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Revista Brasileira de Epidemiologia

Print version ISSN 1415-790XOn-line version ISSN 1980-5497

Rev. bras. epidemiol. vol.21  supl.1 São Paulo  2018  Epub Nov 29, 2018

http://dx.doi.org/10.1590/1980-549720180020.supl.1 

ORIGINAL ARTICLE

Trends in risk and protective factors for non-communicable diseases in the population with health insurance in Brazil from 2008 to 2015

Deborah Carvalho MaltaI 

Regina Tomie Ivata BernalII 

Eduardo Vieira NetoIII 

Katia Audi CurciIII 

Maria Tereza de Marsillac PasinatoIII 

Raquel Medeiros LisbôaIII 

Renata Fernandes CachapuzIII 

Karla Santa Cruz CoelhoIII 

IMaternal-child and Public Health Nursing Department, Escola de Enfermagem, Universidade Federal de Minas Gerais - Belo Horizonte (MG), Brazil.

IIEpidemiological Research Center in Nutrition and Health, Faculdade de Saúde Pública, Universidade de São Paulo - São Paulo (SP), Brazil.

IIIBoard of Standards and Product Qualification, National Regulatory Agency for Private Health Insurance and Plans - Rio de Janeiro (RJ), Brazil.

ABSTRACT:

Objective:

To analyze trends in risk and protective factors for non-communicable diseases (NCD) and access to preventive tests in the population with health insurance in Brazilian state capitals between 2008 and 2015.

Methods:

This is a cross-sectional study that analyzed data collected from the Surveillance of Risk and Protective Factors for non-communicable diseases (NCD) Telephone Survey (Sistema Nacional de Vigilância de Doenças Crônicas por Inquérito Telefônico - Vigitel) on adults aged 18 years and older. We analyzed trends in NCD indicators among health insurance users in approximately 30 thousand interviews done between 2008 and 2015. We used the simple linear regression model to calculate the trends.

Results:

Health insurance users showed an increase in the prevalence of protective factors such as fruit and vegetable consumption, and physical activity in leisure time. Also, there was a decrease in risk factors such as smoking and soft drink consumption, increase in mammography coverage, and a drop in smoking prevalence. However, overweight, obesity, and diabetes increased.

Conclusion:

There are differences according to gender, and, in general, women accumulate more protective factors and men, more risk factors.

Keywords: Noncommunicable diseases; Health insurance; Time series studies; Tobacco use disorder; Obesity

INTRODUCTION

The World Health Organization (WHO) estimates that non-communicable diseases (NCD) are responsible for about 38 million deaths annually, with 16 million of them corresponding to premature deaths, before 70 years of age, making NCD the largest health problem in the world1. In addition, NCD generate a high level of disability, suffering, and economic impact on global economy2,3. According to the Global Burden of Disease study, NCD represent approximately 75% of mortality in Brazil in 20154.

Evidence shows that among NCD determinants several of them are modifiable risk factors (smoking, physical inactivity, inadequate diet, alcohol consumption, obesity, and dyslipidemia) that can be reduced with actions and integrated strategies to prevent and control these diseases3.

Also, NCD have a long duration and evolve slowly, disproportionately affecting low- and middle-income countries, the poorest and most vulnerable populations3,5. Recent data from the National Health Survey (NHS) in Brazil indicated that people with higher schooling and health insurance have more access to health services and medical appointments6,7. They also have a lower prevalence of risk factors8 and are less affected by disabilities caused by NCD, which illustrates the inequalities in health6.

In the United States, investigations such as the Behavior Risk Factor Surveillance System (BRFSS), done by telephone, reveal that populations with health insurance have more protective factors - e.g., healthy diet and physical activity -, more access to preventive tests, and lower frequency of risk factors - e.g., tobacco9,10. WHO recommends monitoring NCD and their risk factors as an important public health action3, in addition to following different population segments and their singularities.

Since 2006, the Surveillance of Risk and Protective Factors for Chronic Diseases by Telephone Survey (Sistema Nacional de Vigilância de Doenças Crônicas por Inquérito Telefônico - Vigitel) monitor NCD risk factors in Brazilian state capitals11. In 2008, a question was added to the questionnaire about whether the participant had health insurance or not, allowing studies on this specific population12,13. However, even though more than 27% of the Brazilian population have health insurance, about 50 million Brazilians14,15, studies deepening this theme are still rare, particularly ones that analyze the behavioral trends in this population. The progress of trend analysis is essential to follow its evolution16,17, with the purpose of supporting preventive actions and population control.

The current article analyzed trends in risk and protective factors for NCD and access to preventive tests in the population with health insurance in Brazilian state capitals between 2008 and 2015.

METHODS

This is a cross-sectional study that analyzed data from Vigitel between 2008 and 2015 on adults (≥ 18 years) living in the 26 Brazilian state capitals and the Federal District.

The sampling procedures used by Vigitel aim at obtaining probabilistic samples of the adult population (≥ 18-year-olds) who resides in households with at least one landline telephone. The system establishes a minimum sample size of approximately 2 thousand individuals in each city to estimate the frequency of the main risk factors for NCD in the adult population, with a confidence coefficient of 95% and a maximum error of 2%. Maximum errors of 3% are expected for specific estimates, according to gender, assuming the ratio of men and women in the sample are similar.

