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Epidemiologia e Serviços de Saúde

versión impresa ISSN 1679-4974versión On-line ISSN 2237-9622

Epidemiol. Serv. Saúde vol.29 no.3 Brasília jun. 2020  Epub 10-Jun-2020 

Original article

Factors associated with glycemic control in a sample of individuals with Diabetes Mellitus taken from the Longitudinal Study of Adult Health, Brazil, 2008-2010*

Helaine Aparecida Bonatto de Moraes (orcid: 0000-0002-6557-0931)1  , Sotero Serrate Mengue (orcid: 0000-0002-3349-8541)2  , Maria del Carmem Bisi Molina (orcid: 0000-0001-8746-5860)1  , Nágela Valadão Cade (orcid: 0000-0001-6073-504X)1 

1Universidade Federal do Espírito Santo, Programa de Pós-Graduação em Saúde Coletiva, Vitória, ES, Brazil

2Universidade Federal do Rio Grande do Sul, Programa de Pós-Graduação em Epidemiologia, Porto Alegre, RS, Brazil



to investigate factors associated with glycemic control in individuals with diabetes mellitus (DM).


this was a cross-sectional study, with participants in the Longitudinal Study of Adult Health with self-reported DM; binomial logistic regression was used.


1,242 individuals were included; 54.2% had glycated hemoglobin ≥6.5%, showing inadequate glycemic control; factors associated with inadequate glycemic control were male sex (OR=1.39 – 95%CI 1.05;1.85), black skin color (OR=1.74 – 95%CI 1.22;2.48) or brown skin color (OR=1,57 – 95%CI 1.14;2.16), average occupation level (OR=1.63 – 95%CI 1.02;2.58), not having health insurance (OR=1.47 – 95%CI 1.09;1.96), use of insulin (OR=7.34 – 95%CI 3.56;15.15), increased waist-to-hip ratio (OR=1.87 – 95%CI 1.19;2.93), smoking (OR=1.73 – 95%CI 1.09;2.74), and poor or very poor self-rated health (OR=2.37 – 95%CI 1.17;4.83).


the results reinforce the multicausal context in glycemic control, which was associated with sociodemographic factors, lifestyles and health conditions.

Key words: Diabetes Mellitus; Glycated Hemoglobin A; Diabetes Complications; Hyperglycemia


Diabetes mellitus (DM) currently represents a global epidemic, a huge challenge for national health systems. Factors such as urbanization and industrialization, the global increase in life expectancy and lifestyles characterized by physical inactivity and eating habits that tend towards accumulation of body fat, have contributed to the advance of the DM epidemic worldwide.1

Brazil is the country with the fourth highest number of cases of the disease in adults in the world (14.3 million individuals); in 2015 alone there were 130,700 deaths caused by DM.2 A household survey about DM occurrence conducted in Brazil in 2013 revealed that self-reported prevalence of the disease was 6.2%, with a higher proportion among women and people living in urban areas.3

DM is a heterogeneous group of metabolic disorders the striking characteristic of which is hyperglicemia. In type 1 DM, the body fails to produce insulin and its treatment necessarily requires exogenous insulin. Type 2 DM, which accounts for more than 90% of cases, is related to defects in insulin action and secretion and regulation of hepatic glucose production. Its treatment implies a series of measures being taken in order to achieve dietary control which include combating obesity, promoting regular physical activity and use of single or combination oral antidiabetic medication.1

Glycated Hemoglobin A (hemoglobin A1c) stands out as a standardized test for assessing glycemic control.1 There is ample evidence that good control of blood glucose and of other risk factors such as, for instance, obesity, physical inactivity and hypercaloric diet, prevent both acute and chronic complications of the disease. An evaluation study with individuals with decompensated DM identified that at the beginning of follow-up none of the patients had all parameters (glycemic control, blood pressure control and lipid control) within the recommended target limits.4 Authors draw attention to the importance of changing lifestyle habits for weight control, diet and physical activity.5 In this sense, understanding determining factors of DM control has shown itself to be fundamental: the earlier the intervention takes place, the better the clinical course of the disease and the lesser the probability of complications emerging.1

The objective of this study was to investigate factors associated with glycemic control in individuals with diabetes mellitus who took part in the Longitudinal Study of Adult Health (ELSA-Brasil).


This is a cross-sectional study using baseline data from the Longitudinal Study of Adult Health (ELSA-Brasil). This information was collected between August 2008 and December 2010.6

The ELSA-Brasil cohort was comprised of 15,105 in-service or retired civil servants aged between 35 and 74 years old, from six public higher education institutions in the following Brazilian state capitals: São Paulo (São Paulo), Belo Horizonte (Minas Gerais), Salvador (Bahia), Porto Alegre (Rio Grande do Sul), Rio de Janeiro (Rio de Janeiro) and Vitória (Espírito Santo).6

ELSA-Brasil baseline data collection was done by teams that were trained and certified centrally and that conducted standardized interviews, clinical assessments and sample collections for performance of biochemical tests. Every three months the participants are contacted for them to undergo new procedures with the aim of accompanying their health and monitoring individual outcomes.7

This study included all individuals who took part in the ELSA-Brasil baseline who self-reported DM and were taking oral antidiabetic drug(s) or insulin. Those who did not have results for all the biochemical parameters studied and for the ‘medication adherence’ variable were excluded from the study.

