Conceived and designed the experiments: AA HB. Performed the experiments: LA AA. Analyzed the data: LA AA. Contributed reagents/materials/analysis tools: AA HB. Wrote the paper: LA AA SS. Classified occupations: LA SS. Interpreted the data and revised the manuscript for important intellectual content: LA AA SS HB.
The authors have declared that no competing interests exist.
The evaluation of the gender-specific prevalence of cardiovascular risk factors across socioeconomic position (SEP) categories may unravel mechanisms involved in the development of coronary heart disease. Using a sample of 1704 community dwellers of a Portuguese urban center aged 40 years or older, assessed in 1999–2003, we quantified the age-standardized prevalence of nine established cardiovascular risk factors (diabetes mellitus, hypertension, hypercholesterolemia, smoking, sedentariness, abdominal obesity, poor diet, excessive alcohol intake and depression) across SEP and gender categories. Data on individual education and occupation were collected by questionnaire and used to characterize SEP. The prevalence of seven out of nine well-established risk factors was higher in men. Among women, the prevalence of most of the studied risk factors was higher in lower SEP groups. The main exception was smoking, which increased with education and occupation levels. Among men, socioeconomic gradients were less clear, but lower SEP was associated with a higher prevalence of diabetes, excessive alcohol intake and depression in a graded mode. The historical cultural beliefs and practices captured throughout the lifecourse frame the wide socioeconomic gradients discernible in our study conducted in an unequal European developed population. While men were more exposed to most risk factors, the clearer associations between SEP and risk factors among women support that their adoption of particular healthy behaviors is more dependent on material and symbolic conditions. To fully address the issue of health inequalities, interventions within the health systems should be complemented with population-based policies specifically designed to reduce socioeconomic gradients.
Guidelines for coronary heart disease (CHD) prevention have increasingly considered the use of risk factor scoring systems
Socioeconomic position (SEP) refers to the location groups of individuals hold within the structure of a society
Gender is another social dimension associated with health-related behaviors
Portugal is recognized as one of Europe’s most unequal countries
In this paper, we quantify the gender-specific prevalence of nine established risk factors for CHD across educational and occupational levels in a population-based sample of urban Portuguese adults.
The study design has been extensively described previously
Subjects aged 40 years or older were eligible for the current analysis (n = 2000). Housewives who never had a paid occupation (n = 175) and those unemployed at the time of data collection (n = 67) were excluded. The Mini-Mental State Examination (MMSE)
Trained interviewers collected data on sociodemographic and behavioral characteristics, including diet, alcohol consumption, physical activity and smoking, and personal and family medical history, using structured questionnaires.
Age was categorized in 4 groups: 40–49, 50–59, 60–69 and 70 years or more. Education was recorded as completed years of schooling and aggregated in 3 categories: less than 5, 5–11, and more than 11 years. Occupations were classified by major professional groups, according to the National Classification of Occupations - version 1994 (NCO-94)
Participants were classified as never-smoker, former smoker (a person that stopped smoking at least 6 months ago) and current smoker, including both daily and occasional smokers
Physical activity was evaluated using a questionnaire exploring all professional, domestic and leisure time activities over the past 12 months
Dietary intake was estimated using an 82-item semiquantitative food frequency questionnaire, covering the previous year
