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Clustering of risk behaviors for chronic noncommunicable diseases: A population-based study in southern Brazil
Preventive Medicine 56 (2013) 20–24
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Clustering of risk behaviors for chronic noncommunicable diseases: A population-based study in southern Brazil Diego A.S. Silva a,⁎, Karen G. Peres b, Antonio F. Boing b, David A. González-Chica c, Marco A. Peres b a b c
Federal University of Santa Catarina, Post-Graduate Program in Physical Education, Florianópolis, Brazil Federal University of Santa Catarina, Post-Graduate Program in Public Health, Florianópolis, Brazil Federal University of Santa Catarina, Post-Graduate Program in Nutrition, Florianópolis, Brazil
a r t i c l e
i n f o
Available online 31 October 2012 Keywords: Risk factors Chronic diseases Prevention and control Health inequalities Social iniquities Adults' health Lifestyle Cross-sectional studies
a b s t r a c t Objective. The purpose of this study was to investigate the prevalence and identify factors associated with simultaneous risk behaviors for chronic noncommunicable diseases in adults in a southern capital in Brazil. Method. A cross-sectional, population-based study was carried out with 1720 adults in Florianópolis, Brazil. The simultaneous occurrence of tobacco smoking, abusive drinking, inadequate or unhealthy diet, and physical inactivity during leisure was assessed. The independent variables were demographic and socioeconomic characteristics. Results. Only 8.3% of the respondents did not have any of these factors, whereas the simultaneous occurrence of two or more risk behaviors was 59.4%. The simultaneous presence of four risk behaviors (3.4%) was 220% higher of what would be expected by combining the individual prevalence of these factors (1.5%). The likelihood of individuals having two or more risk behaviors simultaneously was greater in young men, with black skin color, living without a partner, with lower household per capita income, and lower education. Conclusion. It is necessary to implement programs that reduce the risk behaviors for chronic noncommunicable diseases among adults in Brazil, especially between young men with low education and income. © 2012 Elsevier Inc. All rights reserved.
Introduction The four major risk factors for changeable chronic noncommunicable diseases (CNCDs) are tobacco smoking, alcohol consumption, unhealthy eating habits, and physical inactivity (World Health Organization, 2002, 2009; Yusuf et al., 2004). More worrisome than the exposure to one risk factor is the simultaneous exposure to more than one of these behaviors (Yusuf et al., 2004). Thus, evaluating aggregate health risk factors facilitates (Poortinga, 2007): i) the identification of the health risk factors that influence the occurrence of other factors; ii) the evaluation of the negative effect on health caused by simultaneous exposure, which is higher than the sum of the effects of exposure to each factor alone, suggesting that the health effects of lifestyle risk factors are multiplicative rather than additive; and iii) the promotion of health programs based on actions oriented to modify simultaneous behaviors because this strategy has shown to be more effective than those based on isolate behaviors. The lack of studies in Latin America on this subject also makes it difficult to detect the most common behavior patterns and the ⁎ Corresponding author at: Universidade Federal de Santa Catarina, Centro de Desportos, Programa de Pós Graduação em Educação Física, Campus Universitário Trindade, CEP: 88010-970, Florianópolis, SC, Brazil. Fax: +55 48 37218562. E-mail address: [email protected] (D.A.S. Silva). 0091-7435/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ypmed.2012.10.022
groups that are more susceptible to developing unhealthy habits, thus limiting any possible intervention to reduce the exposure to these risks. This work aimed to identify the prevalence and the sociodemographic factors associated with the simultaneous presence of chronic noncommunicable diseases risk behaviors in adults in a capital city in southern Brazil. Methods EpiFloripa, a population-based cross-sectional study was performed in Florianópolis, southern Brazil, from September 2009 to January 2010 (www.epifloripa.ufsc.br). The city has 421,203 inhabitants and ranks highest in terms of social and health indicators compared to other Brazilian capitals (Brazilian Institute of Geography and Statistics, 2009). Sampling was conducted in two phases (Höfelmann et al., 2012). In the first phase, 420 urban census tracts were stratified by deciles of household income being systematically drawn 63 tracts (sampling fraction equal to seven), totaling six tracts in each decile. In the second phase, the sampling units were households. The number of inhabited households ranged from 61 to 810 (coefficient of variation, 32%), and 18 were systematically chosen at random in each of the geographical units (average of 1.78 adults per domicile) or 32 adults in each census tract. The sample size for prevalence (n = 1820) was calculated assuming a target population of 249,530 adults aged between 20 and 59, a prevalence of 50% for the outcome (to maximize the number of individuals), sample error
D.A.S. Silva et al. / Preventive Medicine 56 (2013) 20–24
of 3.5 p.p., confidence level of 95%, design effect of 2.0 (because of clustering sample) and percentage of non-answers of 10%. Because of the multiple objectives of the EpiFloripa study (to evaluate diverse health outcomes) (Höfelmann et al., 2012), the sample size was increased to 2016 individuals. A posteriori we calculated the minimum odds ratio (OR) detectable with this sample, considering a range of exploratory variable factors (exposures) and the cluster of risk factors (outcome = 50%). For exposure prevalence higher than 5% the study is able to detect OR higher than 1.32 (risk) or lower than 0.76 (protection) for exposure prevalence higher than 5%, adopting an alpha of 5% and power of 80%. The home visits included the administration of a face-to-face questionnaire applied with the use of a personal digital assistant. Thirty-five interviewers were intensively trained prior to field work; the questionnaire pre-testing was performed on 35 adults. The pilot study included almost 100 adults living close to the research headquarters and in two census tracts not included in the sample. A short version of the questionnaire (10 questions) was administered through a telephone interview to 15% of the whole sample (n = 248) for quality control, with kappa and intra class correlation coefficient values ranging from 0.6 to 0.9. The dependent variable was the simultaneity of health-risk behaviors: tobacco smoking, abusive alcohol consumption, poor eating habits, and physical inactivity. A score ranging from 0 (no risk behavior) to 4 (four simultaneous risk behaviors) was generated. Smoking was assessed as nonsmoker, former smoker, or current smoker. For the purposes of the analysis, the “nonsmoker” and “former smoker” categories were grouped and considered not of risk to health. The Alcohol Use Disorders Identification Test was administered to identify people with problematic use of alcohol (no =score from 0 to 7; yes =score ≥8) (Lima et al., 2005). This tool allows identifying people who are at risk of alcohol abuse, harmful use and dependence (Lima et al., 2005). Physical activities and eating habits were assessed by means of the questionnaire used in the Surveillance of Risk Factors and Protection for Chronic Diseases Through Telephone Inquiries (VIGITEL), Brazil (Florindo et al., 2009; Jaime et al., 2009). Adults who reported not practicing physical activity during leisure or practicing less than once a week for the three months preceding the interview were considered physically inactive (Florindo et al., 2009). The subjects who reported consuming fruits and vegetables b5 days a week were considered as having an inadequate diet (Jaime et al., 2009). The independent variables were sex; age in completed years (20–29, 30–39, 40–49, and 50–59); self-reported skin color (Brazilian Institute of Geography and Statistics, 2011), classified as white or light- or dark-skinned black (results of those who reported yellow skin or indigenous were not presented in the tables because of their low frequency (2.2%) but were included in adjusted analyses); household per capita income, in Brazilian currency (R$), was collected according to household monthly gross income (1st tertile=up to R$ 566.9, 2nd tertile=R$ 567.0–1300.0, and 3rd tertile=R$ 1300.1–33,333.0). Successfully completed schooling years (0–4, 5–8, 9–11, and ≥12), marital status (married or with partner, and unmarried or without partner), and occupation in most of his/her life (nonmanual and manual activities) (Szreter, 1984). People who have never worked, students, housewives and those who did not answer the question were considered as missing (n=119). Chi-square tests for heterogeneity and linear trend were performed to assess the groups' differences. To evaluate the most frequent risk behavior combinations, the ratio between the observed and expected (O/E) prevalence was calculated for each possible combination, as described by Schuit et al. (2002). The observed prevalence was identified in our sample. The expected prevalence was calculated by multiplying the individual probabilities of each risk factor based on their occurrence in the study population. Therefore, it was possible to investigate which combinations were above or below the expectation, assuming that the risk factors occur independently in the population under study (Schuit et al., 2002). To identify the associated factors with the dependent variable “simultaneity,” we used the polytomous logistic regression, using the multinomial logit model (Hamilton and Seyfrit, 1993), with estimates of odds ratio (OR), and respective 95%CI, with no health-risk behavior as the reference category. Additionally, we tested any possible interactions of different socioeconomic and demographic variables on simultaneity of risk factors. All analyses were performed using Stata 11.0 (STATA Corp. College Station, Texas USA), considering the design effect and the sampling weight. The study was approved by the ethics committee on Human Research of the Federal University of Santa Catarina (no. 351/08).
