Biodemography and Social Biology, 61:18–39, 2015 Copyright © Society for Biodemography and Social Biology ISSN: 1948-5565 print / 1948-5573 online DOI: 10.1080/19485565.2014.937000
The Role of Education in Explaining Racial/Ethnic Allostatic Load Differentials in the United States JEFFREY T. HOWARD AND P. JOHNELLE SPARKS Department of Demography, University of Texas at San Antonio, San Antonio, Texas, USA This study expands on earlier findings of racial/ethnic and education–allostatic load associations by assessing whether racial/ethnic differences in allostatic load persist across all levels of educational attainment. This study used data from four recent waves of the National Health and Nutrition Survey (NHANES). Results from this study suggest that allostatic load differs significantly by race/ethnicity and educational attainment overall, but that the race/ethnicity association is not consistent across education level. Analysis of interactions and education-stratified models suggest that allostatic load levels do not differ by race/ethnicity for individuals with low education; rather, the largest allostatic load differentials for Mexican Americans (p < .01) and non-Hispanic blacks (p < .001) are observed for individuals with a college degree or more. These findings add to the growing evidence that differences in socioeconomic opportunities by race/ethnicity are likely a consequence of differential returns to education, which contribute to higher stress burdens among minorities compared to non-Hispanic whites.
Introduction Recent research in the areas of chronic stress and aging has begun to uncover important mechanisms linking socioeconomic conditions to health and mortality outcomes. One pathway linking socioeconomic conditions to health and mortality is known as allostatic load, which is a measure of the cumulative biological “wear and tear,” or dysregulation, resulting from exposure to chronic stress (McEwen 1998; McEwen 2003; McEwen and Seeman 1999). Recent research has found evidence linking measures of socioeconomic position (SEP), such as poverty (Crimmins, Kim, and Seeman 2009), income (Seeman et al. 2008), and educational attainment (Seeman et al. 2010; Seeman et al. 2008), as well as age (Crimmins et al. 2003; Geronimus et al. 2006) and race/ethnicity (Geronimus et al. 2006; Peek et al. 2010; Seeman et al. 2008), to levels of allostatic load. Based on such findings, a clearer picture is beginning to emerge regarding how social conditions get into the body through biological stress responses to ultimately affect health and longevity. It has been noted that racial/ethnic differences in health outcomes often persist even when multivariable regression models adjust for confounders such as age, sex, income, poverty status, and educational attainment (Aday 2001; Barr 2008; Geronimus et al. 2006; Haas et al. 2003; Hummer et al. 1999; LaVeist 2002; Peek et al. 2010; Seeman et al. 2008). Three competing explanations for the persistence of racial/ethnic differences include (1) biological susceptibility (Haiman et al. 2006); (2) weathering, or the accelerated aging of racial/ethnic minorities as a result of persistent exposure to stress brought about by Address correspondence to Jeffrey T. Howard, 19527 Gran Roble, San Antonio, TX 78258, USA. E-mail: [email protected]
18
Racial/Ethnic Allostatic Load Differentials
19
racial discrimination (Geronimus 1992; Geronimus et al. 2006); and (3) differential returns on social capital (Crimmins and Saito 2001; Crosnoe 2005; Farmer and Ferraro 2005; Kimbro et al. 2008; Masters, Hummer, and Powers 2012; Roscigno and Ainsworth-Darnell 1999). Under the biological susceptibility hypothesis, one would expect to see racial/ethnic differences persist across levels of other socioeconomic variables, such as education and income. Likewise, under the weathering hypothesis, one might also expect to observe racial/ethnic differentials across all levels of socioeconomic variables, but through mechanisms of social stratification and discrimination leading to increased stress exposure for racial/ethnic minorities. In contrast, the differential returns hypothesis suggests that observed differences are the result of (1) racial/ethnic minorities benefiting less from health-protective socioeconomic factors than whites, and (2) the use of statistical models in which interactions between socioeconomic variables and race/ethnicity are not included. In addition, the fact that measures of SEP often differ across racial/ethnic groups complicates attempts to assess racial/ethnic differences, because comparisons are not being made between similar groups (LaVeist 2005; LaVeist et al. 2007). In many cases, the use of interaction terms and stratification in statistical models can help overcome such issues by creating subsets of analysis that are more alike in their distribution of other covariates (Brambor, Clark, and Golder 2005; Schmoor, Caputo, and Schmacher 2008). The patterning of SEP by educational attainment, however, is complicated by racial/ethnic confounding, particularly in studies seeking to tease apart racial/ethnic disparities in health outcomes, because observed associations among race/ethnicity, measures of SEP, and health often do not operate in the same way for all individuals with similar levels of education (LaVeist 2005; LaVeist et al. 2007). For example, fewer Mexican Americans and African Americans are represented in higher educational categories than are non-Hispanic whites. Likewise minorities may not receive the same return on investment in higher education in terms of comparable salaries or potential long-term wealth as nonHispanic whites. As a result, it is important to examine racial/ethnic and SEP associations with health outcomes within like groups. While recent studies have found evidence of significantly higher levels of allostatic load for racial/ethnic minorities (Crimmins and Saito 2001; Geronimus et al. 2006; Kaestner et al. 2009; Peek et al. 2010; Seeman et al. 2010) and for individuals with low educational attainment (Hickson et al. 2012; Seeman et al. 2010; Seeman et al. 2008), none have addressed race/ethnicity–education interactions explicitly. As a result, it remains unclear whether or not the presence of differential returns to education may explain racial/ethnic differences in allostatic load. Much is still unknown about interactions between fundamental sociodemographic variables such as racial/ethnic background and education, which potentially impact allostatic load by establishing, typically by early adulthood, different trajectories for stress exposure and health over the life course (Hayward and Gorman 2004; Link and Phelan 1995; Seeman et al. 2010). The purpose of this study was to expand on earlier findings of racial/ethnic and education–allostatic load (AL) associations by (1) testing the biological susceptibility and differential returns hypotheses to determine whether or not racial/ethnic differences in AL persist across all levels of educational attainment, and (2) exploring how other demographic and socioeconomic factors are associated with AL within different educational attainment strata. The central hypothesis of this study is that racial/ethnic differences in allostatic load arise from differential returns on educational attainment.
20
J. T. Howard and P. J. Sparks
Data and Methods Sample Public use data from four waves of the National Health and Nutrition Examination Survey (NHANES), collected from 2003 through 2010, were compiled for this study (National Center for Health Statistics [NCHS] 2005; NCHS 2007; NCHS 2009; NCHS 2011a). A set of 10 biomarkers was used to calculate AL scores for each participant. Of particular importance is the fact that the lipid panel used to measure triglycerides is only administered to the fasting subset of the total NHANES sample (NCHS 2011b). As a result, this study was limited to the fasting subset of the NHANES sample from each of the four waves (N = 14,282). Several data restrictions were imposed on the total fasting dataset as a result of a number of important considerations. First, since this study is focused on adults aged 25 years and older, the exclusion of individuals younger than age 25 reduced the sample to 9,324. The sample was further restricted to three racial/ethnic groups, as recommended in the NCHS analytical guidelines and supplemental documentation, which resulted in a reduction of the sample to 8,202 (NCHS 2006). Pregnancy is a confounding factor for many of the biological measures used in calculating allostatic load. Pregnant women will typically have higher BMI scores as a result of pregnancy-related weight gain and can often experience changes in blood pressure, heart rate, and other measures that might not otherwise be present. Therefore, an additional 204 individuals were excluded because they were pregnant at the time the survey and/or the examination were conducted, which left a total sample of 7,998. Finally, there were 1,008 individuals with nonpositive survey weights, which resulted in exclusion from statistical procedures involving adjustments for complex survey design. After applying each of these exclusions, the final total fasting sample used for this study was 6,990. Measures Allostatic Load. Following previous research using population-based samples, such as NHANES, allostatic load was calculated using a 10-biomarker algorithm with clinically determined threshold cut-points for defining the high-risk point for each biomarker (Crimmins, Kim, and Seeman 2009; Juster, McEwen, and Lupien 2010; Seeman et al. 2008). The 10 biomarkers include diastolic blood pressure, calculated as the mean of four measurement attempts (Chobanian et al. 2003); systolic blood pressure, calculated as the mean of