To estimate lifetime alcohol consumption, participants were asked about the lifetime mean frequency of consumption of different types of alcoholic beverages, including wine, beer, and spirits - liquors, gin, rum, vodka, cocktails or other mixed drinks. The period of highest exposure was considered. The average portion consumed was asked to be lower, equal or higher than a glass of 125 ml for wine, a bottle or can of 330 ml for beer, and a cup of 40 ml for spirits. The alcoholic beverages consumption was converted into total alcohol intake with the software Food Processor Plus® using an algorithm that assumed the following alcohol concentrations in volume: 12% for wine, 4.7% for beer, 25% for liquors and similar beverages, and 50% for vodka and the like. The algorithm was adapted to Portuguese drinks (e.g. Port wine). Two classes of alcohol consumption were defined by the cut points 15.0 grams per day (g/day) for women and 30.0 g/day for men, according to the American Heart Association recommendations
The Beck Depression Inventory (BDI)
Anthropometrics were obtained after overnight fasting with the participant in light clothing and barefoot. Waist circumference was measured to the nearest centimeter using a flexible and non-distensible tape, midway between the lower limit of the rib cage and the iliac crest. Waist circumferences equal to or greater than 102 or 88 cm were used to define abdominal obesity in men and women, respectively
Blood pressure was measured on a single occasion following the American Heart Association recommendations
Participants with missing data in each outcome ranged from 0.82% (sedentariness and smoking) to 11.4% (hypercholesterolemia) of the total sample size. The exception was depressive symptoms that were quantified in 50.8% of participants for two main reasons. First, illiterate subjects were not eligible to answer this self-administered questionnaire. Second, the option of allowing participants to return the questionnaire later, after answering it at home, inevitably resulted in a lower proportion of participation specifically in this regard. Participants without information on depressive symptoms were older [mean (standard deviation) age: 58.7 (11.9) vs. 56.6 (10.7), p<0.001], less educated [median (interquartile range) completed schooling years: 4 (4–10) vs. 9 (4–12), p<0.001] and had occupations located at the bottom of socioeconomic hierarchy (blue collar: 47.9% vs. 31.1%, p<0.001). No significant difference was found in gender (women: 58.9% vs. 56.6%, p = 0.323).
Descriptive data are presented as count (percentage) for categorical variables. The χ2 test was used to compare proportions between groups. The t-test or Mann-Whitney-test was used to compare continuous variables between groups, as appropriate. The gender-specific crude risk factor prevalence was computed for each age category. The gender-specific risk factor prevalence in each education and occupation group was age-standardized using the European standard population. Ninety five percent confidence intervals were computed for each standardized prevalence estimate.
With the exception of abdominal obesity and depression, the prevalence of all other cardiovascular risk factors was higher in males than in females. In
Education (years) | Occupation | |||||||
>11 | 5–11 | <5 | Upper white collar | Lower white collar | Blue collar | |||
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40–49 | n (%) | 119 (38.1) | 109 (34.9) | 84 (26.9) | 126 (40.4) | 89 (28.5) | 97 (31.1) | |
50–59 | n (%) | 82 (28.0) | 69 (23.6) | 142 (48.5) | 100 (34.1) | 75 (25.6) | 118 (40.3) | |
60–69 | n (%) | 25 (11.0) | 52 (22.9) | 150 (66.1) | 45 (19.8) | 56 (24.7) | 126 (55.5) | |
≥70 | n (%) | 19 (12.5) | 27 (17.8) | 106 (69.7) | 26 (17.1) | 28 (18.4) | 98 (64.5) | |
pa) | <0.001 | <0.001 | ||||||
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40–49 | n (%) | 80 (42.9) | 74 (27.9) | 40 (14.8) | 100 (51.6) | 49 (25.3) | 45 (23.2) | |
50–59 | n (%) | 55 (29.6) | 62 (24.9) | 69 (24.8) | 84 (45.2) | 45 (24.2) | 57 (30.7) | |
60–69 | n (%) | 24 (13.8) | 68 (26.0) | 96 (33.1) | 66 (35.1) | 46 (24.5) | 76 (40.4) | |
≥70 | n (%) | 22 (13.8) | 55 (21.2) | 75 (27.5) | 48 (31.6) | 51 (33.6) | 53 (34.9) | |
pa) | <0.001 | <0.001 |
a) The χ2 test was used to compare proportions between groups.
