Abstract
Objective:
To assess whether unhealthy behaviors moderated the relationship between allostatic load (AL) and future significant depressive symptoms (SDSs) among 1,789 older Latinos.
Method:
Longitudinal data included baseline AL, three unhealthy behaviors (UBs), and 2-year follow-up SDS. Multivariable logistic regression analyses, stratified by birthplace (U.S. vs. foreign born), modeled the effects of AL, UB count (range = 0–3), and their interaction on follow-up SDS.
Results:
Compared with U.S.-born, foreign-born participants engaged in fewer UBs (0.52 vs. 0.60 behaviors, p = .01) and had higher baseline SDS (31% vs. 20%, p < .001). Among foreign-born participants, the effect of AL on future SDS (adjusted odds ratios [aORs]; 95% confidence interval [CI]) significantly increased across UB counts of 0 to 3: 1.06 [0.83, 1.35], 1.46 [1.14, 1.87], 2.00 [1.18, 3.41], and 2.75 [1.18, 6.44], respectively.
Discussion:
Among foreign-born Latinos, these results were most pronounced for women and adults above age 80, which may represent higher risk groups requiring more intensive screening for depression.
Keywords: physiological stress, allostasis, health behavior, depression, Hispanic Americans
Life stressors experienced during adulthood can lead to an increased likelihood of depression over time (Hammen, 2005; Paykel, 2003). For racial/ethnic minorities, such as Latinos, inequalities in socioeconomic opportunities and environmental conditions may lead to higher levels of chronic stress and subsequently mental illness. Among older adults, Latinos report a greater level of chronic stress compared with Whites (Rodriquez, Gregorich, Livaudais-Toman, & Pérez-Stable, 2017). In addition, the 12-month prevalence of major depression is higher among older Latinos (8.2%) compared with older Whites (5.0%; Jimenez, Alegria, Chen, Chan, & Laderman, 2010). Social determinants could contribute to inequities in mental health via their impact on health behaviors. Based on previous analyses, older Latinos report engaging in significantly more unhealthy behaviors than Whites (Rodriquez et al., 2017). Research among Latinos on the relationships between chronic stress and health risk behaviors on mental health outcomes is limited. The higher prevalence of major depression among older Latinos may be partially explained by differential effects of stress and health risk behaviors.
Unhealthy behaviors, singularly or in combination, may influence the relationship between chronic stress and depression (Jackson, Knight, & Rafferty, 2010; Lipton, 1997; Rodriquez et al., 2017). The Environmental Affordances Model has been proposed as a theoretical framework, which explains how chronic stress and health risk behaviors may interact to affect both physical and mental health (Mezuk et al., 2013). The model posits that the effect of chronic stress on depression is attenuated by unhealthy behaviors, particularly among African Americans. In contrast, among Latino older adults, one study found that those who experienced more chronic stress and had more unhealthy behaviors were more likely to experience future significant depressive symptoms (Rodriquez et al., 2017). Another study found that foreign-born Mexicans who experienced chronic stress reported more depression if they engaged in excessive alcohol use (Lipton, 1997). Research has suggested biological mechanisms that partially explain the relationships of chronic stress with substance use, unhealthy food consumption, and limited physical activity (Dallman et al., 2003; Koob et al., 1998; Lutz, Lochbaum, Lanning, Stinson, & Brewer, 2007; Piazza & Le Moal, 1998; Wadsworth, 2015). These findings warrant research to understand how the social and biological mechanisms that link chronic stress, unhealthy behaviors, and depression may differ by race/ethnicity.
Research among Mexican Americans found that the relationships between life stressors, such as discrimination and acculturative stress, and depressive symptoms differed by birthplace (Finch, Kolody, & Vega, 2000). A more recent study among a diverse sample of Latinos had similar findings regarding exposure to stressful life events and depressive symptoms comparing foreign born with U.S. born (Tillman & Weiss, 2009). There is a need to expand our knowledge of the longitudinal effects of life stressors, health risk behaviors, and their contributions to mental health disparities among Latinos by birthplace.
This study used secondary data from two waves of the Sacramento Area Latino Study on Aging (SALSA) to assess whether unhealthy behaviors moderated the relationship between cumulative chronic stress, measured by allostatic load, and future significant depressive symptoms among Latinos by birthplace. Allostatic load has been associated with mental health outcomes (Schulz et al., 2012; Seeman, Singer, Rowe, Horwitz, & McEwen, 1997), and based on previous findings among Latinos, we hypothesized that engaging in a greater number of unhealthy behaviors would intensify the relationship between allostatic load (i.e., chronic stress) and future significant depressive symptoms for foreign-born Latinos.
Method
Study Design and Sample
This was a secondary analysis of data from SALSA (National Archive of Computerized Data on Aging, n.d.). Participants were 1,786 older Latinos, mostly of Mexican origin, from six California counties (Sacramento, Yolo, Sutter, Placer, Solano, and Yuba) recruited between March 1998 and June 1999 to participate in a longitudinal cohort study. Based on the 1990 U.S. Census, census tracts with greater than 10% Latino composition were selected for enumeration of Latino households. Participants were eligible if they selfidentified as being of Latino or Hispanic ancestry, were at least 60 years old, and spoke English, Spanish, or both as primary languages. Interviews were conducted in participants’ homes by bilingual/bicultural field staff to obtain demographic, health, and functional status data. A brief clinical assessment was conducted, including anthropometry, blood pressure measurement, and blood draw. Biannual telephone interviews were conducted to update current health status and medication use and a follow-up visit was conducted at 24 to 30 months. The final sample included participants who completed interviews and assessments at both baseline and follow-up visits. Further description of the study procedures has been published (Haan et al., 2003). SALSA was approved by the institutional review boards of the collaborating institutions.
