Abstract

Section:

Objectives. To assess how personal social network characteristics moderated mental health declines during the COVID-19 pandemic in emerging adults compared with other age groups.

Methods. The Person to Person Health Interview Study, a representative, probability-based cohort study (n = 2485) in Indiana, collected data through face-to-face (baseline) and phone (follow-up) interviews before and during the pandemic. We used survey-weighted growth curve models to examine network effects on computer-adaptive testing measures of depression and anxiety severity.

Results. Respondents reported significantly increased depression and anxiety in 2021, which returned almost to baseline levels for most age groups by 2022 (P < .001). Stronger ties to others and more interconnected ties were significantly associated with lower depression (B = −0.112 [P < .05]; B = −0.086 [P < .001]) and anxiety (B = −0.101 [P < .05]; B = −0.063 [P < .01]) severity across the pandemic. Interaction models revealed disproportionate protective effects of network characteristics on depression (B = −0.456 [P < .001]; B = −0.268 [P < .001]) and anxiety (B = −0.388 [P < .001]; B = −0.284 [P < .001]) for emerging adults.

Conclusions. Cohesive and affectively strong personal networks promote resiliency to common mental health challenges during periods of crisis, particularly for emerging adults whose social roles and relationships were disrupted during a critical period of development. (Am J Public Health. 2024;114(S3):S258–S267. https://doi.org/10.2105/AJPH.2023.307426)

The COVID-19 pandemic had a profound impact on psychological well-being and demand for mental health services, initiating a mental health crisis that may persist for decades.¹^–¹⁰ While population effects were widespread, the mental health consequences of the COVID-19 pandemic appear to have disproportionately affected emerging adults—individuals aged 18 to 25 years in the period between adolescence and adulthood.¹¹^,¹² Multiple population-based studies suggest that emerging adults have experienced larger increases in distress, anxiety, and depression compared with middle-aged and older adults.⁸^,¹³^–¹⁵ The pandemic disrupted educational and occupational trajectories, resulted in school and workplace closures and mobility restrictions, and fundamentally altered social life. Furthermore, social distancing policies constrained opportunities for forming and maintaining relationships at a critical period in young people’s social and emotional development.¹³^,¹⁶ Understanding how emerging adults were affected and the role of social networks in protecting their mental health provides a window into whether, how, and for whom interventions during future crises should mobilize social as well as medical resources.

Personal social networks, defined as the set of relationships in which an individual is embedded,¹⁷ provide critical resources and promote resilience during periods of crisis and uncertainty.¹⁸^,¹⁹ In particular, personal networks fulfill the human drive for safety and security, which is fostered by cooperation, belonging, support, and cohesion, and promote active, healthy coping.²⁰^–²² Consistent with this perspective, various indicators of social connectedness have emerged as resiliency factors for reducing stress and maintaining mental health during the pandemic.¹⁶^,²³^–²⁵

More broadly, research suggests that being embedded in strong, positive relationships is essential for adjustment during emerging adulthood.²⁶^,²⁷ However, during this period, emerging adults become more independent from their families of origin and embrace new social roles and opportunities.²⁸^,²⁹ As such, their social networks tend to be larger and less stable, tightly knit, and kin-centered than those of older adults, who instead cultivate small and emotionally fulfilling core networks as they approach the end of life. However, few existing studies have examined whether the beneficial mental health effects of cohesive and affectively strong networks vary by age group.

The current study addresses this gap by examining age-group differences in mental health dynamics during the COVID-19 pandemic using social network data from a stratified household probability sample of Indiana residents. Specifically, we asked, “Did depression and anxiety symptoms increase during the pandemic to a greater extent among emerging adults relative to other age groups? Furthermore, did having close and cohesive personal social networks disproportionately moderate the negative mental health impact of the pandemic for emerging adults?”