The first Vigitel sampling stage consists of drawing at least 5 thousand telephone lines per city. This procedure - systematic and stratified by zip code (CEP) - uses the electronic records of residential landlines of telephone companies. The second stage comprises the drawing of one of the adults (≥ 18-year-olds) living in the household selected. This stage is performed after the identification of eligible lines for the system, among the ones drawn. Lines considered non-eligible for the system were the ones that belonged to companies, no longer existed, were out of service, or did not answer after six call attempts made on different days and times, including Saturdays, Sundays, and at night, and that probably corresponded to closed households.

To reduce the possible bias of not representing individuals with no landline telephone, we adopted post-stratification weights for each individual from the Vigitel sample, calculated with the rake method using a specific routine of the Statistical Analysis Software (SAS)18. This method uses iterative procedures that consider successive comparisons between distribution estimates of each sociodemographic variable in the Vigitel sample and the total population of the city. These comparisons culminate in finding weights that, when applied to the Vigitel sample, match their sociodemographic distribution to the one estimated for the total population who has health insurance in the city. The distribution of the population with health insurance in each sociodemographic variable for each city was collected from the Beneficiaries Information System (Sistema de Informação de Beneficiários - SIB) of the National Regulatory Agency for Private Health Insurance and Plans (Agência Nacional de Saúde Suplementar - ANS)19 to build post-stratification weights using the rake method. The population under study consists of adults aged 18 years or older, who live in one of the 26 state capitals or the Federal District and have health insurance. These procedures aim at matching the distribution of the population of the survey to the one estimated by Vigitel in each capital. More details can be found in other publications11,20. Thus, we used a subsample of adults with health insurance extracted from the Vigitel sample19, based on the question “Do you have health insurance?”. Specific weights were employed to analyze the population with health insurance.

The Vigitel survey comprises approximately 90 questions and includes topics such as sociodemographic characteristics, eating habits, physical activity, reported weight and height, cigarette and alcohol consumption, reported morbidity, preventive tests, among others. In the current study, we analyzed trends in the population with health insurance in the following indicators:

  • tobacco: prevalence of smokers (% of smokers/number of individuals interviewed); 20 or more cigarettes smoked per day (heavy use); passive smokers at home; passive smokers at work;

  • overweight (body mass index ≥ 25 kg/m2) and obesity (body mass index ≥ 30 kg/m2);

  • eating habits: recommended consumption of fruits and vegetables (five or more daily servings on five or more days a week); consumption of meat with visible fat (red meat with visible fat or chicken with skin); regular consumption of soft drinks or artificial juices (five or more days a week); regular consumption of beans (five or more days a week);

  • recommended practice of physical activity during leisure time (at least 150 minutes of mild- or moderate-intensity physical activity per week, or at least 75 minutes of vigorous-intensity physical activity per week, regardless of the number of days per week);

  • abusive alcohol consumption (4 or more doses for women and 5 or more doses for men in a single occasion in the last 30 days, considering dose as a shot of distilled drink, a can of beer, or a glass of wine); driving after alcohol consumption (% of adults who reported driving an automotive vehicle after consuming alcoholic drinks);

  • reported morbidity (report a prior medical diagnosis of hypertension and diabetes);

  • undergoing tests for early cancer detection in women: mammography (for women aged 50 to 69 years) and pap smear (for women aged 25 to 59 years).

The indicators, except for those relating to a specific age and gender, were calculated taking the total number of adults interviewed as denominator.

We calculated time series analyses, and the first methodology stage consisted of building the time series of each indicator (Zt) using secondary data. Estimates of risk and protective factors were extracted from Vigitel databases in each of the years under study - from 2008 to 2015 - on the population who reported having health insurance. Approximately 30 thousand adults were interviewed each year, totaling about 240 thousand interviews between 2008 and 2015. The last year of the series, 2015, had 30,549 interviews.

The second stage corresponded to time series (Zt) modeling to identify if the trend was significant. In this study, we used the simple linear regression model, given by the expression: [Z t = α + β* t + a t ], with t as the time prevalence value - ranging from 2008 to 2015 -, α as the intercept, β as the angular coefficient, also known as regression coefficient, and a t , as the residual (random error). A positive regression coefficient points to an increasing trend, while a negative one indicates a decrease. The measures evaluated to adjust the model were: analysis of variance (ANOVA), which informs whether the function adjusted by the linear model (Z t = α + β* t + a t ) reduces the residual variance when compared to the simple model (Z t = µ + a t ), in which we tested the hypothesis that H 0 : β = 0, with a significance level of 5%; determination coefficient, which measures the strength of correlation between indicator and time; analysis of residuals, expressed by the difference between actual (Z t ) and adjusted ( t ) prevalence, indicating the adjustment quality: standardized residuals between -2 and 2 show no outliers that can underestimate or overestimate the trend; and p value of α (intercept or intersection) and β (regression) coefficients. The criteria adopted to consider the series trend significant included an ANOVA significance level of less than 5% and R2 greater than or equal to 70%. The premise of a time series analysis is that what happened in the past will happen again in the future21.