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Results We analyzed 1720 adults (response of 85.3%), with a mean age of 38.1 years (±11.6 years). More than half of the sample were female and younger than 40 years, and most of them self-reported as white (Table 1). The mean household income and years of school education was R$ 866.7 and 11 years, respectively. More than two thirds of the samples had nonmanual occupation. We observed that nearly 20% of the subjects were smokers, and a similar proportion had abusive alcohol consumption, 81.2% had poor eating habits, and slightly more than half did not practice regular physical activities during leisure. Men showed higher prevalence of smoking, problematic alcohol consumption, and unhealthy diet when compared with women. On the other hand, women were more physically inactive than men. The dark-skinned black subjects were more inactive than the white and light-skinned blacks. The individuals without a partner showed higher prevalence of abusive use of alcohol and inadequate diet than those living with a partner. Among the latter, physical inactivity was more frequent. There were direct association between age and the prevalence of smoking and physical inactivity. On the other hand, abusive drinking and unhealthy diet were more frequent among young adults. The study also showed an inverse association between household income and education in relation to smoking, inadequate diet, and physical inactivity, whereas abusive drinking was more prevalent among those with higher household per capita income and higher education (Table 1). The expected prevalence for the four risk behaviors was 1.5%, but 3.4% presented the four risk behaviors simultaneously, which represents an increase of 220% of what would be expected randomly (O/E ratio = 2.2). Regarding the simultaneity of the three risk factors, the most common was the combination of smoking, unhealthy diet, and physical inactivity (7.2%). However, the simultaneous presence of smoking, abusive drinking, and unhealthy diet (2.6%) is relevant because it was 90% higher than what would be expected if the factors were independent. As for the simultaneous occurrence of two risk behaviors, the most prevalent was the simultaneous exposure to unhealthy diet and physical inactivity during leisure (30.6%) (Table 2). Only 8.2% of the subjects did not have any factor, 32.2% had one risk factor, 42.0% had two, 14.2% had three, and 3.4% had four risk behaviors (Table 3). The likelihood of adults having one risk behavior when compared with those without any risk factor almost doubled for men and in the subjects aged 20 to 29 years. The likelihood of individuals having two or three risk factors simultaneously, when compared with the reference group was approximately two times higher among men, younger adults, and the poorer and four times higher among those with low educational background. The odds of adults having four risk factors simultaneously was almost 10 times higher among men, more than four times among the black individuals and those with lower education, and nearly three times higher among those without a partner and those who fall in the poorest income bracket. None of the tested interactions were statistically significant (Table 3).
Discussion This study has as novelty of studying health disparities and processes within a developed and service-industry oriented city such as Florianopolis, within the context of a fast-developing nation such as Brazil. Three major findings can be highlighted. First, the occurrence of the studied risk behaviors in the population is high: 91.8% of the adults in Florianópolis reported at least one risk factor for CNCD. Second, the behavior pattern that indicated a greater increase than that expected at random was the simultaneous occurrence of the four risk factors. Finally, the most vulnerable groups to the simultaneous occurrence of two or more risk behaviors for CNCDs were identified: young
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D.A.S. Silva et al. / Preventive Medicine 56 (2013) 20–24
Table 1 Risk factor characteristics of the adults according to demographic and socioeconomic variables. Florianópolis, Brazil (2009–2010). Variables
Total Sex Females Males Skin color White Lighter skinned black Dark skinned black Age (years) 20–29 30–39 40–49 50–59 Marital status With partner Without partner Household per capita income (R$)c >1300.00 566.80–1300.00 0–566.70 No. of successful schooling years ≥12 9–11 5–8 0–4 Occupation Nonmanual Manual
Total sample
Smoking
Abusive drinking
Unhealthy diet
Physical inactivity
n
n
% (95%CI)
n
% (95%CI)
n
% (95%CI)
n
% (95%CI)
1711
19.2 (17.1–21.3) p = 0.023a 17.5 (14.8–20.2) 21.3 (18.2–24.4) p = 0.209a 18.4 (16.2–20.5) 24.9 (17.6–32.2) 20.8 (12.6–29.1) p = 0.004b 18.0 (14.6–21.4) 15.7 (11.7–19.8) 18.6 (15.0–22.2) 26.1 (21.8–30.4) p = 0.185a 18.2 (15.6–20.7) 20.7 (17.0–24.5) p b 0.001b 14.6 (10.9–18.3) 19.4 (15.1–23.8) 23.9 (20.4–27.4) p b 0.001b 13.9 (11.4–16.5) 21.4 (17.7–25.2) 25.9 (17.9–33.9) 27.0 (19.2–34.8) p = 0.559a 19.0 (16.5–21.4) 19.6 (16.1–23.2)
1720
18.5 (15.5–21.4) p b 0.001a 9.6 (6.9–12.3) 29.6 (25.3–33.9) p = 0.069a 17.3 (14.8–19.8) 28.4 (19.6–37.2) 19.6 (9.2–30.1) p b 0.001b 26.4 (20.3–32.4) 15.6 (11.3–19.9) 15.5 (11.9–19.1) 12.6 (8.8–16.3) p b 0.001a 13.8 (11.5–16.0) 25.6 (20.6–30.7) p = 0.025b 19.1 (15.0–23.3) 20.5 (16.3–24.8) 15.0 (12.0–18.1) p = 0.034b 19.2 (15.3–23.1) 20.5 (15.8–25.1) 14.6 (8.7–20.5) 14.3 (7.0–21.6) p = 0.946a 18.6 (14.6–22.6) 19.0 (15.6–22.5)
1719
81.2 (78.3–84.1) p b 0.001a 76.9 (73.6–80.1) 86.5 (83.2–89.9) p = 0.424a 80.6 (77.7–83.6) 87.0 (80.8–93.2) 83.2 (73.9–92.4) p b 0.001b 89.2 (86.2–92.3) 82.5 (78.0–87.0) 75.1 (70.1–80.0) 73.9 (68.3–79.6) p = 0.001a 79.1 (75.8–82.4) 84.3 (80.5–88.1) p b 0.001b 72.2 (67.0–77.4) 83.2 (80.0–86.4) 88.2 (85.4–91.1) p b 0.001b 74.9 (70.1–79.8) 84.9 (82.0–87.7) 87.6 (83.4–91.8) 88.1 (82.4–93.8) p = 0.885a 80.9 (77.6–84.1) 81.3 (77.2–85.4)
1718
53.1 (48.8–57.4) p b 0.001a 58.5 (53.8–63.3) 46.3 (40.7–51.8) p = 0.024a 51.9 (43.1–52.8) 55.9 (47.7–64.2) 68.3 (60.2–76.4) p b 0.001b 45.9 (40.5–51.2) 56.4 (50.7–62.0) 55.5 (49.6–61.4) 58.3 (50.4–66.1) p = 0.006a 56.4 (51.6–61.3) 48.1 (42.5–53.6) p b 0.001b 39.1 (33.9–44.3) 53.6 (49.1–58.2) 67.7 (62.0–73.4) p b 0.001b 38.7 (35.1–32.4) 57.3 (52.1–62.5) 68.8 (62.0–75.7) 82.6 (75.8–89.3) p = 0.779a 52.9 (49.0–56.7) 52.6 (46.0–59.1)
% (95%CI)
1720 959 761
55.5 (53.4–57.7) 44.5 (42.3–46.6)
955 756
1444 147 87
85.7 (82.2–89.2) 9.1 (6.6–11.5) 5.2 (3.3–6.9)
1436 146 87
540 392 438 350
32.7 (28.1–37.2) 22.9 (20.2–25.5) 25.0 (21.8–28.1) 19.4 (16.9–21.9)
538 391 435 347
1043 677
60.1 (56.5–63.6) 39.9 (36.3–43.4)
1037 674
559 562 564
32.6 (26.1–39.0) 33.3 (29.6–36.8) 34.1 (27.8.40.5)
556 559 563
737 568 253 158
43.9 (36.9–50.8) 33.4 (28.8–37.9) 14.0 (11.2–16.7) 8.7 (6.4–11.1)
733 566 252 156
1111 490
70.3 (65.0–75.5) 29.7 (24.5–34.9)
1107 485
959 761 1444 147 87 540 392 438 350 1043 677 559 562 564 737 568 253 158 1111 490
958 761 1443 147 87 540 392 438 349 1042 677 559 561 564 737 568 253 157 1111 489
958 760 1442 147 87 540 392 437 349 1042 676 559 560 564 737 568 253 156 1110 489
CI — confidence interval; 1R$ = 1.7 US$ at data collection. a Heterogeneity chi-square test. b Linear trend chi-square test. c R$ (real): Brazilian currency.
