Intragenerational Social Mobility and Changes in Blood Pressure: Longitudinal Analysis From the ELSA-Brasil Study

Intragenerational Social Mobility and Changes in Blood Pressure: Longitudinal Analysis From the... Abstract BACKGROUND During the past 4 decades, the highest worldwide blood pressure (BP) levels have shifted from high-income countries to low- and middle-income countries. We investigated the association of intragenerational social mobility with changes in BP and also with the incidence of hypertension over a 4-year follow-up. METHODS Data for 6,529 baseline participants from ELSA-Brasil born between 1938 and 1975 were used. Based on a social mobility matrix, occupational social mobility was defined as the change in occupational social class between participants’ first occupation and current occupation (stable high; upward; downward; stable low). Incident hypertension was defined as systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg or use of antihypertensive medication. Hypertensive participants at baseline were excluded. Mixed effects regression models were used. RESULTS Compared to the stable high group, the downwardly mobile group showed a higher increase over time in both SBP (β = 1.49, 95% CI 0.60; 2.37) and DBP (β = 0.96, 95% CI 0.32; 1.59) after adjustments for background characteristics and also proximal risk factors such as health-related behaviors and body mass index as time-dependent covariates, and diabetes. In contrast, upward mobility had no influence on BP changes (β = 0.67, 95% CI −0.07; 1.41 for SBP, and β = 0.47, 95% CI −0.05; 1.00 for DBP). Social mobility was not associated with the incidence of hypertension. CONCLUSIONS We showed socioeconomic inequalities in BP progression over the life course. The longitudinal changes in BP varied by social mobility groups in the context of low- and middle-income countries, where high BP has become most prevalent. blood pressure change, Brazil, hypertension, longitudinal design, social mobility During the past 4 decades, the highest worldwide blood pressure (BP) levels have shifted from high-income countries to low- and middle-income countries.1 In addition, from 2000 to 2010, hypertension awareness, treatment, and control increased substantially in high-income countries, whereas there was less improvement in low- and middle-income countries.2 It is well recognized that high BP is a major modifiable risk factor for cardiovascular diseases (CVDs).3 Although low socioeconomic condition has been associated with elevated BP 4,5 and higher incidence of hypertension,6,7 the life course social processes behind these associations have scarcely been investigated in low- and middle-income nations. Social mobility is a concept from Sociology and it primarily applies to societies, not to individuals.8,9 It is a structural determinant of health, since it lies beyond the individual and is thus distal to individual health outcomes.10 Social mobility is defined as the change in socioeconomic status (SES) or position within adulthood (intragenerational mobility) or between generations (inter-generational mobility), and it has been identified as an important driver of health.11 The general finding is that upward social mobility is protective for health while downward mobility is detrimental.11,12 Potential mechanisms linking social mobility to CVD have been attributed to differences in health-related behaviors,13 cardiometabolic factors,14 or psychological stress,15 between individuals with different socioeconomic trajectories. Brazil is a large middle-income country which has undergone great structural changes in society over the past half century, transforming from an agricultural society to an urban one, in a context of rapid late industrialization.16 Economic growth, poverty reduction, educational expansion, and increase in female labor market participation have all contributed to an increase in upward social mobility rates.16,17 More recently, since the 2000s, Brazilian society has faced a large reduction in income inequality due to increases in the minimum wage, declining unemployment, and anti-poverty policies such as cash transfer programs.17,18 Despite these positive social changes, striking socioeconomic inequalities still persist, including marked inequalities in life chances and opportunity.17 Using longitudinal data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we investigated whether intragenerational social mobility is associated with changes in BP and also with the incidence of hypertension over a 4-year follow-up. We hypothesized that downward social mobility and stable low groups would show a more rapid increase in BP over time, and a higher incidence of hypertension, than upward mobility and stable high groups. METHODS Study design The ELSA-Brasil is a prospective cohort study designed to investigate social and biological determinants of CVDs and diabetes.19,20 A total of 15,105 active or retired civil servants (35–74 years) of universities or research institutions were enrolled in 6 Brazilian cities at baseline (2008–2010). The first follow-up visit was performed approximately 4 years later (2012–2014), with a retention rate of 93% (n = 14,014). For the present study, participants with prevalent hypertension at baseline (n = 4,847), or missing information on hypertension (n = 70) or social mobility (n = 1,536) were excluded. Retired participants (n = 1,032) were also excluded, since we cannot ascertain their pattern of social mobility from retirement until baseline. We focused on 6,529 participants for analysis, born between 1938 and 1975. ELSA-Brasil was approved at each of the 6 study centers by the local Institutional Review Board addressing research in human subjects, and all participants gave written consent to participate. Measures Intragenerational occupational social mobility was created based on retrospective reports of each participant’s first occupation. It was defined as the change in participants’ occupational social class from the retrospectively reported first occupation (origin) to the current occupation (destination), obtained from the baseline (2008–2010) face-to-face interview. Since our participants started working at the mean age of 17, the earliest cohort of ELSA-Brasil (born in the 1930s) first entered the labor market in the 1950s and the latest cohort (born in the 1970s) first entered the labor market in the 1990s. Because occupation does not necessarily infer social position in Brazil, we created a composite index of occupational social class based on: the occupation held by the participant, the expected income based on the participant’s educational level (average market value), and the observed income. First, we calculated the mean between the expected and observed income for each participant. Second, we created a score indicating occupational SES using the mean income of individuals within each occupational title. Occupations which require enhanced qualifications and offer greater earnings have higher scores, compared to those occupations with lower educational and income levels. Using these scores, strata were defined in order to achieve a minimum intrastratum variation and the maximum variation between strata, according to the method proposed by Pastore21 and Silva.22 Seven occupational strata, or categories, were defined based on these methods: low SES (upper and lower), middle SES (upper, middle, and lower), and high SES (upper and lower). Based on a mobility matrix, 4 different intragenerational social mobility patterns were then identified across these 7 categories: stable high (reference), upward mobility, downward mobility, and stable low (Table 1). Table 1. Intragenerational social mobility matrix (%). Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Upward mobility (above diagonal), downward mobility (below diagonal), stable high (diagonal, middle or high SES in both occasions), and stable low (diagonal, low SES in both occasions). % referred to N = 6,529. View Large Table 1. Intragenerational social mobility matrix (%). Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Upward mobility (above diagonal), downward mobility (below diagonal), stable high (diagonal, middle or high SES in both occasions), and stable low (diagonal, low SES in both occasions). % referred to N = 6,529. View Large Systolic and diastolic blood pressure (SBP and DBP) were taken using a validated certified oscillometric device (Omron HEM 705CPINT) after a 5-minute rest with the subject in a sitting position in a quiet, temperature-controlled room (20–24 °C). Three measurements were taken and the average of the second and third measurements was considered in the analyses.23 Incident hypertension was defined as SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or confirmed use of antihypertensive medication at first follow-up visit (2012–2014) (in the interview, participants showed prescriptions and/or medicine packs and were asked: “Are any of the drugs you have taken in the past two weeks for hypertension?”).24 Background characteristics were: age (continuous), sex, and self-reported color/race (White, “Pardo,” Black, Asian descent, Indigenous). Health-related behaviors capture those associated with high BP and hypertension risk. Pack-years of smoking (continuous) was captured based on the average number of cigarettes smoked per day times the number of years smoked. Leisure-time physical activity (continuous) was captured using the International Physical Activity Questionnaire (IPAQ)25 in min/week. Alcohol consumption (continuous) was assessed through the amount ingested in g/week. Salt consumption (continuous) was evaluated using a 12-hour urine sample in g/day.26 Body mass index (BMI) (continuous) was obtained using measured weight and height (kg/m2). Diabetes was defined according to the American Diabetes Association as described elsewhere.20 According to our theoretical model (Figure 1), downward occupational social mobility might lead to a more rapid increase in BP over the follow-up, and to a higher incidence of hypertension through changes in health-related behaviors (i.e., unhealthier behaviors of the social class they enter, such as smoking, physical inactivity, alcohol consumption, and salt consumption), which could negatively impact BMI, an important proximal risk factor for high BP and hypertension.27 Thus, health-related behaviors and BMI may partially mediate the association between social mobility and changes in BP and incidence of hypertension. Moreover, these relationships would occur independently of individual background characteristics (age, sex, and color/race). Figure 1. View largeDownload slide Theoretical model for the relationship between intragenerational social mobility and changes in blood pressure and incidence of hypertension. Abbreviation: BMI, body mass index. Figure 1. View largeDownload slide Theoretical model for the relationship between intragenerational social mobility and changes in blood pressure and incidence of hypertension. Abbreviation: BMI, body mass index. Statistical analysis The association between social mobility and BP changes was examined using linear mixed effects models, and the association between social mobility and hypertension incidence was examined using mixed effects logistic regression models. Mixed effects models accounted for both the correlation between repeated measures on the same individual over time and on the individuals from the same study center. SBP, DBP, and hypertension incidence were analyzed in separate models. Analyses were adjusted for age, sex, color/race, time since baseline, and also proximal risk factors that may partially act as mediators such as smoking, physical activity, alcohol consumption, salt consumption, BMI, and diabetes. SBP and DBP analyses were also adjusted for antihypertensive medication use at first follow-up visit (2012–2014). Age, physical activity, alcohol consumption, salt consumption, and BMI were treated as time-dependent covariates (both baseline (2008–2010) and first follow-up visit (2012–2014) measures were included in the analysis). The inclusion of an interaction term (social mobility * time since baseline) in the fully adjusted models was not significant, for none of the outcomes investigated (P > 0.05). Analyses were performed using the R software, version 3.2.4. RESULTS Most of ELSA-Brasil’s respondents (60.5%) experienced upward occupational social mobility between their first and their current job, whereas downward mobility occurred for 18.5%. Occupational social class remained consistently high (stable high) for 12.2%, while 8.8% were persistently low (stable low). Higher mean BP at baseline was observed among those with a stable low pattern of social mobility. Individuals in the downward mobility group had higher mean BP at baseline than those in the upward mobility group, for both SBP and DBP. Higher mean BP at baseline was also observed among male sex, older age (only for SBP), lower education, Indigenous, and Black participants, those with a greater amount of pack-years smoked, those with greater alcohol and salt consumption, and those with excess weight (Table 2). Table 2. Characteristics of study participants and means of blood pressure (BP) at baseline (2008–2010)   Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5    Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Table 2. Characteristics of study participants and means of blood pressure (BP) at baseline (2008–2010)   Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5    Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Regarding 4-year changes in BP (mean follow-up = 3.9, median = 4.0, range = 2–6 years), both SBP and DBP increased over time in all groups of social mobility. Increases in SBP were greater in the stable low group than in the downward mobility group, whereas increases in DBP were greater in the downward mobility group than in the stable low one. Those in the upward mobility and stable high groups showed the lowest increases in both SBP and DBP over time. Differences between the 4 groups of social mobility were also seen for the incidence of hypertension. Stable low group showed the highest hypertension incidence, followed by the downwardly mobile group, the upwardly mobile group, and the stable high group (Table 3). Overall incidence of hypertension among study participants (N = 6,529) was 15% (data not shown). Table 3. Mean 4-year changes in blood pressure (BP) and incidence of hypertension (%) between baseline and first follow-up visit, by social mobility groupsa   4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7    4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Table 3. Mean 4-year changes in blood pressure (BP) and incidence of hypertension (%) between baseline and first follow-up visit, by social mobility groupsa   4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7    4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Adjusted 4-year increases in BP were markedly greater in downward and stable low groups than in stable high group (Table 4). Compared to the stable high group, the downward and stable low groups showed a more rapid increase over time in both SBP and DBP after adjustment for age, sex, color/race, time since baseline, antihypertensive medication use at first follow-up visit (Model 1) and also proximal risk factors such as smoking, physical activity, alcohol consumption, salt consumption (Model 2), BMI and diabetes (Model 3). In contrast, upward mobility showed a much lower influence on SBP and DBP changes over time. Estimates for the association between social mobility and hypertension incidence were weak and nonsignificant. Compared to the stable high group, downward mobility and upward mobility groups showed similar results, both in minimally and fully adjusted models. Stable low group showed the highest incidence of hypertension, although nonsignificant (Table 4). Table 4. Adjusted 4-year changes in blood pressure (BP) and adjusted odds ratios (ORs) of hypertension incidence between baseline and first follow-up visit, by social mobility groups.   Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)    Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)  Model 1: Adjusted for age § + sex + race + time since baseline (2008-2010) + antihypertensive medication use at first follow-up visit (2012-2014) [SBP and DBP analyses only]. Model 2: + smoking + physical activity § + alcohol consumption § + salt consumption §. Model 3: + body mass index § + diabetes. §As time-dependent covariates. *P ≤ 0.05, **P ≤ 0.001. View Large Table 4. Adjusted 4-year changes in blood pressure (BP) and adjusted odds ratios (ORs) of hypertension incidence between baseline and first follow-up visit, by social mobility groups.   Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)    Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)  Model 1: Adjusted for age § + sex + race + time since baseline (2008-2010) + antihypertensive medication use at first follow-up visit (2012-2014) [SBP and DBP analyses only]. Model 2: + smoking + physical activity § + alcohol consumption § + salt consumption §. Model 3: + body mass index § + diabetes. §As time-dependent covariates. *P ≤ 0.05, **P ≤ 0.001. View Large DISCUSSION In this large Brazilian sample followed for approximately 4 years, we found that the increase in SBP and DBP was more pronounced in those who experienced downward mobility or a stable low pattern than in those with upward mobility or a stable high pattern. Our cohort has aged over the follow-up, but our findings showed that over and above the aging-related changes in BP over time, BP changes did not occur homogeneously across social mobility groups, which demonstrate socioeconomic inequalities in BP progression over the life course. To the best of our knowledge, this is the first study that has investigated the association between social mobility and longitudinal changes in BP. Other studies, looking at SES differences (but not SES mobility) on BP changes have also shown higher increases in BP over time among socioeconomically disadvantaged groups,4,5,28,29 although some authors found no association.3 Despite the existing evidence, using SES individual-level factors, our study demonstrated the impact of a structural-level social determinant, i.e., social mobility,9,10 on individuals’ health. In our study, social mobility was not associated with the incidence of hypertension (Table 4). This could be partly explained by the relatively short follow-up period of our cohort. We could speculate that downwardly mobile and stable low participants (whose increases in BP between baseline and first follow-up visit were the highest) would achieve BP values compatible with hypertension classification, if they were followed up for a longer period. We will be able to confirm this in future follow-ups of ELSA-Brasil. In contrast to our results, other studies with longer follow-ups (10 years or more)6,30 showed that declines in SES trajectory throughout the life course predicted incident hypertension, which lends support for our argument. Those who were consistently exposed to low SES over adulthood (stable low) showed the greatest increases in BP during the follow-up (Table 4). This might suggest that the relationship between life course SES and high BP in adulthood operates also through a cumulative way, or according to a cumulative risks model, besides the mobility one. The accumulation model posits that the greater the time spent in adverse SES conditions over the life course, the worse is the cumulative damage to biological systems, which translates to worse CVD outcomes.27 Thus, our findings illustrate that these life course models (social mobility vs. accumulation), although contrasting, are interrelated in such a way that they cannot be disentangled, as other authors have demonstrated empirically.11 According to our theoretical model, health-related behaviors (smoking, physical activity, alcohol consumption, and salt consumption) and BMI are part of the mechanism that links social mobility to high BP and hypertension incidence (Figure 1). In other words, they may partially mediate this relationship. However, the adjustment for health behaviors had little effect on the associations we found, which could suggest complementary potential mechanisms linking social mobility to BP changes. Recent studies have demonstrated an important role of psychological stress on the association between intragenerational social mobility and health, through potential effects of downward mobility on higher hair cortisol concentrations (an endocrine marker of stress-related responses on health),31 higher levels of functional somatic symptoms,15 and also DNA methylation in genes related to stress reactivity.32 Taken together, these results suggest that downward intragenerational class transitions in the social hierarchy are related to losses of social status and prestige, which might cause feelings of self-blame, distress, and perceived failure in one’s own career, promoting chronic psychological stress. In turn, greater stress reactivity and poor stress recovery are associated with higher future BP 28 and development of CVD.33 There are a number of important strengths to our study. First, the longitudinal design and the prospective assessment of BP ensured a temporal progression from social mobility to increase in BP between baseline and first follow-up visit, which provides greater support for the causal pathway. Second, our indicator of occupational social class is a composite measure based on occupation, income, and education, allowing us to capture more fully the participants’ social class mobility and the socioeconomic patterning of BP changes. Third, our results are based on a large sample from Brazil, a middle-income country which has undergone high social mobility rates since the 1970s16,17 (over 85% of our participants first entered the labor market from 1970 on). ELSA-Brasil is uniquely positioned to consider the impact of these demographic changes on the health of the population as it ages. Furthermore, health-selected downward mobility does not seem to be likely in our study because ELSA-Brasil is a population of healthy workers, and retired participants were excluded from this analysis as well as those hypertensive subjects at baseline. In spite of these strengths, there are a number of limitations. Our study was limited to about 4-year follow-up period. This could have limited the time window necessary for hypertension to develop and be detected (or “latency period”) among the participants. Moreover, our analyses would benefit from additional longitudinal BP measures, which could generate a more robust trend in BP changes. In addition, since social mobility is operationalized with 2 time points in adulthood, participants might have experienced some class transitions that we were unable to capture. Lastly, ELSA-Brasil is not a population-based study. It includes civil servants from universities and research institutions and it does not include those at the extremes of the social hierarchy (i.e., the highest- and the lowest-income strata). Since social mobility in Brazil has been shown to be higher in the intermediary social stratum,34 it is possible that social mobility estimates are overrepresented in our study. In conclusion, we found that the longitudinal increase in BP was not equally distributed, nor determined solely by the aging of our population. Instead, we showed that BP changes were socially patterned and varied by intragenerational social mobility groups. The magnitude of increase