The National Committee for Ethics in Research with Human Beings of the Ministry of Health approved the Vigitel survey, no. 355.590/2013; and the interviewee verbally agreed to participate at the time of the call.

RESULTS

In the trend analysis of the total population with health insurance between 2008 and 2015, the number of smokers decreased from 12.4 to 7.7% (p < 0.00). Other tobacco indicators also had a statistically significant decrease in this period among the total population, such as smoking 20 or more cigarettes a day, being a passive smoker at home and at work (Table 1).

Overweight increased from 45.8 to 51.6% and obesity, from 12.9 to 16.5% (p < 0.00). The indicator of the recommended daily consumption of fruits and vegetables improved (24.9 to 30.9%; p < 0.00). Consumption of meat with excess fat remained stable, and regular consumption of soft drinks (five or more times a week) dropped. Consumption of beans also remained stable among the population with health insurance (Table 1).

Table 1. Time series trend analysis of indicators of chronic non-communicable diseases in the population with health insurance. Brazilian state capitals, 2008 to 2015. 

Indicator 2008 2009 2010 2011 2012 2013 2014 2015 Regression coefficient p- value R2 Trend
Smoker 12.39 11.20 10.9 10.06 9.37 8.61 8.39 7.68 -0.65 0.00 0.98 Drop
20 or more cigarettes a day 3.48 3.02 3.17 2.98 2.97 2.30 2.21 2.07 -0.20 0.00 0.89 Drop
Passive smokers at home 11.02 9.53 9.82 8.81 9.76 8.30 7.95 -0.42 0.01 0.75 Drop
Passive smokers at work 9.70 8.46 9.28 8.57 8.34 7.51 6.67 -0.43 0.01 0.81 Drop
Overweight 45.81 45.62 48.6 49.35 50.96 49.66 50.43 51.57 0.82 0.00 0.82 Rise
Obesity 12.93 13.72 14.3 15.05 16.24 16.42 16.03 16.50 0.52 0.00 0.88 Rise
Fruits and vegetables (5 times a day) 24.90 24.93 23.9 25.91 28.28 28.33 28.71 30.95 0.91 0.00 0.85 Rise
Meat with excess fat 28.00 28.38 28.6 29.71 27.89 27.95 26.94 28.27 -0.11 0.41 0.12 _
Soft drinks (5 or more times a week) 26.06 25.75 26.6 25.85 24.03 21.76 19.65 17.74 -1.25 0.00 0.84 Drop
Beans (5 or more times a week) 59.77 59.82 61.5 63.82 63.35 62.54 62.69 60.64 0.28 0.29 0.19 _
Sufficient physical activity during leisure time 35.40 36.4 37.64 39.49 39.27 41.32 43.89 1.32 0.00 0.95 Rise
Inactive 16.45 16.1 15.93 15.40 17.05 16.05 15.98 -0.02 0.88 0.01 _
Abusive alcohol consumption 17.09 18.82 19.4 16.56 19.03 16.77 16.69 17.07 -0.19 0.33 0.16 _
Driving after alcohol consumption 9.25 9.53 6.85 7.20 6.57 -0.77 0.06 0.75 _
Mammography every two years (50- to 69-year-olds) 84.94 84.95 85.7 85.70 86.73 85.82 86.37 87.06 0.28 0.00 0.78 Rise
Pap smear every three years (25- to 64-year-olds) 89.94 88.79 89.6 87.75 89.03 88.99 87.62 86.02 -0.40 0.02 0.60 -
Hypertension 23.77 22.77 22.5 22.79 23.05 22.79 21.57 22.46 -0.17 0.07 0.44
Diabetes 6.03 5.96 6.51 6.29 6.78 6.57 6.80 6.67 0.11 0.01 0.70 Rise

Sufficient physical activity during leisure time increased from 35 to 43.9% (p < 0.00). There was no change in the trend in inactive people. Trends in indicators of alcohol consumption and driving after drinking did not change in the period. The trend in hypertension remained the same, but diabetes increased from 6% (2008) to 6.7% (2015) (p < 0.01) among the population with health insurance.

Table 2 presents the trends for men, which shows stability in the prevalence of smokers and a drop in the consumption of 20 cigarettes or more and passive smokers at work. Overweight remained stable and obesity increased from 13.9 to 16.3% (p < 0.01). Regarding eating habits, the recommended consumption of fruits and vegetables increased, and soft drink consumption decreased. Driving after alcohol consumption dropped, and other indicators remained stable. Diabetes increased from 5.1 to 6.5%, but the difference was not significant.

Table 2. Time series trend analysis of indicators of chronic non-communicable diseases in the male population with health insurance. Brazilian state capitals, 2008 to 2015. 