METHODS

Section:

The Person to Person Health Interview Study (P2P) is an omnibus health survey that uses a stratified probability sample of households across the state of Indiana. Sampling, recruitment, and survey methodology for the baseline interview were designed to parallel the national General Social Survey (see the Appendix, available as a supplement to the online version of this article at https://ajph.org).³⁰ Participants were noninstitutionalized, cognitively capable adults aged 18 years and older. Baseline data were collected face to face between October 2018 and July 2021. A total of 2685 respondents completed the baseline survey (response rate = 29%), and 2142 of these consented to future contact. Wave 2 data were not used, wave 3 data were collected from January to April 2021 (n = 1290; 60% response rate), and wave 4 data were collected from June to September 2022 (n = 1407; 66% response rate). Most attrition was attributable to the inability to reach the respondent.

Social Networks Methods

The current study, conducted in Indiana, employed egocentric, or personal social network, methodology, which enhances our understanding of and ability to intervene on objective structural and functional social factors that promote resiliency.³¹^,³² A hallmark of this method is the collection of data about the structure, function, and composition of individuals’ social ties across a range of different social spheres (e.g., family, friends, coworkers, neighbors). Network members are individually distinguishable, and characteristics of each person are collected to provide information about the nature of interactions occurring within the network.¹⁷^,³³

We captured social network characteristics at baseline using 4 name-generating prompts that asked respondents to list the names of people with whom they discussed important matters and health matters, whom they spent free or leisure time with, and who tried to influence their health behaviors (Appendix Table A). There was no limit on the number of people respondents could name. Subsequently, they responded to 10 questions about the people they listed and those people’s relationships to one another. This information was aggregated to construct variables that operationalize social network structure and tie strength.

Measures

Social networks

Social network size, the number of people named, was included in all models (range = 0–24). Mean network strength, an aggregate measure of how strong a person’s relationship is with each individual in their network (range = 0–10), captures feelings of trust, intimacy, and closeness a person derives from relationships to those in the network. Network density, a measure of the average closeness among alters in an ego’s network, ranges from 0 (“don’t know each other”) to 3 (“very close”), and reflects social groups that are tight-knit and cohesive, providing a strong sense of belonging and security. To facilitate interpretation, we standardized mean network strength and network density for multivariate analyses. Network emotional support (i.e., percentage of network that listens and tells you they care) was included in these analyses but later dropped because of nonsignificance.

Mental health outcomes

We assessed dependent variables measuring depression and anxiety severity (range = 0–100) by using the novel Computerized Adaptive Test‒Mental Health (CAT-MH),³⁴^,³⁵ an efficient computerized adaptive test based on multidimensional item response theory (see Appendix). The CAT-MH contains validated measures for diagnostic screening and continuous measurement of targeted mental health disorders and is superior to brief assessments used in other studies during the pandemic. Scores lower than 35 are considered “normal,” scores of 35 to 50 are considered “mild,” scores of 51 to 64 are considered “moderate,” and “severe” scores are 65 and higher.³⁰ The CAT-MH was administered in waves 1, 3, and 4. To aid with interpretation, we standardized depression and anxiety severity by wave.

Demographics

We categorized age into 4 groups: 18 to 25 years (emerging adulthood), 26 to 44 years (early middle adulthood), 45 to 64 years (late middle age), and 65 years and older (late adulthood).¹¹^,³⁶^–³⁸ We performed sensitivity analyses using an alternative coding strategy for emerging adults. Results and substantive conclusions from sensitivity analyses were consistent with main findings. In addition, several potentially confounding sociodemographic characteristics were included as controls.³⁹ Race/ethnicity is a time-invariant, 4-category variable that includes White (reference), Black, Hispanic, and other category. Educational attainment is a time-invariant, 3-category variable that includes high school or less (reference), some college, and bachelor’s degree. Employment status is a time-varying, binary variable (0 = not employed; 1 = employed). Marital status is a time-varying, 3-category variable that includes married or cohabiting (reference), never married, and not currently married (i.e., divorced, annulled, separated, or widowed). Finally, time was coded in years with March 2020 (pandemic onset) set to 0 (range = −1.4–2.5). We also calculated time-squared to assess the nonlinearity of growth or decline in mental health over the course of the pandemic.