four measurement attempts (Chobanian et al. 2003); resting pulse rate (Seccareccia et al. 2001); total cholesterol (National Cholesterol Education Program [NECP] Expert Panel 2001); high-density lipoprotein (HDL) cholesterol (NECP Expert Panel 2001); triglycerides (NECP Expert Panel 2001); body mass index (BMI) (Crimmins et al. 2007); glycated hemoglobin (Golden et al. 2003; Osei et al. 2003); albumin (Visser et al. 2005); and C-reactive protein (Ridker 2003). To calculate each individual’s AL score, each biomarker exceeding the clinically determined threshold for high risk was assigned a value of 1, and 0 otherwise. These values were then summed across each of the 10 biomarkers, which resulted in a score ranging from 0 (no high-risk biomarkers present) to 10 (all biomarkers are high-risk) (Crimmins, Kim, and Seeman 2009; Geronimus et al. 2006; Seeman et al. 2008). The interpretation of the AL score is that the higher the score, the more biological risk an individual has accumulated.
Racial/Ethnic Allostatic Load Differentials
21
Missing cases for each biomarker were imputed using multiple regression procedures (Shrive et al. 2006). No individual biomarker variable had more than 3.3 percent of the sample with missing values, and 94.4 percent of the sample had complete data for all biomarkers. Of those cases with missing data for biomarkers, 281 cases (4 percent) were missing more than two biomarkers, and only 33 cases (0.5 percent) were missing more than four biomarkers. There were no cases that were missing all 10 biomarkers. Table 1 provides a listing of each biomarker, basic descriptive statistics with and without imputation, the clinically determined high-risk threshold, and the percentage of the sample falling outside of these cut-points. Sensitivity analysis of imputed and un-imputed means and standard errors for each biomarker, as well as comparisons of models including and excluding cases having more than two missing biomarkers, suggests that inclusion of these cases with imputed values had no effect on the underlying distributions for each biomarker and did not significantly affect the results of subsequent regression analyses. Demographic, Socioeconomic, and Health Behavior Measures. Of primary interest in this study were the race/ethnicity and educational attainment variables. Race/ethnicity was measured as the following categories: (1) Mexican American, (2) non-Hispanic white (reference group), and (3) non-Hispanic black. Educational attainment was measured as a categorical variable representing the highest educational level completed: (1) less than high school, (2) high school graduate or equivalent, (3) some college, and (4) college degree or higher. There were 11 cases (0.16 percent) missing education level, and these cases were imputed because survey design–adjusted generalized models require a balanced design for interaction terms. Additional sensitivity analysis excluding these cases suggested that the results of regression analyses were not changed by the inclusion of the cases with imputed values. Additional demographic and socioeconomic variables were also included, including age (continuous single year of age), age squared, sex, nativity (U.S. or foreign born), family income (< $20,000, $20,000–$64,999, and $65,000 or more), and marital status (married, divorced/separated/widowed, never married, and cohabitating). Current smoking status (nonsmoker/never smoked, current smoker, former smoker, or missing) was used in this analysis to account for potentially negative health behavior. Smoking status has been extensively studied with respect to its negative association with a range of morbidity and mortality outcomes, and only seven cases in the NHANES sample were missing smoking status data. A similar measure of alcohol consumption, however, was not included for two reasons. First, analysis of the alcohol consumption variable revealed that approximately 20 percent of the sample had missing data. Second, preliminary models containing a variable for alcohol consumption with a missing indicator suggested that interpretation of the variable’s coefficients was questionable. Last, since this study involves the pooling of four separate waves of NHANES data, a survey wave indicator variable was created to control for period effects. Period effects can create bias when changes in important variables under study change over time, independent of other covariates. To account for period effects, a categorical variable was created as (1) Wave 2003–2004 (reference group), (2) Wave 2005–2006, (3) Wave 2007–2008, and (4) Wave 2009–2010. Statistical Analysis While prior studies of allostatic load have used ordinary least squares (OLS) and ordinal logistic regression (OLR) techniques, hypothesis tests for this study were conducted using multivariable negative binomial regression models for the allostatic load outcome.