Hypertension | Diabetes mellitus | Hypercholesterolemia | ||||||||||
Women (n = 949) | Men (n = 688) | Women (n = 893) | Men (n = 662) | Women (n = 881) | Men (n = 629) | |||||||
n/total | % (95%CI) | n/total | %(95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | |
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40–49 | 83/289 | 28.7 (23.6–34.3) | 65/181 | 35.9 (28.9–43.3) | 12/289 | 4.15 (2.16–7.14) | 8/177 | 4.52 (1.97–8.71) | 79/287 | 27.5 (22.4–33.1) | 92/175 | 51.6 (44.9–60.2) |
50–59 | 151/289 | 52.2 (46.3–58.1) | 101/181 | 55.8 (48.2–63.2) | 16/267 | 5.99 (3.46–9.55) | 21/176 | 11.9 (7.54–17.6) | 121/265 | 45.7 (39.6–51.9) | 108/168 | 64.3 (56.5–71.5) |
60–69 | 168/221 | 76.0 (69.8–81.5) | 139/180 | 77.2 (70.4–83.1) | 34/206 | 16.5 (11.7–22.3) | 26/172 | 15.1 (10.1–21.4) | 135/205 | 65.8 (58.9–72.3) | 118/164 | 72.0 (64.4–78.7) |
≥70 | 134/150 | 89.3 (83.3–93.8) | 112/146 | 76.7 (69.0–83.3) | 21/131 | 16.0 (10.2–23.4) | 16/137 | 11.7 (6.82–18.3) | 76/124 | 61.3 (52.1–69.9) | 71/122 | 58.2 (48.9–67.1) |
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>11 | 93/234 | 50.1 (44.1–56.2) | 84/168 | 56.0 (49.3–62.7) | 6/229 | 2.38 (0.44–4.32) | 10/168 | 6.81 (2.59–11.0) | 83/230 | 41.5 (34.6–48.3) | 99/159 | 65.2 (57.8–72.6) |
5–11 | 113/245 | 49.5 (44.1–54.9) | 151/248 | 56.8 (50.9–62.6) | 20/239 | 8.89 (5.36–12.4) | 22/237 | 8.93 (5.27–12.6) | 94/230 | 42.4 (36.3–48.6) | 131/226 | 55.8 (49.6–62.0) |
<5 | 330/470 | 62.9 (58.2–67.5) | 182/272 | 57.1 (50.6–63.7) | 57/425 | 11.2 (8.30–14.2) | 39/257 | 12.5 (8.57–16.4) | 234/421 | 50.2 (45.1–55.2) | 159/244 | 63.2 (56.1–70.2) |
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Upper white collar | 118/283 | 49.0 (44.1–54.0) | 160/282 | 56.0 (50.6–61.5) | 12/275 | 4.64 (2.00–7.28) | 27/271 | 9.58 (6.22–12.9) | 107/275 | 43.7 (37.8–49.5) | 166/255 | 65.9 (60.3–71.5) |
Lower white collar | 132/239 | 55.6 (50.0–61.3) | 106/184 | 51.4 (44.1–58.6) | 22/230 | 9.21 (5.64–12.8) | 15/175 | 7.63 (3.68–11.6) | 106/224 | 48.2 (41.8–54.5) | 100/167 | 58.6 (51.0–66.1) |
Blue colar | 286/427 | 60.1 (55.7–64.6) | 151/222 | 59.4 (52.6–66.2) | 49/388 | 10.7 (7.82–13.5) | 29/216 | 11.8 (7.45–16.1) | 298/382 | 46.7 (41.8–51.7) | 123/207 | 55.7 (48.5–62.8) |
Prevalences were age-adjusted using the European standard population.