The ‘outcome’ variable of this study was glycated hemoglobin (HbA1c). In its most recent guidelines, the Brazilian Diabetes Society defines HbA1c values greater than and/or equal to 6.5% as the criterion for diagnosing established DM.1In order to assess glycemic control, this study used laboratory data on glycated hemoglobin (HbA1c). Results were considered to be adequate when the value was less than 6.5%.8

The exposure variables were as follows:

a) socio-economic and demographic variables

  • age (in years: 35-44; 45-54; 55-64; 65 or over)

  • sex (male; female);

  • race/skin color (white; black; brown; yellow/indigenous);

  • marital status (married/living with someone; divorced or separated; single);

  • schooling (incomplete elementary education; complete elementary education; complete high school education; higher education); and

  • per capita income (in minimum wages: less than 4; from 4 to less than 8; from 8 to less than 12; from 12 to less than 16; 16 or more);

b) lifestyle habit variables

  • physical activity (active; not active);

  • alcohol consumption (never; former user; user); and

  • tobacco smoking (never; smoker; former smoker);

c) dietary variables

  • - total daily diet calories (<2,000; 2,000<3,000; 3,000<4,000; ≥4,000);

d) nutritional variables, measured based on

  • body mass index (BMI) – low weight (BMI <18,5kg/m2)/normal weight (BMI <25kg/m2); overweight (BMI <30kg/m2); obesity (BMI ≥30kg/m2) –;

  • waist-to-hip ratio (ideal; changed); and

  • abdominal circumference (ideal; changed);

e) psychosocial variables

  • religious activity (no; yes); and

  • self-rated health (very good; good; regular; poor/very poor);

f) general characteristics and health characteristic variables

  • - job category (expressed as level of schooling permitted for the job: support; high school [technical position]; higher/teacher);

  • private health insurance (no; yes; no information);

g) DM medication variables (oral antidiabetic drugs; insulin; both); and

h) medication treatment adherence variables (low/medium adherence; high adherence).

The ‘self-rated health’ variable was measured by asking the question “In comparison to people of your age, how do you rate your general health status?”, with reply options categorized into strata: very good; good; regular; poor; very poor; no information (NS [didn’t know]/NQR [didn’t wish to answer]).

With regard to the ‘alcohol consumption’ variable, the ‘user’ category was recorded by asking the question “Do you currently consume alcoholic beverages?” with ‘no’ or ‘yes’ as reply options; the ‘former user’ category was determined by asking “Have you ever consumed alcoholic beverages?” with ‘no’ or ‘yes’ as reply options’; while the ‘never’ category was attributed to those individuals who informed that they had never used alcohol.

In order to define the ‘diet calories’ variable, total energy value was investigated by administering a previously validated Food Frequency Questionnaire (QFA ELSA-Brasil) containing 114 food items.9 Following this, the nutritional composition and energy value of the food consumed were estimated. Total diet energy value was numerically categorized in calories (cal) for each individual.

The ‘job category’ variable was defined according to the level of schooling required for each civil service position: support; technical; or higher/teacher. The ‘support’ category covers the range of jobs for which complete elementary education is required.

The ‘medication adherence’ variable was assessed by applying four-item Morisky Medication Adherence Scale (MMAS-4),10 according to which 1 (one) point is given for each positive answer, while 0 (zero) is given for each negative answer. Adherence was classified in the following manner: high, when all 4 answers were negative; medium when 3 or 2 answers were negative; and low when only one answer was negative, or when all the answers were positive. People classified by MMAS-4 as having high adherence were considered to be adherent to medication, while those classified as having medium or low adherence were considered to be non-adherent.11 Despite MMAS-4 having low sensitivity (43.6%) and reasonable specificity (81%),12 this scale continues to be used in many studies.10

Waist circumference (WC) was categorized as ideal when it was less than 90cm for men and less than 80cm for women, in accordance with International Diabetes Federation recommendations.13 Waist-to-hip ratio (WHR) was assessed according to the criteria of the Brazilian Association for the Study of Obesity and Metabolic Syndrome and was categorized as ideal when values were under 0.90 for men and under 0.85 for women.14

Assessment of physical activity was based on the answers to the long version of the International Physical Activity Questionnaire (IPAQ). People who reported 150 minutes of moderate physical activity a week were considered to be sufficiently active.15 The variables relating to treatment with medication consisted of the description of the profile of medication use, distributed into three categories: oral hypoglycemic agents; use of insulin; or both.

