated, depending on their personal circumstances.
YLLi: Based on the most recent data, Switzerland counted 67008 deaths in 2018 that were
distributed across age groups as follows: category 1 (ages: 0-19): 0.8%; cat 2 (20-39): 1.2%; cat3 (40-
64): 11.1%; cat 4 (65-79): 25.0%; cat5 (80+): 61.9% (22). There are no data informing on the baseline
number of deaths that can be attributed to social disconnection or loneliness. In response, we
averaged the life-expectancy of men and women and calculated YLLi according to the following
steps: 1) life expectancy by category was taken from the Federal Statistics office of Switzerland
which gives remaining life expectancy at birth, 30, 50, 65 and 80 years of age. (51). 2) We then
conservatively adjusted YLL for each age category as a very rough approximation. This approximation
is -if anything- aimed at underestimating the remaining life expectancy (cat 1: 73.45, cat 2: 54.2, cat
3: 34.8, cat 4: 15.5, cat 5: 4.95). 3) Overall, remaining life expectancy was then multiplied with the
percentage of deaths in each age group, leading to a cumulative YLLi =12.03 for each additional
death.
PRi: Having more social connections has been associated with lower death rate with an odds ratio of
1.5 (52). Conversely, a comprehensive meta-analysis indicated that social isolation is associated with
an increase of all-cause mortality by a factor of 1.29 (53). Similarly, a large-scale study estimated that
social isolation increased the hazard risk by a factor of 1.26 after adjusting for multiple potential
confounders including anxiety, depression and lower socio-economic status (54). In our model, we
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use the most conservative estimate of 1.26. In a phase of confinement, we assume that a majority of
75% of the population experiences either reduced social connectedness and/or increased social
isolation. This would lead to a PRi of 1.208.
PICi: Based on the 2018 data on the number of deaths in Switzerland (n=67008) (22), PICi was
estimated as 67008*0.208*0.25=3484.
PYLLi: 3484*12.03=41’912.
Summary Statistics:
The findings presented above are summarized in Table 1. The contribution of the different risk
factors to PYLL in descending order was: Alcohol use disorder: 901’170, Depression: 645’260,
Divorce: 103’723, Suicide: 52’239, Reduction of Social contact: 41’912, Averse childhood events due
to domestic violence: 13’194.
The sum of all PYLLi was 1757’498, this represents 0.205 PYLL per person in Switzerland
(1757498/8.57 million) (Table 1). In other words, we project a loss of 10 weeks and 5 days due to
COVID-19 related mitigation strategies if YLL is equally distributed in the entire Swiss population. The
sum of all PICi was 179520 which represents 2.1% of the Swiss population (179520/8.57 million).
Assuming that this subpopulation will be most impacted, the average PYLL was estimated to be 9.79
(1757498/179’520=9.79).
Discussion
The current study focused on years of life lost due to the social mitigation strategies implemented in
response to the COVID19 pandemic, with a primary focus on the consequences of at home
confinement and restriction to freedom of movement.
The literature suggests that increased duration of confinement is associated with worse outcomes
for psychological health of those confined (4). While some of the stress related problems ensuing
from confinement may remit, an important portion of this damage may prove to be hard or
impossible to reverse and the affected individuals may experience on going suffering. Our projection
suggests that the Swiss population will incur a substantial increase in mortality as a consequence of
confinement related psychosocial stress, which should be considered in forming public health
responses to the pandemic. It is important that policy makers factor mental health when conduction
cost-benefit analyses of mitigation strategies.
The present study hopes to have achieved two aims: 1) to provide information that helps authorities
to consider whether and, if so, how to enact these countermeasures and what resources to employ
for mitigation of their adverse consequences: 2) to make the case for more comprehensive
modelling of the effect of pandemic-responses beyond the immediate risk attributed to acute
infection.