Abdominal obesity | Sedentariness | Smoking | ||||||||||
Women (n = 969) | Men (n = 711) | Women (n = 975) | Men (n = 715) | Women (n = 974) | Men (n = 716) | |||||||
n/total | % (95%CI) | n/total | %(95%CI) | n/total | %(95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | |
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40–49 | 97/310 | 31.3 (26.2–36.8) | 25/194 | 12.9 (8.52–18.4) | 136/312 | 43.6(38.0–49.3) | 87/194 | 44.8(37.7–52.1) | 87/311 | 28.0(23.1–33.3) | 90/194 | 46.4(39.2–53.7) |
50–59 | 135/288 | 46.9 (41.0–52.8) | 32/182 | 17.6 (12.3–23.9) | 103/292 | 35.3(29.8–41.0) | 69/186 | 37.1(30.1–44.5) | 37/292 | 12.7(9.08–17.0) | 56/186 | 30.1(23.6–37.2) |
60–69 | 130/225 | 57.8 (51.0–64.3) | 55/186 | 29.6 (23.1–36.7) | 65/226 | 28.8(29.8–41.0) | 56/187 | 29.9(23.5–37.1) | 13/226 | 5.75(3.10–9.63) | 44/187 | 23.5(17.6–30.3) |
≥70 | 94/146 | 64.4 (56.0–72.1) | 30/149 | 20.1 (14.0–27.5) | 25/145 | 17.2(11.5–24.4) | 31/148 | 20.9(14.7–28.4) | 2/145 | 1.38(0.17–4.89) | 19/149 | 12.6(7.86–19.2) |
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>11 | 66/239 | 31.8 (25.8–37.8) | 26/178 | 15.6 (10.0–21.3) | 69/245 | 24.0(18.5–29.5) | 58/181 | 32.1(25.5–38.8) | 71/245 | 23.0(18.4–27.7) | 60/181 | 28.7(22.7–34.6) |
5–11 | 95/257 | 37.8 (31.8–43.8) | 52/259 | 19.0 (14.2–23.6) | 97/257 | 35.4 (29.9–40.9) | 97/259 | 39.1(33.3–45.0) | 43/256 | 15.3(11.2–19.4) | 68/259 | 28.3(22.7–33.9) |
<5 | 295/473 | 58.2 (53.3–63.2) | 64/274 | 20.0 (14.8–25.2) | 163/473 | 37.0(32.2–41.7) | 88/275 | 36.0(29.3–42.6) | 25/473 | 8.17(5.06–11.3) | 81/276 | 36.0(29.4–42.7) |
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Upper white collar | 83/291 | 31.0 (25.5–36.5) | 53/294 | 17.5 (13.3–21.7) | 77/297 | 24.0(19.2–28.9) | 92/298 | 31.5(26.2–36.8) | 71/297 | 20.0(16.1–24.0) | 87/298 | 28.9(24.1–33.7) |
Lower white collar | 113/248 | 44.8 (38.8–50.8) | 41/189 | 18.5 (13.1–23.8) | 98/247 | 39.5(33.7–45.3) | 63/190 | 34.0(27.0–40.9) | 40/246 | 16.1(11.7–20.4) | 58/190 | 34.1(26.8–41.3) |
Blue colar | 260/430 | 56.2 (51.4–61.1) | 48/228 | 20.1 (14.4–25.7) | 154/431 | 38.6(33.9–43.3) | 88/227 | 42.7(35.9–49.5) | 28/431 | 8.75(5.79–11.7) | 64/228 | 32.6(26.1–39.2) |
Prevalences were age-adjusted using the European standard population.
Less than 5 portions of fruits or vegetables per day | Excessive alcohol intake | Depression | ||||||||||
Women (n = 970) | Men (n = 711) | Women (n = 947) | Men (n = 687) | Women (n = 490) | Men (n = 376) | |||||||
n/total | %(95%CI) | n/total | %(95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | n/total | % (95%CI) | |
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40–49 | 130/311 | 41.8 (36.3–47.5) | 105/194 | 54.1 (46.8–61.3) | 56/302 | 18.5(14.3–23.4) | 91/181 | 50.3(42.8–57.8) | 34/172 | 19.8(14.1–26.5) | 6/99 | 6.06(2.26–12.7) |
50–59 | 106/296 | 36.4 (30.9–42.2) | 106/185 | 57.3 (49.8–64.5) | 76/283 | 26.9(21.8–32.4) | 116/179 | 64.8(57.3–71.8) | 46/152 | 30.3(23.1–38.2) | 12/105 | 11.4(6.05–19.1) |
60–69 | 121/224 | 54.0 (47.2–60.7) | 85/187 | 45.4 (38.2–52.9) | 60/220 | 27.3(21.5–33.7) | 118/183 | 64.5(57.1–71.4) | 36/114 | 31.6(23.2–40.9) | 7/103 | 6.80(2.78–13.5) |
≥70 | 87/144 | 60.4 (51.9–68.5) | 64/145 | 44.1 (35.9–52.6) | 43/142 | 30.3(22.9–38.5) | 80/144 | 55.6(47.0–63.8) | 21/52 | 40.4(27.0–54.9) | 12/69 | 17.4(9.32–28.4) |
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>11 | 80/243 | 27.4 (22.3–32.5) | 87/181 | 48.6 (41.4–55.7) | 41/239 | 19.9(13.9–26.0) | 78/174 | 45.0(37.6–52.4) | 19/150 | 17.4(8.61–26.3) | 8/112 | 6.50(2.10–10.9) |
5–11 | 105/255 | 41.7 (35.4–47.9) | 131/256 | 51.9 (45.8–58.0) | 48/249 | 18.3(13.6–22.9) | 137/248 | 53.3(47.2–59.5) | 46/153 | 31.1(23.8–38.4) | 13/140 | 9.34(4.44–14.2) |