in BP over time was substantially different depending on the direction of SES change, i.e., upward or downward. Downwardly mobile and stable low individuals showed the highest increases in BP, putting them at risk for future development of hypertension and CVD. This is especially concerning since individuals in this socioeconomic position often have reduced access to material resources and medical care, and high BP may goes untreated and undetected among them. Although BP increase among downward and stable low groups seems small, it is relevant from the public health perspective (e.g., a reduction of 2 mm Hg in population-mean SBP may reduce mortality from stroke and ischemic heart disease by 10% and 7%, respectively35). The negative consequences of downward social mobility and persistently low SES for adult BP could potentially be reduced by providing opportunities for upward social mobility through equal access to educational opportunities.9,12,16 Our findings shed light on the role of structural-level factors, i.e., social class mobility, as a potential driver of socioeconomic inequalities in BP change in the context of low- and middle-income countries, where high BP has become most prevalent. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS This work was supported by the Brazilian Ministry of Health (Department of Science and Technology) and Ministry of Science, Technology and Innovation (FINEP, Financiadora de Estudos e Projetos) (grant numbers 01 06 0010.00, 01 06 0212.00, 01 06 0300.00, 01 06 0278.00, 01 06 0115.00, 01 06 0071.00) and CNPq (the National Council for Scientific and Technological Development). This work was also supported by a grant from the University of Michigan and Brazil Partnership Agreement/FIOCRUZ (grant number U043503). J.M.N.G. was supported by a Postdoctoral Research Fellowship from PNPD/CAPES (Coordination for the Improvement of Higher Education Personnel). We thank all ELSA-Brasil participants who agreed to take part in the study. REFERENCES 1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet  2017; 389: 37– 55. CrossRef Search ADS PubMed  2. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, Chen J, He J. Global disparities of hypertension prevalence and control: a systematic analysis of population-based studies from 90 countries. Circulation  2016; 134: 441– 450. Google Scholar CrossRef Search ADS PubMed  3. Theodore RF, Broadbent J, Nagin D, Ambler A, Hogan S, Ramrakha S, Cutfield W, Williams MJ, Harrington H, Moffitt TE, Caspi A, Milne B, Poulton R. Childhood to early-midlife systolic blood pressure trajectories: early-life predictors, effect modifiers, and adult cardiovascular outcomes. Hypertension  2015; 66: 1108– 1115. Google Scholar PubMed  4. Diez Roux AV, Chambless L, Merkin SS, Arnett D, Eigenbrodt M, Nieto FJ, Szklo M, Sorlie P. 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Intergenerational inequality: a sociological perspective. J Econ Perspect  2002; 16: 31– 44. Google Scholar CrossRef Search ADS   9. Beller E, Hout M. Welfare states and social mobility: how educational and social policy may affect cross-national differences in the association between occupational origins and destinations. Res Soc Stratif Mobil  2006; 24: 353– 365. Google Scholar CrossRef Search ADS   10. World Health Organization (WHO). A conceptual framework for action on the social determinants of health: debates, policy & practice, case studies. 2010. http://apps.who.int/iris/bitstream/10665/44489/1/9789241500852_eng.pdf. 11. Hallqvist J, Lynch J, Bartley M, Lang T, Blane D. Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program. Soc Sci Med  2004; 58: 1555– 1562. Google Scholar CrossRef Search ADS PubMed  12. Tiikkaja S, Hemstrom O. Does intergenerational social mobility among men affect cardiovascular mortality? A population-based register study from Sweden. Scand J Public Health  2008; 36: 619– 628. Google Scholar CrossRef Search ADS PubMed  13. Karvonen S, Rimpelä AH, Rimpelä MK. Social mobility and health related behaviours in young people. J Epidemiol Community Health  1999; 53: 211– 217. Google Scholar CrossRef Search ADS PubMed  14. Elovainio M, Ferrie JE, Singh-Manoux A, Shipley M, Batty GD, Head J, Hamer M, Jokela M, Virtanen M, Brunner E, Marmot MG, Kivimäki M. Socioeconomic differences in cardiometabolic factors: social causation or health-related selection? Evidence from the Whitehall II Cohort Study, 1991-2004. Am J Epidemiol  2011; 174: 779– 789. Google Scholar CrossRef Search ADS PubMed  15. Jonsson F, Sebastian MS, Hammarström A, Gustafsson PE. Intragenerational social mobility and functional somatic symptoms in a northern Swedish context: analyses of diagonal reference models. Int J Equity Health  2017; 16: 1. Google Scholar CrossRef Search ADS PubMed  16. Torche F, Ribeiro CC. Pathways of change in social mobility: industrialization, education and growing fluidity in Brazil. Res Soc Stratif Mobil  2010; 28: 291– 307. Google Scholar CrossRef Search ADS   17. Costa Ribeiro CA. Four decades of social mobility in Brazil. Dados  2012; 55: 641– 679. Google Scholar CrossRef Search ADS   18. Rasella D, Aquino R, Santos CA, Paes-Sousa R, Barreto ML. Effect of a conditional cash transfer programme on childhood mortality: a nationwide analysis of Brazilian municipalities. Lancet  2013; 382: 57– 64. Google Scholar CrossRef Search ADS PubMed  19. Aquino EM, Barreto SM, Bensenor IM, Carvalho MS, Chor D, Duncan BB, Lotufo PA, Mill JG, del Molina MC, Mota EL, Passos VM, Schmidt MI, Szklo M. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): objectives and design. Am J Epidemiol  2012; 175: 315– 324. Google Scholar CrossRef Search ADS PubMed  20. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, Aquino EM, Passos VM, Matos SM, del Molina MC, Carvalho MS, Bensenor IM. Cohort profile: longitudinal study of adult health (ELSA-Brasil). Int J Epidemiol  2015; 44: 68– 75. Google Scholar CrossRef Search ADS PubMed  21. Pastore J. Inequality and social mobility in Brazil [Internet]. The University of Wisconsin Press: Madison, WI, 1982. http://www.galileoco.com/hallerLit/OCRHaller82a.pdf. 22. do Valle Silva N. The analytical scheme and the occupational classification. In Hasenbalg CA (ed), Origins and Destinations: Social Inequalities over the Life Course . Topbooks: Rio de Janeiro, Brazil, 2003. 23. Mill JG, Pinto K, Griep RH, Goulart A, Foppa M, Lotufo PA, Maestri MK, Ribeiro AL, Andreão RV, Dantas EM, Oliveira I, Fuchs SC, de Cunha RS, Bensenor IM. Medical assessments and measurements in ELSA-Brasil. Rev Saude Publica  2013; 47( Suppl 2): 54– 62. Google Scholar CrossRef Search ADS PubMed  24. Chor D, Pinho Ribeiro AL, Sá Carvalho M, Duncan BB, Andrade Lotufo P, Araújo Nobre A, Aquino EM, Schmidt MI, Griep RH, del Molina MC, Barreto SM, Passos VM, Benseñor IJ, Matos SM, Mill JG. Prevalence, awareness, treatment and influence of socioeconomic variables on control of high blood pressure: results of the ELSA-Brasil study. PLoS One  2015; 10: e0127382. Google Scholar CrossRef Search ADS PubMed  25. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc  2003; 35: 1381– 1395. Google Scholar CrossRef Search ADS PubMed  26. Pereira TS, Benseñor IJ, Meléndez JG, Faria CP, Cade NV, Mill JG, del Molina MC. Sodium and potassium intake estimated using two methods in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Sao Paulo Med J  2015; 133: 510– 516. Google Scholar CrossRef Search ADS PubMed  27. Murray ET, Mishra GD, Kuh D, Guralnik J, Black S, Hardy R. Life course models of socioeconomic position and cardiovascular risk factors: 1946 birth cohort. Ann Epidemiol  2011; 21: 589– 597. Google Scholar CrossRef Search ADS PubMed  28. Carroll D, Ring C, Hunt K, Ford G, Macintyre S. Blood pressure reactions to stress and the prediction of future blood pressure: effects of sex, age, and socioeconomic position. Psychosom Med  2003; 65: 1058– 1064. Google Scholar CrossRef Search ADS PubMed  29. Hardy R, Kuh D, Langenberg C, Wadsworth ME. Birthweight, childhood social class, and change in adult blood pressure in the 1946 British birth cohort. Lancet  2003; 362: 1178– 1183. Google Scholar CrossRef Search ADS PubMed  30. Waitzman NJ, Smith KR. The effects of occupational class transitions on hypertension: racial disparities among working-age men. Am J Public Health  1994; 84: 945– 950. Google Scholar CrossRef Search ADS PubMed  31. Serwinski B, Salavecz G, Kirschbaum C, Steptoe A. Associations between hair cortisol concentration, income, income dynamics and status incongruity in healthy middle-aged women. Psychoneuroendocrinology  2016; 67: 182– 188. Google Scholar CrossRef Search ADS PubMed  32. Needham BL, Smith JA, Zhao W, Wang X, Mukherjee B, Kardia SL, Shively CA, Seeman TE, Liu Y, Diez Roux AV. Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: the multi-ethnic study of atherosclerosis. Epigenetics  2015; 10: 958– 969. Google Scholar CrossRef Search ADS PubMed  33. Chida Y, Steptoe A. Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status: a meta-analysis of prospective evidence. Hypertension  2010; 55: 1026– 1032. Google Scholar CrossRef Search ADS PubMed  34. Silva AA. Is income inequality decreasing in Brazil? Cad Saude Publica  2015; 31: 1125– 1126. Google Scholar CrossRef Search ADS PubMed  35. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet  2002; 360: 1903– 1913. Google Scholar CrossRef Search ADS PubMed  © American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Hypertension Oxford University Press

Intragenerational Social Mobility and Changes in Blood Pressure: Longitudinal Analysis From the ELSA-Brasil Study

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Oxford University Press
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© American Journal of Hypertension, Ltd 2018. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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0895-7061
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1941-7225
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10.1093/ajh/hpy026
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Abstract

Abstract BACKGROUND During the past 4 decades, the highest worldwide blood pressure (BP) levels have shifted from high-income countries to low- and middle-income countries. We investigated the association of intragenerational social mobility with changes in BP and also with the incidence of hypertension over a 4-year follow-up. METHODS Data for 6,529 baseline participants from ELSA-Brasil born between 1938 and 1975 were used. Based on a social mobility matrix, occupational social mobility was defined as the change in occupational social class between participants’ first occupation and current occupation (stable high; upward; downward; stable low). Incident hypertension was defined as systolic blood pressure (SBP) ≥ 140 mm Hg or diastolic blood pressure (DBP) ≥ 90 mm Hg or use of antihypertensive medication. Hypertensive participants at baseline were excluded. Mixed effects regression models were used. RESULTS Compared to the stable high group, the downwardly mobile group showed a higher increase over time in both SBP (β = 1.49, 95% CI 0.60; 2.37) and DBP (β = 0.96, 95% CI 0.32; 1.59) after adjustments for background characteristics and also proximal risk factors such as health-related behaviors and body mass index as time-dependent covariates, and diabetes. In contrast, upward mobility had no influence on BP changes (β = 0.67, 95% CI −0.07; 1.41 for SBP, and β = 0.47, 95% CI −0.05; 1.00 for DBP). Social mobility was not associated with the incidence of hypertension. CONCLUSIONS We showed socioeconomic inequalities in BP progression over the life course. The longitudinal changes in BP varied by social mobility groups in the context of low- and middle-income countries, where high BP has become most prevalent. blood pressure change, Brazil, hypertension, longitudinal design, social mobility During the past 4 decades, the highest worldwide blood pressure (BP) levels have shifted from high-income countries to low- and middle-income countries.1 In addition, from 2000 to 2010, hypertension awareness, treatment, and control increased substantially in high-income countries, whereas there was less improvement in low- and middle-income countries.2 It is well recognized that high BP is a major modifiable risk factor for cardiovascular diseases (CVDs).3 Although low socioeconomic condition has been associated with elevated BP 4,5 and higher incidence of hypertension,6,7 the life course social processes behind these associations have scarcely been investigated in low- and middle-income nations. Social mobility is a concept from Sociology and it primarily applies to societies, not to individuals.8,9 It is a structural determinant of health, since it lies beyond the individual and is thus distal to individual health outcomes.10 Social mobility is defined as the change in socioeconomic status (SES) or position within adulthood (intragenerational mobility) or between generations (inter-generational mobility), and it has been identified as an important driver of health.11 The general finding is that upward social mobility is protective for health while downward mobility is detrimental.11,12 Potential mechanisms linking social mobility to CVD have been attributed to differences in health-related behaviors,13 cardiometabolic factors,14 or psychological stress,15 between individuals with different socioeconomic trajectories. Brazil is a large middle-income country which has undergone great structural changes in society over the past half century, transforming from an agricultural society to an urban one, in a context of rapid late industrialization.16 Economic growth, poverty reduction, educational expansion, and increase in female labor market participation have all contributed to an increase in upward social mobility rates.16,17 More recently, since the 2000s, Brazilian society has faced a large reduction in income inequality due to increases in the minimum wage, declining unemployment, and anti-poverty policies such as cash transfer programs.17,18 Despite these positive social changes, striking socioeconomic inequalities still persist, including marked inequalities in life chances and opportunity.17 Using longitudinal data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we investigated whether intragenerational social mobility is associated with changes in BP and also with the incidence of hypertension over a 4-year follow-up. We hypothesized that downward social mobility and stable low groups would show a more rapid increase in BP over time, and a higher incidence of hypertension, than upward mobility and stable high groups. METHODS Study design The ELSA-Brasil is a prospective cohort study designed to investigate social and biological determinants of CVDs and diabetes.19,20 A total of 15,105 active or retired civil servants (35–74 years) of universities or research institutions were enrolled in 6 Brazilian cities at baseline (2008–2010). The first follow-up visit was performed approximately 4 years later (2012–2014), with a retention rate of 93% (n = 14,014). For the present study, participants with prevalent hypertension at baseline (n = 4,847), or missing information on hypertension (n = 70) or social mobility (n = 1,536) were excluded. Retired participants (n = 1,032) were also excluded, since we cannot ascertain their pattern of social mobility from retirement until baseline. We focused on 6,529 participants for analysis, born between 1938 and 1975. ELSA-Brasil was approved at each of the 6 study centers by the local Institutional Review Board addressing research in human subjects, and all participants gave written consent to participate. Measures Intragenerational occupational social mobility