Life Course Socioeconomic Status, Daily Stressors, and Daily Well-Being: Examining Chain of Risk Models

Transcription

1 Life Course Socioeconomic Status, Daily Stressors, and Daily Well-Being: Examining Chain of Risk Models Agus Surachman 1,2, Britney Wardecker 2, Sy-Miin Chow 1, and David M. Almeida 1,2 1 Human Development and Family Studies, The Pennsylvania State University 2 Center for Healthy Aging, The Pennsylvania State University

2 Funding: Since 1995 the MIDUS study has been funded by the John D. and Catherine T. MacArthur Foundation Research Network and National Institute on Aging (P01-AG & U19-AG051426).

3 Outline Introduction Childhood SES, daily stress process, and health The chain of risk models: trigger effect versus additive model Method Multilevel Structural Equation Modeling (MSEM) Results Discussion Strengths and Limitations

4 Introduction

5 Childhood SES, Daily Stress Process, and Health Daily stress processes are one pathway through which early life adversity is associated with disparities in health in adulthood Daily stressors (or daily hassles): minor yet frequent day-to-day disruptions, such as family arguments and work deadlines (Almeida, 2005) Low Childhood SES Stressor exposure Stressor severity Daily wellbeing HEALTH Daily Stressors

6 Stressor Severity, Daily Well-Being, and Long-Term Health Outcomes Higher daily stressor severity lower levels of daily positive affect, higher daily negative affect, and higher daily physical symptoms (Almeida, 2005; Almeida et al., 2005; Grzywacz et al., 2004) Lower levels of daily well-being associated with daily stressors significantly predicted: Chronic physical and psychological distress (Charles et al., 2013; Piazza et al., 2013) Chronic conditions and functional impairment (Leger et al., 2015) Elevated levels of inflammation (Sin, Graham-England, Ong, & Almeida, 2015) Mortality risk (Chiang, Turiano, Mroczek, & Miller, 2017; Mroczek et al., 2015)

7 The Chain of Risk Models The precise mechanisms through which childhood SES and daily stressor affect daily well-being? Chain of risk: Early life disadvantages increase one s exposure to later hardships, forming what is known as the chain of risk of health in adulthood (Ben-Shlomo, Mishra, & Kuh, 2014) Majority of individuals: accumulate of either socioeconomic opportunities (high SES) or risks (low SES) across their life course Childhood SES education level adult SES daily stressors daily well-being Chain of risk models: Trigger effect: no direct effect from childhood SES on daily well-being Additive: direct effect from childhood SES on daily well-being

8 This Study Versus MSEM A: chain of risk trigger effect model MSEM B: chain of risk additive model

9 Method

10 Participants and Procedure Current analysis: 782 participants who completed the baseline survey and daily stress diary portion of MIDUS Refresher Daily diary study participants were randomly selected from baseline survey participants (N = 3,572 participants) Daily diary study Micro longitudinal study Participants completed telephone interviews for eight consecutive evenings regarding their daily experiences On average, participants completed 7.5 of the 8 interview days, Total = 5,849 daily diary days data

11 Participants and Procedure Daily diary participants: 55.6% female Mean age = years Age range = years 82.6% White 53.9% hold bachelor s degree or above Average household income: US$84,458.33

12 Measures Day-level variables Daily positive affect Average score from 14 items associated with positive feelings or emotions (adapted from Mroczek & Kolarz, 1998) Daily negative affect Average score from 14 items from the adapted version of the Non-Specific Psychological Distress Scale (Kessler et al., 2002) Daily physical symptoms Sum score from 22-physical symptoms in the physical symptoms checklist (Leger et al., 2015) Daily Stressor exposure Any stressor event covered in the Daily Inventory of Stressor Events (DISE; Almeida et al., 2002) Daily stressor severity The severity ( How stressful was this stressor for you? ) of each reported stressor in each study day using a 4-point Likert scale (0 = not at all, 1 = not very, 2 = somewhat, 3 = very stressful)