<5 | 259/472 | 53.5 (48.7–58.4) | 142/274 | 57.6 (51.0–64.1) | 146/459 | 30.1(25.5–34.6) | 190/265 | 72.8(66.5–79.2) | 72/187 | 35.1(28.0–42.2) | 16/124 | 12.2(5.98–18.4) |
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Upper white collar | 101/295 | 32.3 (27.0–37.6) | 142/295 | 47.2 (41.7–52.6) | 54/291 | 19.1(14.4–23.9) | 144/289 | 49.8(44.1–55.6) | 31/176 | 20.0(13.3–26.6) | 14/172 | 7.37(3.67–11.1) |
Lower white collar | 115/245 | 48.3 (42.2–54.3) | 102/189 | 54.7 (47.2–62.2) | 47/240 | 19.4(14.4–24.5) | 111/183 | 60.7(53.2–68.2) | 39/141 | 27.4(20.1–34.6) | 12/108 | 10.7(5.03–16.3) |
Blue colar | 228/430 | 50.8 (46.0–55.6) | 116/227 | 54.2 (47.3–61.0) | 134/416 | 30.3(25.8–34.9) | 150/215 | 69.4(63.0–75.8) | 67/173 | 36.7(29.6–43.8) | 11/96 | 12.6(5.70–19.5) |
Prevalences were age-adjusted using the European standard population.
Among women, we observed an age-independent variation in the prevalence of most cardiovascular risk factors across educational groups. Although in different directions, the largest relative differences were observed in the prevalence of diabetes mellitus and smoking. Specifically, the prevalence of diabetes mellitus was highest among women with less than 5 completed years of education (<5 years vs. >11 years: 11.2%, 95%CI: 8.30–14.2 vs. 2.38%, 95%CI: 0.44–4.32). Conversely, the least educated women smoked less frequently (<5 years vs. >11 years: 8.17%, 95%CI: 5.06–11.3 vs. 23.0%, 95%CI: 18.4–27.7). Abdominal obesity and consumption of less than 5 portions of fruits or vegetables per day were consistently more prevalent with decreasing levels of education. A prevalence gradient was not observed in sedentariness, depression, hypertension, hypercholesterolemia, excessive alcohol intake. Specifically, only the most educated women presented a lower relative frequency of sedentariness and depression. On the other hand, hypertension, hypercholesterolemia and excessive alcohol intake were more common in the least educated women.
Among men, there was a smaller variation in the relative frequency of most risk factors with education and gradients were less clear. The prevalence of diabetes mellitus, excessive alcohol intake and depression increased with decreasing levels of education. A prevalence gradient was not observed for hypercholesterolemia and fruit and vegetable daily consumption. Hypercholesterolemia was less prevalent among men with 5–11 years of completed education and low daily fruit and vegetable consumption was more prevalent among the least educated men. Hypertension, abdominal obesity, sedentariness, smoking and depression did not vary with education.
Among women, we observed a monotonic variation in the prevalence of the majority of risk factors across occupation categories. Although in different directions, the largest relative differences were also observed in the prevalence of diabetes and smoking. Whereas blue collar women were more commonly diabetic (blue collar vs. upper white collar: 10.7%, 95%CI: 7.82–13.5 vs. 4.64%, 95%CI: 2.00–7.28), smoking was less prevalent among women with less differentiated occupations (blue collar vs. upper white collar: 8.75%, 95%CI: 5.79–11.7 vs. 20.0%, 95%CI: 16.1–24.0). An inverse prevalence gradient was also observed for hypertension, abdominal obesity and depression, but not for sedentariness, fruit and vegetable consumption and alcohol consumption. Whereas sedentariness and low fruit and vegetable consumption were less frequent among upper white collar women, excessive alcohol intake was more common among blue collar women. Hypercholesterolemia was not associated with occupation among women.