22 69.93 122.12 71.43 200.51 54.42 139.31 5.57 28.92 4.21 0.43
6,909 6,933 6,920 6,967 6,881 6,910 6,961
Mean
6,715 6,715 6,765
n
0.007 0.011
0.728 0.286 2.021 0.015 0.109
0.276 0.349 0.278
SE
6,990 6,990
6,990 6,990 6,990 6,990 6,990
6,990 6,990 6,990
n
4.21 0.43
200.51 54.44 139.29 5.57 28.92
69.93 122.18 71.46
Mean
SE
0.007 0.011
0.721 0.284 1.997 0.015 0.108
0.269 0.342 0.271
With imputed values
Note: All figures were adjusted for complex survey design using SDMVPSU, SDMVSTRA, and WTSAF8YR variables.
Cardiovascular markers Diastolic blood pressure (mmHg) Systolic blood pressure (mmHg) Pulse rate at 60 seconds Metabolic markers Total cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) Glycohemoglobin (%) Body mass index (kg/m2) Inflammation markers Albumin (g/dL) C-reactive protein (mg/dL)
Biomarker
Without imputed values
Table 1 Weighted descriptive statistics for 10 allostatic load biomarkers with and without imputation
4.34 14.29 7.89 16.18 16.03 30.23 7.20 35.69 6.90 37.30
≥240
The Role of Education in Explaining Racial/Ethnic Allostatic Load Differentials in the United States JEFFREY T. HOWARD AND P. JOHNELLE SPARKS Department of Demography, University of Texas at San Antonio, San Antonio, Texas, USA This study expands on earlier findings of racial/ethnic and education–allostatic load associations by assessing whether racial/ethnic differences in allostatic load persist across all levels of educational attainment. This study used data from four recent waves of the National Health and Nutrition Survey (NHANES). Results from this study suggest that allostatic load differs significantly by race/ethnicity and educational attainment overall, but that the race/ethnicity association is not consistent across education level. Analysis of interactions and education-stratified models suggest that allostatic load levels do not differ by race/ethnicity for individuals with low education; rather, the largest allostatic load differentials for Mexican Americans (p < .01) and non-Hispanic blacks (p < .001) are observed for individuals with a college degree or more. These findings add to the growing evidence that differences in socioeconomic opportunities by race/ethnicity are likely a consequence of differential returns to education, which contribute to higher stress burdens among minorities compared to non-Hispanic whites.