Analysis

We conducted all analyses in Stata 17 (StataCorp LP, College Station, TX). Descriptive statistics for each age group are reported with the adjusted Wald and F-test used to examine the statistical significance of mean or proportion differences across age categories. We dropped missing data listwise (n = 200; 7.4% of the full sample), resulting in a final analytic sample of 2485 respondents and 5002 observations. As a sensitivity analysis, we performed multiple imputation by chained equations.⁴⁰

Multilevel linear regression models explored relationships among age, network size, mean network strength, network density, and mental health. These models have a 2-level structure where observations (level 1) are nested within respondents (level 2). For example, the basic mixed-effects model with 2 levels predicting mental health using 2 independent variables takes the following form:

(1)	$y_{i j} = β_{0} + β_{1} x_{1 i j} + β_{2} x_{2 i j} + ζ_{j} + ε_{i j}$

In this model, i corresponds to time (level 1) and j to respondent (level 2). The symbol $ζ_{j}$ represents the random intercept at the respondent level. The symbol $ε_{i j}$ represents the level-1 residual. Both $ζ_{j}$ and $ε_{i j}$ represent the random part of the model, while other components are fixed.

First, regression models examined associations between key study variables and each mental health outcome (see Appendix Table B). Model 1 for each outcome, the baseline model, included age, time, and time-squared (i.e., a growth curve model). Model 2 added sociodemographic and network controls, which continued to be included in each subsequent model. Models 3 and 4 added mean network strength and network density, respectively. Next, a series of models (see Appendix Table C) added interaction terms between age and mean network strength (model 1) and network density (model 2) to assess whether age differences in depression and anxiety were moderated by social network characteristics. All analyses were weighted to adjust for poststratification, clustering (by county), nonresponse, and attrition. Covariates used to predict attrition included age, sex, education, current living arrangements, Hispanic ethnicity, race (simplified 3-category version), and country of origin.

We performed analyses to assess the robustness of findings to alternative specifications. First, we reran all models using an alternative coding strategy for emerging adults (Appendix Tables D and E). Second, we conducted analyses with controls for whether respondents and their close friend(s) or family member(s) had been infected with COVID-19 to adjust for the confounding effect of age differences in exposure and grief (see Appendix Tables F and G). Third, we replicated all analyses using multiple imputation by chained equations rather than listwise deletion (see Appendix Tables H and I). Results from sensitivity analyses were nearly identical to results from the restricted models presented and confirmed the robustness of findings.

RESULTS

Section:

Patterns of mental health revealed an age gradient, as shown using weighted descriptive statistics for each age group (Table 1). Emerging adults (aged 18–25 years) reported the highest depression (mean = 0.13) and anxiety severity (mean = 0.18) scores at baseline. Respondents in late adulthood (aged 65 years and older) reported the lowest levels of depression (mean = −0.31) and anxiety severity (mean = −0.34). Emerging adults consistently reported the most elevated levels of depression and anxiety severity, both in absolute terms and with respect to their baseline, across all waves. As shown in Figure 1, overall levels of depression severity and anxiety severity peaked during the second wave of this analysis which represented the height of the pandemic (January–April 2021). However, they resolved substantially by the third wave (June–September 2022).

TABLE 1— Descriptive Statistics for Study Variables, Stratified by Age Group: Person to Person Health Interview Study (P2P), Indiana, 2019–2022