Indicator 2008 2009 2010 2011 2012 2013 2014 2015 Regression coefficient p- value R2 Trend
Smoker 14.22 13.74 12.61 11.93 12.32 11.14 10.20 10.09 -0.35 0.08 0.43 -
20 or more cigarettes a day 4.42 4.32 3.74 3.76 4.27 3.27 2.93 3.06 -0.21 0.01 0.74 Drop
Passive smokers at home 10.75 8.98 9.26 8.04 9.66 8.02 7.60 -0.39 0.05 0.58 -
Passive smokers at work 13.16 11.98 13.14 12.48 11.58 10.48 9.99 -0.50 0.01 0.77 Drop
Overweight 54.02 52.71 55.86 57.05 57.42 56.20 57.29 57.40 0.57 0.02 0.64 -
Obesity 13.94 14.29 14.15 15.45 16.53 17.58 16.62 16.30 0.47 0.01 0.72 Rise
Fruits and vegetables (5 times a day) 19.19 19.35 19.07 20.87 23.39 23.18 23.92 25.80 1.00 0.00 0.91 Rise
Meat with excess fat 38.44 38.67 40.18 39.73 37.23 37.81 35.96 39.43 -0.19 0.41 0.11 -
Soft drinks (5 or more times a week) 31.27 29.27 31.43 30.65 27.59 25.61 22.43 21.55 -1.46 0.00 0.84 Drop
Beans (5 or more times a week) 67.84 67.10 68.39 71.38 70.68 69.41 69.48 67.88 0.17 0.49 0.08 -
Sufficient physical activity during leisure time 45.06 47.09 46.85 47.98 47.12 47.20 53.15 0.88 0.05 0.57 -
Inactive 14.96 14.60 14.11 14.07 15.63 15.97 13.86 0.03 0.84 0.01 -
Abusive alcohol consumption 26.42 29.53 29.66 24.74 28.68 25.04 24.47 24.57 -0.57 0.11 0.37 -
Driving after alcohol consumption 16.22 16.40 12.23 12.28 11.12 -1.43 0.03 0.84 Drop
Hypertension 21.74 20.71 20.08 20.60 21.05 21.36 20.33 20.83 -0.05 0.61 0.05 -
Diabetes 5.14 5.75 6.37 6.17 6.23 6.22 6.51 6.17 0.13 0.05 0.51 -

Table 3 presents the trends for women. All tobacco indicators dropped. The prevalence of women smokers decreased from 10.9 to 5.6%. Overweight increased from 39.3 to 44.7% (p < 0.00), but remained below the indicators for men, and obesity rose from 12.1 to 16.6% (p < 0.00). Regarding eating habits, the recommended consumption of fruits and vegetables increased, and soft drink consumption decreased. Sufficient physical activity during leisure time increased from 27.7 to 36.1% (p < 0.00). Prevalence of preventive mammography increased, while pap smear remained stable and high, above 85%, among women with health insurance throughout the period. Other indicators remained stable (Table 3).

Table 3. Time series trend analysis of indicators of chronic non-communicable diseases in the female population with health insurance. Brazilian state capitals, 2008 to 2015. 2015. 

Indicator 2008 2009 2010 2011 2012 2013 2014 2015 Regression coefficient p- value R2 Trend
Smoker 10.94 9.20 9.62 8.48 6.95 6.56 6.87 5.67 -0.70 0.00 0.92 Drop
20 or more cigarettes a day 2.73 1.99 2.72 2.32 1.89 1.52 1.61 1.25 -0.19 0.01 0.75 Drop
Passive smokers at home 11.22 9.97 10.30 9.44 9.84 8.53 8.23 -0.44 0.00 0.85 Drop
Passive smokers at work 6.97 5.67 6.04 5.38 5.72 5.03 3.90 -0.39 0.01 0.79 Drop
Overweight 39.30 40.01 42.88 42.87 45.68 44.36 44.71 46.71 0.98 0.00 0.85 Rise
Obesity 12.12 13.28 14.47 14.70 16.01 15.49 15.53 16.66 0.56 0.00 0.86 Rise
Fruits and vegetables (5 times a day) 29.42 29.33 27.79 30.14 32.27 32.50 32.70 35.24 0.88 0.00 0.79 Rise
Meat with excess fat 19.72 20.24 19.45 21.29 20.25 19.95 19.41 18.98 -0.11 0.37 0.13 _
Soft drinks (5 or more times a week) 21.94 22.96 22.73 21.82 21.12 18.64 17.33 14.57 -1.10 0.00 0.81 Drop
Beans (5 or more times a week) 53.37 54.07 55.96 57.48 57.35 56.97 57.03 54.61 0.31 0.24 0.22 _
Sufficient physical activity during leisure time 27.76 28.01 29.89 32.55 32.91 36.41 36.18 1.61 0.00 0.95 Rise
Inactive 17.63 17.36 17.47 16.49 18.20 16.12 17.74 -0.05 0.75 0.02 _
Abusive alcohol consumption 9.70 10.35 11.20 9.68 11.14 10.06 10.19 10.82 0.06 0.56 0.06 _
Driving after alcohol consumption 3.39 3.92 2.48 2.95 2.78 -0.22 0.27 0.38 _
Hypertension 25.37 24.40 24.45 24.62 24.69 23.96 22.61 23.82 -0.25 0.03 0.58 _
Diabetes 6.74 6.12 6.63 6.39 7.23 6.85 7.03 7.10 0.10 0.07 0.44 _
Mammography every two years (50- to 69-year-olds) 84.94 84.95 85.68 85.70 86.73 85.82 86.37 87.06 0.28 0.00 0.78 Rise
Pap smear every three years (25- to 64-year-olds) 89.94 88.79 89.59 87.75 89.03 88.99 87.62 86.02 -0.40 0.02 0.60 _

DISCUSSION

It is important to study the characteristics of the population with health insurance as it represents more than a quarter of Brazilians and can reach half the population in some capitals15. In general, this group has a higher schooling and income, greater frequency of insertion in the production process, and better health outcomes12,22,23,24.