When analyzing the data, the comparison between categorical variables was done using Pearson’s chi-square test. The multivariate analysis was done using binomial logistic regression, taking glycated hemoglobin<6.5% as the reference category, and included all variables associated with the result the p-value of which was <0.100 in the crude analysis. SPSS (Statistical Package for the Social Sciences) version 22 and BioEstat 5.3 were used for the statistical analyses, taking a 5% significance level.

The ELSA-Brasil Project was approved by the National Research Ethics Committee/National Health Council on August 4th 2006, (approval number 13,065), and by the Research Ethics Committees of the six institutions forming the consortium. This study was submitted to and approved by the ELSA-Brasil Scientific Committee.


The study included 1,242 individuals. DM prevalence in the population studied was 8.2%. Males were predominant (53.1%); 41.4% of the participants were in the 55-64 age range, 43.9% were of black race/skin color, 78.4% had high school and higher education, 61.2% reported per capita income below four minimum wages – 25.3% had income between four and up to eight minimum wages (Table 1).

Table 1 – Characterization of the socio-economic, demographic, income and lifestyle and health habits of individuals with diabetes mellitus taking part in the baseline of the Longitudinal Study of Adult Health, Brazil, 2008-2010 

Variable Categorization n %
Sex Male 659 53.1
Female 583 46.9
Age range (in years) 35-44 80 6.4
45-54 354 28.5
55-64 514 41.4
≥65 294 23.7
Race/skin color Black 545 43.9
Brown 273 22.0
White 343 27.6
Yellow/indigenous 61 4.9
No information 20 1.6
Schooling Incomplete elementary 143 11.5
Complete elementary 125 10.1
Complete high school 464 37.3
Higher education 510 41.1
Marital status Married or living with someone 808 65.1
Divorced. separated or widowed 308 24.8
Single 125 10.0
No information 1 0.1
Per capita income (in minimum wages) <4 760 61.2
4 to <8 315 25.3
8 to <12 120 9.7
12 to <16 11 0.9
≥16 31 2.5
No information 5 0.4
Physical activity Active 42 3.4
Not active 1,200 96.6
Smoking habit Never 629 50.6
Former smoker 480 38.6
Smoker 132 10.7
No information 1 0.1
Alcohol consumption Never 176 14.1
Former user 369 29.7
User 697 56.2
Medication adherence Low/medium adherence 748 60.2
High adherence 494 39.8
Medication type Oral antidiabetic 1,074 86.5
Insulin 71 5.7
Both 97 7.8
Glycemic control Glycated hemoglobin≥6.5% 673 54.2
Glycated hemoglobin<6.5% 569 45.8
Total 1,242 100.0

There was a predominance of physically inactive people (96.6%), people who had never smoked (50.6%) and people who consumed alcoholic beverages (56.2%). Of the 697 individuals who self-reported alcohol use, 10.2% were classified as excessive drinkers (Table 1).

The majority (60.2%) had low/medium adherence to medication and 54.2% of individuals with DM had inadequate glycemic control (Table 1).

With regard to the categories of drugs used by individuals with DM, 86.5% only used oral antidiabetics, 7.8% used oral antidiabetics and insulin jointly, and 5.7% only used insulin as a form of treatment (Table 1). According to the medication classes of the oral antidiabetics and respective active ingredients, of the 1,174 individuals with DM taking oral antidiabetics, the majority used biguanide (57.2%), followed by those who used biguanide and sulfonylurea combinations (24.2%); only 8.9% used sulfonylurea on its own; other combination treatments accounted for 9.5%. Of the total sample, 0.2% were not able to state the name of the medication they were taking. No information was available on the ELSA-Brasil Project database about the number of medications used by each individual with diabetes.

In Table 2, it can be seen that together overweight and obesity account for 82.8% of the sample, changed waist circumference and changed WHR each account for more than 90%, and almost 40% had abdominal obesity. 45.8% of the sample self-rated their health as good and 63.4% had health insurance.

Table 2 – Characterization of nutritional, dietary, psychosocial and functional variables of individuals with diabetes mellitus taking part in the baseline of the Longitudinal Study of Adult Health, Brazil, 2008-2010 

Variable Categorization n %
Body mass index (BMI) Low/normal weight 214 17.2
Overweight 518 41.7
Obesity 510 41.1
Waist circumference (WC) Ideal 122 9.8
Changed 1,119 90.1
No information 1 0.1
Waist-to-hip ratio (WHR) Ideal 117 9.4
Changed 1,124 90.5
No information 1 0.1
Abdominal obesity No 315 25.4
Yes 484 39.0
No information 443 35.6
Total daily diet calories <2,000 265 21.3
2,000<3,000 567 45.7
3,000<4,000 255 20.5
≥4,000 154 12.4
No information 1 0.1
Self-rated health (SRH) Very good 108 8.7
Good 569 45.8
Regular 483 38.9
Poor/very poor 80 6.4
No information 2 0.2
Private health insurance No 454 36.5
Yes 787 63.4
No information 1 0.1
Occupational history Working 754 60.7
Retired 487 39.2
No information 1 0.1
Job category Support 404 32.5
Technical 452 36.4
Higher/teacher 386 31.1
Religious activity No 354 28.5
Yes 888 71.5
Total 1,242 100.0

In the crude analysis (Tables 3 and 4), there was a higher proportion of adequate glycemic control (HbA1c <6.5%) in females, people of white race/skin color, people with higher education, those who only used oral antidiabetics, those who had religious activities and health insurance, those who had never smoked, those with good self-rated health and a daily diet consumption of 2,000 to 3,000 calories. Poorer medication adherence and lower per capita income were found in those with poorer glycemic control (HbA1c ≥6.5%).