was created based on retrospective reports of each participant’s first occupation. It was defined as the change in participants’ occupational social class from the retrospectively reported first occupation (origin) to the current occupation (destination), obtained from the baseline (2008–2010) face-to-face interview. Since our participants started working at the mean age of 17, the earliest cohort of ELSA-Brasil (born in the 1930s) first entered the labor market in the 1950s and the latest cohort (born in the 1970s) first entered the labor market in the 1990s. Because occupation does not necessarily infer social position in Brazil, we created a composite index of occupational social class based on: the occupation held by the participant, the expected income based on the participant’s educational level (average market value), and the observed income. First, we calculated the mean between the expected and observed income for each participant. Second, we created a score indicating occupational SES using the mean income of individuals within each occupational title. Occupations which require enhanced qualifications and offer greater earnings have higher scores, compared to those occupations with lower educational and income levels. Using these scores, strata were defined in order to achieve a minimum intrastratum variation and the maximum variation between strata, according to the method proposed by Pastore21 and Silva.22 Seven occupational strata, or categories, were defined based on these methods: low SES (upper and lower), middle SES (upper, middle, and lower), and high SES (upper and lower). Based on a mobility matrix, 4 different intragenerational social mobility patterns were then identified across these 7 categories: stable high (reference), upward mobility, downward mobility, and stable low (Table 1). Table 1. Intragenerational social mobility matrix (%). Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Upward mobility (above diagonal), downward mobility (below diagonal), stable high (diagonal, middle or high SES in both occasions), and stable low (diagonal, low SES in both occasions). % referred to N = 6,529. View Large Table 1. Intragenerational social mobility matrix (%). Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Occupational social class of the first occupation (origin)  Occupational social class of the current occupation (destination)  Low-lower  Low-upper  Middle-lower  Middle-middle  Middle-upper  High-lower  High-upper  Total  Low-lower  6.0  0.7  2.9  1.9  0.4  1.2  0.0  13.2  Low-upper  9.0  2.8  9.9  11.9  2.1  9.2  0.3  45.2  Middle-lower  1.3  1.0  4.4  5.3  1.0  5.7  0.0  18.7  Middle-middle  0.2  0.0  0.5  1.1  0.1  1.6  0.1  3.8  Middle-upper  0.3  0.5  1.1  2.5  0.7  5.8  0.0  11.0  High-lower  0.0  0.0  0.3  0.4  0.0  5.8  0.0  6.6  High-upper  0.0  0.0  0.0  0.2  0.0  0.9  0.1  1.3  Total  16.8  5.2  19.0  23.4  4.5  30.2  0.9  100.0%  Upward mobility (above diagonal), downward mobility (below diagonal), stable high (diagonal, middle or high SES in both occasions), and stable low (diagonal, low SES in both occasions). % referred to N = 6,529. View Large Systolic and diastolic blood pressure (SBP and DBP) were taken using a validated certified oscillometric device (Omron HEM 705CPINT) after a 5-minute rest with the subject in a sitting position in a quiet, temperature-controlled room (20–24 °C). Three measurements were taken and the average of the second and third measurements was considered in the analyses.23 Incident hypertension was defined as SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or confirmed use of antihypertensive medication at first follow-up visit (2012–2014) (in the interview, participants showed prescriptions and/or medicine packs and were asked: “Are any of the drugs you have taken in the past two weeks for hypertension?”).24 Background characteristics were: age (continuous), sex, and self-reported color/race (White, “Pardo,” Black, Asian descent, Indigenous). Health-related behaviors capture those associated with high BP and hypertension risk. Pack-years of smoking (continuous) was captured based on the average number of cigarettes smoked per day times the number of years smoked. Leisure-time physical activity (continuous) was captured using the International Physical Activity Questionnaire (IPAQ)25 in min/week. Alcohol consumption (continuous) was assessed through the amount ingested in g/week. Salt consumption (continuous) was evaluated using a 12-hour urine sample in g/day.26 Body mass index (BMI) (continuous) was obtained using measured weight and height (kg/m2). Diabetes was defined according to the American Diabetes Association as described elsewhere.20 According to our theoretical model (Figure 1), downward occupational social mobility might lead to a more rapid increase in BP over the follow-up, and to a higher incidence of hypertension through changes in health-related behaviors (i.e., unhealthier behaviors of the social class they enter, such as smoking, physical inactivity, alcohol consumption, and salt consumption), which could negatively impact BMI, an important proximal risk factor for high BP and hypertension.27 Thus, health-related behaviors and BMI may partially mediate the association between social mobility and changes in BP and incidence of hypertension. Moreover, these relationships would occur independently of individual background characteristics (age, sex, and color/race). Figure 1. View largeDownload slide Theoretical model for the relationship between intragenerational social mobility and changes in blood pressure and incidence of hypertension. Abbreviation: BMI, body mass index. Figure 1. View largeDownload slide Theoretical model for the relationship between intragenerational social mobility and changes in blood pressure and incidence of hypertension. Abbreviation: BMI, body mass index. Statistical analysis The association between social mobility and BP changes was examined using linear mixed effects models, and the association between social mobility and hypertension incidence was examined using mixed effects logistic regression models. Mixed effects models accounted for both the correlation between repeated measures on the same individual over time and on the individuals from the same study center. SBP, DBP, and hypertension incidence were analyzed in separate models. Analyses were adjusted for age, sex, color/race, time since baseline, and also proximal risk factors that may partially act as mediators such as smoking, physical activity, alcohol consumption, salt consumption, BMI, and diabetes. SBP and DBP analyses were also adjusted for antihypertensive medication use at first follow-up visit (2012–2014). Age, physical activity, alcohol consumption, salt consumption, and BMI were treated as time-dependent covariates (both baseline (2008–2010) and first follow-up visit (2012–2014) measures were included in the analysis). The inclusion of an interaction term (social mobility * time since baseline) in the fully adjusted models was not significant, for none of the outcomes investigated (P > 0.05). Analyses were performed using the R software, version 3.2.4. RESULTS Most of ELSA-Brasil’s respondents (60.5%) experienced upward occupational social mobility between their first and their current job, whereas downward mobility occurred for 18.5%. Occupational social class remained consistently high (stable high) for 12.2%, while 8.8% were persistently low (stable low). Higher mean BP at baseline was observed among those with a stable low pattern of social mobility. Individuals in the downward mobility group had higher mean BP at baseline than those in the upward mobility group, for both SBP and DBP. Higher mean BP at baseline was also observed among male sex, older age (only for SBP), lower education, Indigenous, and Black participants, those with a greater amount of pack-years