Measures
We used allostatic load as a biomarker of stress. Using 10 established biological indicators (Beckie, 2012), a continuous allostatic load variable was calculated at baseline as an index of overall physiological dysregulation due to the cumulative burden of stress on the body. The index was originally developed using data from the MacArthur Studies of Successful Aging (Seeman et al., 1997). Indicators were (a and b) systolic and diastolic blood pressure, (c) waist–hip circumference ratio, (d) total serum cholesterol, (e) high-density lipoprotein (HDL) cholesterol, (f) ratio of total to HDL cholesterol, (g) interleukin-6, (h) C-reactive protein, (i) albumin, and (j) estimated creatinine clearance based on the 2009 Chronic Kidney Disease Epidemiology Collaboration equation for estimating glomerular filtration rate expressed for specified sex and serum creatinine level (Levey et al., 2009).
Allostatic load risk categories included low, moderate, or high risk using clinically relevant cut points for all biomarkers except interleukin-6, which was based on tertiles. Clinically relevant low, moderate, and high cut points, respectively, are as follows: systolic blood pressure <120 mm Hg, 120 to 149 mm Hg, and ≥150 mm Hg; diastolic blood pressure <80 mm Hg, 80 to 89 mm Hg, and ≥90 mm Hg; waist–hip circumference ratio for men ≤0.95, >0.95 to <1.0, and ≥1.0; waist–hip circumference ratio for women, ≤0.80, >0.80 to <0.85, and ≥0.85; total cholesterol <200 mg/dL, 200 to 239 mg/dL, and ≥240 mg/dL; HDL cholesterol ≥60 mg/dL, 40 to 59 mg/dL, and <40 mg/dL; total/HDL cholesterol ratio <5, 5 to <6, and ≥6; interleukin-6 <2.9, 2.9 to 5.0, and >5.0; C-reactive protein <1 mg/L, 1 to 3 mg/L, and >3 mg/L; albumin ≥3.8 mg/L, 3 to <3.8 mg/L, and <3 mg/L; and creatinine clearance ≥60 mL/min/1.73 m2, 30 to 59 mL/min/1.73 m2, and <30 mL/min/1.73 m2. Higher values correspond to higher risk of all biomarkers except for HDL cholesterol, albumin, and creatinine clearance, where lower values indicate a higher risk.
By assigning zero points for low risk, a half point for moderate risk, and 1 point for high risk, an allostatic load score (range = 0–10) was calculated by summing the values for all 10 biomarkers. Participants taking medications that may have influenced the level of a biological indicator were not subject to adjustment because improved levels of any indicator(s) would successfully reduce the effects of stress on regulatory systems and subsequently lower allostatic load.
Three self-reported unhealthy behaviors were assessed at baseline: current cigarette smoking, excessive/binge drinking of alcohol, and body mass index (BMI) as a proxy measure of poor diet and physical inactivity. Current smoking was defined as smoking in the last 30 days. The following established definitions (National Institute on Alcohol Abuse and Alcoholism, n.d.) were used to define excessive drinking: for those <65 years old, >2 drinks per day on average or >14 drinks per week for men and >1 drink per day on average or >7 drinks per week for women; for women and men ≥65 years old, >1 drink per day on average or >7 drinks per week. Binge drinking was defined as ≥4 and ≥5 drinks of alcohol per day for women and men, respectively. Height and weight were determined by physical examination and used to calculate BMI. Levels of obesity were defined as follows: normal/not obese = BMI <25 kg/m2, overweight = BMI ≥25 kg/m2 to BMI <30 kg/m2, and obese = BMI ≥30 kg/m2. A count of the unhealthy behaviors, ranging from 0 to 3, was created by summing the “yes” responses of current smoking, excessive or binge drinking, and being obese.
Depressive symptoms were the outcomes of interest and were assessed at baseline and follow-up visits using the 20-item Center for Epidemiologic Studies–Depression (CES-D) Scale (Radloff, 1977). Items assessed the frequency of depressive symptoms using a four-level response scale (never, little of the time, some of the time, and most or all the time, coded as 0, 1, 2, and 3, respectively). The sum of the items represents the total score with a possible range of 0 to 60. A standard cut point of ≥16 for adults was used as an indicator of significant depressive symptoms (Gonzalez, Haan, & Hinton, 2001). Longitudinal depressive symptom outcomes were restricted to those collected at Visit 2 (Jackson et al., 2010; Rodriquez et al., 2017).
Demographic covariates were age, birthplace, gender, educational attainment (less than ninth grade, ninth to 12th grade, high school graduate or equivalent, and some college or higher), household income, and language preference and were assessed at baseline by self-report and considered as potential covariates.
Statistical Analyses
T tests and chi-square tests were used to examine bivariate associations between individual-level characteristics and birthplace. We fit bivariate, multivariable, and multinomial logistic regression models for the overall sample and stratified by birthplace to assess whether engaging in one or more unhealthy behaviors would intensify the relationship between allostatic load and significant depressive symptoms among Latinos.
Bivariate relationships between allostatic load at baseline, unhealthy behaviors at baseline, and significant depressive symptoms at follow-up (i.e., 24–30 months after baseline) were modeled using logistic and linear regression, stratified by birthplace, and fit prior to the construction of multivariable models.
Multivariable models estimated the effects of allostatic load score and unhealthy behavior count at baseline, as well as their interaction, on predicting significant depressive symptoms at follow-up, controlling for age, gender, educational attainment, and significant depressive symptoms at baseline (Figure 1). The interaction term between allostatic load score and unhealthy behavior count in each model tested our moderation hypothesis. Approximately 12% of participants were missing data for at least one variable. Listwise deletion was used to exclude participants with missing data for any variable. Analyses were conducted using SAS 9.4 (SAS Institute, Inc., Cary, North Carolina).
Figure 1.
Hypothesized relationships between allostatic load score and depressive symptoms moderated by three unhealthy behaviors.