Introduction Recent research in the areas of chronic stress and aging has begun to uncover important mechanisms linking socioeconomic conditions to health and mortality outcomes. One pathway linking socioeconomic conditions to health and mortality is known as allostatic load, which is a measure of the cumulative biological “wear and tear,” or dysregulation, resulting from exposure to chronic stress (McEwen 1998; McEwen 2003; McEwen and Seeman 1999). Recent research has found evidence linking measures of socioeconomic position (SEP), such as poverty (Crimmins, Kim, and Seeman 2009), income (Seeman et al. 2008), and educational attainment (Seeman et al. 2010; Seeman et al. 2008), as well as age (Crimmins et al. 2003; Geronimus et al. 2006) and race/ethnicity (Geronimus et al. 2006; Peek et al. 2010; Seeman et al. 2008), to levels of allostatic load. Based on such findings, a clearer picture is beginning to emerge regarding how social conditions get into the body through biological stress responses to ultimately affect health and longevity. It has been noted that racial/ethnic differences in health outcomes often persist even when multivariable regression models adjust for confounders such as age, sex, income, poverty status, and educational attainment (Aday 2001; Barr 2008; Geronimus et al. 2006; Haas et al. 2003; Hummer et al. 1999; LaVeist 2002; Peek et al. 2010; Seeman et al. 2008). Three competing explanations for the persistence of racial/ethnic differences include (1) biological susceptibility (Haiman et al. 2006); (2) weathering, or the accelerated aging of racial/ethnic minorities as a result of persistent exposure to stress brought about by Address correspondence to Jeffrey T. Howard, 19527 Gran Roble, San Antonio, TX 78258, USA. E-mail: [email protected]
18
Racial/Ethnic Allostatic Load Differentials
19
racial discrimination (Geronimus 1992; Geronimus et al. 2006); and (3) differential returns on social capital (Crimmins and Saito 2001; Crosnoe 2005; Farmer and Ferraro 2005; Kimbro et al. 2008; Masters, Hummer, and Powers 2012; Roscigno and Ainsworth-Darnell 1999). Under the biological susceptibility hypothesis, one would expect to see racial/ethnic differences persist across levels of other socioeconomic variables, such as education and income. Likewise, under the weathering hypothesis, one might also expect to observe racial/ethnic differentials across all levels of socioeconomic variables, but through mechanisms of social stratification and discrimination leading to increased stress exposure for racial/ethnic minorities. In contrast, the differential returns hypothesis suggests that observed differences are the result of (1) racial/ethnic minorities benefiting less from health-protective socioeconomic factors than whites, and (2) the use of statistical models in which interactions between socioeconomic variables and race/ethnicity are not included. In addition, the fact that measures of SEP often differ across racial/ethnic groups complicates attempts to assess racial/ethnic differences, because comparisons are not being made between similar groups (LaVeist 2005; LaVeist et al. 2007). In many cases, the use of interaction terms and stratification in statistical models can help overcome such issues by creating subsets of analysis that are more alike in their distribution of other covariates (Brambor, Clark, and Golder 2005; Schmoor, Caputo, and Schmacher 2008). The patterning of SEP by educational attainment, however, is complicated by racial/ethnic confounding, particularly in studies seeking to tease apart racial/ethnic disparities in health outcomes, because observed associations among race/ethnicity, measures of SEP, and health often do not operate in the same way for all individuals with similar levels of education (LaVeist 2005; LaVeist et al. 2007). For example, fewer Mexican Americans and African Americans are represented in higher educational categories than are non-Hispanic whites. Likewise minorities may not receive the same return on investment in higher education in terms of comparable salaries or potential long-term wealth as nonHispanic whites. As a result, it is important to examine racial/ethnic and SEP associations with health outcomes within like groups. While recent studies have found evidence of significantly higher levels of allostatic load for racial/ethnic minorities (Crimmins and Saito 2001; Geronimus et al. 2006; Kaestner et al. 2009; Peek et al. 2010; Seeman et al. 2010) and for individuals with low educational attainment (Hickson et al. 2012; Seeman et al. 2010; Seeman et al. 2008), none have addressed race/ethnicity–education interactions explicitly. As a result, it remains unclear whether or not the presence of differential returns to education may explain racial/ethnic differences in allostatic load. Much is still unknown about interactions between fundamental sociodemographic variables such as racial/ethnic background and education, which potentially impact allostatic load by establishing, typically by early adulthood, different trajectories for stress exposure and health over the life course (Hayward and Gorman 2004; Link and Phelan 1995; Seeman et al. 2010). The purpose of this study was to expand on earlier findings of racial/ethnic and education–allostatic load (AL) associations by (1) testing the biological susceptibility and differential returns hypotheses to determine whether or not racial/ethnic differences in AL persist across all levels of educational attainment, and (2) exploring how other demographic and socioeconomic factors are associated with AL within different educational attainment strata. The central hypothesis of this study is that racial/ethnic differences in allostatic load arise from differential returns on educational attainment.