Table 3 – Socio-economic, demographic, medication type and adherence variables of individuals with diabetes mellitus, according to glycemic control, taking part in the baseline of the Longitudinal Study of Adult Health, Brazil, 2008-2010 

Variable Categorization Inadequate glycemic control (HbA1c ≥6.5%) p-valuea
n %
Sex Male 377 57.2 0.023
Female 296 50.8
Age range (in years) 35-44 46 57.5 0.080
45-54 210 59.3
55-64 270 52.5
≥65 147 50.0
Race/skin color Black 244 44.8 <0.001
Brown 177 64.8
White 209 60.9
Yellow/indigenous 35 57.4
Schooling Incomplete elementary 101 70.6 <0.001
Complete elementary 89 71.2
Complete high school 274 59.1
Higher education 209 40.9
Per capita income (in minimum wages) <4 470 61.8 <0.001
4 a <8 138 43.8
8 a <12 48 40.0
12 a <16 6 54.5
≥16 9 29.0
Marital status Married/living with someone 437 54.0 0.587
Divorced or separated 172 55.8
Single 63 50.4
Type of medication Oral antidiabetic 528 49.2 <0.001
Insulin 61 85.9
Both 85 87.6
Medication adherence Low/medium 426 56.9 0.016
High 247 50.0

a) p-value: calculated using Pearson’s chi-square test.

Table 4 – Nutritional. occupational, psychosocial, lifestyle and dietary habit characteristics of individuals with diabetes mellitus, according to glycemic control, taking part in the baseline of the Longitudinal Study of Adult Health, Brazil, 2008-2010 

Variable Categorization Inadequate glycemic control (HbA1c >6.5%) p-valuea
n %
Body mass index (BMI) Low/normal weight 117 54.7  
Overweight 273 52.7 0.661
Obesity 283 55.5  
Waist circumference (WC) Ideal 65 53.3 0.839
Changed 607 54.2  
Waist-to-hip ratio (WHR) Ideal 50 42.7 0.009
Changed 622 55.3  
Religious activity No 176 49.7 0.046
Yes 497 55.9
Job category Support 273 67.6 <0.001
Technical 260 57.5
Higher/teacher 140 36.3
Private health insurance No 300 66.1 <0.001
Yes 373 47.4
Occupational history Working 417 55.3 0.310
Retired 255 52.4
Self-rated health (SRH) Very good 44 40.7 <0.001
Good 278 48.8
Regular 292 60.4
Poor/very poor 58 72.5
Physical activity Active 25 59.5 0.480
Not active 648 54.0
Smoking habit Never 320 50.9 0.001
Former smoker 262 54.6
Smoker 90 68.2
Alcohol consumption Never 102 57.9 0.039
Former user 214 58.0
User 355 50.9
Total daily diet calories <2,000 138 52.1 <0.001
2,000<3,000 281 49.5
3,000<4,000 148 58.0
≥4,000 105 68.2

a) p- value: calculated using Pearson’s chi-square test.

In the multivariate analysis (Table 5), the following variables were associated with inadequate glycemic control: male sex (OR=1.39 – 95%CI 1.05;1.85); black race/skin color (OR=1.74 – 95%CI 1.22;2.48) or brown race/skin color (OR=1.57 – 95%CI 1.14;2.16); use of insulin on its own (OR=7.34 – 95%CI 3.56;15.15) or in combination with oral antidiabetics (OR=7.58 – 95%CI 3.96;14.52); changed waist-to-hip ratio (WHR) (OR=1.87 – 95%CI 1.19;2.93); occupation compatible with high school education (technical position) (OR=1.63 – 95%CI 1.02;2.58), compared to those with higher education; poor or very poor self-rated health (OR=2.37 – 95%CI 1.17;4.83); not having private health insurance (OR=1.47 – 95%CI 1.09;1.96); and being a smoker (OR=1.73 – 95%CI 1.09;2.74).