Covariates	Age 18–25 Years (Emerging Adulthood; n = 234), Mean (SD) or Proportion	Age 26–44 Years (Early Middle Adulthood; n = 806), Mean (SD) or Proportion	Age 45–64 Years (Late Middle Adulthood; n = 764), Mean (SD) or Proportion	Aged 65 Years and Older (Late Adulthood; n = 681), Mean (SD) or Proportion
Female	0.46	0.53	0.55	0.52
Race/ethnicity***
White	0.64	0.79	0.85	0.91
Black	0.17	0.12	0.10	0.07
Hispanic	0.15	0.05	0.02	0.01
Other	0.04	0.04	0.04	0.02
Employed***	0.66	0.80	0.68	0.16
Education***
High school or less	0.35	0.25	0.27	0.37
Some college	0.49	0.38	0.36	0.35
Bachelor’s degree	0.16	0.37	0.37	0.28
Marital status***
Married or partnered	0.22	0.61	0.63	0.61
Never been married	0.76	0.27	0.09	0.03
Widowed, divorced, annulled, or separated	0.01	0.12	0.28	0.36
Mental health
Depression severity (baseline)***	0.13 (1.00)	0.03 (1.00)	‒0.03 (1.04)	‒0.31 (0.87)
Depression severity (T2)***	0.27 (0.64)	0.02 (0.99)	0.01 (0.92)	‒0.30 (1.22)
Depression severity (T3)***	0.18 (0.71)	0.02 (0.92)	0.02 (0.99)	‒0.37 (1.02)
Anxiety severity (baseline)***	0.18 (1.06)	0.14 (1.05)	‒0.09 (1.00)	‒0.34 (0.76)
Anxiety severity (T2)***	0.28 (0.69)	0.12 (1.00)	0.03 (0.94)	‒0.28 (1.12)
Anxiety severity (T3)***	0.20 (0.78)	0.02 (0.91)	0.02 (1.05)	‒0.34 (0.98)
Social network characteristics
Network size	5.12 (2.63)	4.96 (2.44)	5.12 (2.63)	5.39 (3.06)
Mean network strength	8.94 (1.03)	8.84 (1.29)	8.81 (1.61)	8.97 (1.45)
Network density	1.89 (0.74)	1.85 (0.74)	1.81 (0.79)	1.85 (0.80)

Note. T2 = time 2; T3 = time 3.The sample size was n= 2485. All variables, except for mental health measures, are taken from the baseline P2P survey. Standardized means are presented for depression and anxiety severity. The adjusted Wald and F-test were used to assess the statistical significance of mean or proportion differences across age groups.

*P < .05, **P < .01, ***P < .001 (2-tailed test).

FIGURE 1— Predicted Values Over Time as a Function of Age for (a) Depression and (b) Anxiety Severity: Person to Person Health Interview Study, Indiana, 2019‒2022

Note. WHO = World Health Organization. Based on a model that regresses mental health on time, time-squared, and the interaction terms age × time and age × time-squared. Covariates (race/ethnicity, gender, employment status, education, and marital status) are held constant.

With respect to social network characteristics, mean network strength was high across all age categories (emerging adults = 8.94; early middle adulthood = 8.84; late middle adulthood = 8.81; late adulthood = 8.97; range = 0–10). Average network density scores were between 1.8 and 1.9 (range = 0–3), suggesting that respondents across all age groups were embedded in networks where alters were “sort of close” with one another. There was also modest age variation in the size of networks (emerging adults = 5.12; early middle adulthood = 4.96; late middle adulthood = 5.12; late adulthood = 5.39; range = 0–24). Mean differences in network characteristics across age groups were not statistically significant.

Descriptive patterns are confirmed by results from multilevel linear regression models examining relationships among age, mean network strength, network density, and depression and anxiety severity net of controls (see Appendix Table B). An initial increase in depression and anxiety severity occurred during the onset of the pandemic, which decelerated as the pandemic progressed. More striking were age differences in mental health. Emerging adults had depression and anxiety severity scores that were 0.539 (P < .001) and 0.636 (P < .001) standard deviations higher, respectively, than adults aged 65 years and older. After we adjusted for covariates, age disparities in depression and the anxiety persisted and widened, particularly between emerging adults and respondents in late middle adulthood and late adulthood. Multivariate findings also confirm that respondents embedded in stronger networks had lower depression (b = −0.112; P < .05) and anxiety severity (b = −0.101; P < .05), while those embedded in more densely connected networks also had lower depression (b = −0.086; P < .001) and anxiety severity (b = −0.063; P < .01).

Interaction models assessing whether age disparities in mental health were moderated by social network characteristics suggested that the magnitude of the association between mean network strength and density and depression and anxiety severity differed across age groups (see Appendix Table C). Being embedded in a stronger network was associated with the greatest decrease in depression (b = −0.456; P < .001) and anxiety severity (b = −0.388; P < .001) among emerging adults (Figure 2). Likewise, being embedded in a densely connected network was associated with a disproportionately large reduction in depression (b = −0.268; P < .001) and anxiety severity (b = −0.284; P < .001) among emerging adults relative to other age groups (Figure 3). As such, age disparities in depression and anxiety severity were attenuated among respondents who had stronger and more interconnected networks.