The trend in smoking decreased in most indicators, which has also been identified in other studies indicating the success of anti-tobacco measures in the country, such as banning advertising, raising taxes and prices of tobacco products; legislation on smoke-free environments, banning smoking in public places; warnings on cigarette packets, among others25,26. The study presented differences from previous analyses, by indicating a reduction of smokers only among women. Studies on the Brazilian population point to a decrease among men as well25,26. Men have twice the women prevalence and should receive greater attention when monitoring future trends and specific policies. Heavy tobacco consumption dropped for both genders, a fact associated with cardiovascular events and lung cancer27.

Passive smoking exposes nonsmoking populations to the same types of smoker’s diseases28. The study shows a decrease in passive smoking at work for both genders, and also in passive smoking at home for women. This represents a great improvement in this behavior, which could have been motivated by the restrictive measures adopted, especially the 2011 law on smoke-free environments, and the executive order26.

The WHO recommends daily consumption of 400 g of fruits and vegetables to prevent cardiovascular diseases. Fruits and vegetables are also beneficial in overweight prevention and treatment29, a serious health issue in the country. The regular and recommended intake of fruits and vegetables grew among women and men, although it was higher among women throughout the period, as already highlighted in other studies30.

A sedentary lifestyle is the fourth main cause of death in the world31, and studies point to significant differences regarding gender, with the practice of physical activity during leisure time being more common among men and young people32,33. The current study showed that men practice physical activity in their leisure time approximately 50% more than women in Brazil. However, the trend remained stable among males and increased in females, which differs from previous studies34.

Vigitel researches some proxy indicators of saturated fat, such as removing the fat from the meat. Previous studies point out that the Brazilian population consumes saturated fat within the maximum limit recommended by WHO (10% of total calories)35. The consumption of free sugar by the Brazilian population exceeds more than 50% the maximum limit recommended by WHO (10% of total calories)35,36. Thus, the drop in consumption of soft drinks and artificial juices shown here is important. We also underline that this number is lower than the 23% identified by the NHS 201335.

Although eating habits and physical activity indicators have improved, obesity and overweight showed an increasing trend in the population with health insurance. Overweight and obesity are risk factors for the global burden of disease due to their association with cardiovascular diseases, diabetes, colon, rectum, and breast cancer, cirrhosis, among others2,3. In light of its magnitude and steady growth in the world, the WHO included in its global targets the challenge of halting the increase in obesity by 20253. However, countries have not positively advanced to reach this goal1. Brazil shows an increasing trend in obesity37, the same trend found in this study for both genders. Obesity in the general population estimated by Vigitel was present in 18.1% of men and 19.7% of women in 2015, values higher than the ones of this study38. Factors such as higher schooling and income in this specific population can justify the lower prevalence in the previous study37.

Differences related to overweight reveal stability among men with health insurance, and growth in the general male population, from 49.8% (2008) to 57.6% (2015)38. We underline that in 2015 prevalence rates were similar for men in the general population and those with health insurance. Women showed increasing trends in both populations.

Studies in the United States indicate that the population with health insurance has a lower prevalence of risk factors, and more access to health and preventive services9,10, such as mammography, colonoscopy, and pap smear9. In Brazil, the NHS pointed to improvements in access to health services and the importance of the public health system in its universalization. However, the country still faces inequalities in access to health services according to region, as well as coverage of tests, such as mammography and pap smear7,39.

Cervix cancer preventive tests, such as pap smear, are recommended for fertile women every three years. To prevent breast cancer, women aged 50 to 69 years should get a mammography every two years. The population with health insurance has been reaching this goal, with coverage rates above 85%, and mammography coverage has been growing. This constitutes a virtually concluded objective by the universality of these procedures among women with health insurance. A study analyzing data from the NHS confirms that women with health insurance have greater coverage of breast cancer preventive tests39.

In this period, trends in hypertension did not change, while self-reported diabetes increased among the total population. Studies have demonstrated the association between diabetes and non-localized obesity, characterized by a high body mass index40,41. The growing obesity in the Brazilian population has contributed to the rise of diabetes and population ageing42.

The trend in abusive alcohol consumption remained stable, and there was a decrease in driving after alcohol consumption among men. In the latter, we underline that the population with health insurance has higher frequencies than the total population - almost three times greater -, which can be due to their better income and, therefore, the ownership of a car43. Vigitel data also indicate that drinking and driving is more common in the population with higher schooling and has decreased among men44.