Table 5 – Factors associated with glycemic control in individuals with diabetes mellitus taking part in the baseline of the Longitudinal Study of Adult Health, Brazil, 2008-2010 

Variable Categorization Adjusted analysis p-valuec
Adjusted (95%CI)b
Sex Female
Male 1.39 (1.05;1.85) 0.021
Age range (in years) 35-44
45-54 1.05 (0.60;1.81) 0.856
55-64 0.90 (0.52;1.54) 0.700
≥65 0.98 (0.54;1.76) 0.954
Schooling Incomplete elementary 1.03 (0.55;1.96) 0.905
Complete elementary 1.38 (0.74;2.57) 0.300
Complete high school 0.95 (0.62;1.46) 0.832
Higher education
Per capita income (in minimum wages) <4 1.55 (0.59;4.06) 0.370
4 to <8 1.25 (0.48;3.23) 0.633
8 to <12 1.562 (0.58;4.18) 0.375
12 to <16 3.02 (0.59;15.43) 0.184
Religious activity Yes
No 0.87 (0.64;1.18) 0.379
Race/skin color White
Black 1.74 (1.22;2.48) 0.002
Brown 1.57 (1.14;2.16) 0.006
Yellow/indigenous 1.65 (0.91;2.99) 0.098
Job category Support 1.67 (0.94;2.96) 0.078
Technical 1.63 (1.02;2.58) 0.039
Health insurance Yes
No 1.47 (1.09;1.96) 0.010
Type of medication Oral antidiabetic
Insulin 7.34 (3.56;15.15) <0.001
Both 7.58 (3.96;14.52) <0.001
Waist-to-hip ratio (WHR) Ideal
Changed 1.87 (1.19;2.93) 0.007
Smoking habit Never
Former smoker 0.97 (0.73;1.28) 0.817
Smoker 1.73 (1.09;2.74) 0.019
Self-rated health (SRH) Very good
Good 1.29 (0.80;2.09) 0.290
Regular 1.53 (0.93;2.52) 0.092
Poor/very poor 2.37 (1.17;4.83) 0.017
Total daily diet calories <2,000
2,000<3,000 0.89 (0.64;1.25) 0.530
3,000<4,000 1.17 (0.78;1.73) 0.433
≥4,000 1.53 (0.95;2.45) 0.075
Alcohol consumption Never
Former user 0.92 (0.60;1.40) 0.709
User 0.97 (0.64;1.46) 0.894
Medication adherence Low/medium adherence 1.20 (0.92;1.56) 0.166
High adherence

a) OR: odds ratio.

b) 95%CI: 95% confidence interval.

c) The multivariate analysis was performed using binomial logistic regression and included all variables associated with the result with p-value <0.100 in the bivariate analysis.


Diabetes mellitus prevalence in the population studied was 8.2%. The study showed that factors associated with greater odds of inadequate glycemic control included socio-economic and demographic variables (male sex; black or brown race/skin color), lifestyle (tobacco smoking; increased waist-to-hip ratio), health status (use of insulin; poor self-rated health), as well as job category compatible with high school education (technical position) and not having health insurance.

Among the socio-economic and demographic variables, the male sex was associated with poorer glucose level results. Another national study with people with self-reported DM also indicated difference between the sexes, with higher prevalence among females (7.0% – 95%CI 6.5;7.5), this fact is put down to women using health services more, especially during pregnancy, as well as women’s greater awareness of taking care of their own health.3

Notwithstanding the literature pointing to more cases of the disease among women, in this study in particular, men had greater desglycemic discontrol. This relationship between the sexes can be attributed to a series of behavioral and cultural factors regarding men’s health, for many of whom illness may be seen as a sign of weakness, leading them to avoid medical consultations and taking care of their health.16 Male fear of discovering they have a serious disease must also be taken into consideration, this being yet another reason for explaining men’s absence in health services, hindering the establishment of prevention habits among them.17

In this study, an individual being of black or brown race/skin color increased the odds of poor glycemic control. Similarly, another study identified ethnic differences in the appearance of DM, with the disease being two times more frequent in people belonging to the Black ethnic group.18 Despite these studies having the same findings with regard to black/brown race/skin color in relation to the appearance and control of the disease, these disparities do not appear to be so well-established: for some authors they are due to difficulties in overcoming iniquities, such as differences in formal education, communication and access to health services, reiterating the existence of ethnic and social disparity in the progression of DM, apart from the need for a discussion about the identification of barriers possibly related to poorer control of the disease by individuals of black or brown race/skin color.19

Individuals whose job category was compatible with high school education (technical position) had greater odds of having inadequate glycemic control, when compared to those with higher education. This association between fewer years of study and more likelihood of having DM has also been found in other studies.20 Following comparison between the job categories studied, the majority of individuals with higher education were teachers and researchers who, due to the nature of the institutions involved, have advantages in relation to the other job categories (technical and support), as well as greater working time flexibility, more than 30 days holiday a year and workload adaptable to other environments, so that these factors may be considered to be a factor contributing to the differences found in the process of people taking care of their health and controlling chronic diseases, as is the case of DM. The finding with regard to desglycemic discontrol in individuals with less schooling reinforces the importance of education in the process of having knowledge of the disease and attitudes towards health status. Identifying how people live, work and age demonstrates the importance of social determinants, both in the process of becoming ill and also in taking care of their health.22