13 Measures Person-level variables Childhood SES (treated as latent variable), composite score from four indicators: 1. Parent s highest level of education (0 = < high school, 1 = high school/ GED, 2 = some college and above) 2. Parent s employment status during childhood (0 = not working at all/ a little of time, 1 = some of the time/ most of the time, 2 = all the time) 3. Whether the family of origin received welfare (0 = all the time/ most of the time, 1 = some of the time/ a little of the time, 2 = never in welfare) 4. Financial situation growing up (0 = a lot/ somewhat/ a little worse off than average family, 1 = same as average family, 2 = a lot/ somewhat, a little better off than average family) Education (no bachelor s degree versus bachelor s degree and above)

14 Measures Person-level variables Adult SES (treated as latent variable), composite score from four indicators: 1. Household-size adjusted income to poverty ratio (0 = less than 300%, 1 = more than or equal to 300% but less than 600%, 2 = more than equal to 600%) 2. Current financial situation (0 = worse, 1 = average, 2 = best) 3. Availability of money to meet basic needs (0 = not enough money, 1 = just enough money, 2 = more money than need) 4. Difficulty level paying bills (0 = very/ somewhat difficult, 1 = not very difficult, 2 = not at all difficult) Covariates: age, gender, and race

15 Statistical Analysis Multilevel structural equation modeling (MSEM) MSEM partitions nested data into within- and between-person components Incorporates multivariate information by modeling means and variancecovariances structures of the data Simultaneously test measurement models of latent variables and relations among the latent variables Level 1 (within-level) of the models explains within-person associations among daily-level variables Level 2 (between-level) of the MSEMs serves to delineate the links among between-person predictor variables and individuals latent person-specific means of day-level variables

16 Hypothesis Versus MSEM A: chain of risk trigger model MSEM B: chain of risk additive model

17 Results

18 MSEM A: Trigger Effect Model The average cluster size for MSEM A was 7.45, with intraclass correlations (ICC) ranging from.16 to.76. Design effect for the outcomes ranged from 2.0 to 5.9, confirming the need to account for the within-person associations in the data The model showed overall acceptable goodness-of-fit criteria Assessment of parameter estimates indicated that all the hypothesized paths of chain of risk were significant, except that adult SES was only marginally associated with daily stressor exposure (p <.10)

19 MSEM B: Additive Model Similar design effect values and goodness-of-fit values Likelihood ratio test was used to compare the change in fit from MSEM B to MSEM A. When additional direct paths from MSEM B were omitted to yield MSEM A, significant decrement in fit was observed (χ 2 diff = 31.77, df diff = 9, p <.001) MSEM B is preferable Final model: modified version of MSEM B with only the significant paths were retained

20 Final Model

21 Discussion

22 Discussion Childhood SES links to education, and education, in turn, links to adult SES Adult SES is associated with severity of daily stressors, but not daily stressor exposure Daily stressor severity is a better predictor of daily well-being compared to daily stressor exposure Childhood SES was directly associated with between-person levels of daily physical and psychological well-being, especially daily physical symptoms and daily negative affect Childhood SES was indirectly associated with overall well-being through the chain of risk of life course SES and daily stressor exposure and severity

23 Discussion This paper is among the first to show that childhood SES has a direct influence on day-to-day well-being Childhood may be a sensitive period that has salient implications for daily well-being in adulthood Public health implications, given that previous studies have shown that higher daily physical symptoms and negative affect due to daily stressors are associated with long-term health outcomes Future studies should prioritize examination of the linkages among childhood SES, daily stressors, daily well-being, daily biological functioning, and long-term health outcomes

24 Strengths MSEM method: simultaneously investigate within-person links among childhood SES, daily stressor exposure and severity, and daily well-being, as well as between-person differences in such links This study incorporated multiple indicators to measure childhood and adult SES, combining both subjective and objective indicators This study incorporated three measures of daily well-being and analyzed them simultaneously, minimalizing the symptom perception bias

25 Limitations Life course data for the analyses were collected using a retrospective cross-sectional research design Participants in this sample did not reflect the national distribution of the United States population (racial and socioeconomic composition) Our analyses excluded other types of early life hardship such as early traumatic experiences (e.g., child abuse) This study did not incorporate the timing and duration of hardships, which are also important predictors of health in adulthood Our model is unable to explain the interindividual variability regarding mobility of SES across the life course and the influence of education, personality, and resiliency on buffering life course hardships

