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Epidemiologia e Serviços de Saúde

versão impressa ISSN 1679-4974versão On-line ISSN 2237-9622

Epidemiol. Serv. Saúde vol.29 no.2 Brasília maio 2020  Epub 06-Maio-2020 

Original article

Prevalence of self-reported diabetes mellitus in women and associated factors: a population-based study in São Leopoldo, Rio Grande do Sul, Brazil, 2015 *

Juvenal Soares Dias-da-Costa (orcid: 0000-0003-3160-6075)1  , Cassiane Silocchi (orcid: 0000-0002-8538-9849)1  , Sheila Cristiane Schwendler (orcid: 0000-0001-7483-0400)1  , Tissiani Morimoto (orcid: 0000-0001-5722-2163)1  , Vitoria Hana Muller Mottin (orcid: 0000-0002-0428-6128)1  , Vera Maria Vieira Paniz (orcid: 0000-0003-3186-9991)1  , Fernanda Souza de Bairros (orcid: 0000-0002-8611-058X)2  , Maria Teresa Anselmo Olinto (orcid: 0000-0002-3950-4594)1 

1Universidade do Vale do Rio dos Sinos, Programa de Pós-Graduação em Saúde Coletiva, São Leopoldo, RS, Brazil

2Universidade Federal do Rio Grande do Sul, Departamento de Saúde Coletiva, Porto Alegre, RS, Brazil



to analyze the prevalence of Diabetes Mellitus (DM) and associated factors in women aged 20 to 69 years in São Leopoldo, RS, Brazil.


this was a population-based cross-sectional study, the outcome of which was self-reported medical diagnosis of DM. Poisson regression was used.


1,128 women took part, 8.16% (95%CI2.56;13.74) reported DM diagnosis; in the adjusted analysis association was found between the outcome and the 50-59 years age group (PR=15.73 – 95%CI4.84;71.00) and the over 60s (PR=8.95 – 95%CI1.98;40.49), economic classes D/E (PR=2.37 – 95%CI1.17;4.83), obesity (PR=1.41 – 95%CI0.85;2.32), arterial hypertension (PR=2.78 – 95%CI1.73;4.46), common mental disorders (PR=1.47 – 95%CI1.04;2.05), dyslipidemia (PR=2.16 – 95%CI1.45;3.23), regular/poor self-perception of health (PR=2.80 – 95%CI1.20;6.56), and not working (PR=1.98 – 95%CI1.11;3.53).


DM was associated with situations of economic and social vulnerability, such as poverty and being outside the labor market, as well as with the presence of other diseases.

Key words: Diabetes Mellitus; Women; Prevalence; Cross-Sectional Studies; Multimorbidity


Population aging and the increase in overweight people can lead to growth in diabetes mellitus (DM) prevalence.1 A review article of population-based studies on DM in different parts of the world found that age-standardized diabetes prevalence among adults increased or, at the best, remained unchanged between 1980 and 2014. The study also found that this condition increased more rapidly in low and middle-income countries when compared to high-income countries. In 2014, half the adults with DM lived in five countries: China, India, United States, Brazil and Indonesia.2 In 2017, the global disease burden study found that DM had become the world’s fourth leading cause of disability.3

A study on diabetes conducted in nine large Brazilian cities at the end of the 1980s found prevalence of 7.6% among the population.4 This result is similar to the 7.5% prevalence rate found by the Social Dimensions of Inequalities ( Dimensões Sociais das Desigualdades ) study conducted in 2008; however, DM frequency was seen to increase among individuals over 65 years old and reached 16.7%.5 The 2013 National Health Survey found that 6.2% of the total population had DM and that this rate was above 19.0% in those over 65 years old. The Survey also found that prevalence was higher among females (7.0%) than among males (5.4%).6

DM complications have been classified as both microvascular and macrovascular disorders and can cause retinopathy, nephropathy, neuropathy, coronary disease, cerebrovascular disease and peripheral artery disease. DM can also contribute directly or indirectly to complication of other health conditions.7

With regard to its economic impact, a systematic review of articles published between 1990 and 2015 found that the individual economic burden of the disease and its complications was considerably high, despite government subsidies of some supplies, such as medication and laboratory tests.8

Population-based epidemiological studies can contribute to health sector planning and management, by providing representative information about health-related events. Estimates enable the magnitude of problems to be identified, thus allowing realistic provision of supplies.9 In turn, determining factors associated with diseases enables identification of more vulnerable population groups and informs preparation of policies and actions aimed at minimizing their consequences.10

The objective of this study was to analyze diabetes mellitus (DM) prevalence and associated factors among women aged 20-69 living in the municipality of São Leopoldo, Rio Grande do Sul, Brazil.