men, with black skin color, living without a partner, who are poor and less education. The prevalence order of risk behaviors in our study is more similar to the findings from high-income countries than those from Brazil. Data from the MORGEN study (Schuit et al., 2002) conducted with Dutch individuals aged 20 to 65 years showed that the most common health-risk behavior was poor diet, followed by low levels of physical activity, smoking, and abusive drinking. A population-based survey derived from the 2003 Health Survey for England (Poortinga, 2007) showed the same pattern. Regarding the combination of simultaneous risk factors for CNCD, at least two factors were present in 59.4% of the respondents, and the
most frequent pattern was the simultaneity of inadequate diet and physical inactivity (30.6%). In Holland (Schuit et al., 2002), 57.7% of men and 52.9% of women had at least two of the same four risk factors of this study (the most prevalent are physical inactivity and low consumption of fruits/vegetables for both sexes). Although the prevalence of the four risk behaviors in our study was low (3.4%), this result is significant because it represents an increase of 220% of what would be expected randomly. In the adult population in Holland (Schuit et al., 2002) and England (Poortinga, 2007), the observed prevalence was 170% and 200% higher than what would be expected if the four risk factors were independent. The second combination that had a higher observed prevalence than
Table 2 Prevalence of combinations of health risk behaviors in the adult population. Florianópolis, Brazil (2009–2010). Risk factors
Smoking
Drinking
Unhealthy diet
Physical inactivity
4 3
+ + + + – + + + – – – – – – + –
+ + + – + + – – + + – – – + – –
+ + – + + – + – + – + – + – – –
+ – + + + – – + – + + + – – – –
2
1
0
Prevalence Observed % (95%CI)
Expected %
O/E
3.4 (2.6–4.3) 2.6 (1.9–3.3) 0.4 (0.1–0.6) 7.2 (5.9–8.4) 4.0 (3.0–4.9) 0.2 (0.0–0.4) 3.2 (2.3–4.0) 1.3 (0.8–1.8) 6.2 (5.1–7.3) 0.5 (0.1–0.7) 30.6 (28.4–32.8) 5.7 (4.6–6.8) 24.0 (22.0–26.0) 1.2 (0.7–1.7) 1.3 (0.8–1.8) 8.2 (6.9–9.5)
1.5 1.4 0.4 6.7 6.4 0.3 6.0 1.6 5.7 1.5 28.4 6.6 25.1 1.3 1.4 5.8
2.2 1.9 1.1 1.1 0.6 0.6 0.5 0.8 1.1 0.3 1.1 0.9 1.0 0.9 0.9 1.4
CI: confidence interval; + presence of risk behavior; – absence of risk behavior; O: Observed prevalence; E: Expected prevalence; O/E: Ratio between observed and expected prevalence.
D.A.S. Silva et al. / Preventive Medicine 56 (2013) 20–24
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Table 3 Association between risk factors and demographic and socioeconomic variables in adults. Florianópolis, Brazil (2009–2010). One risk factor n (%) Total Sex Female Male Skin color White Lighter skinned black Dark skinned black Age (years) 20–29 30–39 40–49 50–59 Marital status With partner Without partner Household per capita income (R$)d >1300.00 566.80–1300.00 0–566.70 No. of successful schooling years ≥12 9–11 5–8 0–4 Occupation Nonmanual Manual
Two risk factors OR (95%CI)a
551 (32.2)
n (%)
Three risk factors
OR (95%CI)a
718 (42.0) b
n (%)
Four risk factors
OR (95%CI)a
241 (14.2)
OR (95%CI)a
59 (3.4)
308 (32.3) 243 (32.1)
1.0 1.7 (1.1–2.7)
417 (43.7) 301 (39.8)
1.0 1.6 (1.1–2.4)
114 (11.9) 127 (16.8)
1.0 2.2 (1.2–3.7)
16 (1.7) 43 (5.7)
1.0b 9.9 (3.9–15.6)
476 (33.1) 33 (22.6) 29 (33.3)
1.0 0.6 (0.3–1.4) 1.6 (0.4–5.5)
608 (42.3) 61 (41.7) 32 (36.7)
1.0 0.7 (0.3–1.7) 0.9 (0.3–3.3)
190 (13.2) 33 (22.6) 15 (17.2)
1.0 1.3 (0.5–3.1) 1.1 (0.3–3.6)
42 (2.9) 7 (4.7) 7 (8.1)
1.0b 1.4 (0.3–5.3) 4.1 (1.1–15.7)
182 132 139 98
(33.8) (33.7) (32.0) (28.2)
1.0c 0.9 (0.5–1.9) 0.4 (0.2–0.8) 0.4 (0.2–0.6)
224 182 178 134
1.0c 1.2 (0.5–2.2) 0.4 (0.2–0.8) 0.3 (0.2–0.5)
(15.8) (12.0) (11.9) (16.4)
1.0c 1.0 (0.4–2.3) 0.4 (0.2–0.9) 0.4 (0.2–0.8)
22 (4.1) 6 (1.5) 17 (3.9) 14 (4.0)
1.0 0.5 (0.1–2.4) 0.7 (0.2–2.1) 0.6 (0.2–1.8)
344 (33.1) 207 (30.7)
1.0 1.0 (0.6–1.6)
435 (41.9) 283 (42.0)
1.0 1.2 (0.7–1.09)
137 (13.2) 104 (15.4)
1.0 1.6 (0.9–2.7)
27 (2.6) 32 (4.8)
1.0c 3.0 (1.2–7.6)
224 (40.2) 181 (32.4) 138 (24.5)
1.0 1.4 (0.8–2.6) 1.4 (0.6–3.3)
188 (33.8) 246 (44.0) 271 (48.1)
1.0b 2.2 (1.2–3.8) 2.7 (1.2–6.2)
55 (9.8) 74 (13.2) 104 (18.4)
1.0b 2.0 (1.0–4.3) 2.6 (1.1–4.3)
14 (2.5) 20 (3.5) 25 (4.4)
1.0b 2.7 (1.1–7.1) 3.3 (1.1–10.4)
303 157 67 23
(41.3) (27.7) (26.5) (14.8)
1.0 1.6 (0.9–2.9) 1.5 (0.6–3.5) 1.1 (0.3–3.7)
264 254 117 80
1.0c 2.9 (1.7–4.9) 2.6 (1.2–5.6) 4.0 (1.2–13.1)
60 102 44 35
1.0c 5.1 (2.5–10.0) 4.8 (1.7–13.3) 7.2 (1.8–28.4)
21 (2.8) 18 (3.1) 11 (4.3) 9 (5.8)
1.0c 2.5 (0.9–6.8) 4.4 (1.1–17.5) 6.1 (1.2–32.5)
365 (33.0) 141 (29.1)
1.0 0.8 (0.5–1.1)
467 (42.2) 205 (42.2)
1.0 0.8 (0.5–1.2)
154 (13.9) 67 (13.8)
1.0 0.8 (0.4–1.2)
34 (3.1) 22 (4.5)
1.0 1.2 (0.6–2.6)
(41.6) (46.5) (41.0) (38.6)
(36.0) (44.8) (46.4) (51.6)
b
n (%)
85 47 52 57
(8.1) (18.0) (17.4) (22.5)
b
OR: odds ratio; CI: confidence interval; 1R$ = 1.7 US$ at the time of data collection. a Adjusted analysis for all independent variables. b p b 0.05. c p b 0.01. d R$ (real): Brazilian currency.