26 Limitations Limitations regarding measures of life course SES parental employment status does not distinguish part-time or full-time employment status, number of jobs held, or spells of unemployment The item regarding family welfare receipt may be biased toward single mother households Questions about financial situation may depend on social comparison, which may depend on neighborhood level SES These limitations may lead to biased estimation of the influence of childhood SES on daily well-being. Replications are needed to test the consistency of these results using different data sets More studies are needed to investigate the validity of these childhood SES measures

27 Thank you! Agus Surachman: David Almeida:

28 Statistical Analysis Latent person-specific in the level 2 = individuals overall stress exposure and severity and overall levels on those three well-being variables, refer as: Between-person daily stressor exposure and between-person daily stressor severity Between-person daily physical symptoms, between-person daily positive affect, and between-person daily negative affect Level 2 provides a proxy for testing targeted aspects of the two chain of risk models Trigger effect model: no direct paths from distal risk factors (life course SES) to any of the three latent well-being variables at level 2 Additive model: inclusion of direct paths from all distal risk factors (life course SES) to those three latent well-being variables at level 2 Analysis was conducted using Mplus version 7.3

29 Appendix Daily positive and negative affect: across all persons and days, the mean of daily positive affect was 2.52 (SD = 2.62, minmax = 0-4) and the mean of daily negative affect was.22 (SD =.07, min-max = ). Daily physical symptoms: across all persons and days, the mean of daily physical symptoms was 1.87 (SD = 2.33, min-max = 0-22). Daily stressor exposure: participants in the MIDUS Refresher reported at least one daily stressor on 40% of the study days Daily stressor severity: across all persons and days, the mean of daily stressor severity was.75 (SD = 1.02, min-max = 0-3)

30 Appendix Childhood SES: the mean childhood SES score was 5.95 (SD = 1.60, median = 6, min-max = 0-8) Education: 46.1% reported that they had at least a bachelor s degree Adult SES: the mean adult SES score was 3.80 (SD = 2.31, median = 4, min-max = 0-8)

31 Appendix

32 Understanding Childhood Risk Factors for Patterns of Alcohol Use, Smoking, and Distress in Adulthood Presenter: Yuen Wai H ung Co-authors: Bethany Bray, Jennifer Maggs M A R CH 9 TH, N C D S 6 0 Y EARS O F O U R L I V ES, LO N D ON

33 Acknowledgement The National Child Development Study and participants The Prevention and Methodology Training Program (T32 DA017629; P50 DA039838) Funding from the National Institute on Drug Abuse (NIDA) The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDA or NIH

34 Background Alcohol and tobacco Most commonly used substances Heavy alcohol use and smoking - leading causes of chronic health problems and early mortality (Whiteford et al., 2013) Psychological distress Associated with chronic diseases and premature mortality (Batty et al., 2017)(Russ et al., 2012) Both excessive alcohol use and tobacco use related to psychological distress Current smokers more likely depressed than former and never smokers (Luger et al., 2014) Presence of alcohol disorder or major depression increases risk of the other (Boden & Fergusson, 2011)

35 Background Interrelationship between alcohol use, smoking, and psychological distress Adult smokers more likely to use alcohol, have internalizing and externalizing problems (Conway et al., 2017) Higher psychological distress Heavy drinking and smoking pattern (Staff & Maggs, 2017) Non-drinking compared to light/moderate drinking (Mathiesen et al., 2012) Potential subset of former heavy drinkers (Lucas et al., 2010)

36 Background: Childhood Risk Factors Childhood risk factors of adult mental health and substance use Behavioral Externalizing behavioral disinhibition (Hayatbakhsh et al., 2008)(Fischer et al., 2012) Internalizing behavioral inhibition (Hussong et al., 2011) Social environment Adverse childhood experiences (Choi et al., 2016)(Merrick et al., 2017)(Fothergill et al., 2016) Dysfunctional household environment, abuse, and neglect

37 Objectives 1. Identify distinct patterns of drinking, smoking, and distress from early to mid-adulthood 2. Examines childhood predictors of these patterns 1. Childhood internalizing and externalizing behaviors 2. Adverse childhood experiences