In parallel to these considerations is the unquestionable importance of income between different job categories. Professionals with higher education have more attractive pay, more advantageous career progression and, consequently, better access to health services, including having health insurance and using first-line medication with fewer side effects. This is how these issues are understood as social determinants of health.22

The result of this study showed that not having private health insurance was also associated with inadequate glycemic control. A study that compared use of health services by people with diabetes who had private health insurance and those who relied on Public Health facilities found that access (defined as lack of difficulty in getting medical appointments) was greater among those with health insurance than among those who used public services covered by the Family Health Strategy or traditional primary health care centers.23 This fact suggests the existence of important differences between access by those who have private health insurance and those who depend on public services, not just with regard to the possibility of first contact with a specialist doctor but also, and above all, with regard to the frequency and ease with which those with private health insurance can access health services. This may be a determining factor of adequate glycemic control.

The variables related to the disease, treatment and self-perception of health increased substantially the odds of increased glycated hemoglobin. According to this study, use of insulin was also related to increase in glucose and this result has also been found by other researchers.4 This relationship may possibly be related to changes in the daily routine of patients with diabetes. These changes are often stressful, caused by daily use of insulin, the need to adjust daily habits, particularly with regard to meal times, as well as storing and transporting medication at controlled temperatures, use of syringes to administer it and adequate disposal of waste and sharps. A study that investigates variables and their relationship with stress in people with DM found that emotional load and stress, either in relation to the treatment regime or interpersonal relationships, were associated with being on insulin therapy, reinforcing even more the negative emotional load deriving from use of insulin treatment to control this disease.24

Alongside exclusive use of insulin, treatment with a combination of insulin and oral antidiabetics also contributed to inadequate glycemic control by individuals with DM analyzed in this study. The need for complex treatment regimes, combining insulin with oral antidiabetics, is a necessary practice in the routine of people with diabetes who are unable to achieve glycemic control targets by other means. A study conducted to verify the role of polymedication in individuals with type 2 DM or hypertension taking part in a treatment group at a Family Health Strategy health centre in a municipality in Southern Brazil found that patients on polymedication had higher glucose levels compared to those who only took one type of medication.25 In this sense, the need for complex regimes is a big challenge for people with DM, given the need for specialized knowledge in order to handle them, which may be prejudicial to metabolic self-control.

As in this study, poorer self-rated health was found among diabetic Korean adults with glycemic discontrol.26 For a person with diabetes, increased glycated hemoglobin can represent poorer health status and, because of their knowledge of their metabolic process, result in their health being self-rated as poor. One of the factors which may also contribute to negative perception of health status by an individual with DM may be related to being diagnosed as having the disease, since this discovery necessarily implies a series of lifestyle changes – and, from the point of view of the diagnosed individual, a reduction in their quality of life.27

Being a smoker contributed to poorer glycemic control, as was also found by a study conducted in Saudi Arabia: smokers were more prone to having higher HbA1c than non-smokers.28 In addition to being associated with metabolic discontrol, tobacco smoking has been shown to be a risk factor for the development of DM. A study conducted in Japan with workers from twelve industrial companies also found, even after adjusting the variables, type 2 DM incidence among current and former smokers, and risk of DM increased as cigarette consumption increased among smokers.29 According to these conclusions, a person who has DM and also smokes demonstrates that they have not adapted to the disease, in view of their maintaining a lifestyle not in keeping with their clinical status, and that they may also present other conditions and behaviors unfavorable to glycemic control.

Changed waist-to-hip ratio (WHR) was also associated with inadequate glycemic control. This finding differs from other studies, other than when referring solely to incidence of type 2 DM cases.30 Changed WHR may also be a reflection of changed nutritional parameters among the majority of participants, especially those that had inadequate glycemic control. Together, those who were overweight and obese accounted for 82.8%; and almost 40% of these had abdominal obesity and over 90% changed waist circumference measurements. Moreover, 56.2% regularly consumed alcoholic beverages, 96.6% were physically inactive, and more than a third reported having a diet with more than 3,000 calories a day. These changes were also found in metabolic terms: 54.2% had HbA1c values >6.5%, providing evidence of metabolic discontrol.

A limitation of this study lies in its cross-sectional design, which does not allow investigation of the relationship of temporality with most of the variables. However, as it was an exploratory study, it was capable of raising hypotheses to be answered by cohort studies and, at the same time, providing information to health services in support of the health promotion of people with diabetes mellitus.

The results presented reinforce the multicausal context and its power to act as a barrier or facilitator of glycemic control, considering the complexity of the interaction of social determinants of health in this process, while focusing on several dimensions such as, for instance, diet quality, quantity and frequency, physical activity type, regularity and duration, absence of obesity, control of alcohol consumption, as well as taking medication correctly, assuming that it has been prescribed correctly.

In conclusion, there is a need to implement care lines for people with diabetes by strengthening the chronic conditions care model and including it in the care network for people with chronic diseases.