FIGURE 2— Predicted Values as a Function of Age and Mean Network Strength for (a) Depression and (b) Anxiety Severity: Person to Person Health Interview Study, Indiana, 2018‒2022

Note. Based on models 1a and 1b, Appendix Table C (available as a supplement to the online version of this article at https://ajph.org).

FIGURE 3— Predicted Values as a Function of Age and Network Density for (a) Depression and (b) Anxiety Severity: Person to Person Health Interview Study, Indiana, 2018‒2022

Note. Based on models 2a and 2b, Appendix Table C (available as a supplement to the online version of this article at https://ajph.org).

DISCUSSION

Section:

The present study examined age-group differences in change in depression and anxiety severity over 3 points in time spanning before the pandemic and 2 follow-up waves in 2021 and 2022. Depression and anxiety peaked in January to April 2021 and were significantly more pronounced among emerging adults aged 18 to 25 years compared with other age groups, consistent with previous studies.¹³^,⁴¹ These patterns parallel the trajectory of COVID-19 transmission rates and related public health responses in Indiana,⁴² including restricted travel, masking, and social distancing recommendations and the closure of many schools and businesses.

These conditions likely contributed to elevated levels of depression and anxiety observed among emerging adults, and to a lesser extent older adults, during the worst months of the pandemic. Contradicting the dire predictions of long-term impact on mental health, we found that depression returned nearly to prepandemic levels in a relatively short period, while anxiety remained somewhat elevated, especially among emerging adults. However, even temporary increases in depression may have contributed to the observed spike in suicidality—including ideation, attempt, and mortality—which corresponded to months when COVID-19–related stressors and community responses peaked.⁴³^,⁴⁴

In addition, we found that being embedded in social networks that contained stronger ties, on average, and more densely connected relationships among network members conferred resiliency to the adverse mental health effects of the pandemic. Protective effects of networks providing a sense of security and social bonding were disproportionately strong among emerging adults such that social network characteristics fully attenuated age-group differences in mental health. Specifically, emerging adults in the top third of the distribution with respect to strength of ties and network density did not report elevated symptoms of depression and anxiety during the pandemic and experienced outcomes similar to middle-aged peers.

There are at least 3 potential explanations for the strong and robust relationships between emerging adults’ mental health outcomes and social network integration and cohesion. First, it is possible that emerging adults were more impacted by stay-at-home orders and the closing of businesses and institutions than other age groups. Research suggests that emerging adults experienced significant declines in physical and social activity during the pandemic, with number of steps and time spent in social interaction decreasing by more than 50% and screen time more than doubling.⁴⁵ These lifestyle changes were, in turn, associated with poorer mental health.⁴⁵^,⁴⁶

Second, many emerging adults are in a liminal state between the security of their family of origin and starting their own families. Emerging adulthood is a period in which levels of communication with and perceived affection from families of origin diminish.²⁶^,²⁹ At the same time, many have yet to settle into long-term romantic partnerships or parenthood. As such, emerging adults may rely more on networks outside the home for feelings of integration and security, but they were largely homebound or restricted from visiting friends and family during the worst months of the pandemic.

Third, emerging adulthood is a critical period of social and emotional development and volatility in which young people experience shifts in feelings of belongingness vis-à-vis family, peers, and communities. Moreover, major transitions experienced in young adulthood are inherently stressful.⁴⁷ Any disruption to the formation or maintenance of social bonds or periods of elevated challenge and uncertainty may disproportionately impact young people at this vulnerable developmental stage.²⁸^,⁴⁸ Research suggests that social connectedness, and particularly relationship quality, is essential for adjustment during emerging adulthood.²⁶^,²⁷ To the extent that emerging adults were able to maintain a sense of integration and a strong social support system during the pandemic, this source of security and social resources would be a powerful driver of resilience.

Finally, we did not identify a protective effect of perceived emotional support on mental health during the pandemic. Previous research suggests that social support does not exhibit a clear dose-dependent effect on well-being; that is, a larger number of supportive ties does not guarantee better mental health. Rather, it is the absence of such network members that most reliably contributes to distress and disorder.⁴⁹ Similarly, the perceived adequacy of support matters more than the objective amount of support received.⁵⁰ For these reasons, having a greater proportion of network members who listen and care may offer diminishing returns for mental health.