38 Sample The National Child Development Study (NCDS), born in 1958 Childhood data (ages 7 & 11) Adult drinking, smoking, and distress level (ages 23,33,42) Mid-adulthood health and functioning (age 50) Sample: surveyed at age 50 and at least once in 23, 33, and 42 (n=9577)

39 Measures (childhood) Childhood internalizing and externalizing behaviors Teacher-rated Bristol Social Adjustment Guides Behavior items grouped into syndromes Relevant syndromes scores summed Top 13% of summed scores considered as high behavior level

40 Measures (childhood) Adverse childhood experience (ACEs) Parental death Parental separation Child in public care services or foster care Family member in probation or prison Family member with mental illness Family member with alcohol abuse problem Child frequently bullied Physical neglect (undernourished or dirty) Proportion of sample with cumulative types of ACEs by age none 1 type of ACE 2 types of ACE 3 or more types of ACE

41 Measures (adulthood) Alcohol use Frequency; Units of Alcohol in prior week Combined into: Never nowadays Less than once/month or only special occasions Low-risk drinking (<=14 units) Smoking Exceed moderate drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Average daily cigarette use Combined into: Not current smoking 1-19 cigarettes per day >=20 cigarettes per day Psychological Distress Malaise Inventory, 15-item subscale (α: ) High psychological distress: 5 or more symptoms (Power et al., 2011) Health and Functioning SF-36 (Ware & Sherbourne, 1992) Multi-item scale that assesses 8 health concepts Risk of Alcohol Problems AUDIT Women: score >=13; Men: score >=15 (Brown et al., 2009)

42 Analytic Methods Repeated-measures latent class analysis Identify unique patterns of drinking, smoking, and distress (ages 23, 33, 42) Unconditional class estimation: 1-9 class solutions tested using Mplus Covariates estimation using R3step Bivariate association with latent classes Multivariate association Internalizing and externalizing behavior (ages 7, 11) ACEs (by age 11) Controlled for sex, father s social class (age 11), education attainment (age 23), attained social class (age 23)

43 Unconditional class estimation Classes df LL AIC BIC a-bic LRT-value LMR-LRT Stability Entropy

44 Estimated probabilities of drinking level, smoking level, and distress C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Age Past week alcohol use Never nowadays Less than once per month or only on special occasion Low-risk drinking (<=14 units) More than low-risk drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Number of cigarettes usually smoked Not currently smoking cigarettes per day >=20 cigarettes per day Distress No / low distress High distress

45 Estimated probabilities of drinking level, smoking level, and distress C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Age Past week alcohol use Never nowadays Less than once per month or only on special occasion Low-risk drinking (<=14 units) More than low-risk drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Number of cigarettes usually smoked Not currently smoking cigarettes per day >=20 cigarettes per day Distress No / low distress High distress

46 Estimated probabilities of drinking level, smoking level, and distress C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Age Past week alcohol use Never nowadays Less than once per month or only on special occasion Low-risk drinking (<=14 units) More than low-risk drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Number of cigarettes usually smoked Not currently smoking cigarettes per day >=20 cigarettes per day Distress No / low distress High distress

47 Estimated probabilities of drinking level, smoking level, and distress C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Age Past week alcohol use Never nowadays Less than once per month or only on special occasion Low-risk drinking (<=14 units) More than low-risk drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Number of cigarettes usually smoked Not currently smoking cigarettes per day >=20 cigarettes per day Distress No / low distress High distress

48 Estimated probabilities of drinking level, smoking level, and distress C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Age Past week alcohol use Never nowadays Less than once per month or only on special occasion Low-risk drinking (<=14 units) More than low-risk drinking (women: units; men: units) Potential harmful drinking (women: >35 units; men: >50 units) Number of cigarettes usually smoked Not currently smoking cigarettes per day >=20 cigarettes per day Distress No / low distress High distress