1. Sociedade Brasileira de Diabetes (BR). Diretrizes da Sociedade Brasileira de Diabetes 2017-2018 [Internet]. In: Oliveira JEP, Montenegro Júnior RM, Vencio S (organizadores). São Paulo: Editora Clannad; 2017 [citado 2020 maio 19]. 383 p. Disponível em: ]

2. International Diabetes Federation. IDF Diabetes atlas [Internet]. 7th ed. [S.l.]: International Diabetes Federation; 2015 ]cited 2020 May 19]. 142 p. Available from: ]

3. Iser BPM, Stopa SR, Chueiri OS, Szwarcwald CL, Malta DC, Monteiro HOC, et al . Prevalência de diabetes autorreferido no Brasil: resultados da Pesquisa Nacional de Saúde 2013. Epidemiol Serv Saúde [Internet]. 2015 abr-jun [citado 2020 fev 5]; 24(2):305-14. Disponível em: ]

4. Panarotto D, Teles AR, Schumacher MV. Fatores associados ao controle glicêmico em pacientes com diabetes tipo 2. Rev Assoc Med Bras [Internet]. 2008 [citado 2020 maio 19];54(4):314-21. Disponível em: ]

5. Morais GFC, Soares MJGO, Costa MML, Santos IBC. O diabético diante do tratamento, fatores de risco e complicações crônicas. Rev Enferm UERJ [Internet]. 2009 abr-jun [citado 2020 maio 19];17(2):240-5. Disponível em: https://www.facenf.uerj.brv17n2/v17n2a18.pdfLinks ]

6. Aquino EML, Araujo MJ, Almeida MCC, Conceição P, Andrade CR, Cade NV et al. Recrutamento de participantes no estudo longitudinal de saúde do adulto. Rev Saúde Pública [Internet]. 2013 jun [citado 2020 maio 19];47(Suppl 2):10-8. Disponível em: ]

7. Bensenor IM, Griep RH, Pinto KA, Faria KP, Felisbino-Mendes M, Caetano EI, et al. Rotinas de organização de exames e entrevistas no centro de investigação ELSA-Brasil. Rev Saúde Pública [Internet]. 2013 jun [citado 2020 maio 19];47(2):37-47. Disponível em: ]

8. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care [Internet]. 2016 Jan [cited 2020 May 19];39(suppl 1):S4-5. Available from: ]

9. Molina MCB, Benseñor IM, Cardoso LO, Velasquez-Melendez G, Drehmer M, Pereira TSS, et al. Reprodutibilidade e validade relativa do Questionário de Frequência Alimentar do ELSA-Brasil. Cad Saúde Pública [Internet]. 2013 fev [citado 2020 maio 19];29(2):379-89. Disponível em: ]

10. Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich) [Internet]. 2008 May [cited 2020 May 19];10(5):348-54. Available from: [ Links ]

11. Remondi FA, Cabrera MAS, Souza RKT. Não adesão ao tratamento medicamentoso contínuo: prevalência e determinantes em adultos de 40 anos e mais. Cad Saúde Pública [Internet]. 2014 jan [citado 2020 maio 19];30(1):126-36. Disponível em: ]

12. Ben AJ, Neumann CR, Mengue SS. Teste de Morisky-Green e Brief Medication Questionnaire para avaliar adesão a medicamentos. Rev Saúde Pública [Internet] 2012 fev [citado 2020 maio 19];46(2):279-89. Disponível em: ]

13. International Diabetes Federation - IDF. The IDF consensus worldwide definition of the metabolic syndrome [Internet]. Brussels: International Diabetes Federation; 2006 [cited 2020 May 19]. 23 p. Available from: ]

14. Associação Brasileira para Estudo da Obesidade e da Síndrome Metabólica (BR) - ABESO. Diretrizes brasileiras de obesidade 2009/2010 [Internet]. 3. ed. Itapevi: AC Farmacêutica; 2009 [citado 2020 maio 19]. 83 p. Disponível em: ]

15. Hallal PC, Matsudo SM, Matsudo VKR, Araújo TL, Andrade DR, Bertoldi AD. Physical activity in adults from two Brazilian areas: similarities and differences. Cad Saúde Pública [Internet]. 2005 Mar-Apr [cited 2020 May 19];21(2):573-80. Available from: ]

16. Ministério da Saúde (BR). Secretaria de Atenção à Saúde. Política nacional de atenção integral à saúde do homem: princípios e diretrizes [Internet]. Brasília: Ministério da Saúde; 2008 [citado 2020 maio 19]. 40 p. Disponível em: ]

17. Gomes R, Nascimento EF, Araújo FC. Por que os homens buscam menos os serviços de saúde do que as mulheres? As explicações de homens com baixa escolaridade e homens com ensino superior. Cad Saúde Pública [Internet]. 2007 mar [citado 2020 maio 19];23(3):565-74. Disponível em: ]