20
J. T. Howard and P. J. Sparks
Data and Methods Sample Public use data from four waves of the National Health and Nutrition Examination Survey (NHANES), collected from 2003 through 2010, were compiled for this study (National Center for Health Statistics [NCHS] 2005; NCHS 2007; NCHS 2009; NCHS 2011a). A set of 10 biomarkers was used to calculate AL scores for each participant. Of particular importance is the fact that the lipid panel used to measure triglycerides is only administered to the fasting subset of the total NHANES sample (NCHS 2011b). As a result, this study was limited to the fasting subset of the NHANES sample from each of the four waves (N = 14,282). Several data restrictions were imposed on the total fasting dataset as a result of a number of important considerations. First, since this study is focused on adults aged 25 years and older, the exclusion of individuals younger than age 25 reduced the sample to 9,324. The sample was further restricted to three racial/ethnic groups, as recommended in the NCHS analytical guidelines and supplemental documentation, which resulted in a reduction of the sample to 8,202 (NCHS 2006). Pregnancy is a confounding factor for many of the biological measures used in calculating allostatic load. Pregnant women will typically have higher BMI scores as a result of pregnancy-related weight gain and can often experience changes in blood pressure, heart rate, and other measures that might not otherwise be present. Therefore, an additional 204 individuals were excluded because they were pregnant at the time the survey and/or the examination were conducted, which left a total sample of 7,998. Finally, there were 1,008 individuals with nonpositive survey weights, which resulted in exclusion from statistical procedures involving adjustments for complex survey design. After applying each of these exclusions, the final total fasting sample used for this study was 6,990. Measures Allostatic Load. Following previous research using population-based samples, such as NHANES, allostatic load was calculated using a 10-biomarker algorithm with clinically determined threshold cut-points for defining the high-risk point for each biomarker (Crimmins, Kim, and Seeman 2009; Juster, McEwen, and Lupien 2010; Seeman et al. 2008). The 10 biomarkers include diastolic blood pressure, calculated as the mean of four measurement attempts (Chobanian et al. 2003); systolic blood pressure, calculated as the mean of four measurement attempts (Chobanian et al. 2003); resting pulse rate (Seccareccia et al. 2001); total cholesterol (National Cholesterol Education Program [NECP] Expert Panel 2001); high-density lipoprotein (HDL) cholesterol (NECP Expert Panel 2001); triglycerides (NECP Expert Panel 2001); body mass index (BMI) (Crimmins et al. 2007); glycated hemoglobin (Golden et al. 2003; Osei et al. 2003); albumin (Visser et al. 2005); and C-reactive protein (Ridker 2003). To calculate each individual’s AL score, each biomarker exceeding the clinically determined threshold for high risk was assigned a value of 1, and 0 otherwise. These values were then summed across each of the 10 biomarkers, which resulted in a score ranging from 0 (no high-risk biomarkers present) to 10 (all biomarkers are high-risk) (Crimmins, Kim, and Seeman 2009; Geronimus et al. 2006; Seeman et al. 2008). The interpretation of the AL score is that the higher the score, the more biological risk an individual has accumulated.