49 Multivariate multinomial regression of childhood risk factors with latent classes C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%)) C5: Infrequent/low-risk drinking, moderate/heavy smoking, more distress (19%) aor (95% CI) aor (95% CI) aor (95% CI) aor (95% CI) Adverse childhood experiences (reference: none) * ( ) 1.06 ( ) 1.23 ( ) 1.48 *** ( ) ( ) 1.07 ( ) 0.97 ( ) 1.94 *** ( ) ( ) 2.44 * ( ) 2.33 * ( ) 3.38 *** ( ) Age 7 Externalizing behavior 1.05 ( ) 1.31 ( ) 1.48 * ( ) 1.51 ** ( ) Internalizing behavior 0.87 ( ) 0.65 ** ( ) 1.56 ** ( ) 0.96 ( ) Age 11 Externalizing behavior 1.67 ** ( ) 2.34 *** ( ) 1.00 ( ) 2.36 *** ( ) Internalizing behavior 0.94 ( ) 0.86 ( ) 1.19 ( ) 1.31 * ( )

50 Multivariate multinomial regression of childhood risk factors with latent classes C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%)) C5: Infrequent/low-risk drinking, moderate/heavy smoking, more distress (19%) aor (95% CI) aor (95% CI) aor (95% CI) aor (95% CI) Adverse childhood experiences (reference: none) * ( ) 1.06 ( ) 1.23 ( ) 1.48 *** ( ) ( ) 1.07 ( ) 0.97 ( ) 1.94 *** ( ) ( ) 2.44 * ( ) 2.33 * ( ) 3.38 *** ( ) Age 7 Externalizing behavior 1.05 ( ) 1.31 ( ) 1.48 * ( ) 1.51 ** ( ) Internalizing behavior 0.87 ( ) 0.65 ** ( ) 1.56 ** ( ) 0.96 ( ) Age 11 Externalizing behavior 1.67 ** ( ) 2.34 *** ( ) 1.00 ( ) 2.36 *** ( ) Internalizing behavior 0.94 ( ) 0.86 ( ) 1.19 ( ) 1.31 * ( )

51 Multivariate multinomial regression of childhood risk factors with latent classes C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Infrequent/low-risk drinking, moderate/heavy smoking, more distress (19%) aor (95% CI) aor (95% CI) aor (95% CI) aor (95% CI) Adverse childhood experiences (reference: none) * ( ) 1.06 ( ) 1.23 ( ) 1.48 *** ( ) ( ) 1.07 ( ) 0.97 ( ) 1.94 *** ( ) ( ) 2.44 * ( ) 2.33 * ( ) 3.38 *** ( ) Age 7 Externalizing behavior 1.05 ( ) 1.31 ( ) 1.48 * ( ) 1.51 ** ( ) Internalizing behavior 0.87 ( ) 0.65 ** ( ) 1.56 ** ( ) 0.96 ( ) Age 11 Externalizing behavior 1.67 ** ( ) 2.34 *** ( ) 1.00 ( ) 2.36 *** ( ) Internalizing behavior 0.94 ( ) 0.86 ( ) 1.19 ( ) 1.31 * ( )

52 Multivariate multinomial regression of childhood risk factors with latent classes C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%)) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (19%) aor (95% CI) aor (95% CI) aor (95% CI) aor (95% CI) Adverse childhood experiences (reference: none) * ( ) 1.06 ( ) 1.23 ( ) 1.48 *** ( ) ( ) 1.07 ( ) 0.97 ( ) 1.94 *** ( ) ( ) 2.44 * ( ) 2.33 * ( ) 3.38 *** ( ) Age 7 Externalizing behavior 1.05 ( ) 1.31 ( ) 1.48 * ( ) 1.51 ** ( ) Internalizing behavior 0.87 ( ) 0.65 ** ( ) 1.56 ** ( ) 0.96 ( ) Age 11 Externalizing behavior 1.67 ** ( ) 2.34 *** ( ) 1.00 ( ) 2.36 *** ( ) Internalizing behavior 0.94 ( ) 0.86 ( ) 1.19 ( ) 1.31 * ( )

