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Adverse childhood experiences, dispositional mindfulness, and adult health
Preventive Medicine 67 (2014) 147–153
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Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Adverse childhood experiences, dispositional mindfulness, and adult health Robert C. Whitaker a,b,⁎, Tracy Dearth-Wesley a, Rachel A. Gooze c, Brandon D. Becker a, Kathleen C. Gallagher d, Bruce S. McEwen e a
Department of Public Health, Temple University, Philadelphia, PA, USA Department of Pediatrics, Temple University, Philadelphia, PA, USA Child Trends, Bethesda, MD, USA d Frank Porter Graham Child Development Institute, University of North Carolina, Chapel Hill, NC, USA e Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology, The Rockefeller University, New York, NY, USA b c
a r t i c l e
i n f o
Available online 30 July 2014 Keywords: Child abuse Child neglect Child maltreatment Mindfulness Stress, Psychological Chronic disease Quality of life Health behavior
a b s t r a c t Objective. To determine whether greater dispositional mindfulness is associated with better adult health across a range of exposures to adverse childhood experiences (ACEs). Methods. In 2012, a web-based survey of 2160 Pennsylvania Head Start staff was conducted. We assessed ACE score (count of eight categories of childhood adversity), dispositional mindfulness (Cognitive and Affective Mindfulness Scale—Revised), and the prevalence of three outcomes: multiple health conditions (≥3 of 7 conditions), poor health behavior (≥2 of 5 behaviors), and poor health-related quality of life (HRQOL) (≥2 of 5 indicators). Results. Respondents were 97% females, and 23% reported ≥3 ACEs. The prevalences of multiple health conditions, poor health behavior, and poor HRQOL were 29%, 21%, and 13%, respectively. At each level of ACE exposure, health outcomes were better in those with greater mindfulness. For example, among persons reporting ≥3 ACEs, those in the highest quartile of mindfulness had a prevalence of multiple health conditions two-thirds that of those in the lowest quartile (adjusted prevalence ratio (95% confidence interval) = 0.66 (0.51, 0.86)); for those reporting no ACEs, the ratio was 0.62 (0.41, 0.94). Conclusion. Across a range of exposures to ACEs, greater dispositional mindfulness was associated with fewer health conditions, better health behavior, and better HRQOL. © 2014 Elsevier Inc. All rights reserved.
Introduction Over half of US adults have experienced one or more types of adverse childhood experiences (ACEs), such as abuse and household dysfunction (Centers for Disease Control and Prevention, 2010). These exposures are associated with an increased risk of several chronic health conditions (Felitti and Anda, 2010; Felitti et al., 1998) and mortality (Brown et al., 2009). Childhood traumas are thought to worsen adult health through changes in the structure and function of the body's stress-response systems (Danese and McEwen, 2012; Danese et al., 2009) and through poor health behaviors, such as smoking, which may be adopted to help cope with stress (Anda et al., 1999). These unhealthy biologic and behavioral responses to childhood adversity can be reactivated in adults during the course of their work providing human services to children experiencing trauma (Kluft et al., 2000).
⁎ Corresponding author at: Temple University, 3223 North Broad Street, Suite 175, Philadelphia, PA 19140, USA. Fax: +1 215 707 6475. E-mail address: [email protected] (R.C. Whitaker).
http://dx.doi.org/10.1016/j.ypmed.2014.07.029 0091-7435/© 2014 Elsevier Inc. All rights reserved.
This reactivation may worsen the health and workplace functioning of these adult caregivers (Perry et al., 1995). Dispositional mindfulness is the general tendency to have awareness that results from purposefully paying attention to sensations, thoughts, and feelings in the present moment while suspending judgments (Bishop et al., 2004; Kabat-Zinn, 2003). Mindfulness practices, such as meditation, can increase dispositional mindfulness (Carmody and Baer, 2008; Kuyken et al., 2010) and alleviate psychological and somatic symptoms, such as depression and pain, which can accompany exposure to traumatic experiences (de Vibe et al., 2012; Goyal et al., 2014). These practices can result in favorable changes in brain structure and function and in physiologic parameters of the stress response— changes opposite to those that can result from exposure to repeated or severe stress (Davidson and McEwen, 2012; Shonkoff and Garner, 2012). While ACEs can lead to poor health, and mindfulness practices can improve health, we know of no studies that have examined the relationships between ACEs, dispositional mindfulness, and health, nor any that have examined ACEs and health among human service providers. Using data from a survey of staff in Head Start, a large federally-funded early
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childhood education program serving children in poverty, we hypothesized that staff with greater dispositional mindfulness would have fewer health conditions, better health behavior, and higher healthrelated quality of life (HRQOL), and that this would be true across a range of exposures to their own ACEs. Confirmation of this hypothesis could provide evidence to support the testing of mindfulness-based interventions to improve the health and functioning of human service providers. Methods The Pennsylvania Head Start Staff Wellness Survey, conducted in 2012, was an anonymous, voluntary, web-based survey of staff in the state's Head Start and Early Head Start programs. The survey protocol, approved by Temple University's Institutional Review Board, has been previously described (Whitaker et al., 2013). Sixty-six of Pennsylvania's 91 Head Start and Early Head Start programs participated, and 2199 of 3375 (65%) staff members in the participating programs responded to the survey. To assess any pattern of non-response, we used federal program-level data on staff characteristics for teachers and home-visitors (US Department of Health and Human Services, Administration for Children and Families, 2011). Among teachers and homevisitors in the 66 participating programs, 57% had a bachelor's or associate's degree in early childhood education and 85% were White. This compared to 55% and 88%, respectively, among teachers and home-visitors that participated in the survey. Health indicators Sixteen binary (yes/no) health indicators were developed across three domains: health conditions (7 indicators), health behavior (5 indicators), and health-related quality of life (HRQOL) (4 indicators). The wording of questions about health conditions and HRQOL was the same as in the National Health Interview Survey (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014) and the Behavioral Risk Factor Surveillance System (BRFSS) (Centers for Disease Control and Prevention, National Center for Health Statistics, Public Health Surveillance and Informatics Program Office, 2013). Health conditions Participants were asked about seven stress-associated health conditions that are common in mid-life: depression (Hammen, 2005), severe headache or migraine (Nash and Thebarge, 2006), lower back pain (Linton, 2000), obesity (Wardle et al., 2011), hypertension (Rozanski et al., 1999), diabetes or prediabetes (Black, 2003), and asthma (Wisnivesky et al., 2010). In separate questions, participants were asked whether they had “ever been told by a doctor or other health professional” that they had depression, hypertension or high blood pressure, diabetes or sugar diabetes (other than during pregnancy), prediabetes or borderline diabetes, and asthma. They were also asked whether, during the last 3 months, they had “severe headache or migraine that lasted a whole day or more” and “lower back pain that lasted a whole day or more.” Obesity (body mass index ≥ 30 kg/m2) was determined from self-reported height and weight (pre-pregnant weight if pregnant). Health behaviors Participants were asked “Do you smoke cigarettes (yes/no)?” and “How many times in the past year have you had 4 or more drinks in a day?” (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014; Wechsler et al., 1995). Those who reported ≥ 12 occasions (≥ 1/month) were considered to binge drink (Substance Abuse and Mental Health Services Administration, 2013). Binge eaters were those who reported an eating binge (“eating an amount of food that most people, like your friends, would consider to be very large, in a short period of time”) once a week or more often during the past year and feeling “out of control” during those binges (American Psychiatric Association, 2013; Field et al., 2004). Participants were classified as inactive if they reported that they did not “at least once a week engage in any regular activity like brisk walking, jogging, bicycling, etc., long enough to work up a sweat” (Paffenbarger et al., 1993). Low nighttime sleep (b 6 h) (Cappuccio et al., 2011) was based on responses to the following question, “During the past month, how many hours of actual sleep did you get at night?” (Buysse et al., 1989).