Results
The baseline sample of 1,789 Latinos comprised 871 U.S. born (56.6% women) and 908 foreign born participants (60.4% women; Table 1). U.S. born participants were similar in age to their foreign born counterparts (70.1 years vs. 71.2 years, p = .001). Almost 80% of foreign born participants reported less than ninth-grade education, whereas most U.S. born participants (58.5%) obtained a ninth-grade education or higher (p < .0001). Current smoking (p = .95), excessive drinking (p = .13), and binge drinking (p = .25) did not differ significantly by birthplace. Obesity was common in both groups (44.6% of U.S. born vs. 39.8% of foreign born; p = .07). U.S. born participants had a higher mean number of unhealthy behaviors than foreign born participants (0.60 vs. 0.52, p = .01). The prevalence of significant depressive symptoms at baseline was higher among foreign born than U.S. born participants (30.6% vs. 20.2%, p < .0001). At follow-up, this difference narrowed but remained significantly different (26.9% vs. 21.4%, p = .03).
Table 1.
Demographics and Unhealthy Behaviors at Baseline (1998–1999) and Significant Depressive Symptoms at Baseline and Follow-Up (24–30 Months After Baseline) by Birthplace: Sacramento Area Latino Study on Aging.
| Characteristics | Total n = 1,789 100%a | U.S. born n = 871 49%a | Foreign born n = 908 51%a | p value |
|---|---|---|---|---|
| Age, M (SD) | 70.6 (7.1) | 70.1 (6.4) | 71.2 (7.7) | .001 |
| Age, n (%) | ||||
| 60–69 years | 919 (51.5) | 465 (53.4) | 447 (49.2) | <.001 |
| 70–79 years | 683 (38.2) | 348 (40.0) | 335 (36.9) | |
| ≥80 years | 184 (10.3) | 58 (6.7) | 126 (13.9) | |
| Women, n (%) | 1,044 (58.4) | 493 (56.6) | 548 (60.4) | .11 |
| Education, n (%) Less than ninth grade |
1,083 (60.9) | 361 (41.5) | 722 (79.5) | <.001 |
| Ninth grade to high school | 177 (10.0) | 119 (13.7) | 58 (6.4) | |
| High school graduate or equivalent | 225 (12.7) | 166 (19.1) | 59 (6.5) | |
| Some college or higher | 294 (16.5) | 225 (25.8) | 69 (7.6) | |
| Current smoking, n (%) | 203 (11.4) | 99 (11.4) | 104 (11.5) | .95 |
| Excessiveb or bingec drinking, n (%) | ||||
| Excessive drinkingb | 103 (5.9) | 58 (6.8) | 45 (5.1) | .13 |
| Binge drinkingc | 24 (1.4) | 9 (1.0) | 15 (1.7) | .25 |
| Obesity, n (%) | .07 | |||
| Normal: BMI < 25 kg/m2 | 311 (19.1) | 140 (17.3) | 171 (21.0) | |
| Overweight: 25 kg/m2 ≤ BMI < 30 kg/m2 | 629 (38.7) | 309 (38.1) | 320 (39.3) | |
| Obese: BMI ≥ 30 kg/m2 | 686 (42.2) | 362 (44.6) | 324 (39.8) | |
| Unhealthy behavior count,d M (SD) | 0.56 (0.62) | 0.60 (0.64) | 0.52 (0.61) | .01 |
| Unhealthy behavior count,d n (%) | .10 | |||
| 0 | 902 (51.1) | 417 (48.4) | 485 (53.6) | |
| 1 | 748 (42.3) | 379 (44.0) | 369 (40.8) | |
| 2 | 109 (6.2) | 62 (7.2) | 47 (5.2) | |
| 3 | 8 (0.5) | 4 (0.5) | 4 (0.4) | |
| Significant depressive symptoms at baseline, n (%) | 439 (25.5) | 172 (20.2) | 267 (30.6) | <.001 |
| Significant depressive symptoms at follow-up, n (%) | 279 (24.0) | 130 (21.4) | 149 (26.9) | .03 |
Note. Center for Epidemiologic Studies–Depression Scale score of 16 or greater. BMI = body mass index.
Percentages based on nonmissing values.
For <65 years old: >2 drinks per day or >14 drinks per week on average for men and >1 drinks per day or >7 drinks per week on average for women; for women and men ≥65 years old: >1 drink per day or >7 drinks per week on average.
Based on ≥4 drinks per day for women and ≥5 drinks per day for men.
Assigned 1 point each for current smoking, excessive or binge drinking, or being obese.
Biomarkers of Stress and Allostatic Load
Overall, a quarter of participants had elevated systolic blood pressure (≥150 mm Hg or high risk), with similar rates across birthplace groups (24.6% U.S. born vs. 25.0% foreign born, p = .83; Table 2). About 10% of the total sample had elevated diastolic blood pressure, with similar rates by birthplace (10.8% U.S. born vs. 9.7% foreign born, p = .74). Less than half of participants were categorized as highest risk on waist–hip circumference ratio, with no differences by birthplace (44.4% U.S. born vs. 46.3% foreign born, p = .70). The proportion classified as highest risk based on total cholesterol levels (≥240 mg/dL) was similar for foreign born and U.S. born groups (22.2% vs. 21.9%, p = .30). Based on interleukin-6 levels, a greater proportion of foreign born than U.S. born Latinos were classified as lowest risk (36.8% vs. 30.0%, p<.01). Overall, half of participants were categorized as being high risk on C-reactive protein levels, with greater risk among U.S. born than foreign born participants (56.6% vs. 49.3%, p = .01). Overall, most participants were classified as lowest risk based on albumin and creatinine clearance levels, with no significant differences observed by birthplace. Mean values for the allostatic load score were similar for U.S. born (M [SD] = 3.96 [1.32]) and foreign born (M [SD] = 3.82 [1.38], p = .06) participants.
Table 2.