53 Association with health, functioning, and risk of alcohol dependence at age 50 C1: Low-risk drinking, non-smoking, low distress (38%) C2: Higher-risk drinking, non-smoking, low distress (20%) C3: Higher-risk drinking, heavy smoking, slight distress (12%) C4: Infrequent drinking, non-smoking, more distress (12%) C5: Low-risk/infrequent drinking, moderate/heavy smoking, more distress (18%) Mean/prop Mean/prop Mean/prop Mean/prop Mean/prop General health Overall relationship between classes 5<2,4,1; 3,4<2,1; 2<1 Physical functioning <3,2,1; 3,4<2,1; Pain <1 Energy/fatigue Role limitationphysical Role limitationemotional 5<3,2,1; 3,4<2, <2,1; 3,4<2,1; <1 Emotional well-being ,3,4<2,1 Social functioning ,2<1; 5<1 3>2,5,1,4; Risk of alcohol 0.7% 6.1% 10.9% 0.1% 2.0% 2>5,1,4; dependence 5>4,1

54 Discussion Tobacco use associated with internalizing symptoms (Conway et al., 2017) Infrequent/abstaining alcohol use associated with distress (Staff & Maggs, 2017) Higher risk alcohol use associated with worse health than low-risk alcohol use (Salonsalmi et al., 2017) ACEs association with adult smoking and psychological distress (Ford et al., 2011) Not with individuals with higher risk drinking but not smoking and low distress (Fang & McNeil, 2017) Important to consider psychological distress when assessing the health risk of life course alcohol use and smoking Life course implication of childhood risk factors on health behaviors and distress Future work may consider pathways of these associations Association with earlier health status and other life style factors Extend beyond mid-life to understand long-term patterns

55 Strengths and Limitations Strengths Prospective measures throughout different life stages Limitations Attrition over time Not comprehensive assessment of adverse child experience Not included adolescence experience and behavior Association between latent classes and health and functioning at age 50 not controlled for health status, sex, and other social factors

56 Thank you

57 Emotional Reactivity to Daily Stressors Predicts 20-Year Risk of Mortality David Manuel Almeida Nancy Sin Center for Healthy Aging

58 Basic Definitions of Stress Engineering Definition: The force exerted upon a body that tends to strain or deforms it shape. Webster's New World Dictionary (2002) Human Definition: Stress refers to the pressure that life exerts on us and the way this pressure makes us feel. McEwen (2002)

59 Daily Stress Paradigm Challenges and frustrations of daily life (disagreements, malfunctions, time pressures) Intensive assessment: Daily Diaries Advantages 1. Assess naturally occurring tangible events: capture life as it is lived 2. Minimize memory bias 3. Evaluate daily exposures 4. Calculate stressor reactivity (Withinperson slopes)

60 Stressor Exposure Daily Inventory of Stressful Events (DISE) In the past 24 hours Did you have an argument or disagreement? Did anything happen at work that people would consider stressful? Did anything happen at home that people would consider stressful? Did anything happen to a close friend or relative that was stressful for you? Did anything else happen to you that people would consider stressful? Almeida, D. M., Wethington, E., & Kessler, R. C. (2002). The Daily Inventory of Stressful Experiences (DISE): An interviewbased approach for measuring daily stressors. Assessment, 9, 41-55

61 Daily Stressor Affective Reactivity Within-person Slopes between Stressor and Affect High Reactivity Low Reactivity High Reactivity Low Reactivity High Reactivity Low Reactivity

62 Why Should Emotional Reactivity be Related to Health Outcomes? Repetti, Taylor & Seeman (2002) and others have theorized that greater responsiveness to stress indicating poorer handling or managing of stress is itself likely to have adverse effects on downstream physical health outcomes, probably through dysregulated HPA function.

63 National Study of Daily Experiences Telephone Diary Study Across Eight Consecutive Evenings National sample of participants from the daily diary project of MIDUS Two waves of data (1996 & 2005) Third Wave! (2017) Refresher Cohort (2008) (Npeople = 2,765, Ndays=30,165) Mean Age = 46 (SD = 12, Range = 25 94)

64 Emotional Reactivity r= 0.37*** Wave 1 Wave 2 Stressors 0.14*** 0.13*** Negative Affect Stressors Negative Affect Variance in Stressor Reactivity Slope σ 2 u1 = 0.04*** σ 2 u1 = 0.02*** Individual Differences in How Much they React to Daily Stressors