HRQOL Participants were classified as having poor or fair health status based on responses to the question, “Would you say your health in general is poor, fair, good, very good, or excellent?” (DeSalvo et al., 2006). Following the Centers for Disease Control and Prevention, we calculated separately the prevalence of frequent (≥14 days/month) physically unhealthy days and mentally unhealthy days (Moriarty et al., 2003). The prevalence of ≥10 work absences/year due to illness was based on responses to the question, “During the past 12 months, about how many days did you miss work because of your own illness or injury?” (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014). Primary outcomes We summed the number of poor health indicators in each domain and created three binary outcome variables: multiple health conditions (≥3 of 7 conditions), poor health behavior (≥ 2 of 5 behaviors), and poor HRQOL (≥ 2 of 4 indicators). These variables were made to avoid multiple comparisons in the analyses and because indicators within each domain often co-occur. The cut point for the binary outcome variable in each domain was selected to produce a prevalence that was closest to the mean of the prevalences of the indicators in that domain. Exposure measures ACEs Participants were asked whether they experienced each of eight categories of childhood adversity related to abuse and household dysfunction (Table A.1). Following the work of other researchers (Felitti et al., 1998), an ACE score (0 to 8) was made by summing the number of categories of adversity, and for analysis, participants were divided into groups based on ACE score (0, 1, 2, and ≥3). Mindfulness We assessed dispositional mindfulness with the 12-item Cognitive and Affective Mindfulness Scale—Revised (CAMS-R) (Feldman et al., 2007), a singlefactor scale correlated with other scales of dispositional mindfulness (Baer et al., 2006). Each item describes an attitude or approach toward the experience of one's emotions or thoughts in four areas—focusing attention, being oriented to the present moment, being aware of an experience, and having an attitude of acceptance or nonjudgment toward an experience. The scale scores, with a possible range from 12 to 48 (low to high mindfulness), had a normal distribution and an internal consistency (Cronbach alpha) of 0.85. To facilitate interpretation, the scores were divided into quartiles (high, medium-high, medium-low, and low) for analyses. Covariates The survey asked participants about their gender, age, race, ethnicity, education, relationship status, job position, and whether they or their own children had ever attended Head Start. Participants were also asked about five categories (yes/no) of economic hardship that they may have experienced during the prior year: food insufficiency (Ribar and Hamrick, 2003), receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare. Data analysis Our analyses involved 2160 participants, excluding 39 who were missing data on mindfulness or ACE score. We used chi-square tests to examine the relationships between each covariate and the prevalences of high ACE score (≥3), high mindfulness (upper quartile), and each health outcome. A chi-square test for trend (Cochran–Armitage test) was used to examine the associations between the health outcomes and levels of ACEs and mindfulness. For multivariable analyses, 90 cases (4%) of the sample were missing one or more covariates, so we first imputed these missing values with sequential regression imputation (Raghunathan et al., 2001) using Stata software (StataCorp, 2013). We created 20 imputed datasets with an imputation model that included all covariates, ACE score, mindfulness score, and the three health outcomes (Graham et al., 2007). In regression models, we accounted for the clustering of participants within Head Start programs using Taylor series linearization methods (Heeringa et al., 2010). Using separate logistic regression models for those at each ACE score (0, 1, 2, ≥3) and adjusting for covariates, we computed prevalence ratios to estimate associations between mindfulness and each of the three outcomes, with the lowest quartile of mindfulness as the reference group
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(Cummings, 2009, 2011). In this method, we used regression-based margins, standardized to the distribution of covariates in the study population, to estimate the standardized prevalences of the three health outcomes at each level of ACE score (0, 1, 2, ≥3) and mindfulness. The prevalence ratios describe the standardized prevalence of the health outcome (multiple health conditions, poor health behavior, or poor HRQOL) in those with high, medium-high, and medium-low mindfulness relative to the standardized prevalence of the same health outcome in those with low mindfulness. In logistic regression models using all 2160 subjects and all covariates, we used Wald tests to assess the main effects of both ACEs and mindfulness (and the multiplicative interaction between ACEs and mindfulness) on each health outcome.
Results The participants were predominantly non-Hispanic White females (Table 1). Approximately one fifth had experienced two or more economic hardships in the last year, and approximately one fourth had a child who had attended Head Start (Table 1). Three or more of the seven health conditions were reported by 29%, two or more of the five poor health behaviors by 22%, and two or more of the four indicators of poor HRQOL by 13%. Emotional abuse was more common than physical abuse or sexual abuse (Table A.1). The prevalences of 0, 1, 2, and ≥3 ACEs were 41, 22, 14, and 23%, respectively. The mean (SD) of the mindfulness score was 35 (6). All covariates, except for relationship status, were significantly associated with having ≥3 ACEs (Table 2). Those who had more economic hardships more often reported having ≥3 ACEs and were less often in the upper quartile of mindfulness (Table 2). All covariates were also related to one or more of the three outcomes (Table A.2). As the level of mindfulness decreased from the highest to lowest quartile, the prevalence of having three or more ACEs increased (17%, 23%, 25%, and 27%; p b .001). Those with a higher number of ACEs had a significantly higher prevalence of each of the 16 health indicators, except for high blood pressure, diabetes/pre-diabetes, and binge drinking. Those with higher levels of mindfulness had a significantly lower prevalence of each health indicator, except for obesity, high blood pressure, diabetes/pre-diabetes, and work absences (Table 3). In logistic regression models (one for each of the three health outcomes), there was a significant main effect of the level of mindfulness (p b .001) and the level of ACEs (p b .01) on each outcome. There was statistical evidence of an interaction between levels of ACEs and mindfulness for poor HRQOL (p = .04) and poor health behavior (p = .06), but not for multiple health conditions (p = .25). However, in stratified analyses, at every level of ACEs, the prevalence of having multiple health conditions, poor health behavior, and poor HRQOL decreased as mindfulness increased (Table 4), even after adjusting for economic hardships and other covariates (Table 4 and Fig. 1). Discussion In this population-based study of staff working with low-income children and families in Pennsylvania's Head Start programs, we found that the likelihood of having multiple health conditions, poor health behavior, and poor HRQOL increased with the level of exposure to ACEs. However, these same health outcomes were all less common in those who reported higher levels of mindfulness, and this was true across a range of exposure to ACEs. This cross-sectional analysis does not allow us to make causal inferences about the relationships between ACEs or mindfulness and health outcomes. However, these relationships are biologically plausible, large in magnitude, graded in nature, and consistent across several health outcomes. ACEs and health Our findings were consistent with those of the original ACE Study, which showed a graded relationship between the number of ACEs and
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Table 1 Characteristics of participants (PA Head Start Staff Wellness Survey, 2012). No. (%)a Sociodemographic characteristic Female Age, years 18–29 30–39 40–49 ≥50 Race/ethnicity White, non-Hispanic Black, non-Hispanic Other race, non-Hispanic Hispanic, any race Education High school Associate's degree Bachelor's degree Master's or doctoral degree Relationship status Married Cohabitating Other Own child attended Head Start Attended Head Start as a child Number of economic hardshipsb 0 1 2 3–5 Job position Lead teacher Assistant teacher Home visitor Family support Manager Health indicator Depression Severe headache or migraine Lower back pain Obesity High blood pressure Diabetes or prediabetes Asthma Smoking Binge drinking Binge eating Inactivity Low nighttime sleep Fair or poor health status Physically unhealthy days (≥14 days/month) Mentally unhealthy days (≥14 days/month) Absences due to illness (≥10 days/year)
2089 (97) 373 (18) 561 (27) 502 (24) 666 (32) 1842 (86) 128 (6) 53 (2) 119 (6) 423 (20) 411 (19) 1100 (51) 212 (10) 1331 (62) 243 (11) 566 (26) 554 (26) 131 (6) 1354 (63) 363 (17) 258 (12) 174 (8) 589 (27) 452 (21) 305 (14) 323 (15) 491 (23)
505 (24) 683 (32) 802 (37) 738 (37) 488 (23) 264 (12) 391 (18) 283 (13) 278 (13) 101 (5) 782 (36) 390 (18) 322 (15) 218 (10) 385 (18) 188 (9)
a Percentages across levels of a characteristic may not add to 100% due to rounding. Participants were missing data on characteristics as follows: gender (9), age (58), race/ ethnicity (18), education (14), relationship status (20), own child attended Head Start (16), attended Head Start as a child (7), number of economic hardships (11), and job position (0). Participants were missing data on health indicators as follows: depression (7), severe headache or migraine (3), lower back pain (4), obesity (182), high blood pressure (6), diabetes or prediabetes (10), asthma (7), smoking (12), binge drinking (40), binge eating (18), inactivity (5), low nighttime sleep (25), health status (3), physically unhealthy days (38), mentally unhealthy days (42), and absences due to illness (21). b Sum of five indicators of economic hardship experienced during the prior 12 months: food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare.