Limitations of this study include the interviews being conducted only with individuals who have a landline telephone, which can reduce the participation of low-income populations. However, we minimized this bias by using weighting factors to match the same population distribution of Vigitel and the Census 2010. The evolution of time trends outlined here might be due to other factors and cannot be attributed to the population having health insurance. In addition, the trends were measured in different cross-sectional studies and not in the same individual.

CONCLUSION

The time series analysis of NCD indicators among individuals with health insurance showed positive aspects, such as increasing trends in protective factors - consumption of fruits and vegetables and physical activity in leisure time -, decrease in risk factors - smoking and soft drink consumption -, and higher mammography coverage. Among indicators with negative performance, we have the rise in overweight, obesity, and diabetes. There are differences according to gender, and, in general, women accumulate more protective factors and men, more risk factors.

REFERENCES

1. World Health Organization. Global status report on noncommunicable diseases 2014 [Internet]. Genebra: World Health Organization; 2014 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://apps.who.int/iris/bitstream/10665/148114/1/9789241564854_eng.pdf?ua=1Links ]

2. World Health Organization. Global status report on noncommunicable diseases 2010 [Internet]. Genebra: World Health Organization ; 2011 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://apps.who.int/iris/bitstream/10665/44579/1/9789240686458_eng.pdfLinks ]

3. World Health Organization. Global action plan for the prevention and control of NCDs 2013-2020 [Internet]. Genebra: WHO; 2013 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1Links ]

4. Malta DC, França E, Abreu DMX, Perillo RD, Salmen MC, Teixeira RA, et al. Mortality from non-communicable diseases in Brazil, 1990 and 2015, according to estimates from the Global Burden of Disease study. São Paulo Med J. 2017. http://dx.doi.org/10.1590/1516-3180.2016.0330050117Links ]

5. World Economic Forum, World Health Organization. From burden to “best buys”: reducing the economic impact of non-communicable diseases in low- and middle-income countries [Internet]. Genebra: World Economic Forum; 2011 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://www.who.int/nmh/publications/best_buys_summary.pdf?ua=1Links ]

6. Malta DC, Bernal RTI, Lima MG, Araújo SSC, Silva MMA, Freitas MIF, et al. Noncommunicable diseases and the use of health services: analysis of the National Health Survey in Brazil. Rev Saúde Pública. 2017; 51(Supl. 1): 4s. http://dx.doi.org/10.1590/s1518-8787.2017051000090Links ]

7. Stopa SR, Malta DC, Monteiro CN, Szwarcwald CL, Goldbaum Moisés, Cesar CLG. Acesso e uso de serviços de saúde pela população brasileira, Pesquisa Nacional de Saúde 2013. Rev Saúde Pública [Internet]. 2017; 51(Supl. 1): 3s. http://dx.doi.org/10.1590/s1518-8787.2017051000074Links ]

8. Barros MBA, Lima MG, Medina LPB, Szwarcwald CL, Malta DC. Social inequalities in health behaviors among Brazilian adults: National Health Survey, 2013. Int J Equity Health. 2016; 15: 148. http://dx.doi.org/10.1186/s12939-016-0439-0Links ]

9. Ahluwalia JB, Bolen J, Garvin B. Health insurance coverage and use of selected preventive services by working age women, BRFSS 2006. J Womens Health (Larchmt). 2007; 16(7): 935-40. https://doi.org/10.1089/jwh.2007.CDC8Links ]

10. Nelson KM, Chapko MK, Reiber G, Boylo EJ. The association between health insurance coverage and diabetes care; data from the 2000 behavior risk factor surveillance system. Health Serv Res. 2005; 40(2): 361-72. https://doi.org/10.1111/j.1475-6773.2005.00361.xLinks ]

11. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos Não transmissíveis e Promoção da Saúde. VIGITEL Brasil 2016: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico [Internet]. Brasília: Ministério da Saúde; 2017 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://portalarquivos.saude.gov.br/images/pdf/2017/junho/07/vigitel_2016_jun17.pdfLinks ]

12. Malta DC, Bernal RTI. Comparação dos fatores de risco e proteção de doenças crônicas na população com e sem planos de saúde nas capitais brasileiras, 2011. Rev Bras Epidemiol. 2014; 17(Supl. 1): 241-55. http://dx.doi.org/10.1590/1809-4503201400050019Links ]

13. Malta DC, Bernal RTI, Oliveira M. Tendências dos fatores de risco de doenças crônicas não transmissíveis, segundo a posse de planos de saúde, Brasil, 2008 a 2013. Ciênc Saúde coletiva. 2015; 20(4): 1005-16. http://dx.doi.org/10.1590/1413-81232015204.14712014Links ]

14. Viacava F, Bellido JG. Condições de saúde, acesso a serviços e fontes de pagamento, segundo inquéritos domiciliares. Ciênc Saúde Coletiva. 2016; 21(2): 351-70. http://dx.doi.org/10.1590/1413-81232015212.19422015Links ]