This was a population-based cross-sectional study of a representative sample of women between 20 and 69 years old living in the urban area of São Leopoldo, RS.

The study was part of a research project conducted by the Universidade do Vale do Rio dos Sinos (Unisinos) between February and October 2015, entitled ‘Living and health conditions of adult women: a population-based study in the Vale do Rio dos Sinos –Evaluation after 10 years’, which investigated lifestyle habits, nutritional aspects, psychological aspects, preventive procedures, contraceptive methods, morbidity and use of health services.

Initially the sample size for the research mentioned above was calculated using Epi Info 6.0 based on the female population of São Leopoldo (109,845 females, according to the 2010 Demographic Census 2010)11 , as well as being based on several of the project’s outcomes. A 5.7% prevalence rate for late cytopathology examinations12 was the outcome that required the largest sample size. This sample size was used to calculate a 3% sampling error, plus 10% for losses and 15% for controlling confounding factors, resulting in a total of 1,281 women. Later, once field work had been done to check that the sample size was sufficient – taking into account a 95% confidence interval %, 80% statistical power, a 50% exposed/unexposed ratio, 4.6% prevalence among unexposed women (DM prevalence in economic classes A/B) and a 2.1 risk ratio –, we concluded that 1.022 participants were needed.

Sampling was conducted in multiple stages. The 371 urban census tracts in São Leopoldo were classified in descending order, starting with the sector with the highest nominal monthly income of people aged 10 or over ( with or without income ). In order to randomly select 40 tracts, first of all the 371 tracts were divided by 40, resulting in 9.275; this number was rounded down to 9 and this was the number adopted for the intervals between the tracts. Using the Sorteador website (, the following random selection was performed: all numbers from 1 to 9 were input in order to draw a number that would be the first tract to be selected. The result of the draw (from 1 to 9) was number 9, i.e. the study began in census tract number 9 (in the ascending numerical order of the tracts). Taking this number, a further 9 were added, so that the next tract was tract number 18, and so on until 40 tracts were reached. At the end of the field work, the number of participants was found to be insufficient, so a further five tracts were added using the same process.

For each tract the block and street corner for starting the study were selected at random. Following the first household visit, the study jumped two households before the next household visit, until 36 households per sector were included. All women living in the households visited were invited to take part.

Women in the households at the time of the visit but who did not live there were excluded, as were pregnant women and those unable to answer the questionnaire. If women who lived in the household were absent at the time of the visit, two further attempts were made on different days and at different times, including weekends and at night.

Standardized pre-coded and pre-tested questionnaires were used. The following measurements were also taken: weight, height and blood pressure, using scales, stadiometers and digital automatic arm sphygmomanometers (Omron, model HEM 720).

The interviewers were trained in order to standardize instrument use and they took part in a pilot study in a census tract that had not been included in the sample. Quality was also assessed, by means of administering a shortened version of the questionnaire via telephone interview with a random sample of 10% of the people included in the study.

The study outcome was ‘self-reported medically diagnosed DM’ and it was measured by the answers to the following question:

“Has a doctor ever told you that you have diabetes?”

The independent variables assessed by the study were classified as demographic, socio-economic, reproductive, lifestyle habits, morbidities (not all) and self-perception of health:

a) Demographic variables

  • - age (in years: 20-29; 30-39; 40-49; 50-59; 60-69);

  • - self-reported race/skin color (White; non-White [Black/brown/indigenous/yellow]);

  • - marital status (single; married/partner; separated/divorced/widowed).

b) Socio-economic variables

  • - schooling (years of study: 15 or more; 11-14; 8-10; 5-7; 0-4);

  • - employment situation (works; retired/out of work; does not work);

  • - per capita family income in minimum wages (>3.0; 1.0-3.0; <1.0) – the regional minimum wage at the time of the study was R$868.00;

  • - economic class (A/B; C; D/E).