the expected one in this study (smoking, drinking, and poor eating habits) had similar results to the studies carried out in Holland (Schuit et al., 2002) and England (Poortinga, 2007). The great similarity of our findings with those from high-income countries may be due to the high socioeconomic indicators that Florianópolis has compared with the rest of the country (Brazilian Institute of Geography and Statistics, 2009), which could influence the choice of behaviors. Men, in general, showed more prone to present risk factors than women. Researchers who have addressed gender differences reported that socioeconomic and cultural factors can influence these behaviors (Fornari et al., 2010). This is an issue of concern if we take into account that men make use of health services less frequently—especially for health prevention—and the higher morbidity and mortality from cardiovascular diseases that men have compared with women (Brasil, 2008). When comparing our data with those on the North American population (Fine et al., 2004), similar results were observed, and together, the risk of having one or more risk factors was higher among the young. This result may be attributed to the young's lack of concern with CNCDs; because of the insidious course of these diseases, they usually manifest clinically in individuals after 40 years old (Barreto et al., 2009). As in other studies (Scafato et al., 2008), we found that the individuals who live without a partner are more likely to develop the four risk behaviors simultaneously. This can explain the relationship between marital status and health that could be related to a protective effect on the health status by the social and economic support between married people. As a consequence, improved health behaviors and a healthier lifestyle can be developed by the couple (Scafato et al., 2008). Health inequalities are a core issue in Brazil. Despite the country's improvement in the global economy, there are still discrepancies
between rich and poor regarding income distribution, availability, and provision of medicines and health-care services (Boing et al., 2011). In this study, lower levels of household per capita income and education were strongly associated with the presence of two or more risk factors for CNCDs. This social gradient was also observed in studies about the simultaneity of risk factors in England (Poortinga, 2007), Holland (Schuit et al., 2002), and the United States (Fine et al., 2004). These inequalities in health could be strongly tackled if we consider the principle of proportionate universalism. To reduce the steepness of the social gradient in health, actions must be universal but with a scale and intensity that is proportionate to the level of disadvantage (Marmot Review, 2010). Another example of health inequalities found in this study was regarding skin color. Dark-skinned blacks are 4.1 times more likely to develop the four risk factors for CNCD simultaneously than whites. In Brazil, these people make up the population stratum most impacted by health inequities (Boing et al., 2011). Some population-based studies examined the risk factors in a stratified approach for men and women (Poortinga, 2007; Schuit et al., 2002). Others investigated the changeable risk behaviors without stratification (Fine et al., 2004). The studies that had a gender-stratification approach investigated the different prevalence on risk behaviors and in associations between men and women, particularly in high-income countries. However, in our study, no interaction was identified. The assessment of a representative adult sample by using complex sampling, the even distribution of losses in deciles of household income, and the similarity in the distribution by sex and age in comparison with IBGE's (Brazilian Institute of Geography and Statistics, 2011) estimates for the adult population in Florianopolis were key factors in our study. The British-based (Szreter, 1984) classification criteria for occupations might be a limitation of this study because the occupational status in income-emerging countries like Brazil differs drastically from
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that found in high-income countries. Despite its limitation, this classification was chosen because of the lack of a similar reference in Brazil. In addition, the measures used to assess physical inactivity and unhealthy diet are not fully in line with literature, which recommends obtaining more information about these variables. However, such measures are used in the VIGITEL system and allow comparability with other data from Brazil (Barreto et al., 2009; Del Duca et al., 2012; Florindo et al., 2009; Jaime et al., 2009). The cross-sectional design doesn't identify whether such corrupted lifestyles make it difficult to get a high income job or such lifestyles were present as a result of an outlet for frustration about low income. Further studies can investigate more distal determinants in the hypothetically causal chain of risk factors for CNCDs in the population studied. Contextual variables related to cultural and environmental conditions may bring more information on indicators related to health risk factors (Diez Roux, 2012). The results found in this research in relation to health inequities can serve as a warning to other cities in Brazil and Latin America that have with better social indicators, and even in this context individuals exposed to adverse socio-economic conditions are more susceptible to chronic noncommunicable diseases than the wealthiest. It is necessary to implement programs that reduce the risk behaviors for CNCDs among adults in Brazil, especially between young men with low education and income. Funding This project was funded by the National Council for the Scientific and Technological Development — CNPq (no. 485327/2007-4). Conflicts of interest The authors declare that there are no conflicts of interest.
Acknowledgments The authors thank to the Brazilian Institute of Geography and Statistics (IBGE) for the training support; to Professor Dr. Nilza Nunes da Silva of the Faculty of Public Health, University of São Paulo, for the contributions in sampling; to the Municipal Secretary of Health in Florianópolis for the support in the development of the research works.