a range of health conditions and behaviors (Felitti and Anda, 2010; Felitti et al., 1998). In that study, the prevalence of an ACE score of three or higher was similar to our study (Dube et al., 2003). State reports have replicated many of the original ACE Study findings using BRFSS data (Anda and Brown, 2010; Minnesota Department of Public Health, 2013; O'Connor et al., 2012). Consistent with these state reports, we also showed strong graded relationships between ACEs and indicators
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Table 2 The prevalences of frequent adverse childhood experiences (≥3 ACEs) and high (upper quartile) mindfulness by participant characteristics (PA Head Start Staff Wellness Survey, 2012). Characteristic Gender Female Male Age, years 18–29 30–39 40–49 ≥50 Race/ethnicity White, non-Hispanic Black, non-Hispanic Other race, non-Hispanic Hispanic, any race Education High school Associate's degree Bachelor's degree Master's or doctoral degree Relationship status Married Cohabitating Other Own child attended Head Start Yes No Attended Head Start as a child Yes No Number of economic hardshipsb 0 1 2 3–5 Job position Lead teacher Assistant teacher Home visitor Family support Manager
3 or more ACEs No. (%) 480 (23) 7 (11) 83 (22) 154 (27) 114 (23) 130 (20) 392 (21) 41 (32) 13 (25) 42 (35) 111 (26) 108 (26) 226 (21) 43 (20) 292 (22) 64 (26) 132 (23)
p valuea
.03
.01
b.001
.02
.31
High mindfulness No. (%) 539 (26) 13 (21) 75 (20) 100 (18) 126 (25) 236 (35) 445 (24) 43 (34) 19 (36) 40 (34) 109 (26) 116 (28) 270 (25) 56 (26) 371 (28) 49 (20) 128 (23)
p valuea
.39
b.001
.004
.53
.01
169 (31) 320 (20)
b.001
131 (24) 421 (26)
.19
51 (39) 438 (22)
b.001
34 (26) 520 (26)
.95
237 (18) 101 (28) 86 (33) 65 (37) 106 (18) 89 (20) 81 (27) 95 (29) 121 (25)
b.001
b.001
402 (30) 80 (22) 41 (16) 30 (17) 141 (24) 113 (25) 77 (25) 88 (27) 136 (28)
b.001
.64
ACEs = adverse childhood experiences (emotional abuse, physical abuse, sexual abuse, mother treated violently, parental separation or divorce, household substance abuse, household mental illness, and incarcerated household member). a p value for chi-square comparing the prevalence of high mindfulness or 3 or more ACEs across levels of a participant characteristic. b Sum of five indicators of economic hardship experienced during the prior 12 months: food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare.
of HRQOL (Anda and Brown, 2010; O'Connor et al., 2012) and economic hardships (Minnesota Department of Public Health, 2013) plus absent or weak relationships between ACEs and high blood pressure (Anda and Brown, 2010), diabetes/pre-diabetes (Anda and Brown, 2010; Minnesota Department of Public Health, 2013), and binge drinking (Anda and Brown, 2010; O'Connor et al., 2012).
Mindfulness and health We know of no other studies that have examined the relationship between dispositional mindfulness and the prevalence of health conditions, behaviors, or HRQOL in a large, non-clinical sample of adults. However, meta-analyses of experimental studies suggest that mindfulness-based interventions can enhance both mental and physical health (de Vibe et al., 2012; Goyal et al., 2014; Hofmann et al., 2010). These interventions have been particularly successful in alleviating symptoms of depression and anxiety, which may occur in some individuals with ACEs and mediate the relationship between trauma exposure and poor physical health (Chartier et al., 2009; Jimenez et al., 2010). These experimental studies are consistent with a variety of mechanistic studies in humans demonstrating that social experience, both positive (mindfulness-based
interventions) and negative (childhood neglect and abuse), can induce neuroplasticity and alter physiologic stress responses (Davidson and McEwen, 2012). We have demonstrated that greater levels of mindfulness among Head Start staff are associated with indicators of better health, such as less depression, more nighttime sleep, and fewer mentally unhealthy days, all of which could improve work-related functioning for the staff as well as program outcomes for children and families. Interventions to increase mindfulness in teachers could play a role in improving children's educational outcomes (Jennings et al., 2011; Meiklejohn et al., 2012), and may do so, in part, by enhancing teachers' emotionregulation (Chambers et al., 2009) to help them improve classroom management (Raver et al., 2012). This may be especially important in early childhood education programs like Head Start that serve children experiencing multiple social stressors that can affect classroom behavior and learning (Ursache et al., 2011).
Limitations The survey respondents were not representative of all staff working in Head Start, and our findings could be biased by non-response within
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Table 3 The prevalences of health outcomes by number of adverse childhood experiences and level of mindfulness (PA Head Start Staff Wellness Survey, 2012). Number of adverse childhood experiencesa
Level of mindfulnessb
0 1 2 ≥3 p for trend Low Med-low Med-high High p for trend (n = 887) (n = 467) (n = 314) (n = 492) (n = 529) (n = 535) (n = 541) (n = 555) Health conditions, % Depression Severe headache or migraine Lower back pain Obesity High blood pressure Diabetes or prediabetes Asthma ≥3 of 7 health conditions
14 25 28 33 23 11 17 20
23 31 39 37 22 14 17 29
30 40 40 37 20 9 16 33
38 39 51 45 24 15 23 42
b.001 b.001 b.001 b.001 .87 .30 .01 b.001
39 44 47 39 21 16 20 39
26 33 41 38 25 11 19 32
18 29 33 35 21 11 18 24
13 22 29 37 24 12 15 21
b.001 b.001 b.001 .43 .57 .07 .02 b.001
Health behaviors, % Smoking Binge drinking Binge eating Inactivity Low nighttime sleep ≥2 of 5 poor health behaviors
8 12 3 34 16 16
13 12 4 34 15 17
20 14 5 38 19 27
18 15 8 43 25 31
b.001 .09 b.001 .001 b.001 b.001
17 19 10 43 29 34
13 16 4 36 16 21
13 8 3 33 16 17
10 10 2 33 12 13
.001 b.001 b.001 b.001 b.001 b.001
Health-related quality of life indicators, % Fair or poor health status Physically unhealthy days (≥14 days/month) Mentally unhealthy days (≥14 days/month) Absences due to illness (≥10 days/year) ≥2 of 4 indicators
11 8 13 7 9
13 9 19 8 12
17 13 22 10 16
22 14 26 12 19
b.001 b.001 b.001 b.001 b.001
24 18 38 9 24
17 10 18 9 13
10 8 11 10 9
9 6 6 8 6
b.001 b.001 b.001 .50 b.001
a A count of 8 categories of adverse childhood experiences (emotional abuse, physical abuse, sexual abuse, mother treated violently, parental separation or divorce, household substance abuse, household mental illness, and incarcerated household member). b Quartiles of mindfulness.