According to the criterion proposed by the Brazilian Association of Survey Companies (ABEP), this classification of economic class is based on ownership of material goods, head of family schooling and number of household employees.

c) Reproductive variables

  • - age at first menstruation (years: 8-11; 12-13; 14 or over);

  • - number of children (none; 1-3; 4 or more);

  • - menopause (no; yes).

d) Lifestyle habits

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

  • - excessive alcohol consumption (no; yes). Degree of alcohol consumption was defined by frequency, type of drink and amount consumed, classified as excessive when equal to or greater than 30g of ethanol/day.13

  • - physical activity during leisure (yes; no). Physical activity was assessed according to short version of the International Physical Activity Questionnaire and was taken to be 150 minutes of activity a week.14

e) Nutritional status (normal weight; overweight; obese)

Nutritional status was classified based on body mass index (BMI), i.e. weight divided by height squared. BMI of up to 24.9kg/m2 was considered to be normal weight, 25.0-29.9kg/m2 as overweight and 30.0kg/m2 or more as obesity.

f) Morbidities

  • - systemic arterial hypertension (no; yes). Women were considered to be hypertensive when average blood pressure readings were equal to or greater than 140 x 90 mmHg and/or women whose readings were below this level but who reported using antihypertensive medication.15

  • - common mental disorders (no; yes). These were rated using the Self Reporting Questionnaire (SRQ-20), comprised of 20 dichotomous questions, whereby participants with a score of 7 or more were considered to have common mental disorders.16

  • - dyslipidemia (no; yes).

Prescribed use of lipid-modifying medication was used as a proxy dyslipidemia variable, estimated by the following question:

“Are you currently taking any medication prescribed by a doctor?”

Medication used was classified according to the respective pharmacological groups defined by the Anatomical Therapeutic Chemical Classification System ( Participants were categorized as ‘yes’ when their reply to the above question stated continuous use of the following medication: simvastatin, atorvastatin, rosuvastatin, gemfibrozil, ciprofibrate or fenofibrate.

g) Common mental disorders (no; yes).

h) Self-perception of health (excellent/very good; good; regular/poor).

The data were input twice, so that any typing errors could be corrected afterwards. The analyses were performed using Stata version 13.0 (StataCorp, College Station, TX, USA), taking the ample design effect into account by means of the svy command. The crude analysis provided prevalence ratios (PR), with their respective 95% confidence intervals (95%CI); dichotomous and nominal categorical variables were analyzed using Pearson’s chi-square test with a p-value for proportion heterogeneity, while ordinal categorical variables were analyzed according to linear trend p-value. The adjusted analysis was performed by means of Poisson regression using an analysis model.

The first block of the analysis model was comprised of the demographic variables (age; race/skin color; marital status) and the socio-economic variables (schooling; employment status; per capita family income in minimum wages; economic class). The second block contained the reproductive variables (age at first menstruation; number of children; menopause), the behavioral variables (tobacco smoking; excessive alcohol consumption; physical activity during leisure) and morbidities (obesity; arterial hypertension; common mental disorders; dyslipidemia). Self-perception of health was analyzed in the third block. All the variables determined the outcome. The ABEP ‘economic class’ variable was chosen in the analysis to explore association between economic status and diabetes, due to colinearity with schooling, given that 51,8% of the women stated that they were heads of household.

Variables obtaining a p-value of up to 0.20 in the crude analysis were selected for the adjusted analysis and only those variables with statistical significance with a p-value <0.05 were kept in the model.

The study project was approved by the Research Ethics Committee of the Universidade do Vale do Rio dos Sinos: Protocol No. 653.394, issued on May 20th2014. All participants signed a Free and Informed Consent form.


We interviewed 1,128 of the 1,281 women visited, representing 11.9% losses or refusals. Among the study participants, 92 (8.16% – 95%CI2.56;13.74) self-reported medical diagnosis of DM.