65 % change in risk of chronic condition 10 years later Stressor reactivity predicts 10% to 34% increased risk of reporting a chronic condition 10 years later. 40 p< NS p<.01 p<.05 NS p<.05 Stressor Exposure (1-Unit Increase) Stressor Reactivity (1-Unit Increase) -10 Age (+10 years) Entire Sample (N = 435) Initially Disease-Free (N = 105) Sample Piazza, J. R., Charles, S. T., Sliwinski, M. J., Mogle, J., & Almeida, D. M. (2013). Affective reactivity to daily stressors and long-term risk of reporting a chronic physical health condition. Annals of Behavioral Medicine, 45(1),

66 Daily stress and mortality We know that affective reactivity is related to health, but what about mortality? Among 181 men in the VA Normative Aging Study, PA reactivity (but neither NA reactivity nor stressor exposure) increased the 10-year risk of mortality (Mroczek et al., 2015) Objective: To examine whether affective reactivity (assessed at MIDUS I) was prospectively related to mortality risk across 20 years of follow-up.

67 Data Analysis 1. We employed within-person variation multilevel models (Hoffman & Stawski, 2009) to estimate emotional reactivity coefficients. 2. Proportional Hazards (Cox) models were used to predict mortality risk. All predictors were standardized in z-units so that hazard ratios reflected the risk of dying (allcause) per 1 standard deviation (SD) difference in each predictor variable.

68 Emotional Reactivity The reactivity variables indicate the degree to which a stress day was associated with increased negative affect and decreased in positive affect. These individual-level reactivity estimates (slopes) were entered as predictors of mortality.

69 Participant characteristics (N = 1253) 203 deaths (16%) across mean follow-up of 17.8 years Variable Mean SD Survival in years Diaries completed Age at baseline Stressor frequency 41% 21% PA reactivity NA reactivity

70 Kaplan-Meier survival curve for lower vs. higher NA reactivity

71 NA reactivity (per 1 SD) predicted twofold greater mortality risk Predictor Hazard Ratio (95% CI) p NA reactivity 2.15 (1.07, 4.35) 0.03 Age (per year) 1.11 (1.09, 1.12) <0.001 Male 1.33 (1.00, 1.76) <0.05 Some college 0.85 (0.63, 1.13) 0.26 Stressor frequency 1.49 (0.75, 2.95) 0.26

72 Kaplan-Meier survival curve for lower vs. higher PA reactivity

73 PA reactivity (per 1 quartile) predicted 18% greater mortality risk Predictor Hazard Ratio (95% CI) p PA reactivity 1.18 (1.04, 1.34) Age (per year) 1.11 (1.09, 1.12) <0.001 Male 1.39 (1.05, 1.85) 0.02 Some college 0.83 (0.62, 1.11) 0.21 Stressor frequency 1.65 (0.83, 3.24) 0.15

74 PA reactivity uniquely predictive of mortality risk? Predictor Hazard Ratio (95% CI) p PA reactivity 1.15 (1.02, 1.31) 0.03 NA reactivity 1.80 (0.81, 4.01) 0.15 Age (per year) 1.11 (1.09, 1.12) <0.001 Male 1.37 (1.03, 1.82) 0.03 Some college 0.86 (0.64, 1.15) 0.30 Stressor frequency 1.58 (0.80, 3.12) 0.19

75 Discussion Perhaps it is less increases in NA that puts us at risk for worse health and mortality, but rather decreases in PA that places us at risk. PA may play a buffering role when stress occurs. Those who can remain positive may stem their health risks -- perhaps it allows them to continue keeping up their health behaviors, or allows continued good interpersonal functioning in the face of stress.

76 It's not the lost lover that brings us to ruin, or the barroom brawl, or the con game gone bad, or the beating taken in the alleyway. But the lost car keys, The broken shoelace, The overcharge at the gas pump Which we broach without comment these are the things that eat away at life, these constant vibrations In the web of the unremarkable. Jay Hopler (Meditation on Ruin, 2006)

77 Acknowledgement of Funding The MIDUS and NSDE are supported by NIH Grants P01 AG , R01 AG19239, T32 MH and the Network on Successful Mid-Life Development of the John D. and Catherine T. MacArthur Foundation