participating programs. Bias could have been introduced if social desirability led to the report of both better health behavior and greater mindfulness or if greater recall of childhood trauma occurred in those with negative affect (Edwards et al., 2001). Our findings could also be confounded by unmeasured factors, such as household income. Response rates may have been higher if we had identified individual staff members and provided each of them with reminders and monetary incentives to complete the survey. However, our anonymous survey design
provided greater assurance of confidentiality and was intended to reduce social desirability bias in the survey responses. Implications Our findings are consistent with the possibility that dispositional mindfulness, which can be increased by mindfulness practices, may lead to better health and functioning. Our data further suggest that
Table 4 Prevalences and adjusted prevalence ratios of poor health outcomes by number of adverse childhood experiences and level of mindfulness (PA Head Start Staff Wellness Survey, 2012). Category of ACE and mindfulness
Multiple health conditionsa
Poor health behaviorb
Poor health-related quality of lifec
Number of ACEs
Quartile of mindfulness
No. in category
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
0
Low Med-low Med-high High Low Med-low Med-high High Low Med-low Med-high High Low Med-low Med-high High
171 200 245 271 119 123 108 117 97 77 66 74 142 135 122 93
49 (29) 42 (21) 41 (17) 46 (17) 44 (37) 39 (32) 29 (27) 23 (20) 40 (41) 27 (35) 21 (32) 17 (23) 73 (51) 61 (45) 40 (33) 32 (34)
1.00 reference 0.74 (0.52, 1.06) 0.58 (0.44, 0.77) 0.62 (0.41, 0.94) 1.00 reference 0.87 (0.68, 1.11) 0.75 (0.57, 1.00) 0.53 (0.33, 0.86) 1.00 reference 0.76 (0.55, 1.05) 0.73 (0.51, 1.05) 0.51 (0.32, 0.79) 1.00 reference 0.91 (0.70, 1.19) 0.66 (0.51, 0.85) 0.66 (0.51, 0.86)
44 (26) 36 (18) 32 (13) 31 (11) 35 (29) 20 (16) 11 (10) 12 (10) 43 (44) 18 (23) 11 (17) 12 (16) 59 (42) 38 (28) 34 (28) 19 (20)
1.00 reference 0.71 (0.54, 0.95) 0.54 (0.32, 0.90) 0.48 (0.36, 0.63) 1.00 reference 0.56 (0.37, 0.87) 0.35 (0.22, 0.55) 0.31 (0.20, 0.48) 1.00 reference 0.52 (0.36, 0.75) 0.48 (0.30, 0.76) 0.41 (0.24, 0.69) 1.00 reference 0.74 (0.58, 0.94) 0.77 (0.55, 1.09) 0.60 (0.38, 0.95)
35 (20) 21 (11) 9 (4) 13 (5) 22 (18) 17 (14) 12 (11) 3 (3) 22 (23) 13 (17) 9 (14) 6 (8) 47 (33) 18 (13) 16 (13) 12 (13)
1.00 reference 0.51 (0.30, 0.87) 0.18 (0.11, 0.31) 0.25 (0.13, 0.47) 1.00 reference 0.87 (0.53, 1.43) 0.59 (0.35, 1.00) 0.15 (0.03, 0.65) 1.00 reference 0.81 (0.52, 1.26) 0.60 (0.37, 0.95) 0.39 (0.15, 1.01) 1.00 reference 0.43 (0.28, 0.67) 0.43 (0.25, 0.71) 0.43 (0.25, 0.75)
1
2
≥3
ACEs = adverse childhood experiences. a ≥3 of 7 health conditions (depression, severe headache or migraine, lower back pain, obesity, high blood pressure, diabetes or prediabetes, and asthma). The participant was classified as not having a condition if data on that condition were missing. b ≥2 of 5 poor health behaviors (smoking, binge drinking, binge eating, inactivity, and low nighttime sleep). The participant was classified as not having a poor health behavior if data on that behavior were missing. c ≥2 of 5 poor health-related quality of life indicators (fair or poor health status, ≥14 physically unhealthy days/month, ≥14 mentally unhealthy days/month, and ≥10 absences/year due to illness). The participant was classified as not having a poor health-related quality of life indicator if data on that indicator were missing. d Adjusted for the following variables: gender, age, race/ethnicity, education, relationship status, own child attended Head Start, attended Head Start as a child, job position, and number of 5 economic hardships experienced during the prior 12 months (food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare).
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55
55
55
3 ACEs
45
45
1 ACE
40
No ACEs
35 30 25 20 15
30 25 20 15
5
5
0
0
Level of Mindfulness
High
No ACEs
35
10
Med-Low Med-High
1 ACE
40
10
Low
2 ACEs
Low
Med-Low Med-High
Level of Mindfulness
High
3 ACEs
Poor Health-Related Quality of Life, %
50
2 ACEs
Poor Health Behavior, %
Multiple Health Conditions, %
50
3 ACEs
50
2 ACEs
45
1 ACE
40
No ACEs
35 30 25 20 15 10 5 0
Low
Med-Low Med-High
High
Level of Mindfulness
Fig. 1. Standardized prevalences of health outcomes by level of mindfulness and adverse childhood experiences (PA Head Start Staff Wellness Survey, 2012). Multiple health conditions defined as ≥3 of 7 conditions (depression, severe headache or migraine, lower back pain, obesity, high blood pressure, diabetes or prediabetes, and asthma); poor health behavior defined as ≥2 of 5 behaviors (smoking, binge drinking, binge eating, inactivity, and low nighttime sleep); and poor health-related quality of life defined as ≥2 of 5 indicators (fair or poor health status, ≥14 physically unhealthy days/month, ≥14 mentally unhealthy days/month, and ≥10 absences/year due to illness). Each point represents the standardized prevalence of a health outcome at a level of mindfulness and adverse childhood experiences (ACEs). The prevalences are standardized to the distribution of the study population for the following covariates: gender, age, race/ethnicity, education, relationship status, own child attended Head Start, attended Head Start as a child, job position, and number of 5 economic hardships experienced during the prior 12 months (food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare).
dispositional mindfulness may confer these benefits even to those who have been impacted by ACEs (Masten, 2011). Affordable approaches are needed to minimize the societal costs of impaired adult health and functioning that have resulted from ACEs (Fang et al., 2012). Interventions to increase dispositional mindfulness may be one approach to consider. Such interventions could be tested to determine if they improve the health and well-being of staff in early care and education programs while also leading to improved outcomes for the children and families served by these programs. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ypmed.2014.07.029. Conflict of interest statement The authors declare that there are no conflicts of interest.
Acknowledgments The efforts of Rachel A. Gooze, PhD were supported by a University Fellowship from the Graduate School at Temple University, Philadelphia, Pennsylvania. Temple University had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. We thank all the Head Start and Early Head Start staff who completed the survey; the Pennsylvania Head Start Association for assisting with the recruitment of the Head Start and Early Head Start programs that participated in the study; Amy Requa, MSN, CRNP for her encouragement and advice on designing and implementing the study; David F. Tucker for his technical assistance on the web-based survey design; and Vincent J. Felitti, MD, FACP, Matthew J. O'Brien, MD, MPH, and David Williamson, MS, PhD for their helpful suggestions on an earlier draft of this manuscript. References American Psychiatric Association, 2013. Feeding and eating disorders, In: American Psychiatric Association (Ed.), Diagnostic and Statistical Manual of Mental Disorders, Fifth edition American Psychiatric Publishing, Arlington, VA (Available at www. dsm.psychiatryonline.org). Anda, R.F., Brown, D.W., 2010. Adverse childhood experiences and population health in Washington: the face of a chronic public health disaster. Available at http://www. legis.state.wv.us/senate1/majority/poverty/ACEsinWashington2009BRFSSFinal Report%20-%20Crittenton.pdf.