References Barreto, S.M., Passos, V.M., Giatti, L., 2009. Healthy behavior among Brazilian young adults. Rev. Saude Publica 43, S9–S17. Boing, A.C., Bertoldi, A.D., Peres, K.G., 2011. Socioeconomic inequalities in expenditures and income committed to the purchase of medicines in Southern Brazil. Rev. Saude Publica 45, 897–905. Brasil, 2008. Ministério da Saúde. Política Nacional de Atenção Integral a Saúde do Homem – Princípios e Diretrizes. Secretaria de Atenção à Saúde, Departamento de Ações Programáticas Estratégicas, Ministério da Saúde, Brasília. Brazilian Institute of Geography and Statistics, 2009. Estimates of the resident population on 1 July 2009, according to cities. www.ibge.gov.br. Brazilian Institute of Geography and Statistics, 2011. Census 2010. www.ibge.gov.br/ home/estatistica/populacao/censo2010/default.shtm. Del Duca, G.F., Silva, K.S., Garcia, L.M., de Oliveira, E.S., Nahas, M.V., 2012. Clustering of unhealthy behaviors in a Brazilian population of industrial workers. Prev. Med. 54, 254–258. Diez Roux, A.V., 2012. Conceptual approaches to the study of health disparities. Annu. Rev. Public Health 33, 41–58. Fine, L.J., Philogene, G.S., Gramling, R., Coups, E.J., Sinha, S., 2004. Prevalence of multiple chronic disease risk factors. 2001 National Health Interview Survey. Am. J. Prev. Med. 27, S18–S24. Florindo, A.A., Hallal, P.C., Moura, E.C., Malta, D.C., 2009. Practice of physical activities and associated factors in adults, Brazil, 2006. Rev. Saude Publica 43, S65–S73. Fornari, C., Donfrancesco, C., Riva, M.A., et al., 2010. Social status and cardiovascular disease: a Mediterranean case. results from the Italian Progetto CUORE cohort study. BMC Publ. Health 10, 574. Hamilton, L.C., Seyfrit, C.L., 1993. Interpreting multinomial logistic regression. Stata Tech. Bull. 13, 24–27. Höfelmann, D.A., Antunes, J.L., Santos Silva, D.A., Peres, M.A., 2012. Is income area level associated with blood pressure in adults regardless of individual-level characteristics? A multilevel approach. Health Place 18, 971–977. Jaime, P.C., Figueiredo, I.C., Moura, E.C., Malta, D.C., 2009. Factors associated with fruit and vegetable consumption in Brazil, Rev. Saude Publica 43, S57–S64. Lima, C.T., Freire, A.C., Silva, A.P., Teixeira, R.M., Farrell, M., Prince, M., 2005. Concurrent and construct validity of the audit in an urban Brazilian sample. Alcohol Alcohol 40, 584–589. Marmot Review, 2010. Fair Society, Healthy Lives: Strategic Review of Health Inequalities in England Post 2010. Marmot Review, London. Poortinga, W., 2007. The prevalence and clustering of four major lifestyle risk factors in an English adult population. Prev. Med. 44, 124–128. Scafato, E., Galluzzo, L., Gandin, C., et al., 2008. Marital and cohabitation status as predictors of mortality: a 10-year follow-up of an Italian elderly cohort. Soc. Sci. Med. 67, 1456–1464. Schuit, A.J., van Loon, A.J., Tijhuis, M., Ocké, M., 2002. Clustering of lifestyle risk factors in a general adult population. Prev. Med. 35, 219–224. Szreter, S.R.S., 1984. The genesis of the Registrar-Generals social classification of occupations. Br. J. Sociol. 35, 522–546. World Health Organization, 2002. The World Health Report: Reducing Risks, Promoting Healthy Life. World Health Organization, Geneva. World Health Organization, 2009. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. World Health Organization, Geneva. Yusuf, S., Hawken, S., Ounpuu, S., et al., 2004. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet 364, 937–952.
Contents lists available at SciVerse ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Clustering of risk behaviors for chronic noncommunicable diseases: A population-based study in southern Brazil Diego A.S. Silva a,⁎, Karen G. Peres b, Antonio F. Boing b, David A. González-Chica c, Marco A. Peres b a b c
Federal University of Santa Catarina, Post-Graduate Program in Physical Education, Florianópolis, Brazil Federal University of Santa Catarina, Post-Graduate Program in Public Health, Florianópolis, Brazil Federal University of Santa Catarina, Post-Graduate Program in Nutrition, Florianópolis, Brazil
a r t i c l e
i n f o
Available online 31 October 2012 Keywords: Risk factors Chronic diseases Prevention and control Health inequalities Social iniquities Adults' health Lifestyle Cross-sectional studies
a b s t r a c t Objective. The purpose of this study was to investigate the prevalence and identify factors associated with simultaneous risk behaviors for chronic noncommunicable diseases in adults in a southern capital in Brazil. Method. A cross-sectional, population-based study was carried out with 1720 adults in Florianópolis, Brazil. The simultaneous occurrence of tobacco smoking, abusive drinking, inadequate or unhealthy diet, and physical inactivity during leisure was assessed. The independent variables were demographic and socioeconomic characteristics. Results. Only 8.3% of the respondents did not have any of these factors, whereas the simultaneous occurrence of two or more risk behaviors was 59.4%. The simultaneous presence of four risk behaviors (3.4%) was 220% higher of what would be expected by combining the individual prevalence of these factors (1.5%). The likelihood of individuals having two or more risk behaviors simultaneously was greater in young men, with black skin color, living without a partner, with lower household per capita income, and lower education. Conclusion. It is necessary to implement programs that reduce the risk behaviors for chronic noncommunicable diseases among adults in Brazil, especially between young men with low education and income. © 2012 Elsevier Inc. All rights reserved.
Introduction The four major risk factors for changeable chronic noncommunicable diseases (CNCDs) are tobacco smoking, alcohol consumption, unhealthy eating habits, and physical inactivity (World Health Organization, 2002, 2009; Yusuf et al., 2004). More worrisome than the exposure to one risk factor is the simultaneous exposure to more than one of these behaviors (Yusuf et al., 2004). Thus, evaluating aggregate health risk factors facilitates (Poortinga, 2007): i) the identification of the health risk factors that influence the occurrence of other factors; ii) the evaluation of the negative effect on health caused by simultaneous exposure, which is higher than the sum of the effects of exposure to each factor alone, suggesting that the health effects of lifestyle risk factors are multiplicative rather than additive; and iii) the promotion of health programs based on actions oriented to modify simultaneous behaviors because this strategy has shown to be more effective than those based on isolate behaviors. The lack of studies in Latin America on this subject also makes it difficult to detect the most common behavior patterns and the ⁎ Corresponding author at: Universidade Federal de Santa Catarina, Centro de Desportos, Programa de Pós Graduação em Educação Física, Campus Universitário Trindade, CEP: 88010-970, Florianópolis, SC, Brazil. Fax: +55 48 37218562. E-mail address: [email protected] (D.A.S. Silva). 0091-7435/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ypmed.2012.10.022