smoked, those with greater alcohol and salt consumption, and those with excess weight (Table 2). Table 2. Characteristics of study participants and means of blood pressure (BP) at baseline (2008–2010)   Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5    Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Table 2. Characteristics of study participants and means of blood pressure (BP) at baseline (2008–2010)   Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5    Study participants—N = 6,529a  Mean BP at baseline (mm Hg)    N (%)  Systolic  Diastolic  Intragenerational social mobility   Stable high  797 (12.2)  112.0 (P < 0.001)  71.6 (P < 0.001)   Upward  3,951 (60.5)  113.4  72.5   Downward  1,209 (18.5)  115.2  73.3   Stable low  572 (8.8)  116.9  74.1  Sex   Males  3,016 (46.2)  118.1 (P < 0.001)  75.0 (P < 0.001)   Females  3,513 (53.8)  110.3  70.6  Age   35–44  2,181 (33.4)  111.6 (P < 0.001)  71.7 (P < 0.001)   45–54  3,124 (47.8)  114.3  73.2   55–64  1,138 (17.4)  116.5  73.0   65–74  86 (1.3)  120.1  72.0  Education   Incomplete elementary school  242 (3.7)  119.1 (P < 0.001)  75.0 (P < 0.001)   Incomplete secondary school  365 (5.6)  117.8  74.2   Complete secondary school  2,444 (37.4)  114.7  73.2   University degree  3,478 (53.3)  112.5  71.9  Race (self reported)   White  3,381 (52.2)  112.7 (P < 0.001)  71.9 (P < 0.001)   Pardo  1,919 (29.6)  114.7  73.1   Black  943 (14.6)  116.2  74.2   Asian  157 (2.4)  112.9  71.5   Indigenous  72 (1.1)  116.4  73.2  Smoking, pack-years   Never smoker  3,866 (59.4)  113.4 (P < 0.001)  72.4 (P < 0.001)   0.05–19.9  1,855 (28.5)  114.0  72.6   20.0–141.0  785 (12.1)  115.7  73.6  Leisure physical activity (min/week)   None  2,816 (44.0)  113.7 (p = 0.31)  72.7 (p = 0.59)   10–149  1,391 (21.7)  113.8  72.7   150–1,860  2,191 (34.2)  114.2  72.5  Alcohol consumption (g/week)   None  3,386 (51.9)  113.1 (P < 0.001)  72.1 (P < 0.001)   6–99  2,039 (31.2)  113.3  72.3   100–1,190  1,099 (16.9)  117.3  74.8  Salt consumption (g/day, tertiles)   0.03–1.42  2,086 (33.0)  111.3 (P < 0.001)  71.0 (P < 0.001)   1.43–2.17  2,086 (33.0)  113.9  72.5   2.18–174.0  2,150 (34.0)  116.5  74.4  Body mass index (kg/m2)   15.4–24.9  2,875 (44.0)  111.2 (P < 0.001)  70.3 (P < 0.001)   25.0–29.9  2,594 (39.7)  115.5  73.6   30.0–52.7  1,059 (16.2)  116.9  76.5  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Regarding 4-year changes in BP (mean follow-up = 3.9, median = 4.0, range = 2–6 years), both SBP and DBP increased over time in all groups of social mobility. Increases in SBP were greater in the stable low group than in the downward mobility group, whereas increases in DBP were greater in the downward mobility group than in the stable low one. Those in the upward mobility and stable high groups showed the lowest increases in both SBP and DBP over time. Differences between the 4 groups of social mobility were also seen for the incidence of hypertension. Stable low group showed the highest hypertension incidence, followed by the downwardly mobile group, the upwardly mobile group, and the stable high group (Table 3). Overall incidence of hypertension among study participants (N = 6,529) was 15% (data not shown). Table 3. Mean 4-year changes in blood pressure (BP) and incidence of hypertension (%) between baseline and first follow-up visit, by social mobility groupsa   4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7    4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Table 3. Mean 4-year changes in blood pressure (BP) and incidence of hypertension (%) between baseline and first follow-up visit, by social mobility groupsa   4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7    4-year changes in BP, in mm Hg  Incidence of hypertension (%)    Systolic  Diastolic  Intragenerational social mobility   Stable high  1.02  1.18  13.4   Upward  2.06  1.82  13.8   Downward  2.78  2.46  16.2   Stable low  2.98  2.34  22.7  aHypertensive participants at baseline (N = 4,847) were excluded. View Large Adjusted 4-year increases in BP were markedly greater in downward and stable low groups than in stable high group (Table 4). Compared to the stable high group, the downward and stable low groups showed a more rapid increase over time in both SBP and DBP after adjustment for age, sex, color/race, time since baseline, antihypertensive medication use at first follow-up visit (Model 1) and also proximal risk factors such as smoking, physical activity, alcohol consumption, salt consumption (Model 2), BMI and diabetes (Model 3). In contrast, upward mobility showed a much lower influence on SBP and DBP changes over time. Estimates for the association between social mobility and hypertension incidence were weak and nonsignificant. Compared to the stable high group, downward mobility and upward mobility groups showed similar results, both in minimally and fully adjusted models. Stable low group showed the highest incidence of hypertension, although nonsignificant (Table 4). Table 4. Adjusted 4-year changes in blood pressure (BP) and adjusted odds ratios (ORs) of hypertension incidence between baseline and first follow-up visit, by social mobility groups.   Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)    Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)  Model 1: Adjusted for age § + sex + race + time since baseline (2008-2010) + antihypertensive medication use at first follow-up visit (2012-2014) [SBP and DBP analyses only]. Model 2: + smoking + physical activity § + alcohol consumption § + salt consumption §. Model 3: + body mass index § + diabetes. §As time-dependent covariates. *P ≤ 0.05, **P ≤ 0.001. View Large Table 4. Adjusted 4-year changes in blood pressure (BP) and adjusted odds ratios (ORs) of hypertension incidence between baseline and first follow-up visit, by social mobility groups.   Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)    Model 1  Model 2  Model 3  Changes in SBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.75  (-0.001; 1.51)  0.81  (0.05; 1.57)*  0.67  (-0.07; 1.41)   Downward  1.81  (0.91; 2.71)**  1.75  (0.84; 2.66)**  1.49  (0.60; 2.37)**   Stable low  2.67  (1.58; 3.76)**  2.79  (1.69; 3.89)**  2.52  (1.45; 3.59)**  Changes in DBP - β (95% CI)               Stable high    ref    ref    ref   Upward  0.60  (0.05; 1.15)*  0.62  (0.06; 1.18)*  0.47  (-0.05; 1.00)   Downward  1.27  (0.61; 1.93)**  1.21  (0.54; 1.88)**  0.96  (0.32; 1.59)**   Stable low  1.65  (0.85; 2.44)**  1.66  (0.85; 2.47)**  1.44  (0.67; 2.21)**  Hypertension incidence - OR (95% CI)     Stable high    ref    ref    ref   Upward  0.93  (0.75; 1.16)  0.92  (0.73; 1.15)  0.90  (0.72; 1.14)   Downward  1.05  (0.81; 1.35)  1.01  (0.78; 1.31)  0.97  (0.75; 1.27)   Stable low  1.26  (0.95; 1.67)  1.24  (0.93; 1.66)  1.20  (0.89; 1.61)  Model 1: Adjusted for age § + sex + race + time since baseline (2008-2010) + antihypertensive medication use at first follow-up visit (2012-2014) [SBP and DBP analyses only]. Model 2: + smoking + physical activity § + alcohol consumption § + salt consumption §. Model 3: + body mass index § + diabetes. §As time-dependent covariates. *P ≤ 0.05, **P ≤ 0.001. View Large DISCUSSION In this large Brazilian sample followed for approximately 4 years, we found that the increase in SBP and DBP was more pronounced in those who experienced downward mobility or a stable low pattern than in those with upward mobility or a stable high pattern. Our cohort has aged over the follow-up, but our findings showed that over and above the aging-related changes in BP over time, BP changes did not occur homogeneously across social mobility groups, which demonstrate socioeconomic inequalities in BP progression over the life course. To the best of our knowledge, this is the first study that has investigated the association between social mobility and longitudinal changes in BP. Other studies, looking at SES