With regard to the demographic variables, approximately a quarter of the women were 40-49 years old (24.5%), the majority reported being of White race/skin color (74.5%) and were married or had a partner (63.8%). In relation to the socio-economic variables, 32.0% of the respondents had between 11 and 14 years of schooling, 56.1% had a job, 44.8% received less than a minimum wage per month and 53.1% fell into ABEP class C. Regarding the reproductive variables, 45.7% of the women menstruated for the first time when they were between 12 and 13 years old, and 67.6% had between 1 and 3 children; 79.4% had not reached the menopause. When assessing lifestyle habits, 18.5% were found to be smokers, 97.0% reported not consuming alcohol excessively and 85.6% were not classified as being physically active during leisure. The majority of the participants (66.1%) were overweight, systemic arterial hypertension prevalence was 35.8%, 39.9% had common mental disorders and 8.9% had dyslipidemia. In addition, 33.7% classified their health as regular/poor ( Table 1 ).

Table 1 – Participant characteristics and self-reported diabetes mellitus prevalence among women (n=1128), São Leopoldo, Rio Grande do Sul, 2015 

Variable n (%) Prevalence
Age (years)
20-29 216 (19.2) 0.9
30-39 244 (21.6) 2.9
40-49 276 (24.5) 6.2
50-59 228 (20.2) 18.4
60-69 164 (14.5) 14.6
Race/skin color
White 840 (74.5) 7.4
Non-White 288 (25.5) 10.4
Marital status    
Single 227 (20.1) 2.6
Married/partner 720 (63.8) 8.5
Separated/divorced/widowed 181 (16.1) 13.8
Schooling (years of study)
≥15 110 (9.8) 2.7
11-14 360 (32.0) 3.6
8-10 199 (17.7) 4.5
5-7 253 (22.4) 11.9
0-4 204 (18.1) 18.1
Employment status
Working 633 (56.1) 4.3
Retired/out of work 148 (13.1) 20.3
Does not work 347 (30.8) 10.1
Per capita family income in minimum wages (MW)
>3.0 MW 129 (11.8) 4.7
1.0-3.0 MW 473 (43.4) 7.4
<1.0 MW 489 (44.8) 9.8
Economic class
A/B 390 (34.8) 4.6
C 596 (53.1) 9.4
D/E 136 (12.1) 13.2
Age at first menstruation (years)
8-11 259 (23.2) 7.0
12-13 510 (45.7) 8.0
≥14 348 (31.1) 9.5
Number of children
None 217 (19.3) 4.2
1-3 761 (67.6) 8.5
≥4 148 (13.1) 12.2
No 896 (79.4) 8.5
Yes 232 (20.6) 6.9
Tobacco smoking
Never smoked 661 (58.9) 7.3
Former smoker 253 (22.6) 11.1
Smoker 208 (18.5) 7.2
Excessive alcohol consumption
No 1.087 (97.0) 8.3
Yes 34 (3.0) 2.9
Physical activity during leisure
Yes 162 (14.4) 6.2
No 966 (85.6) 8.5
Nutritional status
Normal weight 380 (33.9) 4.2
Overweight 373 (33.2) 6.2
Obese 369 (32.9) 14.2
Systemic arterial hypertension
No 722 (64.2) 2.9
Yes 403 (35.8) 17.6
Common mental disorders
No 678 (60.1) 6.1
Yes 450 (39.9) 11.3
No 1.028 (91.1) 5.8
Yes 100 (8.9) 32.0
Self-perception of health
Excellent/very good 233 (20.7) 1.7
Good 515 (45.6) 5.8
Regular/poor 380 (33.7) 15.3

In the crude analysis, association was found between DM prevalence and the following variables: ‘age’, ‘marital status’, ‘schooling’, ‘employment status’, ‘economic class’, ‘number of children’, ‘nutritional status’, ‘arterial hypertension’, ‘common mental disorders’, ‘dyslipidemia’ and ‘self-perception of health’ ( Table 2 ). Direct linearity was found when analyzing by age: DM prevalence increased as age increased. Separated/divorced/widowed women and married women / women with partners had higher DM prevalence rates compared to single women (13.8% and 8.5%, respectively). Women with lower schooling levels had higher DM prevalence (18.1%), compared to those with 15 or more years of schooling (2.7%) and linearity was found for this variable. Retired / out of work women and those who did not work had DM prevalence rates of 20.3% and 10.1% respectively. These rates were higher than that found for women who worked (4.3%). Higher DM prevalence rates were found in women belonging to economic classes C (9.4%) and D/E (13.2%), compared to class A/B women (4.6%). Women who had four or more children had higher DM prevalence (12.2%) compared to those who had no children (4.2%). Obese women had higher DM prevalence (14.2%) compared to those whose weight was considered to be adequate (4.2%). Participants classified as having arterial hypertension and common mental disorders had higher diabetes prevalence than those who did not have these conditions, 17.6% and 11.3% respectively. DM prevalence was 32.0% in women who had dyslipidemia. DM prevalence was also higher among women who classified their health as being regular/poor (15.3%) ( Table 1 ).