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Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Adverse childhood experiences, dispositional mindfulness, and adult health Robert C. Whitaker a,b,⁎, Tracy Dearth-Wesley a, Rachel A. Gooze c, Brandon D. Becker a, Kathleen C. Gallagher d, Bruce S. McEwen e a
Department of Public Health, Temple University, Philadelphia, PA, USA Department of Pediatrics, Temple University, Philadelphia, PA, USA Child Trends, Bethesda, MD, USA d Frank Porter Graham Child Development Institute, University of North Carolina, Chapel Hill, NC, USA e Harold and Margaret Milliken Hatch Laboratory of Neuroendocrinology, The Rockefeller University, New York, NY, USA b c
a r t i c l e
i n f o
Available online 30 July 2014 Keywords: Child abuse Child neglect Child maltreatment Mindfulness Stress, Psychological Chronic disease Quality of life Health behavior
a b s t r a c t Objective. To determine whether greater dispositional mindfulness is associated with better adult health across a range of exposures to adverse childhood experiences (ACEs). Methods. In 2012, a web-based survey of 2160 Pennsylvania Head Start staff was conducted. We assessed ACE score (count of eight categories of childhood adversity), dispositional mindfulness (Cognitive and Affective Mindfulness Scale—Revised), and the prevalence of three outcomes: multiple health conditions (≥3 of 7 conditions), poor health behavior (≥2 of 5 behaviors), and poor health-related quality of life (HRQOL) (≥2 of 5 indicators). Results. Respondents were 97% females, and 23% reported ≥3 ACEs. The prevalences of multiple health conditions, poor health behavior, and poor HRQOL were 29%, 21%, and 13%, respectively. At each level of ACE exposure, health outcomes were better in those with greater mindfulness. For example, among persons reporting ≥3 ACEs, those in the highest quartile of mindfulness had a prevalence of multiple health conditions two-thirds that of those in the lowest quartile (adjusted prevalence ratio (95% confidence interval) = 0.66 (0.51, 0.86)); for those reporting no ACEs, the ratio was 0.62 (0.41, 0.94). Conclusion. Across a range of exposures to ACEs, greater dispositional mindfulness was associated with fewer health conditions, better health behavior, and better HRQOL. © 2014 Elsevier Inc. All rights reserved.
Introduction Over half of US adults have experienced one or more types of adverse childhood experiences (ACEs), such as abuse and household dysfunction (Centers for Disease Control and Prevention, 2010). These exposures are associated with an increased risk of several chronic health conditions (Felitti and Anda, 2010; Felitti et al., 1998) and mortality (Brown et al., 2009). Childhood traumas are thought to worsen adult health through changes in the structure and function of the body's stress-response systems (Danese and McEwen, 2012; Danese et al., 2009) and through poor health behaviors, such as smoking, which may be adopted to help cope with stress (Anda et al., 1999). These unhealthy biologic and behavioral responses to childhood adversity can be reactivated in adults during the course of their work providing human services to children experiencing trauma (Kluft et al., 2000).
⁎ Corresponding author at: Temple University, 3223 North Broad Street, Suite 175, Philadelphia, PA 19140, USA. Fax: +1 215 707 6475. E-mail address: [email protected] (R.C. Whitaker).
http://dx.doi.org/10.1016/j.ypmed.2014.07.029 0091-7435/© 2014 Elsevier Inc. All rights reserved.
This reactivation may worsen the health and workplace functioning of these adult caregivers (Perry et al., 1995). Dispositional mindfulness is the general tendency to have awareness that results from purposefully paying attention to sensations, thoughts, and feelings in the present moment while suspending judgments (Bishop et al., 2004; Kabat-Zinn, 2003). Mindfulness practices, such as meditation, can increase dispositional mindfulness (Carmody and Baer, 2008; Kuyken et al., 2010) and alleviate psychological and somatic symptoms, such as depression and pain, which can accompany exposure to traumatic experiences (de Vibe et al., 2012; Goyal et al., 2014). These practices can result in favorable changes in brain structure and function and in physiologic parameters of the stress response— changes opposite to those that can result from exposure to repeated or severe stress (Davidson and McEwen, 2012; Shonkoff and Garner, 2012). While ACEs can lead to poor health, and mindfulness practices can improve health, we know of no studies that have examined the relationships between ACEs, dispositional mindfulness, and health, nor any that have examined ACEs and health among human service providers. Using data from a survey of staff in Head Start, a large federally-funded early
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childhood education program serving children in poverty, we hypothesized that staff with greater dispositional mindfulness would have fewer health conditions, better health behavior, and higher healthrelated quality of life (HRQOL), and that this would be true across a range of exposures to their own ACEs. Confirmation of this hypothesis could provide evidence to support the testing of mindfulness-based interventions to improve the health and functioning of human service providers. Methods The Pennsylvania Head Start Staff Wellness Survey, conducted in 2012, was an anonymous, voluntary, web-based survey of staff in the state's Head Start and Early Head Start programs. The survey protocol, approved by Temple University's Institutional Review Board, has been previously described (Whitaker et al., 2013). Sixty-six of Pennsylvania's 91 Head Start and Early Head Start programs participated, and 2199 of 3375 (65%) staff members in the participating programs responded to the survey. To assess any pattern of non-response, we used federal program-level data on staff characteristics for teachers and home-visitors (US Department of Health and Human Services, Administration for Children and Families, 2011). Among teachers and homevisitors in the 66 participating programs, 57% had a bachelor's or associate's degree in early childhood education and 85% were White. This compared to 55% and 88%, respectively, among teachers and home-visitors that participated in the survey. Health indicators Sixteen binary (yes/no) health indicators were developed across three domains: health conditions (7 indicators), health behavior (5 indicators), and health-related quality of life (HRQOL) (4 indicators). The wording of questions about health conditions and HRQOL was the same as in the National Health Interview Survey (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014) and the Behavioral Risk Factor Surveillance System (BRFSS) (Centers for Disease Control and Prevention, National Center for Health Statistics, Public Health Surveillance and Informatics Program Office, 2013). Health conditions Participants were asked about seven stress-associated health conditions that are common in mid-life: depression (Hammen, 2005), severe headache or migraine (Nash and Thebarge, 2006), lower back pain (Linton, 2000), obesity (Wardle et al., 2011), hypertension (Rozanski et al., 1999), diabetes or prediabetes (Black, 2003), and asthma (Wisnivesky et al., 2010). In separate questions, participants were asked whether they had “ever been told by a doctor or other health professional” that they had depression, hypertension or high blood pressure, diabetes or sugar diabetes (other than during pregnancy), prediabetes or borderline diabetes, and asthma. They were also asked whether, during the last 3 months, they had “severe headache or migraine that lasted a whole day or more” and “lower back pain that lasted a whole day or more.” Obesity (body mass index ≥ 30 kg/m2) was determined from self-reported height and weight (pre-pregnant weight if pregnant). Health behaviors Participants were asked “Do you smoke cigarettes (yes/no)?” and “How many times in the past year have you had 4 or more drinks in a day?” (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014; Wechsler et al., 1995). Those who reported ≥ 12 occasions (≥ 1/month) were considered to binge drink (Substance Abuse and Mental Health Services Administration, 2013). Binge eaters were those who reported an eating binge (“eating an amount of food that most people, like your friends, would consider to be very large, in a short period of time”) once a week or more often during the past year and feeling “out of control” during those binges (American Psychiatric Association, 2013; Field et al., 2004). Participants were classified as inactive if they reported that they did not “at least once a week engage in any regular activity like brisk walking, jogging, bicycling, etc., long enough to work up a sweat” (Paffenbarger et al., 1993). Low nighttime sleep (b 6 h) (Cappuccio et al., 2011) was based on responses to the following question, “During the past month, how many hours of actual sleep did you get at night?” (Buysse et al., 1989).