Limitations

Although we leveraged a representative probability sample of adults, a limitation of this study is that it is restricted to residents of 1 state, and results may not be generalizable to the country at large. Although the magnitude and timing of these shocks may have varied among states, disruptions to social life across different domains (e.g., work, school)—factors that likely contributed to age disparities in mental health and elevated the importance of social networks in promoting resiliency during the pandemic—were widespread in the United States. However, some early evidence indicates that Republican-led states, including Indiana, had less-stringent policies during the pandemic and began easing COVID-19 restrictions earlier compared with Democrat-led states. Thus, it is possible that our results provide conservative estimates of the protective role of social bonding and cohesion on mental health, particularly among emerging adults who were likely disproportionately affected by social distancing policies.

In addition, because of the risks of in-person contact during the pandemic, follow-up waves of data collection were conducted via phone interview rather than in person. While this shift might have contributed to variation in response patterns within persons over time, it is unlikely that mode changes explained age-group differences, which are a key contribution of this research. In addition, beginning with in-person data collection provided an opportunity for building trust with respondents, which likely improved the quality of subsequent phone interview data, especially in comparison with online surveys, which constituted the bulk of research during the pandemic.

Implications for Research, Policy, and Practice

These findings highlight the importance of tailoring public health response to pandemics by age group. Specifically, the strong protective effects of social network strength and density on the mental health of emerging adults suggests the critical importance of maintaining social integration in tightly knit core networks at this stage in the life course. As such, the individual and population benefits of social distancing and closure of educational institutions, workplaces, and social gathering spaces to reduce viral transmission must be balanced against the potential adverse impact of social isolation on the mental health of young people.

These findings also highlight the importance of strong, lasting social relationships among young people, in general. Unfortunately, recent cohorts of emerging adults may be at greater risk of mental health problems because their relationships are increasingly likely to be mediated by technology relative to past cohorts.⁵¹ The well-documented rise in mental health problems in American youths⁵² can be attributed in part to the adoption of the smartphone and the widespread use of apps like Instagram, contributing to problems such as distressing social comparison and sleep loss.⁵³^–⁵⁵ Indeed, frequent social media use during the pandemic has been linked to reduced loneliness among older adults but greater loneliness among younger adults.⁵⁶ Social media use privileges breadth over depth of social relationships, potentially encouraging weaker and less dense social ties. Researchers and policymakers seeking to increase psychological resilience among young people may wish to intervene on social media use and its offline effects.

Finally, as the Surgeon General’s recent report concluded,⁵⁷ loneliness and isolation are not mere “feelings”—social connectedness has profound consequences for human physical and mental health, productivity, and well-being. While familiar to many researchers who laid the groundwork for this landmark report, the implications for future research are critical. Specifically, mechanistic studies that identify the specific social, psychological, and biological pathways through which social connectedness affects health are essential to inform policy changes and intervention development. We know relatively little about the configurations, not just the characteristics, of social ties and supports that matter, and are only beginning to look at how this systematically varies by age, race, ethnicity, gender, or social class. Without this crucial scientific foundation, our ability to respond to the Surgeon General’s call to “boost” our “culture of connection” remains compromised. The research presented here begins to reveal how social networks have heterogenous effects under different circumstances, for distinct groups, and across the life course.

ACKNOWLEDGMENTS

Funding for this research was provided by the Russell Sage Foundation (PI: B. L. Perry) and the Indiana University Office of the Vice President for Research through the Precision Health Grand Challenge (PI: B. A. Pescosolido).

We thank Hank Green, Person to Person Health Interview Study (P2P) scientific director, and Alex Capshew, P2P project manager, for their assistance during the course of this research.

CONFLICTS OF INTEREST

The authors report no conflicts of interest.

HUMAN PARTICIPANT PROTECTION

The P2P study was approved by the institutional review board at Indiana University (protocol 1803431862 and protocol 2003938142).

REFERENCES

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