15. Malta DC, Stopa SR, Pereira CA, Szwarcwald CL, Oliveira M, Reis AC. Cobertura de Planos de Saúde na população brasileira, segundo a Pesquisa Nacional de Saúde, 2013. Ciênc Saúde Coletiva. 2017; 22(1): 179-90. http://dx.doi.org/10.1590/1413-81232017221.16782015Links ]

16. Bonita R, de Courten M, Dwyer T, Jamrozik K, Winkelmann R. Surveillance of risk factors for noncommunicable diseases: The WHO STEPwise approach [Internet]. Genebra: World Health Organization ; 2001 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://apps.who.int/iris/bitstream/10665/70475/1/WHO_NMH_CCS_01.01_eng.pdfLinks ]

17. Bonita R, Magnusson R, Bovet P, Zhao D, Malta DC, Geneau R, et al. Country actions to meet UN commitments on non-communicable diseases: a stepwise approach. Lancet. 2013; 381(9866): 575-84. https://doi.org/10.1016/S0140-6736(12)61993-XLinks ]

18. Izrael D, Hoaglin, DC, Battaglia MP. A SAS Macro for Balancing a Weighted Sample. In: Proceedings of the Twenty-Fifth Annual SAS Users Group International Conference, 2000 Apr 9-12. Paper 275 [Internet] . Cary (NC): SAS Institute; 2000 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://www2.sas.com/proceedings/sugi25/25/st/25p258.pdfLinks ]

19. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos Não Transmissíveis e Promoção da Saúde. Vigitel Brasil 2015 Saúde Suplementar: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico [Internet]. Brasília: Ministério da Saúde ; 2017 [citado em 18 jul. 2017]. Disponível em: Disponível em: http://portalarquivos.saude.gov.br/images/pdf/2017/marco/07/vigitel_saude_suplementar_2015.pdfLinks ]

20. Bernal R. Inquéritos por telefone: inferências válidas em regiões com baixa taxa de cobertura de linhas residenciais [tese]. São Paulo: Faculdade de Saúde Pública; 2011. http://dx.doi.org/10.11606/T.6.2011.tde-09092011-120701Links ]

21. Paula GA. Modelos de Regressão com apoio computacional. São Paulo: IME-USP; 2004. [ Links ]

22. Lima-Costa MF. Estilos de vida e uso de serviços preventivos de saúde entre adultos filiados ou não a plano privado de saúde (inquérito de saúde de Belo Horizonte). Ciênc Saúde Coletiva. 2004; 9(4): 857-64. http://dx.doi.org/10.1590/S1413-81232004000400008Links ]

23. Lima-Costa MF, Guerra HL, Firmo JO, Vidigal PG, Uchoa E, Barreto SM. The Bambuí Health and Ageing Study (BHAS): private health plan and medical care utilization by older adults. Cad Saúde Pública. 2002; 18(1): 177-86. http://dx.doi.org/10.1590/S0102-311X2002000100018Links ]

24. Instituto Brasileiro de Geografia e Estatística. Coordenação de Trabalho e Rendimento. Pesquisa Nacional de Saúde 2013: acesso e utilização dos serviços de saúde, acidentes e violências: Brasil, grandes regiões e unidades da federação [Internet]. Rio de Janeiro: IBGE; 2015 [citado 18 jul. 2017]. Disponível em: Disponível em: http://biblioteca.ibge.gov.br/visualizacao/livros/liv94074.pdfLinks ]

25. Monteiro CA, Cavalcante TM, Moura EC, Claro RM, Szwarcwald CL. Population-based evidence of a strong decline in the prevalence of smokers in Brazil (1989-2003). Bull World Health Organ. 2007; 85(7): 527-34. [ Links ]

26. Malta DC, Oliveira TP, Luz M, Stopa SR, Silva-Junior JB, Reis AAC. Tendências de indicadores de tabagismo nas capitais brasileiras, 2006 a 2013. Ciênc Saúde Coletiva. 2015; 20(3): 631-40. http://dx.doi.org/10.1590/1413-81232015203.15232014Links ]

27. World Health Organization. WHO Report on the global tobacco epidemic, 2013. Enforcing bans on tobacco advertising, promotion and sponsorship [Internet]. Genebra: WHO ; 2013 [citado em 18 jul. 2017]. Disponível em: http://apps.who.int/iris/bitstream/10665/85380/1/9789241505871_eng.pdf [ Links ]

28. Tilloy E, Cottel D, Ruidavets J-B, Arveiler D, Ducimetière P, Bongard V, et al. Characteristics of current smokers, former smokers, and second-hand exposure and evolution between 1985 and 2007. Eur J Cardiovasc Prev Rehabil. 2010; 17(6): 730-6. http://dx.doi.org/10.1097/HJR.0b013e32833a9a0cLinks ]

29. Alinia S, Hels O, Tetens I. The potential association between fruit intake and body weight: a review. Obes Rev. 2009 Nov; 10(6): 639-47. http://dx.doi.org/10.1111/j.1467-789X.2009.00582.xLinks ]