HRQOL Participants were classified as having poor or fair health status based on responses to the question, “Would you say your health in general is poor, fair, good, very good, or excellent?” (DeSalvo et al., 2006). Following the Centers for Disease Control and Prevention, we calculated separately the prevalence of frequent (≥14 days/month) physically unhealthy days and mentally unhealthy days (Moriarty et al., 2003). The prevalence of ≥10 work absences/year due to illness was based on responses to the question, “During the past 12 months, about how many days did you miss work because of your own illness or injury?” (Centers for Disease Control and Prevention, National Center for Health Statistics, 2014). Primary outcomes We summed the number of poor health indicators in each domain and created three binary outcome variables: multiple health conditions (≥3 of 7 conditions), poor health behavior (≥ 2 of 5 behaviors), and poor HRQOL (≥ 2 of 4 indicators). These variables were made to avoid multiple comparisons in the analyses and because indicators within each domain often co-occur. The cut point for the binary outcome variable in each domain was selected to produce a prevalence that was closest to the mean of the prevalences of the indicators in that domain. Exposure measures ACEs Participants were asked whether they experienced each of eight categories of childhood adversity related to abuse and household dysfunction (Table A.1). Following the work of other researchers (Felitti et al., 1998), an ACE score (0 to 8) was made by summing the number of categories of adversity, and for analysis, participants were divided into groups based on ACE score (0, 1, 2, and ≥3). Mindfulness We assessed dispositional mindfulness with the 12-item Cognitive and Affective Mindfulness Scale—Revised (CAMS-R) (Feldman et al., 2007), a singlefactor scale correlated with other scales of dispositional mindfulness (Baer et al., 2006). Each item describes an attitude or approach toward the experience of one's emotions or thoughts in four areas—focusing attention, being oriented to the present moment, being aware of an experience, and having an attitude of acceptance or nonjudgment toward an experience. The scale scores, with a possible range from 12 to 48 (low to high mindfulness), had a normal distribution and an internal consistency (Cronbach alpha) of 0.85. To facilitate interpretation, the scores were divided into quartiles (high, medium-high, medium-low, and low) for analyses. Covariates The survey asked participants about their gender, age, race, ethnicity, education, relationship status, job position, and whether they or their own children had ever attended Head Start. Participants were also asked about five categories (yes/no) of economic hardship that they may have experienced during the prior year: food insufficiency (Ribar and Hamrick, 2003), receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare. Data analysis Our analyses involved 2160 participants, excluding 39 who were missing data on mindfulness or ACE score. We used chi-square tests to examine the relationships between each covariate and the prevalences of high ACE score (≥3), high mindfulness (upper quartile), and each health outcome. A chi-square test for trend (Cochran–Armitage test) was used to examine the associations between the health outcomes and levels of ACEs and mindfulness. For multivariable analyses, 90 cases (4%) of the sample were missing one or more covariates, so we first imputed these missing values with sequential regression imputation (Raghunathan et al., 2001) using Stata software (StataCorp, 2013). We created 20 imputed datasets with an imputation model that included all covariates, ACE score, mindfulness score, and the three health outcomes (Graham et al., 2007). In regression models, we accounted for the clustering of participants within Head Start programs using Taylor series linearization methods (Heeringa et al., 2010). Using separate logistic regression models for those at each ACE score (0, 1, 2, ≥3) and adjusting for covariates, we computed prevalence ratios to estimate associations between mindfulness and each of the three outcomes, with the lowest quartile of mindfulness as the reference group
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(Cummings, 2009, 2011). In this method, we used regression-based margins, standardized to the distribution of covariates in the study population, to estimate the standardized prevalences of the three health outcomes at each level of ACE score (0, 1, 2, ≥3) and mindfulness. The prevalence ratios describe the standardized prevalence of the health outcome (multiple health conditions, poor health behavior, or poor HRQOL) in those with high, medium-high, and medium-low mindfulness relative to the standardized prevalence of the same health outcome in those with low mindfulness. In logistic regression models using all 2160 subjects and all covariates, we used Wald tests to assess the main effects of both ACEs and mindfulness (and the multiplicative interaction between ACEs and mindfulness) on each health outcome.
Results The participants were predominantly non-Hispanic White females (Table 1). Approximately one fifth had experienced two or more economic hardships in the last year, and approximately one fourth had a child who had attended Head Start (Table 1). Three or more of the seven health conditions were reported by 29%, two or more of the five poor health behaviors by 22%, and two or more of the four indicators of poor HRQOL by 13%. Emotional abuse was more common than physical abuse or sexual abuse (Table A.1). The prevalences of 0, 1, 2, and ≥3 ACEs were 41, 22, 14, and 23%, respectively. The mean (SD) of the mindfulness score was 35 (6). All covariates, except for relationship status, were significantly associated with having ≥3 ACEs (Table 2). Those who had more economic hardships more often reported having ≥3 ACEs and were less often in the upper quartile of mindfulness (Table 2). All covariates were also related to one or more of the three outcomes (Table A.2). As the level of mindfulness decreased from the highest to lowest quartile, the prevalence of having three or more ACEs increased (17%, 23%, 25%, and 27%; p b .001). Those with a higher number of ACEs had a significantly higher prevalence of each of the 16 health indicators, except for high blood pressure, diabetes/pre-diabetes, and binge drinking. Those with higher levels of mindfulness had a significantly lower prevalence of each health indicator, except for obesity, high blood pressure, diabetes/pre-diabetes, and work absences (Table 3). In logistic regression models (one for each of the three health outcomes), there was a significant main effect of the level of mindfulness (p b .001) and the level of ACEs (p b .01) on each outcome. There was statistical evidence of an interaction between levels of ACEs and mindfulness for poor HRQOL (p = .04) and poor health behavior (p = .06), but not for multiple health conditions (p = .25). However, in stratified analyses, at every level of ACEs, the prevalence of having multiple health conditions, poor health behavior, and poor HRQOL decreased as mindfulness increased (Table 4), even after adjusting for economic hardships and other covariates (Table 4 and Fig. 1). Discussion In this population-based study of staff working with low-income children and families in Pennsylvania's Head Start programs, we found that the likelihood of having multiple health conditions, poor health behavior, and poor HRQOL increased with the level of exposure to ACEs. However, these same health outcomes were all less common in those who reported higher levels of mindfulness, and this was true across a range of exposure to ACEs. This cross-sectional analysis does not allow us to make causal inferences about the relationships between ACEs or mindfulness and health outcomes. However, these relationships are biologically plausible, large in magnitude, graded in nature, and consistent across several health outcomes. ACEs and health Our findings were consistent with those of the original ACE Study, which showed a graded relationship between the number of ACEs and
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Table 1 Characteristics of participants (PA Head Start Staff Wellness Survey, 2012). No. (%)a Sociodemographic characteristic Female Age, years 18–29 30–39 40–49 ≥50 Race/ethnicity White, non-Hispanic Black, non-Hispanic Other race, non-Hispanic Hispanic, any race Education High school Associate's degree Bachelor's degree Master's or doctoral degree Relationship status Married Cohabitating Other Own child attended Head Start Attended Head Start as a child Number of economic hardshipsb 0 1 2 3–5 Job position Lead teacher Assistant teacher Home visitor Family support Manager Health indicator Depression Severe headache or migraine Lower back pain Obesity High blood pressure Diabetes or prediabetes Asthma Smoking Binge drinking Binge eating Inactivity Low nighttime sleep Fair or poor health status Physically unhealthy days (≥14 days/month) Mentally unhealthy days (≥14 days/month) Absences due to illness (≥10 days/year)
2089 (97) 373 (18) 561 (27) 502 (24) 666 (32) 1842 (86) 128 (6) 53 (2) 119 (6) 423 (20) 411 (19) 1100 (51) 212 (10) 1331 (62) 243 (11) 566 (26) 554 (26) 131 (6) 1354 (63) 363 (17) 258 (12) 174 (8) 589 (27) 452 (21) 305 (14) 323 (15) 491 (23)
505 (24) 683 (32) 802 (37) 738 (37) 488 (23) 264 (12) 391 (18) 283 (13) 278 (13) 101 (5) 782 (36) 390 (18) 322 (15) 218 (10) 385 (18) 188 (9)
a Percentages across levels of a characteristic may not add to 100% due to rounding. Participants were missing data on characteristics as follows: gender (9), age (58), race/ ethnicity (18), education (14), relationship status (20), own child attended Head Start (16), attended Head Start as a child (7), number of economic hardships (11), and job position (0). Participants were missing data on health indicators as follows: depression (7), severe headache or migraine (3), lower back pain (4), obesity (182), high blood pressure (6), diabetes or prediabetes (10), asthma (7), smoking (12), binge drinking (40), binge eating (18), inactivity (5), low nighttime sleep (25), health status (3), physically unhealthy days (38), mentally unhealthy days (42), and absences due to illness (21). b Sum of five indicators of economic hardship experienced during the prior 12 months: food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare.