Biomarkers of Stress and Allostatic Load Score at Baseline by Birthplace: Sacramento Area Latino Study on Aging, 1998 to 1999.
| Characteristics | Total n = 1,789 100%a | U.S. born n = 871 49%a | Foreign born n = 908 51%a | p value |
|---|---|---|---|---|
| Systolic blood pressure (mm Hg) | ||||
| Low risk (<120) | 236 (14.3) | 122 (14.9) | 114 (13.8) | .83 |
| Moderate risk (120–149) | 1,002 (60.9) | 497 (60.5) | 505 (61.2) | |
| High risk (≥150) | 408 (24.8) | 202 (24.6) | 206 (25.0) | |
| Diastolic blood pressure (mm Hg) | ||||
| Low risk (<80) | 1,038 (63.1) | 513 (62.5) | 525 (63.6) | .74 |
| Moderate risk (80–89) | 439 (26.7) | 219 (26.7) | 220 (26.7) | |
| High risk (≥90) | 169 (10.3) | 89 (10.8) | 80 (9.7) | |
| Waist–hip circumference ratio | ||||
| Low risk (men: ≤0.95, women: ≤0.80) | 505 (30.7) | 253 (30.9) | 252 (30.5) | .70 |
| Moderate risk (men: >0.95 to <1.0, women: >0.80 to <0.85) | 395 (24.0) | 203 (24.8) | 192 (23.2) | |
| High risk (men: ≥1.0, women: ≥0.85) | 746 (45.3) | 364 (44.4) | 382 (46.3) | |
| Total cholesterol (mg/dL) | ||||
| Low risk (<200) | 634 (38.8) | 332 (40.5) | 302 (37.0) | .30 |
| Moderate risk (200–239) | 641 (39.2) | 308 (37.6) | 333 (40.8) | |
| High risk (≥240) | 360 (22.0) | 179 (21.9) | 181 (22.2) | |
| HDL cholesterol (mg/dL) | ||||
| Low risk (≥60) | 389 (23.8) | 197 (24.1) | 192 (23.6) | .97 |
| Moderate risk (40–59) | 988 (60.5) | 493 (60.2) | 495 (60.7) | |
| High risk (<40) | 257 (15.7) | 129 (15.8) | 128 (15.7) | |
| Total/HDL cholesterol ratio | ||||
| Low risk (<5) | 1,216 (74.4) | 621 (75.8) | 595 (73.0) | .24 |
| Moderate risk (5 to <6) | 315 (19.3) | 154 (18.8) | 161 (19.8) | |
| High risk (≥6) | 103 (6.3) | 44 (5.4) | 59 (7.2) | |
| Interleukin-6b | ||||
| Low risk (<2.9) | 520 (33.3) | 236 (30.0) | 283 (36.8) | .01 |
| Moderate risk (2.9–5.0) | 522 (33.4) | 266 (33.8) | 251 (32.6) | |
| High risk (>5.0) | 520 (33.3) | 284 (36.1) | 235 (30.6) | |
| C-reactive protein (mg/L) | ||||
| Low risk (<1) | 286 (18.3) | 132 (16.8) | 154 (20.0) | .01 |
| Moderate risk (1–3) | 448 (28.7) | 209 (26.6) | 236 (30.7) | |
| High risk (>3) | 828 (53.0) | 445 (56.6) | 379 (49.3) | |
| Albumin (mg/L) | ||||
| Low risk (≥3.8) | 1,135 (69.6) | 550 (67.3) | 585 (71.9) | .12 |
| Moderate risk (3 to <3.8) | 476 (29.2) | 255 (31.2) | 221 (27.2) | |
| High risk (<3) | 20 (1.2) | 12 (1.5) | 8 (1.0) | |
| Creatinine clearance (mL/min/1.73 m2)c | ||||
| Low risk (≥60) | 1,395 (85.5) | 689 (84.3) | 706 (86.7) | .64 |
| Moderate risk (30–59) | 200 (12.3) | 108 (13.2) | 92 (11.3) | |
| High risk (<30) | 36 (2.2) | 20 (2.5) | 16 (2.5) | |
| Allostatic load scorea,d M (SD) | 3.89 (1.35) | 3.96 (1.32) | 3.82 (1.38) | .06 |
Note. HDL = high-density lipoprotein.
Percentages based on nonmissing value.
Cut points based on tertiles.
Based on the 2009 Chronic Kidney Disease Epidemiology Collaboration equation for estimating glomerular filtration rate expressed for specified sex and serum creatinine level.
Assigned 0.5 points for each biomarker categorized as moderate risk and 1 point for each biomarker categorized as high risk. Allostatic load score range = 0 to 10.
Associations Between Allostatic Load and Unhealthy Behaviors
As for individual unhealthy behaviors, current smoking was associated with a higher allostatic load score among foreign born Latinos. Allostatic load score was significantly associated with obesity in U.S. born and foreign born groups (Table 3). Regarding the unhealthy behavior count, a greater allostatic load score was associated with significantly higher odds of engaging in one (adjusted odds ratio [aOR] = 1.49, 95% confidence interval [CI] = [1.36, 1.63]) and two unhealthy behaviors (aOR = 1.53, 95% CI = [1.29, 1.83]), but not three unhealthy behaviors (aOR = 1.77, 95% CI = [0.97, 3.24]), compared with those who did not engage in any of these three unhealthy behaviors. Associations that were significant were consistent for both U.S. born (unhealthy behavior count = 1, aOR = 1.47, 95% CI = [1.29, 1.67]; unhealthy behavior count = 2, aOR = 1.47, 95% CI = [1.17, 1.86]) and foreign born (unhealthy behavior count = 1, aOR = 1.52, 95% CI = [1.34, 1.73]; unhealthy behavior count = 2, aOR = 1.55, 95% CI = [1.17, 2.05]) participants.
Table 3.