groups that are more susceptible to developing unhealthy habits, thus limiting any possible intervention to reduce the exposure to these risks. This work aimed to identify the prevalence and the sociodemographic factors associated with the simultaneous presence of chronic noncommunicable diseases risk behaviors in adults in a capital city in southern Brazil. Methods EpiFloripa, a population-based cross-sectional study was performed in Florianópolis, southern Brazil, from September 2009 to January 2010 (www.epifloripa.ufsc.br). The city has 421,203 inhabitants and ranks highest in terms of social and health indicators compared to other Brazilian capitals (Brazilian Institute of Geography and Statistics, 2009). Sampling was conducted in two phases (Höfelmann et al., 2012). In the first phase, 420 urban census tracts were stratified by deciles of household income being systematically drawn 63 tracts (sampling fraction equal to seven), totaling six tracts in each decile. In the second phase, the sampling units were households. The number of inhabited households ranged from 61 to 810 (coefficient of variation, 32%), and 18 were systematically chosen at random in each of the geographical units (average of 1.78 adults per domicile) or 32 adults in each census tract. The sample size for prevalence (n = 1820) was calculated assuming a target population of 249,530 adults aged between 20 and 59, a prevalence of 50% for the outcome (to maximize the number of individuals), sample error
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of 3.5 p.p., confidence level of 95%, design effect of 2.0 (because of clustering sample) and percentage of non-answers of 10%. Because of the multiple objectives of the EpiFloripa study (to evaluate diverse health outcomes) (Höfelmann et al., 2012), the sample size was increased to 2016 individuals. A posteriori we calculated the minimum odds ratio (OR) detectable with this sample, considering a range of exploratory variable factors (exposures) and the cluster of risk factors (outcome = 50%). For exposure prevalence higher than 5% the study is able to detect OR higher than 1.32 (risk) or lower than 0.76 (protection) for exposure prevalence higher than 5%, adopting an alpha of 5% and power of 80%. The home visits included the administration of a face-to-face questionnaire applied with the use of a personal digital assistant. Thirty-five interviewers were intensively trained prior to field work; the questionnaire pre-testing was performed on 35 adults. The pilot study included almost 100 adults living close to the research headquarters and in two census tracts not included in the sample. A short version of the questionnaire (10 questions) was administered through a telephone interview to 15% of the whole sample (n = 248) for quality control, with kappa and intra class correlation coefficient values ranging from 0.6 to 0.9. The dependent variable was the simultaneity of health-risk behaviors: tobacco smoking, abusive alcohol consumption, poor eating habits, and physical inactivity. A score ranging from 0 (no risk behavior) to 4 (four simultaneous risk behaviors) was generated. Smoking was assessed as nonsmoker, former smoker, or current smoker. For the purposes of the analysis, the “nonsmoker” and “former smoker” categories were grouped and considered not of risk to health. The Alcohol Use Disorders Identification Test was administered to identify people with problematic use of alcohol (no =score from 0 to 7; yes =score ≥8) (Lima et al., 2005). This tool allows identifying people who are at risk of alcohol abuse, harmful use and dependence (Lima et al., 2005). Physical activities and eating habits were assessed by means of the questionnaire used in the Surveillance of Risk Factors and Protection for Chronic Diseases Through Telephone Inquiries (VIGITEL), Brazil (Florindo et al., 2009; Jaime et al., 2009). Adults who reported not practicing physical activity during leisure or practicing less than once a week for the three months preceding the interview were considered physically inactive (Florindo et al., 2009). The subjects who reported consuming fruits and vegetables b5 days a week were considered as having an inadequate diet (Jaime et al., 2009). The independent variables were sex; age in completed years (20–29, 30–39, 40–49, and 50–59); self-reported skin color (Brazilian Institute of Geography and Statistics, 2011), classified as white or light- or dark-skinned black (results of those who reported yellow skin or indigenous were not presented in the tables because of their low frequency (2.2%) but were included in adjusted analyses); household per capita income, in Brazilian currency (R$), was collected according to household monthly gross income (1st tertile=up to R$ 566.9, 2nd tertile=R$ 567.0–1300.0, and 3rd tertile=R$ 1300.1–33,333.0). Successfully completed schooling years (0–4, 5–8, 9–11, and ≥12), marital status (married or with partner, and unmarried or without partner), and occupation in most of his/her life (nonmanual and manual activities) (Szreter, 1984). People who have never worked, students, housewives and those who did not answer the question were considered as missing (n=119). Chi-square tests for heterogeneity and linear trend were performed to assess the groups' differences. To evaluate the most frequent risk behavior combinations, the ratio between the observed and expected (O/E) prevalence was calculated for each possible combination, as described by Schuit et al. (2002). The observed prevalence was identified in our sample. The expected prevalence was calculated by multiplying the individual probabilities of each risk factor based on their occurrence in the study population. Therefore, it was possible to investigate which combinations were above or below the expectation, assuming that the risk factors occur independently in the population under study (Schuit et al., 2002). To identify the associated factors with the dependent variable “simultaneity,” we used the polytomous logistic regression, using the multinomial logit model (Hamilton and Seyfrit, 1993), with estimates of odds ratio (OR), and respective 95%CI, with no health-risk behavior as the reference category. Additionally, we tested any possible interactions of different socioeconomic and demographic variables on simultaneity of risk factors. All analyses were performed using Stata 11.0 (STATA Corp. College Station, Texas USA), considering the design effect and the sampling weight. The study was approved by the ethics committee on Human Research of the Federal University of Santa Catarina (no. 351/08).
21
Results We analyzed 1720 adults (response of 85.3%), with a mean age of 38.1 years (±11.6 years). More than half of the sample were female and younger than 40 years, and most of them self-reported as white (Table 1). The mean household income and years of school education was R$ 866.7 and 11 years, respectively. More than two thirds of the samples had nonmanual occupation. We observed that nearly 20% of the subjects were smokers, and a similar proportion had abusive alcohol consumption, 81.2% had poor eating habits, and slightly more than half did not practice regular physical activities during leisure. Men showed higher prevalence of smoking, problematic alcohol consumption, and unhealthy diet when compared with women. On the other hand, women were more physically inactive than men. The dark-skinned black subjects were more inactive than the white and light-skinned blacks. The individuals without a partner showed higher prevalence of abusive use of alcohol and inadequate diet than those living with a partner. Among the latter, physical inactivity was more frequent. There were direct association between age and the prevalence of smoking and physical inactivity. On the other hand, abusive drinking and unhealthy diet were more frequent among young adults. The study also showed an inverse association between household income and education in relation to smoking, inadequate diet, and physical inactivity, whereas abusive drinking was more prevalent among those with higher household per capita income and higher education (Table 1). The expected prevalence for the four risk behaviors was 1.5%, but 3.4% presented the four risk behaviors simultaneously, which represents an increase of 220% of what would be expected randomly (O/E ratio = 2.2). Regarding the simultaneity of the three risk factors, the