Limitations: As we demonstrate here, the evidence base for building such comprehensive models is
limited and accordingly we had to make several assumptions. In this sense, our model projection is
obviously constrained by the limitations of the available literature which, itself, involves a number of
unknowns. Given the time constraints, the uncertainty in those assumptions is increased; the
authors however judge the urgency of such projections to be very high at the moment. Moreover, in
this respect our model is not dissimilar to current projections of the spread and consequences of
COVID19 which are being continually revised as more. Additionally, the present projection is not all-
encompassing concerning potential effects of confinement: such as (prolonged) grief, elder abuse,
increase of sedentary lifestyle or change of diet. The pandemic is also likely to have multiple
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additional consequences including distress due to job losses and financial hardship. The projection
also does not model potentially positive changes in behaviour, for example reductions in car
accidents and air pollution. Due to frequent co-occurrence of certain phenomena it is possible that a
single individual may be affected by more than one of the factors presented. When possible, data
were adjusted for age, sex and socioeconomics. However, for several factors, possibilities to do so
were impeded by virtue of limitations of the current literature.
Competing interests: The authors received no financial support for the submitted work; the authors
report no financial relationships with any organizations that might have an interest in the submitted
work in the previous three years; the authors report no other relationships or activities that could
appear to have influenced the submitted work.
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Table 1: Projection of lost years of life for the population of Switzerland due to demographics and
mental health changes related to a mass confinement of 3 months.
Cause
Estimated Persons
Affected with reduced
longevity (% of the
overall population)
Years of
Life Lost
per Person
Years of life lost per
person across the
entire population
(8.57 million)
Suicidality
1'523 (0.02%)
34.3
0.006
Divorces
(spouses)
20842 (0.24%)
3.5
0.008
Divorces
(affected minors)
7694 (0.09%)
4
0.004
Family Violence
(affected minors)
5’567 (0.06%)
2.37
0.002
Depression
94’613 (1.10%)
6.82
0.075
Alcohol use
Disorder
51’000 (0.60%)
17.67
0.105
Diminished
Social contacts
3484 (0.04%)
12.03
0.005
Total
179’520 (2.09%)
9.79
0.205
14
Table 2 indicates how many Years of life lost selected other countries would be projected to have,
were their disorder and social representation were the same as Switzerland’s (it is simply a
multiplication of the years of life lost by the size of the population). Population numbers for
countries other than Switzerland were sourced from Wikipedia (21).
Country
Population in millions
Years of Life Lost for an isolation
3 months period in millions
Switzerland
8.57
1.76
Germany
83.15
17.05
France
67.08
13.75
Spain
47.10
9.65
United Kingdom
66.44
13.62
Canada
37.99
7.79
United States
329.61
67.58
Japan
125.95
25.82
China
1402.16
287.47
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Background Marital separation and divorce are associated with an increased risk of early mortality, but the specific biobehavioral pathways that explain this association remain largely unknown. Purpose This study sought to identify the putative psychological, behavioral, and biomarker variables that can help explain the association of being separated or divorced and increased risk for early mortality. Methods Using data from the English Longitudinal Study of Ageing, a representative community sample of aging adults (N = 5,786), we examined the association of marital status and life satisfaction, health behaviors measured 2 years later, biomarkers measured 4 years later, and mortality outcomes from the subsequent 4 years. Results Consistent with prior literature, older adults who were separated/divorced evidenced greater risk of mortality relative to those in intact marriages over the study period, OR = 1.46, 95% CI [1.15, 1.86]. Marital status was associated with lower levels of life satisfaction, β = −0.22 [−0.25, −0.19] and greater likelihood of smoking 2 years later β = 0.17 [0.13, 0.21]. Lower life satisfaction predicted less frequent physical activity 2 years later, β = 0.07 [0.03, 0.10]. Smoking, but not physical activity, predicted poorer lung functioning 2 years later, β = −0.43 [−0.51, −0.35], and poorer lung function predicted increased likelihood of mortality over the following 4 years, β = −0.15 [−0.27, −0.03]. There was a significant total indirect effect of marital status on mortality through these psychological, behavioral, and biomarker variables, β = 0.03 [0.01, 0.05], which fully explained this mortality risk. Conclusions For separated/divorced adults, differences in life satisfaction predict health behaviors associated with poorer long-term lung function, and these intermediate variables help explain the association between marital dissolution and increased risk of earlier mortality.