Associations Between Allostatic Load Score and Unhealthy Behaviors at Baseline: Sacramento Area Latino Study on Aging, 1998 to 1999.
| Total |
U.S. born |
Foreign born |
||||
|---|---|---|---|---|---|---|
| Unhealthy behavior | aORa | 95% CI | aORa | 95% CI | aORa | 95% CI |
| Current smoking | 1.15 | [1.02, 1.30] | 1.09 | [0.91, 1.31] | 1.22 | [1.03, 1.45]* |
| Excessive or binge drinking | 0.96 | [0.69, 1.32] | 1.22 | [0.76, 1.95] | 0.89 | [0.59, 1.33] |
| Obese | 1.55 | [1.42, 1.70]*** | 1.53 | [1.35, 1.74]*** | 1.56 | [1.37, 1.77]*** |
| Unhealthy behavior countb,c = 0 | Reference | Reference | Reference | |||
| Unhealthy behavior countb,c = 1 | 1.49 | [1.36, 1.63]*** | 1.47 | [1.29, 1.67]*** | 1.52 | [1.34, 1.73]*** |
| Unhealthy behavior countb,c = 2 | 1.53 | [1.29, 1.83]*** | 1.47 | [1.17, 1.86]*** | 1.55 | [1.17, 2.05]** |
| Unhealthy behavior countb,c = 3 | 1.77 | [0.97, 3.24] | 1.96 | [0.61, 6.23] | 1.95 | [0.91, 4.17] |
Note. Allostatic load score included systolic and diastolic blood pressure, waist–hip circumference ratio, total cholesterol and HDL cholesterol, total/HDL cholesterol ratio, interleukin-6, C-reactive protein, albumin, and creatinine clearance. Respondents were assigned 0.5 points for each biomarker categorized as moderate risk and 1 point for each biomarker categorized as high risk. Allostatic load score range = 0–10. aOR = adjusted odds ratio; CI = confidence interval; HDL = high-density lipoprotein.
Adjusted for age, gender, education, and significant depressive symptoms at baseline.
Unhealthy behavior count included current smoking, excessive or binge drinking, and being obese.
Respondents were assigned 1 point for each behavior reported.
Modeled using multinomial logistic regression.
p < .05.
p < .01.
p < .001.
Main Effects
Greater allostatic load score was associated significantly with follow-up depressive symptoms at the mean unhealthy behavior count (aOR = 1.17, 95% CI = [1.03, 1.33], data not shown). Also, a higher unhealthy behavior count at baseline was significantly associated with follow-up depressive symptoms at the mean allostatic load score (aOR = 0.98, 95% CI = [0.18, 0.99], data not shown).
Interaction Effects
Odds ratios for the interaction effects were estimated from the model that included unhealthy behavior count (Table 4). In the overall sample, the interactive effect of allostatic load on significant depressive symptoms strengthened with increasing numbers of unhealthy behaviors (unhealthy behavior count = 0, aOR = 1.02, 95% CI = [0.86, 1.21], p = .80; unhealthy behavior count = 1, aOR = 1.28, 95% CI = [1.09, 1.50], p = .003; unhealthy behavior count = 2, aOR = 1.61, 95% CI = [1.16, 2.23], p = .005; unhealthy behavior count = 3, aOR = 2.01, 95% CI = [1.19, 3.40], p = .009; Table 4), respectively. In models stratified by birthplace, an interaction effect was only observed among foreign born participants (unhealthy behavior count = 0, aOR = 1.06, 95% CI = [0.83, 1.35], p = .64; unhealthy behavior count = 1, aOR = 1.46, 95% CI = [1.14, 1.87], p = .003; unhealthy behavior count = 2, aOR = 2.00, 95% CI = [1.18, 3.41], p = .01; unhealthy behavior count = 3, aOR = 2.75, 95% CI = [1.18, 6.44], p = .02).
Table 4.
Multivariable Logistic Regression Results Predicting Significant Depressive Symptoms at Follow-Up (24–30 Months After Baseline) by Birthplace: Sacramento Area Latino Study on Aging, 1998 to 1999.
| Total |
U.S. born |
Foreign born |
||||
|---|---|---|---|---|---|---|
| Independent variables at baseline | OR | 95% CI | OR | 95% CI | OR | 95% CI |
| Interaction terms (Allostatic Loada × Unhealthy Behaviorb)d | ||||||
| Allostatic load score at unhealthy behavior count = 0 | 1.02 | [0.86, 1.21] | 0.95 | [0.75, 1.20] | 1.06 | [0.83, 1.35] |
| Allostatic load score at unhealthy behavior count = 1 | 1.28 | [1.09, 1.50]** | 1.09 | [0.88, 1.36] | 1.46 | [1.14, 1.87]** |
| Allostatic load score at unhealthy behavior count = 2 | 1.61 | [1.16, 2.23]** | 1.26 | [0.81, 1.96] | 2.00 | [1.18, 3.41]* |
| Allostatic load score at unhealthy behavior count = 3 | 2.01 | [1.19, 3.40]** | 1.45 | [0.72, 2.93] | 2.75 | [1.18, 6.44]* |
| Covariates | ||||||
| Age, categorical | ||||||
| 60–69 years | Reference | Reference | Reference | |||
| 70–79 years | 0.93 | [0.67, 1.30] | 0.90 | [0.56, 1.43] | 1.04 | [0.63, 1.74] |
| ≥80 years | 2.27 | [1.32, 3.89]** | 2.60 | [1.06, 6.39]** | 2.62 | [1.27, 5.40]** |
| Gender | ||||||
| Men | Reference | Reference | Reference | |||
| Women | 1.80 | [1.27, 2.55]** | 1.32 | [0.83, 2.10] | 2.68 | [1.54, 4.64]*** |
| Education | ||||||
| Less than ninth grade | Reference | Reference | Reference | |||
| Ninth grade to high school | 0.72 | [0.43, 1.20] | 0.99 | [0.51, 1.89] | 0.38 | [0.15, 0.99]* |
| High school graduate or equivalent | 0.91 | [0.58, 1.44] | 1.08 | [0.62, 1.89] | 0.34 | [0.10, 1.12] |
| Some college or higher | 0.52 | [0.32, 0.85]** | 0.45 | [0.23, 0.85]* | 0.69 | [0.29, 1.66] |
| Depressive symptomsc at baseline | ||||||
| Not significant | Reference | Reference | Reference | |||
| Significant | 5.13 | [3.70, 7.11]*** | 4.71 | [2.90, 7.64]*** | 5.75 | [3.60, 9.16]*** |
Note. OR = odds ratio; CI = confidence interval; HDL = high-density lipoprotein.