a range of health conditions and behaviors (Felitti and Anda, 2010; Felitti et al., 1998). In that study, the prevalence of an ACE score of three or higher was similar to our study (Dube et al., 2003). State reports have replicated many of the original ACE Study findings using BRFSS data (Anda and Brown, 2010; Minnesota Department of Public Health, 2013; O'Connor et al., 2012). Consistent with these state reports, we also showed strong graded relationships between ACEs and indicators
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Table 2 The prevalences of frequent adverse childhood experiences (≥3 ACEs) and high (upper quartile) mindfulness by participant characteristics (PA Head Start Staff Wellness Survey, 2012). Characteristic Gender Female Male Age, years 18–29 30–39 40–49 ≥50 Race/ethnicity White, non-Hispanic Black, non-Hispanic Other race, non-Hispanic Hispanic, any race Education High school Associate's degree Bachelor's degree Master's or doctoral degree Relationship status Married Cohabitating Other Own child attended Head Start Yes No Attended Head Start as a child Yes No Number of economic hardshipsb 0 1 2 3–5 Job position Lead teacher Assistant teacher Home visitor Family support Manager
3 or more ACEs No. (%) 480 (23) 7 (11) 83 (22) 154 (27) 114 (23) 130 (20) 392 (21) 41 (32) 13 (25) 42 (35) 111 (26) 108 (26) 226 (21) 43 (20) 292 (22) 64 (26) 132 (23)
p valuea
.03
.01
b.001
.02
.31
High mindfulness No. (%) 539 (26) 13 (21) 75 (20) 100 (18) 126 (25) 236 (35) 445 (24) 43 (34) 19 (36) 40 (34) 109 (26) 116 (28) 270 (25) 56 (26) 371 (28) 49 (20) 128 (23)
p valuea
.39
b.001
.004
.53
.01
169 (31) 320 (20)
b.001
131 (24) 421 (26)
.19
51 (39) 438 (22)
b.001
34 (26) 520 (26)
.95
237 (18) 101 (28) 86 (33) 65 (37) 106 (18) 89 (20) 81 (27) 95 (29) 121 (25)
b.001
b.001
402 (30) 80 (22) 41 (16) 30 (17) 141 (24) 113 (25) 77 (25) 88 (27) 136 (28)
b.001
.64
ACEs = adverse childhood experiences (emotional abuse, physical abuse, sexual abuse, mother treated violently, parental separation or divorce, household substance abuse, household mental illness, and incarcerated household member). a p value for chi-square comparing the prevalence of high mindfulness or 3 or more ACEs across levels of a participant characteristic. b Sum of five indicators of economic hardship experienced during the prior 12 months: food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare.
of HRQOL (Anda and Brown, 2010; O'Connor et al., 2012) and economic hardships (Minnesota Department of Public Health, 2013) plus absent or weak relationships between ACEs and high blood pressure (Anda and Brown, 2010), diabetes/pre-diabetes (Anda and Brown, 2010; Minnesota Department of Public Health, 2013), and binge drinking (Anda and Brown, 2010; O'Connor et al., 2012).
Mindfulness and health We know of no other studies that have examined the relationship between dispositional mindfulness and the prevalence of health conditions, behaviors, or HRQOL in a large, non-clinical sample of adults. However, meta-analyses of experimental studies suggest that mindfulness-based interventions can enhance both mental and physical health (de Vibe et al., 2012; Goyal et al., 2014; Hofmann et al., 2010). These interventions have been particularly successful in alleviating symptoms of depression and anxiety, which may occur in some individuals with ACEs and mediate the relationship between trauma exposure and poor physical health (Chartier et al., 2009; Jimenez et al., 2010). These experimental studies are consistent with a variety of mechanistic studies in humans demonstrating that social experience, both positive (mindfulness-based
interventions) and negative (childhood neglect and abuse), can induce neuroplasticity and alter physiologic stress responses (Davidson and McEwen, 2012). We have demonstrated that greater levels of mindfulness among Head Start staff are associated with indicators of better health, such as less depression, more nighttime sleep, and fewer mentally unhealthy days, all of which could improve work-related functioning for the staff as well as program outcomes for children and families. Interventions to increase mindfulness in teachers could play a role in improving children's educational outcomes (Jennings et al., 2011; Meiklejohn et al., 2012), and may do so, in part, by enhancing teachers' emotionregulation (Chambers et al., 2009) to help them improve classroom management (Raver et al., 2012). This may be especially important in early childhood education programs like Head Start that serve children experiencing multiple social stressors that can affect classroom behavior and learning (Ursache et al., 2011).
Limitations The survey respondents were not representative of all staff working in Head Start, and our findings could be biased by non-response within
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Table 3 The prevalences of health outcomes by number of adverse childhood experiences and level of mindfulness (PA Head Start Staff Wellness Survey, 2012). Number of adverse childhood experiencesa
Level of mindfulnessb
0 1 2 ≥3 p for trend Low Med-low Med-high High p for trend (n = 887) (n = 467) (n = 314) (n = 492) (n = 529) (n = 535) (n = 541) (n = 555) Health conditions, % Depression Severe headache or migraine Lower back pain Obesity High blood pressure Diabetes or prediabetes Asthma ≥3 of 7 health conditions
14 25 28 33 23 11 17 20
23 31 39 37 22 14 17 29
30 40 40 37 20 9 16 33
38 39 51 45 24 15 23 42
b.001 b.001 b.001 b.001 .87 .30 .01 b.001
39 44 47 39 21 16 20 39
26 33 41 38 25 11 19 32
18 29 33 35 21 11 18 24
13 22 29 37 24 12 15 21
b.001 b.001 b.001 .43 .57 .07 .02 b.001
Health behaviors, % Smoking Binge drinking Binge eating Inactivity Low nighttime sleep ≥2 of 5 poor health behaviors
8 12 3 34 16 16
13 12 4 34 15 17
20 14 5 38 19 27
18 15 8 43 25 31
b.001 .09 b.001 .001 b.001 b.001
17 19 10 43 29 34
13 16 4 36 16 21
13 8 3 33 16 17
10 10 2 33 12 13
.001 b.001 b.001 b.001 b.001 b.001
Health-related quality of life indicators, % Fair or poor health status Physically unhealthy days (≥14 days/month) Mentally unhealthy days (≥14 days/month) Absences due to illness (≥10 days/year) ≥2 of 4 indicators
11 8 13 7 9
13 9 19 8 12
17 13 22 10 16
22 14 26 12 19
b.001 b.001 b.001 b.001 b.001
24 18 38 9 24
17 10 18 9 13
10 8 11 10 9
9 6 6 8 6
b.001 b.001 b.001 .50 b.001
a A count of 8 categories of adverse childhood experiences (emotional abuse, physical abuse, sexual abuse, mother treated violently, parental separation or divorce, household substance abuse, household mental illness, and incarcerated household member). b Quartiles of mindfulness.