differences (but not SES mobility) on BP changes have also shown higher increases in BP over time among socioeconomically disadvantaged groups,4,5,28,29 although some authors found no association.3 Despite the existing evidence, using SES individual-level factors, our study demonstrated the impact of a structural-level social determinant, i.e., social mobility,9,10 on individuals’ health. In our study, social mobility was not associated with the incidence of hypertension (Table 4). This could be partly explained by the relatively short follow-up period of our cohort. We could speculate that downwardly mobile and stable low participants (whose increases in BP between baseline and first follow-up visit were the highest) would achieve BP values compatible with hypertension classification, if they were followed up for a longer period. We will be able to confirm this in future follow-ups of ELSA-Brasil. In contrast to our results, other studies with longer follow-ups (10 years or more)6,30 showed that declines in SES trajectory throughout the life course predicted incident hypertension, which lends support for our argument. Those who were consistently exposed to low SES over adulthood (stable low) showed the greatest increases in BP during the follow-up (Table 4). This might suggest that the relationship between life course SES and high BP in adulthood operates also through a cumulative way, or according to a cumulative risks model, besides the mobility one. The accumulation model posits that the greater the time spent in adverse SES conditions over the life course, the worse is the cumulative damage to biological systems, which translates to worse CVD outcomes.27 Thus, our findings illustrate that these life course models (social mobility vs. accumulation), although contrasting, are interrelated in such a way that they cannot be disentangled, as other authors have demonstrated empirically.11 According to our theoretical model, health-related behaviors (smoking, physical activity, alcohol consumption, and salt consumption) and BMI are part of the mechanism that links social mobility to high BP and hypertension incidence (Figure 1). In other words, they may partially mediate this relationship. However, the adjustment for health behaviors had little effect on the associations we found, which could suggest complementary potential mechanisms linking social mobility to BP changes. Recent studies have demonstrated an important role of psychological stress on the association between intragenerational social mobility and health, through potential effects of downward mobility on higher hair cortisol concentrations (an endocrine marker of stress-related responses on health),31 higher levels of functional somatic symptoms,15 and also DNA methylation in genes related to stress reactivity.32 Taken together, these results suggest that downward intragenerational class transitions in the social hierarchy are related to losses of social status and prestige, which might cause feelings of self-blame, distress, and perceived failure in one’s own career, promoting chronic psychological stress. In turn, greater stress reactivity and poor stress recovery are associated with higher future BP 28 and development of CVD.33 There are a number of important strengths to our study. First, the longitudinal design and the prospective assessment of BP ensured a temporal progression from social mobility to increase in BP between baseline and first follow-up visit, which provides greater support for the causal pathway. Second, our indicator of occupational social class is a composite measure based on occupation, income, and education, allowing us to capture more fully the participants’ social class mobility and the socioeconomic patterning of BP changes. Third, our results are based on a large sample from Brazil, a middle-income country which has undergone high social mobility rates since the 1970s16,17 (over 85% of our participants first entered the labor market from 1970 on). ELSA-Brasil is uniquely positioned to consider the impact of these demographic changes on the health of the population as it ages. Furthermore, health-selected downward mobility does not seem to be likely in our study because ELSA-Brasil is a population of healthy workers, and retired participants were excluded from this analysis as well as those hypertensive subjects at baseline. In spite of these strengths, there are a number of limitations. Our study was limited to about 4-year follow-up period. This could have limited the time window necessary for hypertension to develop and be detected (or “latency period”) among the participants. Moreover, our analyses would benefit from additional longitudinal BP measures, which could generate a more robust trend in BP changes. In addition, since social mobility is operationalized with 2 time points in adulthood, participants might have experienced some class transitions that we were unable to capture. Lastly, ELSA-Brasil is not a population-based study. It includes civil servants from universities and research institutions and it does not include those at the extremes of the social hierarchy (i.e., the highest- and the lowest-income strata). Since social mobility in Brazil has been shown to be higher in the intermediary social stratum,34 it is possible that social mobility estimates are overrepresented in our study. In conclusion, we found that the longitudinal increase in BP was not equally distributed, nor determined solely by the aging of our population. Instead, we showed that BP changes were socially patterned and varied by intragenerational social mobility groups. The magnitude of increase in BP over time was substantially different depending on the direction of SES change, i.e., upward or downward. Downwardly mobile and stable low individuals showed the highest increases in BP, putting them at risk for future development of hypertension and CVD. This is especially concerning since individuals in this socioeconomic position often have reduced access to material resources and medical care, and high BP may goes untreated and undetected among them. Although BP increase among downward and stable low groups seems small, it is relevant from the public health perspective (e.g., a reduction of 2 mm Hg in population-mean SBP may reduce mortality from stroke and ischemic heart disease by 10% and 7%, respectively35). The negative consequences of downward social mobility and persistently low SES for adult BP could potentially be reduced by providing opportunities for upward social mobility through equal access to educational opportunities.9,12,16 Our findings shed light on the role of structural-level factors, i.e., social class mobility, as a potential driver of socioeconomic inequalities in BP change in the context of low- and middle-income countries, where high BP has become most prevalent. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS This work was supported by the Brazilian Ministry of Health (Department of Science and Technology) and Ministry of Science, Technology and Innovation (FINEP, Financiadora de Estudos e Projetos) (grant numbers 01 06 0010.00, 01 06 0212.00, 01 06 0300.00, 01 06 0278.00, 01 06 0115.00, 01 06 0071.00) and CNPq (the National Council for Scientific and Technological Development). This work was also supported by a grant from the University of Michigan and Brazil Partnership Agreement/FIOCRUZ (grant number U043503). J.M.N.G. was supported by a Postdoctoral Research Fellowship from PNPD/CAPES (Coordination for the Improvement of Higher Education Personnel). We thank all ELSA-Brasil participants who agreed to take part in the study. REFERENCES 1. NCD Risk Factor Collaboration (NCD-RisC). 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American Journal of HypertensionOxford University Press

Published: Feb 9, 2018

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