Allostatic load score included systolic and diastolic blood pressure, waist-hip circumference ratio, total and HDL cholesterol, total/HDL cholesterol ratio, interleukin-6, C-reactive protein, albumin, and creatinine clearance. Respondents were assigned 0.5 points for each biomarker categorized as moderate risk and 1 point for each biomarker categorized as high risk. Allostatic load score range = 0 to 10.
Unhealthy behavior count included current smoking, excessive or binge drinking, and being obese. Respondents were assigned 1 point for each behavior reported.
Based on a score of 16 or greater on the Left for Epidemiologic Studies–Depression Scale
Each odds ratio was estimated at the mean value of the counterpart interacting variable.
p < .05.
p < .01.
p < .001.
Other Predictors of Depressive Symptoms
Those who were 80 years of age or older, regardless of birthplace, were more likely to report depressive symptoms at follow-up versus those who were 60 to 69 years old. Compared with foreign born men, foreign born women were more than two and a half times more likely (95% CI = [1.54, 4.64]) to report depressive symptoms at follow-up. Among U.S. born participants, those with some college or higher education were less likely than their counterparts with less than a ninth-grade education (aOR = 0.45, 95% CI = [0.23, 0.85]) to report depressive symptoms at follow-up. Among foreign born participants, only those with a ninth grade to high school education were less likely to report depressive symptoms at follow-up compared with those with less than a ninth-grade education (aOR = 0.38, 95% CI = [0.15, 0.99]). Among both groups, participants who reported significant depressive symptoms at baseline were at least 4 times more likely to experience significant depressive symptoms at follow-up compared with those who did not.
Discussion
Our purpose was to assess the effects of allostatic load, the number of unhealthy behaviors, and their interaction on depressive symptoms longitudinally and whether these relationships varied by birthplace among Latinos. Results showed that higher allostatic load increased the risk of future significant depressive symptoms, and that the effect of allostatic load on depressives symptoms increased significantly with more unhealthy behaviors among foreign born Latinos. We also found that foreign born women and adults ≥80 years old were at a greater risk of experiencing significant depressive symptoms in the future, after accounting for allostatic load and unhealthy behaviors. Our data contribute to increased understanding of the effects of cumulative physiologic stress and three unhealthy behaviors on future depressive symptoms and emphasize the importance of evaluating Latinos by birthplace. These findings indicate that the Environmental Affordances Model may not apply to Latinos.
Grounded in neurobiological research on stress and health behavior and sociological theory, the Environmental Affordances Model attempts to explain how life stressors at the individual and societal levels interact with unhealthy behaviors to potentially attenuate the effect of stress on health among racial/ethnic minorities (Mezuk et al., 2013). Previous research has debated whether African Americans engage in unhealthy behaviors to reduce the effect of chronic stress on depression (Bates, Barnes, & Keyes, 2011; Boardman & Alexander, 2011; Mezuk et al., 2010). Two studies did not find an interaction effect between race, chronic stress, and unhealthy behaviors on depression hypothesized by the Environmental Affordances Model (Keyes, Barnes, & Bates, 2011; Walsh, Senn, & Carey, 2013). The nature of the interaction effect between allostatic load and unhealthy behaviors on the risk of future significant depressive symptoms among foreign born Latinos is inconsistent with the Environmental Affordances Model, but is supported by prior research on depressive symptoms among older Latinos and foreign born Mexicans (Lipton, 1997; Rodriquez et al., 2017). Our findings suggest that unhealthy behaviors may place foreign born Latinos at an increased risk of future significant depressive symptoms over time when they are chronically stressed. Therefore, this model may be directly relevant for African Americans (Jackson et al., 2010; Mezuk et al., 2010), but may only be relevant for depressive symptoms among Latinos.
Women, in particular those who were foreign born, were at a higher risk of future significant depressive symptoms. These observations may indicate contributing factors of stress from immigration and possible changes in traditional gender roles. Previous research has established that women are about twice as likely as men to report symptoms of depression (Kuehner, 2017). Depression or significant depressive symptoms were found to be more prevalent among older foreign born Latinos than their U.S. born counterparts (Gonzalez et al., 2001; Jimenez et al., 2010; Rote, Chen, & Markides, 2015). Given that the SALSA cohort was almost entirely comprised of older Mexican Americans (Gonzalez et al., 2001), our findings suggest that the higher risk of depressive symptoms among foreign born women may be due to cumulative deleterious factors that are yet unexplained.
We also observed a higher risk of significant depressive symptoms among adults 80 years of age and older. In the general population, depressive symptoms are either less frequent or similar among older adults compared with middle-aged adults (Blazer, 2003; Haigh, Bogucki, Sigmon, & Blazer, 2018). However, older Mexican Americans may experience depression more frequently than other racial/ethnic groups for unclear reasons, with a possible explanation being increasing disillusionment with longer time in the United States (Blazer, 2003; Gonzalez et al., 2001). Our results support a longitudinal relationship between depressive symptoms, allostatic load, and unhealthy behaviors and risk of future significant depressive symptoms among foreign born elderly Mexican Americans.