most common was the combination of smoking, unhealthy diet, and physical inactivity (7.2%). However, the simultaneous presence of smoking, abusive drinking, and unhealthy diet (2.6%) is relevant because it was 90% higher than what would be expected if the factors were independent. As for the simultaneous occurrence of two risk behaviors, the most prevalent was the simultaneous exposure to unhealthy diet and physical inactivity during leisure (30.6%) (Table 2). Only 8.2% of the subjects did not have any factor, 32.2% had one risk factor, 42.0% had two, 14.2% had three, and 3.4% had four risk behaviors (Table 3). The likelihood of adults having one risk behavior when compared with those without any risk factor almost doubled for men and in the subjects aged 20 to 29 years. The likelihood of individuals having two or three risk factors simultaneously, when compared with the reference group was approximately two times higher among men, younger adults, and the poorer and four times higher among those with low educational background. The odds of adults having four risk factors simultaneously was almost 10 times higher among men, more than four times among the black individuals and those with lower education, and nearly three times higher among those without a partner and those who fall in the poorest income bracket. None of the tested interactions were statistically significant (Table 3).
Discussion This study has as novelty of studying health disparities and processes within a developed and service-industry oriented city such as Florianopolis, within the context of a fast-developing nation such as Brazil. Three major findings can be highlighted. First, the occurrence of the studied risk behaviors in the population is high: 91.8% of the adults in Florianópolis reported at least one risk factor for CNCD. Second, the behavior pattern that indicated a greater increase than that expected at random was the simultaneous occurrence of the four risk factors. Finally, the most vulnerable groups to the simultaneous occurrence of two or more risk behaviors for CNCDs were identified: young
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Table 1 Risk factor characteristics of the adults according to demographic and socioeconomic variables. Florianópolis, Brazil (2009–2010). Variables
Total Sex Females Males Skin color White Lighter skinned black Dark skinned black Age (years) 20–29 30–39 40–49 50–59 Marital status With partner Without partner Household per capita income (R$)c >1300.00 566.80–1300.00 0–566.70 No. of successful schooling years ≥12 9–11 5–8 0–4 Occupation Nonmanual Manual
Total sample
Smoking
Abusive drinking
Unhealthy diet
Physical inactivity
n
n
% (95%CI)
n
% (95%CI)
n
% (95%CI)
n
% (95%CI)
1711
19.2 (17.1–21.3) p = 0.023a 17.5 (14.8–20.2) 21.3 (18.2–24.4) p = 0.209a 18.4 (16.2–20.5) 24.9 (17.6–32.2) 20.8 (12.6–29.1) p = 0.004b 18.0 (14.6–21.4) 15.7 (11.7–19.8) 18.6 (15.0–22.2) 26.1 (21.8–30.4) p = 0.185a 18.2 (15.6–20.7) 20.7 (17.0–24.5) p b 0.001b 14.6 (10.9–18.3) 19.4 (15.1–23.8) 23.9 (20.4–27.4) p b 0.001b 13.9 (11.4–16.5) 21.4 (17.7–25.2) 25.9 (17.9–33.9) 27.0 (19.2–34.8) p = 0.559a 19.0 (16.5–21.4) 19.6 (16.1–23.2)
1720
18.5 (15.5–21.4) p b 0.001a 9.6 (6.9–12.3) 29.6 (25.3–33.9) p = 0.069a 17.3 (14.8–19.8) 28.4 (19.6–37.2) 19.6 (9.2–30.1) p b 0.001b 26.4 (20.3–32.4) 15.6 (11.3–19.9) 15.5 (11.9–19.1) 12.6 (8.8–16.3) p b 0.001a 13.8 (11.5–16.0) 25.6 (20.6–30.7) p = 0.025b 19.1 (15.0–23.3) 20.5 (16.3–24.8) 15.0 (12.0–18.1) p = 0.034b 19.2 (15.3–23.1) 20.5 (15.8–25.1) 14.6 (8.7–20.5) 14.3 (7.0–21.6) p = 0.946a 18.6 (14.6–22.6) 19.0 (15.6–22.5)
1719
81.2 (78.3–84.1) p b 0.001a 76.9 (73.6–80.1) 86.5 (83.2–89.9) p = 0.424a 80.6 (77.7–83.6) 87.0 (80.8–93.2) 83.2 (73.9–92.4) p b 0.001b 89.2 (86.2–92.3) 82.5 (78.0–87.0) 75.1 (70.1–80.0) 73.9 (68.3–79.6) p = 0.001a 79.1 (75.8–82.4) 84.3 (80.5–88.1) p b 0.001b 72.2 (67.0–77.4) 83.2 (80.0–86.4) 88.2 (85.4–91.1) p b 0.001b 74.9 (70.1–79.8) 84.9 (82.0–87.7) 87.6 (83.4–91.8) 88.1 (82.4–93.8) p = 0.885a 80.9 (77.6–84.1) 81.3 (77.2–85.4)
1718
53.1 (48.8–57.4) p b 0.001a 58.5 (53.8–63.3) 46.3 (40.7–51.8) p = 0.024a 51.9 (43.1–52.8) 55.9 (47.7–64.2) 68.3 (60.2–76.4) p b 0.001b 45.9 (40.5–51.2) 56.4 (50.7–62.0) 55.5 (49.6–61.4) 58.3 (50.4–66.1) p = 0.006a 56.4 (51.6–61.3) 48.1 (42.5–53.6) p b 0.001b 39.1 (33.9–44.3) 53.6 (49.1–58.2) 67.7 (62.0–73.4) p b 0.001b 38.7 (35.1–32.4) 57.3 (52.1–62.5) 68.8 (62.0–75.7) 82.6 (75.8–89.3) p = 0.779a 52.9 (49.0–56.7) 52.6 (46.0–59.1)
% (95%CI)
1720 959 761
55.5 (53.4–57.7) 44.5 (42.3–46.6)
955 756
1444 147 87
85.7 (82.2–89.2) 9.1 (6.6–11.5) 5.2 (3.3–6.9)
1436 146 87
540 392 438 350
32.7 (28.1–37.2) 22.9 (20.2–25.5) 25.0 (21.8–28.1) 19.4 (16.9–21.9)
538 391 435 347
1043 677
60.1 (56.5–63.6) 39.9 (36.3–43.4)
1037 674
559 562 564
32.6 (26.1–39.0) 33.3 (29.6–36.8) 34.1 (27.8.40.5)
556 559 563
737 568 253 158
43.9 (36.9–50.8) 33.4 (28.8–37.9) 14.0 (11.2–16.7) 8.7 (6.4–11.1)
733 566 252 156
1111 490
70.3 (65.0–75.5) 29.7 (24.5–34.9)
1107 485
959 761 1444 147 87 540 392 438 350 1043 677 559 562 564 737 568 253 158 1111 490
958 761 1443 147 87 540 392 438 349 1042 677 559 561 564 737 568 253 157 1111 489
958 760 1442 147 87 540 392 437 349 1042 676 559 560 564 737 568 253 156 1110 489
CI — confidence interval; 1R$ = 1.7 US$ at data collection. a Heterogeneity chi-square test. b Linear trend chi-square test. c R$ (real): Brazilian currency.
men, with black skin color, living without a partner, who are poor and less education. The prevalence order of risk behaviors in our study is more similar to the findings from high-income countries than those from Brazil. Data from the MORGEN study (Schuit et al., 2002) conducted with Dutch individuals aged 20 to 65 years showed that the most common health-risk behavior was poor diet, followed by low levels of physical activity, smoking, and abusive drinking. A population-based survey derived from the 2003 Health Survey for England (Poortinga, 2007) showed the same pattern. Regarding the combination of simultaneous risk factors for CNCD, at least two factors were present in 59.4% of the respondents, and the
most frequent pattern was the simultaneity of inadequate diet and physical inactivity (30.6%). In Holland (Schuit et al., 2002), 57.7% of men and 52.9% of women had at least two of the same four risk factors of this study (the most prevalent are physical inactivity and low consumption of fruits/vegetables for both sexes). Although the prevalence of the four risk behaviors in our study was low (3.4%), this result is significant because it represents an increase of 220% of what would be expected randomly. In the adult population in Holland (Schuit et al., 2002) and England (Poortinga, 2007), the observed prevalence was 170% and 200% higher than what would be expected if the four risk factors were independent. The second combination that had a higher observed prevalence than
Table 2 Prevalence of combinations of health risk behaviors in the adult population. Florianópolis, Brazil (2009–2010). Risk factors
Smoking
Drinking
Unhealthy diet
Physical inactivity
4 3
+ + + + – + + + – – – – – – + –
+ + + – + + – – + + – – – + – –
+ + – + + – + – + – + – + – – –
+ – + + + – – + – + + + – – – –
2
1
0
Prevalence Observed % (95%CI)
Expected %
O/E
3.4 (2.6–4.3) 2.6 (1.9–3.3) 0.4 (0.1–0.6) 7.2 (5.9–8.4) 4.0 (3.0–4.9) 0.2 (0.0–0.4) 3.2 (2.3–4.0) 1.3 (0.8–1.8) 6.2 (5.1–7.3) 0.5 (0.1–0.7) 30.6 (28.4–32.8) 5.7 (4.6–6.8) 24.0 (22.0–26.0) 1.2 (0.7–1.7) 1.3 (0.8–1.8) 8.2 (6.9–9.5)
1.5 1.4 0.4 6.7 6.4 0.3 6.0 1.6 5.7 1.5 28.4 6.6 25.1 1.3 1.4 5.8
2.2 1.9 1.1 1.1 0.6 0.6 0.5 0.8 1.1 0.3 1.1 0.9 1.0 0.9 0.9 1.4
CI: confidence interval; + presence of risk behavior; – absence of risk behavior; O: Observed prevalence; E: Expected prevalence; O/E: Ratio between observed and expected prevalence.