Table 2 – Crude and adjusted analysis of self-reported diabetes mellitus prevalence among women (n=1128), São Leopoldo, Rio Grande do Sul, 2015 

Variable Crude PRa(95%CI)b p-value Adjusted PRa(95%CI)b p-value
Age (years)   <0.001c   <0.001d
20-29 1.00   1.00  
30-39 3.10(0.60;16.11)   2.70(0.48;15.18)  
40-49 6.66(1.61;27.66)   6.32(1.46;27.37)  
50-59 19.89(4.61;85.85)   15.73(4.84;71.00)  
60-69 15.80(3.65;68.50)   8.95(1.98;40.49)  
Race/skin color   0.151   0.154d
White 1.00   1.00  
Non-White 1.41(0.88;2.27)   1.41(0.88;2.27)  
Marital status   0.003   0.052d
Single 1.00   1.00  
Married/partner 3.21(1.17;8.77)   2.06(0.78;5.44)  
Separated/divorced/widowed 5.23(1.87;14.59)   1.95(0.73;5.25)  
Schooling (years of study) <0.001c    
≥15 1.00      
11-14 1.32(0.42;4.16)      
8-10 1.66(0.46;6.02)      
5-7 4.35(1.33;14.22)      
0-4 6.65(2.18;20.29)      
Employment status   <0.001   0.013d
Working 1.00   1.00  
Retired/out of work 4.75(2.62;8.63)   2.55(1.32;4.94)  
Does not work 2.36(1.32;4.25)   1.98(1.11;3.53)  
Per capita family income in minimum wages (MW) 0.186   0.497d
>3.0 MW 1.00   1.00  
1.0-3.0 MW 1.59(0.64;3.96)   1.21(0.51;2.90)  
<1.0 MW 2.11(0.87;5.11)   1.30(0.55;3.07)  
Economic class   <0.001c   0.019d
A/B 1.00   1.00  
C 2.04(1.13;3.67)   1.71(0.95;3.09)  
D/E 2.87(1.48;5.55)   2.37(1.17;4.83)  
Age at first menstruation (years) 0.462    
8-11 1.00      
12-13 1.16(0.68;1.98)      
≥14 1.36(0.83;2.24)      
Number of children   0.044   0.743e
None 1.00   1.00  
1-3 2.06(0.95;4.44)   1.02(0.51;2.06)  
≥4 2.93(1.26;6.80)   0.95(0.43;2.10)  
Menopause   0.399    
No 1.00      
Yes 0.81(0.50;1.33)      
Tobacco smoking   0.186   0.751e
Never smoked 1.00   1.00  
Former smoker 1.52(0.95;2.45)   1.06(0.66;1.72)  
Smoker 0.99(0.50;1.99)   0.84(0.45;1.55)  
Excessive alcohol consumption 0.277    
No 1.00      
Yes 0.36(0.05;2.36)      
Physical activity during leisure 0.356    
Yes 1.00      
No 1.38(0.69;2.74)      
Nutritional status   <0.001   0.048e
Normal weight 1.00   1.00  
Overweight 1.46(0.86;2.48)   0.81(0.47;1.39)  
Obese 3.35(2.07;5.40)   1.41(0.85;2.32)  
Systemic arterial hypertension <0.001   <0.001e
No 1.00   1.00  
Yes 6.05(3.97;9.25)   2.78(1.73;4.46)  
Common mental disorders <0.001   <0.001e
No 1.00   1.00  
Yes 1.87(1.33;2.64)   1.47(1.04;2.05)  
Dyslipidemia   <0.001   <0.001e
No 1.00   1.00  
Yes 5.48(3.86;7.80)   2.16(1.45;3.23)  
Self-perception of health <0.001   0.002f
Excellent/very good 1.00   1.00  
Good 3.40(1.17;9.83)   1.95(0.71;5.38)  
Regular/poor 8.90(3.52;22.44)   2.80(1.20;6.56)  

a) PR: prevalence ratio.

b) 95%CI: 95% confidence interval.

c) Linear trend test; p-value <0.05 significant; Wald test, logistic regression.

d) Variables adjusted between each other.

e) Variables adjusted for first block variables and between each other.

f) Variable adjusted for first and second block variables.