Mental health and unhealthy behaviors covary with birthplace, so birthplace is an important factor to include when studying these constructs among Latinos. Among Mexican Americans, the prevalence of mood disorders, such as major depression, has been found to be lower among foreign born adults than U.S. born adults (Grant et al., 2004). Among older Latinos, foreign born adults are significantly less likely to report using substances compared with U.S. born adults (Jimenez et al., 2010). Disaggregating Latinos by birthplace can reveal findings that would have been obscured using aggregated data. Behavioral health interventions need to attend to such differences by birthplace among Latinos when designing prevention and treatment interventions.
The main limitation of the present study is that we assessed self-reported depressive symptoms, which may be more fallible than a clinical diagnosis of depression. The age range of study participants may have prevented us from observing relationships that may exist among middle-aged or younger adults. In addition, not being able to assess poor diet and physical inactivity separately may have limited the effect of the unhealthy behavior count. Finally, the time period of 24 to 30 months between baseline and follow-up assessments may not be the optimal assessment period in which to investigate the relationships targeted in these analyses. Future research may improve upon our analysis by incorporating a clinically relevant measure of depression and investigating the relationships between allostatic load, unhealthy behaviors, and depression among younger to middle-aged U.S. born Latinos with a more refined follow-up time frame.
In summary, using longitudinal data, our findings suggest that the effect of chronic stress on significant depressive symptoms increased with greater counts of unhealthy behaviors among older foreign born Latinos, thus improving our understanding of the mechanisms between chronic stress and mental health. Our findings contribute to the identification of a population group that may require more intensive screening for biomarkers of stress and unhealthy behaviors in primary care settings due to their higher risk of subsequent depression.
Supplementary Material
Acknowledgments
We want to thank Myriam Alcantar Rama for conducting a literature review. We also express gratitude to the faculty and scholars of the Center for Aging in Diverse Communities and the University of California, San Francisco, for their advice. Drs. Erik Rodriquez and Eliseo Pérez-Stable designed the study. Dr. Erik Rodriquez planned the analyses and directed its conduct. Drs. Erik Rodriquez and Melanie Sabado-Liwag cowrote the first draft of the manuscript. Ms. Anne Lee conducted the analyses. Dr. Mary Haan is the principal investigator of the Sacramento Area Latino Study on Aging and provided study design and analytic advice. Dr. Steven Gregorich provided statistical advice. Dr. James Jackson provided contextual advice with respect to the Environmental Affordances Model. Dr. Anna Nápoles provided contextual advice with respect to stress among Latinos. All authors contributed to the writing of the paper and have approved the final manuscript for submission. As the corresponding author, I have full access to all aspects of the research and writing process, and take final responsibility for the manuscript. This study began at the University of California, San Francisco, and an abstract of the manuscript was presented at the Society for Epidemiologic Research Annual Meeting between June 19 and 22, 2018.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported, in part, by grants from the Resource Centers for Minority Aging Research of the National Institute on Aging, National Institutes of Health (Center for Aging in Diverse Communities, P30 AG15272, and diversity supplement, P30 AG15272–19S1), the National Latino Cancer Research Network of the National Cancer Institute, National Institutes of Health (Redes En Acción, U01 CA86117 and U54 CA153511), and the Clinical and Translational Science Institute at the University of California, San Francisco (Strategic Opportunity Support, UL1 RR024131–0X). This study was also supported by the Divisions of Intramural Research of the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities, National Institutes of Health (grant number not applicable). The funders of this work had no role in the study design, data analysis and interpretation, manuscript writing, or decision to submit the manuscript for publication.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Supplemental Material
Supplemental material for this article is available online.
References
- Bates LM, Barnes DM, & Keyes KM (2011). Reconsidering the role of social disadvantage in physical and mental health: Stressful life events, health behaviors, race, and depression. American Journal of Epidemiology, 173, 1343–1351. doi: 10.1093/aje/kwr110 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Beckie TM (2012). A systematic review of allostatic load, health, and health disparities. Biological Research for Nursing, 14, 311–346. doi: 10.1177/1099800412455688 [DOI] [PubMed] [Google Scholar]
- Blazer DG (2003). Depression in late life: Review and commentary. The Journals of Gerontology, Series A: Biological Sciences & Medical Sciences, 58, 249–265. [DOI] [PubMed] [Google Scholar]
- Boardman JD, & Alexander KB (2011). Stress trajectories, health behaviors, and the mental health of black and white young adults. Social Science & Medicine, 72, 1659–1666. doi: 10.1016/j.socscimed.2011.03.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dallman MF, Akana SF, Laugero KD, Gomez F, Manalo S, Bell ME, & Bhatnagar S (2003). A spoonful of sugar: Feedback signals of energy stores and corticosterone regulate responses to chronic stress. Physiology & Behavior, 79, 3–12. [DOI] [PubMed] [Google Scholar]
- Finch BK, Kolody B, & Vega WA (2000). Perceived discrimination and depression among Mexican-origin adults in California. Journal of Health and Social Behavior, 41, 295–313. [PubMed] [Google Scholar]
- Gonzalez HM, Haan MN, & Hinton L (2001). Acculturation and the prevalence of depression in older Mexican Americans: Baseline results of the Sacramento Area Latino Study on Aging. Journal of the American Geriatrics Society, 49, 948–953. [DOI] [PubMed] [Google Scholar]