30. Jaime PC, Stopa SR, Oliveira TP, Vieira ML, Szwarcwald CL, Malta DC. Prevalência e distribuição sociodemográfica de marcadores de alimentação saudável, Pesquisa Nacional de Saúde, Brasil 2013. Epidemiol Serv Saúde. 2015; 24(2): 267-76. http://dx.doi.org/10.5123/S1679-49742015000200009Links ]

31. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012 Jul; 380(9838): 219-29. http://dx.doi.org/10.1016/S0140-6736(12)61031-9Links ]

32. Azevedo MR, Araújo CL, Reichert FF, Siqueira FV, da Silva MC, Hallal PC. Gender differences in leisure-time physical activity. Int J Public Health. 2007; 52(1): 8-15. http://dx.doi.org/10.1007/s00038-006-5062-1Links ]

33. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012; 380(9839): 258-71. http://dx.doi.org/10.1016/S0140-6736(12)60735-1Links ]

34. Malta D, Andrade S, Santos M, Rodrigues G, Mielke G. Tendências dos indicadores de atividade física em adultos: Conjunto de capitais do Brasil 2006-2013. Rev Bras Ativ Fís Saúde. 2015; 20(2): 141-51. http://dx.doi.org/10.12820/rbafs.v.20n2p141Links ]

35. Claro RM, Santos MAS, Oliveira TP, Pereira CA, Szwarcwald CL, Malta DC. Unhealthy food consumption related to chronic non-communicable diseases in Brazil: National Health Survey, 2013. Epidemiol Serv Saúde. 2015; 24(2): 257-65. http://dx.doi.org/10.5123/S1679-49742015000200008Links ]

36. Levy RB, Claro RM, Bandoni DH, Mondini L, Monteiro CA. Disponibilidade de “açúcares de adição” no Brasil: distribuição, fontes alimentares e tendência temporal. Rev Bras Epidemiol. 2012; 15(1): 3-12. http://dx.doi.org/10.1590/S1415-790X2012000100001Links ]

38. Malta DC, Santos MAS, Andrade SSCA, Oliveira TP, Stopa SR, Oliveira MM, et al. Tendência temporal dos indicadores de excesso de peso em adultos nas capitais brasileiras, 2006-2013. Ciênc Saúde Coletiva. 2016; 21(4): 1061-9. http://dx.doi.org/10.1590/1413-81232015214.12292015Links ]

39. Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos Não Transmissíveis e Promoção da Saúde. VIGITEL Brasil 2015: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico [Internet]. Brasília: Ministério da Saúde ; 2016 [citado em 18 jul. 2017 8]. Disponível em: Disponível em: http://portalarquivos.saude.gov.br/images/pdf/2016/outubro/16/vigitel_brasil_2015.pdfLinks ]

40. Silva GA, Souza-Júnior PRB, Damacena GN, Szwarcwald CL. Detecção precoce do câncer de mama no Brasil: dados da Pesquisa Nacional de Saúde, 2013. Rev Saúde Pública [Internet]. 2017 [citado em 2 jun. 2017]; 51(Supl. 1): 14s. Disponível em: Disponível em: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0034-89102017000200303&lng=en http://dx.doi.org/10.1590/s1518-8787.2017051000191 [ Links ]

41. Passos VM, Barreto SM, Diniz LM, Lima-Costa MF. Type 2 diabetes: prevalence and associated factors in a Brazilian community--the Bambui health and aging study. São Paulo Med J. 2005 Mar; 123(2): 66-71. http://dx.doi.org/10.1590/S1516-31802005000200007Links ]

42. Malta DC, Bernal RTI, Iser BPM, Szwarcwald CL, Duncan BB, Schmidt MI. Fatores associados ao diabetes autorreferido segundo a Pesquisa Nacional de Saúde, 2013. Rev Saúde Pública [Internet]. 2017 [citado em 2 jun. 2017]; 51(Supl. 1): 12s. Disponível em: Disponível em: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0034-89102017000200312&lng=en http://dx.doi.org/10.1590/s1518-8787.2017051000011Links ]

43. Schmidt MI, Hoffmann JF, Diniz M de FS, Lotufo PA, Griep RH, Bensenor IM, et al. High prevalence of diabetes and intermediate hyperglycemia - The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Diabetol Metab Syndr. 2014 Nov 18; 6(1): 123. http://dx.doi.org/10.1186/1758-5996-6-123Links ]

44. Malta DC, Berna RTI, Silva MMA, Claro RM, Silva-Júnior JB, Reis AAC. Consumo de bebidas alcoólicas e direção de veículos, balanço da lei seca, Brasil 2007 a 2013. Rev Saúde Pública. 2014; 48(4): 692-966. http://dx.doi.org/10.1590/S0034-8910.2014048005633Links ]

Financial support: National Regulatory Agency for Private Health Insurance and Plans and National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq).

Received: October 18, 2017; Revised: November 08, 2017; Accepted: November 16, 2017

Corresponding author: Deborah Carvalho Malta. Avenida Alfredo Balena, 190, Santa Efigênia, CEP: 30130-100, Belo Horizonte, MG, Brasil. E-mail: dcmalta@uol.com.br

Conflict of interests: nothing to declare

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