18. Chiu M, Austin PC, Manuel DG, Tu JV. Comparison of cardiovascular risk profiles among ethnic groups using population health surveys between 1996 and 2007. CMAJ [Internet]. 2010 May [cited 2020 May 19];182(8):301-10. Available from: ]

19. Lafata JE, Karter AJ, O’Connor PJ, Morris H, Schmittdiel JA, Ratliff S, et al. Medication adherence does not explain black-white differences in cardiometabolic risk factor control among insured patients with diabetes. J Gen Intern Med [Internet]. 2016 Feb [cited 2020 May 19];31(2):188-95. Available from: ]

20. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde. Vigitel Brasil 2014: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico [Internet]. Brasília: Ministério da Saúde; 2015 [citado 2020 maio 19]. 152 p. Disponível em: ]

21. Schmidt MI, Hoffmann JF, Diniz MFS, Lotufo PA, Griep RH, Bensenor IM, et al. High prevalence of diabetes and intermediate hyperglycemia: the brazilian longitudinal study of adult health (ELSA-Brasil) [Internet]. Diabetol Metab [Internet]. 2014 Nov [cited 2020 May 19];6(123). Available from: ]

22. Carvalho AI. Determinantes sociais, econômicos e ambientais da saúde. In: Fundação Oswaldo Cruz (BR). A saúde no Brasil em 2030 - prospecção estratégica do sistema de saúde brasileiro: população e perfil sanitário [Internet]. Rio de Janeiro: Fiocruz/Ipea/Ministério da Saúde; 2013 [citado 2020 maio 19]. p. 19-38. Disponível em: ]

23. Silva SS, Mambrini JVM, Turci MA, Macinko J, Lima-Costa MF. Uso de serviços de saúde por diabéticos cobertos por plano privado em comparação aos usuários do Sistema Único de Saúde no Município de Belo Horizonte, Minas Gerais, Brasil. Cad Saúde Pública [Internet]. 2016 out [citado 2020 maio 19];32(10):e00014615. Disponível em: ]

24. Zanchetta FC, Trevisan DD, Apolinario PP, Silva JB, Lima MH. Variáveis clínicas e sociodemográficas associadas com o estresse relacionado ao diabetes em pacientes com diabetes mellitus tipo 2. Einstein [Internet]. 2016 jul-set [citado 2017 abr 11];14(3):346-51. Disponível em: ]

25. Ames KS, Bassani PH, Motter N, Roratto B, Hammes JLN, Quadro MN, et al. Avaliação de hipertensos e diabéticos usuários de polimedicação em Santo Ângelo/RS. Rev Sau Int [Internet]. 2016 [citado 2020 maio 19];9(17):58-65. Disponível em: ]

26. Lee HW, Song M, Yang JJ, Kang D. Determinants of poor self-rated health in korean adults with diabetes. J Prev Med Public Health [Internet]. 2015 Nov [citado 2020 May 19];48(6):287-300. Available from: ]

27. Feng X, Astell-Burt T. Impact of a type 2 diabetes diagnosis on mental health, quality of life, and social contacts: a longitudinal study. BMJ Open Diabetes Res Care [Internet]. 2017 [cited 2020 May 19];5:e000198. Available from: ]

28. Badedi M, Solan Y, Hussain D, Sabai A, Mahfouz M, Alamodi S, et al. Factors associated with long-term control of type 2 diabetes mellitus. J Diabetes Res [Internet]. 2016 Dec [cited 2020 May 19];2016:2109542. Available from: ]

29. Akter S, Okazaki H, Kuwahara K, Miyamoto T, Murakami T, Shimizu C, et al. Smoking, smoking cessation, and the risk of type 2 diabetes among japanese adults: japan epidemiology collaboration on occupational health study. PLoS One [Internet]. 2015 Jul [cited 2020 May 19];10(7):e0132166. Available from: ]

30. Diabetes Prevention Program Research Group, Knowler WC, Fowler SE, Hamman RF, Brenneman AT, et al.10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet [Internet]. 2009 Nov [cited 2020 May 19];374(9702):1677-86. Available from: ]

*Article derived from the Master’s Degree dissertation entitled ‘Non-association between medication adherence and glycemic control in diabetic participants of the ELSA-Brasil study’, defended by Helaine Aparecida Bonatto de Moraes at the Federal University of Espírito Santo (UFES) Public Health Postgraduate Program in 2017.

Received: January 25, 2019; Accepted: April 20, 2020

Correspondence: Helaine Aparecida Bonatto de Moraes – Rua João Benedito, No. 21, Colatina, Espírito Santo, Brazil. Postcode: 29707-082 E-mail:

Authors’ contributions

Moraes HAB took part in the concept and planning of the study, data interpretation and drafting the manuscript. Mengue SS and Molina MCB took part in critically reviewing the contents of the manuscript. Cade NV took part in the concept and planning of the study, data interpretation and drafting the final version of the manuscript. All the authors have approved the final version of the manuscript and are responsible for all aspects thereof, including the guarantee of its accuracy and integrity.

Associate Editor: Suele Manjourany Duro -

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