- Grant BF, Stinson FS, Hasin DS, Dawson DA, Chou SP, & Anderson K (2004). Immigration and lifetime prevalence of DSM-IV psychiatric disorders among Mexican Americans and non-Hispanic whites in the United States: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General Psychiatry, 61, 1226–1233. doi: 10.1001/archpsyc.61.12.1226 [DOI] [PubMed] [Google Scholar]
- Haan MN, Mungas DM, Gonzalez HM, Ortiz TA, Acharya A, & Jagust WJ (2003). Prevalence of dementia in older Latinos: The influence of type 2 diabetes mellitus, stroke and genetic factors. Journal of the American Geriatrics Society, 51, 169–177. [DOI] [PubMed] [Google Scholar]
- Haigh EAP, Bogucki OE, Sigmon ST, & Blazer DG (2018). Depression among older adults: A 20-year update on five common myths and misconceptions. American Journal of Geriatric Psychiatry, 26, 107–122. doi: 10.1016/j.jagp.2017.06.011 [DOI] [PubMed] [Google Scholar]
- Hammen C (2005). Stress and depression. Annual Review of Clinical Psychology, 1, 293–319. doi: 10.1146/annurev.clinpsy.1.102803.143938 [DOI] [PubMed] [Google Scholar]
- Jackson JS, Knight KM, & Rafferty JA (2010). Race and unhealthy behaviors: Chronic stress, the HPA axis, and physical and mental health disparities over the life course. American Journal of Public Health, 100, 933–939. doi:AJPH.2008.143446 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jimenez DE, Alegría M, Chen CN, Chan D, & Laderman M (2010). Prevalence of psychiatric illnesses in older ethnic minority adults. Journal of the American Geriatrics Society, 58, 256–264. doi: 10.1111/j.1532-5415.2009.02685.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Keyes KM, Barnes DM, & Bates LM (2011). Stress, coping, and depression: Testing a new hypothesis in a prospectively studied general population sample of U.S.-born Whites and Blacks. Social Science & Medicine, 72, 650–659. doi: 10.1016/j.socscimed.2010.12.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Koob GF, Roberts AJ, Schulteis G, Parsons LH, Heyser CJ, Hyytiä P, … Weiss F (1998). Neurocircuitry targets in ethanol reward and dependence. Alcoholism, Clinical & Experimental Research, 22(1), 3–9. [PubMed] [Google Scholar]
- Kuehner C (2017). Why is depression more common among women than among men? Lancet Psychiatry, 4, 146–158. doi: 10.1016/S2215-0366(16)30263-2 [DOI] [PubMed] [Google Scholar]
- Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, … Chronic Kidney Disease Epidemiology Collaboration. (2009). A new equation to estimate glomerular filtration rate. Annals of Internal Medicine, 150, 604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lipton R (1997). The relationship between alcohol, stress, and depression in Mexican Americans and Non-Hispanic Whites. Behavioral Medicine, 23, 101–111. doi: 10.1080/08964289709596366 [DOI] [PubMed] [Google Scholar]
- Lutz RS, Lochbaum MR, Lanning B, Stinson LG, & Brewer R (2007). Cross-lagged relationships among leisure-time exercise and perceived stress in blue-collar workers. Journal of Sport & Exercise Psychology, 29, 687–705. [DOI] [PubMed] [Google Scholar]
- Mezuk B, Abdou CM, Hudson D, Kershaw KN, Rafferty JA, Lee H, & Jackson JS (2013). “White box” epidemiology and the social neuroscience of health behaviors: The environment affordances model. Society and Mental Health, 3, 79–95. doi: 10.1177/2156869313480892 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mezuk B, Rafferty JA, Kershaw KN, Hudson D, Abdou CM, Lee H, … Jackson JS (2010). Reconsidering the role of social disadvantage in physical and mental health: Stressful life events, health behaviors, race, and depression. American Journal of Epidemiology, 172, 1238–1249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- National Archive of Computerized Data on Aging. (n.d.). Sacramento Area Latino Study on Aging (SALSA) Series. Retrieved from https://www.icpsr.umich.edu/icpsrweb/NACDA/series/247
- National Institute on Alcohol Abuse and Alcoholism. (n.d.). How to screen for heavy drinking. Retrieved from http://pubs.niaaa.nih.gov/publications/Practitioner/PocketGuide/pocket_guide5.htm
- Paykel ES (2003). Life events and affective disorders. Acta Psychiatrica Scandinavica, 418, 61–66. [DOI] [PubMed] [Google Scholar]
- Piazza PV, & Le Moal M (1998). The role of stress in drug self-administration. Trends in Pharmacological Sciences, 19, 67–74. [DOI] [PubMed] [Google Scholar]
- Radloff LS (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. [Google Scholar]
- Rodriquez EJ, Gregorich SE, Livaudais-Toman J, & Pérez-Stable EJ (2017). Coping with chronic stress by unhealthy behaviors: A re-evaluation among older adults by race/ethnicity. Journal of Aging and Health, 29, 805–825. doi: 10.1177/0898264316645548 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rote S, Chen NW, & Markides K (2015). Trajectories of depressive symptoms in elderly Mexican Americans. Journal of the American Geriatrics Society, 63, 1324–1330. doi: 10.1111/jgs.13480 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schulz AJ, Mentz G, Lachance L, Johnson J, Gaines C, & Israel BA (2012). Associations between socioeconomic status and allostatic load: Effects of neighborhood poverty and tests of mediating pathways. American Journal of Public Health, 102, 1706–1714. doi: 10.2105/AJPH.2011.300412 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Seeman TE, Singer BH, Rowe JW, Horwitz RI, & McEwen BS (1997). Price of adaptation—Allostatic load and its health consequences. MacArthur studies of successful aging. Archives of Internal Medicine, 157, 2259–2268. [PubMed] [Google Scholar]
- Tillman KH, & Weiss UK (2009). Nativity status and depressive symptoms among Hispanic young adults: The role of stress exposure. Social Science Quarterly, 90, 1228–1250. doi: 10.1111/j.1540-6237.2009.00655.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wadsworth ME (2015). Development of maladaptive coping: A functional adaptation to chronic, uncontrollable stress. Child Development Perspectives, 9, 96–100. doi: 10.1111/cdep.12112 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Walsh JL, Senn TE, & Carey MP (2013). Longitudinal associations between health behaviors and mental health in low-income adults. Translational Behavioral Medicine, 3, 104–113. doi: 10.1007/s13142-012-0189-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
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