Among men, we observed a gradient in the prevalence of excessive alcohol intake across occupational classes. Blue collar men presented a higher prevalence of excessive alcohol consumption (69.4%, 95%CI: 63.0–75.8) than men engaged in lower white collar (60.7%, 95%CI: 53.2–68.2) or upper white collar (49.8%, 95%CI: 44.1–55.6) occupations. Men engaged in lower occupations also tended to be more frequently depressed. Prevalence gradients were not observed for hypercholesterolemia, low fruit and vegetable consumption and sedentariness. Upper white collar men presented the highest prevalence of hypercholesterolemia and the lowest prevalence of low fruit and vegetable intake. Sedentariness was more prevalent only among male blue collar workers. Hypertension, diabetes mellitus, abdominal obesity and smoking were not associated with occupation.
As expected, the prevalence of seven out of nine well-established cardiovascular risk factors was higher in males than in females. Among women, the prevalence of most of the studied cardiovascular risk factors was higher in lower SEP groups. The main exception was smoking, which increased with education and occupation. Among men, lower SEP was associated with a higher prevalence of diabetes, excessive alcohol intake and depression in a graded mode.
Portugal is an interesting case-study due to the behavioral changes associated with a rapid transition to political democracy occurring in the seventies. This transition, catalyzed by the cultural elite, resulted in a rapid improvement in living standards, essentially marked by an increased access to consumer’s goods, not always accompanied by parallel social and cultural changes, particularly among the lowest SEP group. Although these changes were led to by an economic capital improvement across all social strata, this increment was proportionally smaller among the lower SEP groups, resulting in an increasingly unequal society in the economic dimension
While health related practices occurring in the early years of life impact on later health behaviors, they are also strongly associated with other social constructions such as education or occupation in adulthood. Consistent with other studies
Hypertension was more prevalent among lower SEP women, as in other Mediterranean populations
Among women, education, but not occupation, was associated with hypercholesterolemia, which is consistent with other studies
Smoking was more common among higher SEP women, reflecting Portugal’s position in the smoking epidemic
The lowest levels of education or occupation were associated with a higher prevalence of sedentariness among women, which is consistent with other European populations
The prevalence of excessive alcohol intake was higher and the socioeconomic gradient steeper in men than in women. In a review of social inequalities in alcohol consumption, the prevalence of heavy drinking tended to be higher among the most educated women and the least educated men. The exception to this pattern was observed in the Italian population, where heavy alcohol consumption was more common among the least educated women
Gender is grounded on cultural and ideological uses and meanings that vary with time and space
This study adds to the literature a comparison of the gender-specific distribution of several important risk factors across categories of education and occupation in a European developed population. Specifically, the relevance of approaching this issue in Portugal is reinforced by reported large health inequalities
Some limitations of the present study warrant discussion. The proportion of participation of 70% may introduce selection bias. However, in a previous methodological article on the effects of the sampling procedures in this community-based study
The historical cultural beliefs and practices captured throughout the lifecourse frame the wide socioeconomic gradients observable in our study. While men were more exposed to most risk factors, the clearer associations between SEP and risk factors among women support that their adoption of particular healthy behaviors is more dependent on material and symbolic conditions. Thus, the adoption of healthier lifestyles may depend on a reconfiguration of hegemonic gender roles. Although behavioral factors like smoking, physical activity and fruit and vegetable consumption account for an important fraction of cardiovascular disease, preventive efforts focusing entirely on individual behaviors are unlikely to significantly modify socioeconomic inequalities in health outcomes. To fully address the issue of health inequalities, interventions within the health systems should be complemented with population-based policies and health promotion initiatives specifically designed to reduce socioeconomic gradients.
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