Racial/Ethnic Allostatic Load Differentials
21
Missing cases for each biomarker were imputed using multiple regression procedures (Shrive et al. 2006). No individual biomarker variable had more than 3.3 percent of the sample with missing values, and 94.4 percent of the sample had complete data for all biomarkers. Of those cases with missing data for biomarkers, 281 cases (4 percent) were missing more than two biomarkers, and only 33 cases (0.5 percent) were missing more than four biomarkers. There were no cases that were missing all 10 biomarkers. Table 1 provides a listing of each biomarker, basic descriptive statistics with and without imputation, the clinically determined high-risk threshold, and the percentage of the sample falling outside of these cut-points. Sensitivity analysis of imputed and un-imputed means and standard errors for each biomarker, as well as comparisons of models including and excluding cases having more than two missing biomarkers, suggests that inclusion of these cases with imputed values had no effect on the underlying distributions for each biomarker and did not significantly affect the results of subsequent regression analyses. Demographic, Socioeconomic, and Health Behavior Measures. Of primary interest in this study were the race/ethnicity and educational attainment variables. Race/ethnicity was measured as the following categories: (1) Mexican American, (2) non-Hispanic white (reference group), and (3) non-Hispanic black. Educational attainment was measured as a categorical variable representing the highest educational level completed: (1) less than high school, (2) high school graduate or equivalent, (3) some college, and (4) college degree or higher. There were 11 cases (0.16 percent) missing education level, and these cases were imputed because survey design–adjusted generalized models require a balanced design for interaction terms. Additional sensitivity analysis excluding these cases suggested that the results of regression analyses were not changed by the inclusion of the cases with imputed values. Additional demographic and socioeconomic variables were also included, including age (continuous single year of age), age squared, sex, nativity (U.S. or foreign born), family income (< $20,000, $20,000–$64,999, and $65,000 or more), and marital status (married, divorced/separated/widowed, never married, and cohabitating). Current smoking status (nonsmoker/never smoked, current smoker, former smoker, or missing) was used in this analysis to account for potentially negative health behavior. Smoking status has been extensively studied with respect to its negative association with a range of morbidity and mortality outcomes, and only seven cases in the NHANES sample were missing smoking status data. A similar measure of alcohol consumption, however, was not included for two reasons. First, analysis of the alcohol consumption variable revealed that approximately 20 percent of the sample had missing data. Second, preliminary models containing a variable for alcohol consumption with a missing indicator suggested that interpretation of the variable’s coefficients was questionable. Last, since this study involves the pooling of four separate waves of NHANES data, a survey wave indicator variable was created to control for period effects. Period effects can create bias when changes in important variables under study change over time, independent of other covariates. To account for period effects, a categorical variable was created as (1) Wave 2003–2004 (reference group), (2) Wave 2005–2006, (3) Wave 2007–2008, and (4) Wave 2009–2010. Statistical Analysis While prior studies of allostatic load have used ordinary least squares (OLS) and ordinal logistic regression (OLR) techniques, hypothesis tests for this study were conducted using multivariable negative binomial regression models for the allostatic load outcome.
22 69.93 122.12 71.43 200.51 54.42 139.31 5.57 28.92 4.21 0.43
6,909 6,933 6,920 6,967 6,881 6,910 6,961
Mean
6,715 6,715 6,765
n
0.007 0.011
0.728 0.286 2.021 0.015 0.109
0.276 0.349 0.278
SE
6,990 6,990
6,990 6,990 6,990 6,990 6,990
6,990 6,990 6,990
n
4.21 0.43
200.51 54.44 139.29 5.57 28.92
69.93 122.18 71.46
Mean
SE
0.007 0.011
0.721 0.284 1.997 0.015 0.108
0.269 0.342 0.271
With imputed values
Note: All figures were adjusted for complex survey design using SDMVPSU, SDMVSTRA, and WTSAF8YR variables.
Cardiovascular markers Diastolic blood pressure (mmHg) Systolic blood pressure (mmHg) Pulse rate at 60 seconds Metabolic markers Total cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) Glycohemoglobin (%) Body mass index (kg/m2) Inflammation markers Albumin (g/dL) C-reactive protein (mg/dL)
Biomarker
Without imputed values
Table 1 Weighted descriptive statistics for 10 allostatic load biomarkers with and without imputation
4.34 14.29 7.89 16.18 16.03 30.23 7.20 35.69 6.90 37.30
≥240