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Table 3 Association between risk factors and demographic and socioeconomic variables in adults. Florianópolis, Brazil (2009–2010). One risk factor n (%) Total Sex Female Male Skin color White Lighter skinned black Dark skinned black Age (years) 20–29 30–39 40–49 50–59 Marital status With partner Without partner Household per capita income (R$)d >1300.00 566.80–1300.00 0–566.70 No. of successful schooling years ≥12 9–11 5–8 0–4 Occupation Nonmanual Manual
Two risk factors OR (95%CI)a
551 (32.2)
n (%)
Three risk factors
OR (95%CI)a
718 (42.0) b
n (%)
Four risk factors
OR (95%CI)a
241 (14.2)
OR (95%CI)a
59 (3.4)
308 (32.3) 243 (32.1)
1.0 1.7 (1.1–2.7)
417 (43.7) 301 (39.8)
1.0 1.6 (1.1–2.4)
114 (11.9) 127 (16.8)
1.0 2.2 (1.2–3.7)
16 (1.7) 43 (5.7)
1.0b 9.9 (3.9–15.6)
476 (33.1) 33 (22.6) 29 (33.3)
1.0 0.6 (0.3–1.4) 1.6 (0.4–5.5)
608 (42.3) 61 (41.7) 32 (36.7)
1.0 0.7 (0.3–1.7) 0.9 (0.3–3.3)
190 (13.2) 33 (22.6) 15 (17.2)
1.0 1.3 (0.5–3.1) 1.1 (0.3–3.6)
42 (2.9) 7 (4.7) 7 (8.1)
1.0b 1.4 (0.3–5.3) 4.1 (1.1–15.7)
182 132 139 98
(33.8) (33.7) (32.0) (28.2)
1.0c 0.9 (0.5–1.9) 0.4 (0.2–0.8) 0.4 (0.2–0.6)
224 182 178 134
1.0c 1.2 (0.5–2.2) 0.4 (0.2–0.8) 0.3 (0.2–0.5)
(15.8) (12.0) (11.9) (16.4)
1.0c 1.0 (0.4–2.3) 0.4 (0.2–0.9) 0.4 (0.2–0.8)
22 (4.1) 6 (1.5) 17 (3.9) 14 (4.0)
1.0 0.5 (0.1–2.4) 0.7 (0.2–2.1) 0.6 (0.2–1.8)
344 (33.1) 207 (30.7)
1.0 1.0 (0.6–1.6)
435 (41.9) 283 (42.0)
1.0 1.2 (0.7–1.09)
137 (13.2) 104 (15.4)
1.0 1.6 (0.9–2.7)
27 (2.6) 32 (4.8)
1.0c 3.0 (1.2–7.6)
224 (40.2) 181 (32.4) 138 (24.5)
1.0 1.4 (0.8–2.6) 1.4 (0.6–3.3)
188 (33.8) 246 (44.0) 271 (48.1)
1.0b 2.2 (1.2–3.8) 2.7 (1.2–6.2)
55 (9.8) 74 (13.2) 104 (18.4)
1.0b 2.0 (1.0–4.3) 2.6 (1.1–4.3)
14 (2.5) 20 (3.5) 25 (4.4)
1.0b 2.7 (1.1–7.1) 3.3 (1.1–10.4)
303 157 67 23
(41.3) (27.7) (26.5) (14.8)
1.0 1.6 (0.9–2.9) 1.5 (0.6–3.5) 1.1 (0.3–3.7)
264 254 117 80
1.0c 2.9 (1.7–4.9) 2.6 (1.2–5.6) 4.0 (1.2–13.1)
60 102 44 35
1.0c 5.1 (2.5–10.0) 4.8 (1.7–13.3) 7.2 (1.8–28.4)
21 (2.8) 18 (3.1) 11 (4.3) 9 (5.8)
1.0c 2.5 (0.9–6.8) 4.4 (1.1–17.5) 6.1 (1.2–32.5)
365 (33.0) 141 (29.1)
1.0 0.8 (0.5–1.1)
467 (42.2) 205 (42.2)
1.0 0.8 (0.5–1.2)
154 (13.9) 67 (13.8)
1.0 0.8 (0.4–1.2)
34 (3.1) 22 (4.5)
1.0 1.2 (0.6–2.6)
(41.6) (46.5) (41.0) (38.6)
(36.0) (44.8) (46.4) (51.6)
b
n (%)
85 47 52 57
(8.1) (18.0) (17.4) (22.5)
b
OR: odds ratio; CI: confidence interval; 1R$ = 1.7 US$ at the time of data collection. a Adjusted analysis for all independent variables. b p b 0.05. c p b 0.01. d R$ (real): Brazilian currency.
the expected one in this study (smoking, drinking, and poor eating habits) had similar results to the studies carried out in Holland (Schuit et al., 2002) and England (Poortinga, 2007). The great similarity of our findings with those from high-income countries may be due to the high socioeconomic indicators that Florianópolis has compared with the rest of the country (Brazilian Institute of Geography and Statistics, 2009), which could influence the choice of behaviors. Men, in general, showed more prone to present risk factors than women. Researchers who have addressed gender differences reported that socioeconomic and cultural factors can influence these behaviors (Fornari et al., 2010). This is an issue of concern if we take into account that men make use of health services less frequently—especially for health prevention—and the higher morbidity and mortality from cardiovascular diseases that men have compared with women (Brasil, 2008). When comparing our data with those on the North American population (Fine et al., 2004), similar results were observed, and together, the risk of having one or more risk factors was higher among the young. This result may be attributed to the young's lack of concern with CNCDs; because of the insidious course of these diseases, they usually manifest clinically in individuals after 40 years old (Barreto et al., 2009). As in other studies (Scafato et al., 2008), we found that the individuals who live without a partner are more likely to develop the four risk behaviors simultaneously. This can explain the relationship between marital status and health that could be related to a protective effect on the health status by the social and economic support between married people. As a consequence, improved health behaviors and a healthier lifestyle can be developed by the couple (Scafato et al., 2008). Health inequalities are a core issue in Brazil. Despite the country's improvement in the global economy, there are still discrepancies
between rich and poor regarding income distribution, availability, and provision of medicines and health-care services (Boing et al., 2011). In this study, lower levels of household per capita income and education were strongly associated with the presence of two or more risk factors for CNCDs. This social gradient was also observed in studies about the simultaneity of risk factors in England (Poortinga, 2007), Holland (Schuit et al., 2002), and the United States (Fine et al., 2004). These inequalities in health could be strongly tackled if we consider the principle of proportionate universalism. To reduce the steepness of the social gradient in health, actions must be universal but with a scale and intensity that is proportionate to the level of disadvantage (Marmot Review, 2010). Another example of health inequalities found in this study was regarding skin color. Dark-skinned blacks are 4.1 times more likely to develop the four risk factors for CNCD simultaneously than whites. In Brazil, these people make up the population stratum most impacted by health inequities (Boing et al., 2011). Some population-based studies examined the risk factors in a stratified approach for men and women (Poortinga, 2007; Schuit et al., 2002). Others investigated the changeable risk behaviors without stratification (Fine et al., 2004). The studies that had a gender-stratification approach investigated the different prevalence on risk behaviors and in associations between men and women, particularly in high-income countries. However, in our study, no interaction was identified. The assessment of a representative adult sample by using complex sampling, the even distribution of losses in deciles of household income, and the similarity in the distribution by sex and age in comparison with IBGE's (Brazilian Institute of Geography and Statistics, 2011) estimates for the adult population in Florianopolis were key factors in our study. The British-based (Szreter, 1984) classification criteria for occupations might be a limitation of this study because the occupational status in income-emerging countries like Brazil differs drastically from
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that found in high-income countries. Despite its limitation, this classification was chosen because of the lack of a similar reference in Brazil. In addition, the measures used to assess physical inactivity and unhealthy diet are not fully in line with literature, which recommends obtaining more information about these variables. However, such measures are used in the VIGITEL system and allow comparability with other data from Brazil (Barreto et al., 2009; Del Duca et al., 2012; Florindo et al., 2009; Jaime et al., 2009). The cross-sectional design doesn't identify whether such corrupted lifestyles make it difficult to get a high income job or such lifestyles were present as a result of an outlet for frustration about low income. Further studies can investigate more distal determinants in the hypothetically causal chain of risk factors for CNCDs in the population studied. Contextual variables related to cultural and environmental conditions may bring more information on indicators related to health risk factors (Diez Roux, 2012). The results found in this research in relation to health inequities can serve as a warning to other cities in Brazil and Latin America that have with better social indicators, and even in this context individuals exposed to adverse socio-economic conditions are more susceptible to chronic noncommunicable diseases than the wealthiest. It is necessary to implement programs that reduce the risk behaviors for CNCDs among adults in Brazil, especially between young men with low education and income. Funding This project was funded by the National Council for the Scientific and Technological Development — CNPq (no. 485327/2007-4). Conflicts of interest The authors declare that there are no conflicts of interest.
Acknowledgments The authors thank to the Brazilian Institute of Geography and Statistics (IBGE) for the training support; to Professor Dr. Nilza Nunes da Silva of the Faculty of Public Health, University of São Paulo, for the contributions in sampling; to the Municipal Secretary of Health in Florianópolis for the support in the development of the research works.
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