participating programs. Bias could have been introduced if social desirability led to the report of both better health behavior and greater mindfulness or if greater recall of childhood trauma occurred in those with negative affect (Edwards et al., 2001). Our findings could also be confounded by unmeasured factors, such as household income. Response rates may have been higher if we had identified individual staff members and provided each of them with reminders and monetary incentives to complete the survey. However, our anonymous survey design
provided greater assurance of confidentiality and was intended to reduce social desirability bias in the survey responses. Implications Our findings are consistent with the possibility that dispositional mindfulness, which can be increased by mindfulness practices, may lead to better health and functioning. Our data further suggest that
Table 4 Prevalences and adjusted prevalence ratios of poor health outcomes by number of adverse childhood experiences and level of mindfulness (PA Head Start Staff Wellness Survey, 2012). Category of ACE and mindfulness
Multiple health conditionsa
Poor health behaviorb
Poor health-related quality of lifec
Number of ACEs
Quartile of mindfulness
No. in category
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
Prevalence No. (%)
Adjusted prevalence ratio (95% CI)d
0
Low Med-low Med-high High Low Med-low Med-high High Low Med-low Med-high High Low Med-low Med-high High
171 200 245 271 119 123 108 117 97 77 66 74 142 135 122 93
49 (29) 42 (21) 41 (17) 46 (17) 44 (37) 39 (32) 29 (27) 23 (20) 40 (41) 27 (35) 21 (32) 17 (23) 73 (51) 61 (45) 40 (33) 32 (34)
1.00 reference 0.74 (0.52, 1.06) 0.58 (0.44, 0.77) 0.62 (0.41, 0.94) 1.00 reference 0.87 (0.68, 1.11) 0.75 (0.57, 1.00) 0.53 (0.33, 0.86) 1.00 reference 0.76 (0.55, 1.05) 0.73 (0.51, 1.05) 0.51 (0.32, 0.79) 1.00 reference 0.91 (0.70, 1.19) 0.66 (0.51, 0.85) 0.66 (0.51, 0.86)
44 (26) 36 (18) 32 (13) 31 (11) 35 (29) 20 (16) 11 (10) 12 (10) 43 (44) 18 (23) 11 (17) 12 (16) 59 (42) 38 (28) 34 (28) 19 (20)
1.00 reference 0.71 (0.54, 0.95) 0.54 (0.32, 0.90) 0.48 (0.36, 0.63) 1.00 reference 0.56 (0.37, 0.87) 0.35 (0.22, 0.55) 0.31 (0.20, 0.48) 1.00 reference 0.52 (0.36, 0.75) 0.48 (0.30, 0.76) 0.41 (0.24, 0.69) 1.00 reference 0.74 (0.58, 0.94) 0.77 (0.55, 1.09) 0.60 (0.38, 0.95)
35 (20) 21 (11) 9 (4) 13 (5) 22 (18) 17 (14) 12 (11) 3 (3) 22 (23) 13 (17) 9 (14) 6 (8) 47 (33) 18 (13) 16 (13) 12 (13)
1.00 reference 0.51 (0.30, 0.87) 0.18 (0.11, 0.31) 0.25 (0.13, 0.47) 1.00 reference 0.87 (0.53, 1.43) 0.59 (0.35, 1.00) 0.15 (0.03, 0.65) 1.00 reference 0.81 (0.52, 1.26) 0.60 (0.37, 0.95) 0.39 (0.15, 1.01) 1.00 reference 0.43 (0.28, 0.67) 0.43 (0.25, 0.71) 0.43 (0.25, 0.75)
1
2
≥3
ACEs = adverse childhood experiences. a ≥3 of 7 health conditions (depression, severe headache or migraine, lower back pain, obesity, high blood pressure, diabetes or prediabetes, and asthma). The participant was classified as not having a condition if data on that condition were missing. b ≥2 of 5 poor health behaviors (smoking, binge drinking, binge eating, inactivity, and low nighttime sleep). The participant was classified as not having a poor health behavior if data on that behavior were missing. c ≥2 of 5 poor health-related quality of life indicators (fair or poor health status, ≥14 physically unhealthy days/month, ≥14 mentally unhealthy days/month, and ≥10 absences/year due to illness). The participant was classified as not having a poor health-related quality of life indicator if data on that indicator were missing. d Adjusted for the following variables: gender, age, race/ethnicity, education, relationship status, own child attended Head Start, attended Head Start as a child, job position, and number of 5 economic hardships experienced during the prior 12 months (food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare).
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55
55
55
3 ACEs
45
45
1 ACE
40
No ACEs
35 30 25 20 15
30 25 20 15
5
5
0
0
Level of Mindfulness
High
No ACEs
35
10
Med-Low Med-High
1 ACE
40
10
Low
2 ACEs
Low
Med-Low Med-High
Level of Mindfulness
High
3 ACEs
Poor Health-Related Quality of Life, %
50
2 ACEs
Poor Health Behavior, %
Multiple Health Conditions, %
50
3 ACEs
50
2 ACEs
45
1 ACE
40
No ACEs
35 30 25 20 15 10 5 0
Low
Med-Low Med-High
High
Level of Mindfulness
Fig. 1. Standardized prevalences of health outcomes by level of mindfulness and adverse childhood experiences (PA Head Start Staff Wellness Survey, 2012). Multiple health conditions defined as ≥3 of 7 conditions (depression, severe headache or migraine, lower back pain, obesity, high blood pressure, diabetes or prediabetes, and asthma); poor health behavior defined as ≥2 of 5 behaviors (smoking, binge drinking, binge eating, inactivity, and low nighttime sleep); and poor health-related quality of life defined as ≥2 of 5 indicators (fair or poor health status, ≥14 physically unhealthy days/month, ≥14 mentally unhealthy days/month, and ≥10 absences/year due to illness). Each point represents the standardized prevalence of a health outcome at a level of mindfulness and adverse childhood experiences (ACEs). The prevalences are standardized to the distribution of the study population for the following covariates: gender, age, race/ethnicity, education, relationship status, own child attended Head Start, attended Head Start as a child, job position, and number of 5 economic hardships experienced during the prior 12 months (food insufficiency, receipt of benefits from the Supplemental Nutrition Assistance Program, not enough money for housing, not enough money for utilities, and not enough money for healthcare).
dispositional mindfulness may confer these benefits even to those who have been impacted by ACEs (Masten, 2011). Affordable approaches are needed to minimize the societal costs of impaired adult health and functioning that have resulted from ACEs (Fang et al., 2012). Interventions to increase dispositional mindfulness may be one approach to consider. Such interventions could be tested to determine if they improve the health and well-being of staff in early care and education programs while also leading to improved outcomes for the children and families served by these programs. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ypmed.2014.07.029. Conflict of interest statement The authors declare that there are no conflicts of interest.
Acknowledgments The efforts of Rachel A. Gooze, PhD were supported by a University Fellowship from the Graduate School at Temple University, Philadelphia, Pennsylvania. Temple University had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. We thank all the Head Start and Early Head Start staff who completed the survey; the Pennsylvania Head Start Association for assisting with the recruitment of the Head Start and Early Head Start programs that participated in the study; Amy Requa, MSN, CRNP for her encouragement and advice on designing and implementing the study; David F. Tucker for his technical assistance on the web-based survey design; and Vincent J. Felitti, MD, FACP, Matthew J. O'Brien, MD, MPH, and David Williamson, MS, PhD for their helpful suggestions on an earlier draft of this manuscript. References American Psychiatric Association, 2013. Feeding and eating disorders, In: American Psychiatric Association (Ed.), Diagnostic and Statistical Manual of Mental Disorders, Fifth edition American Psychiatric Publishing, Arlington, VA (Available at www. dsm.psychiatryonline.org). Anda, R.F., Brown, D.W., 2010. Adverse childhood experiences and population health in Washington: the face of a chronic public health disaster. Available at http://www. legis.state.wv.us/senate1/majority/poverty/ACEsinWashington2009BRFSSFinal Report%20-%20Crittenton.pdf.
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