Design effect was taken into consideration in the estimates.

In the adjusted analysis of the first block, the ‘age’, ‘employment status’ and ‘economic class’ variables continued to be associated with the diabetes mellitus outcome. With effect from 40, age was found to be associated with DM, whereby women in the 50-59 age group were 15.73 times more likely to report having DM when compared to the reference category. Retired / out of work participants and those who did not work had higher DM prevalence rates compared to those who worked. Women belonging to economic classes D/E were 2.37 times more likely to report having DM compared to those in class A/B ( Table 2 ).

The second block variables were adjusted for each other and for the variables that remained in the first block. ‘Nutritional status’, ‘arterial hypertension’, ‘common mental disorders’ and ‘dyslipidemia’ continued to be associated with DM. DM prevalence was 41% higher among obese women than among women with adequate weight. Women with arterial hypertension were 2.78 times more likely to have DM; while those with common mental disorders were 1.47 times more likely to have DM than the reference categories. Women with dyslipidemia were 2.16 time more likely to report DM than women without this condition ( Table 2 ).

Following adjustment by the two preceding blocks, the ‘self-perception of health’ variable remained associated with the outcome, and women who considered their health to be regular/poor were 2.80 times more likely to report DM ( Table 2 ).


DM prevalence was greater among women who were elderly, poor and who did not work. DM was also found to be associated with obesity, arterial hypertension, common mental disorders, dyslipidemia and regular/poor self-perception of health.

The DM prevalence rate found was higher than the 7.6% frequency found by Malebri & Franco in 1992,4 and higher than the 7.0% identified in women by the 2013 National Health Survey.6 However, it should be highlighted that the proportion of women aged 20-29 was slightly higher in the sample investigated by this study when compared to the female population of São Leopoldo as per the 2010 Demographic Census. Higher DM prevalence among the population assessed could be expected, given that the population is aging and being overweight has been increasing in Brazil in recent years. Notwithstanding, the study confirmed increased DM prevalence with effect from being 50 years old, to the extent of becoming a common event in clinical practice.

As in other studies, association was also found in São Leopoldo between presence of DM and increase in age. Reduction in insulin sensitivity during the course of maturity and aging, especially in those over 50, appears to be associated with changes to and distribution of adipose tissue.18

Evidence was found of DM association with belonging to lower social classes. DM prevalence was also higher among women who did not work and retired / out of work women. Other studies have demonstrated association between DM and socio-economic conditions. A systematic review of case-control and cohort studies found that low levels of schooling, income and employment increased DM likelihood in other countries regardless of whether the population’s average income was classified as high, middle or low.20 In Brazil, studies have indicated that people with less schooling have higher DM prevalence.5

Corroborating the findings of other studies, association was proven between DM and obesity.21 It is known that obese people are at high risk of developing type 2 DM and, although they have not been elucidated, there are two determining hypotheses for this fact: (i) continuous production of insulin by the pancreas, due to cell inability to absorb glucose, leading to what is referred to as insulin resistance; and (ii) reduction in insulin secretion because of hyperglicemia.22

The study also revealed association between DM and other diseases, such as arterial hypertension, common mental disorders and dyslipidemia, indicating its importance in relation to the presence of other diseases.Evidence of association between DM and arterial hypertension has been found, with possible common environmental and genetic factors: both conditions have shown insulin resistance and increased tissue inflammation.23 One study revealed the relationship between DM and depression, indicating risk two times greater of people with DM developing depression. In addition, depression has also been associated with increased risk of DM: the authors of a study published in Portugal indicate that more than being a causal relationship, there is a bidirectional relationship between these variables.24 It should also be highlighted that women who used lipid-modifying medication had greater DM prevalence in this study. Dyslipidemia is common among patients with DM and there is evidence that reduction in cholesterol levels promotes better prognosis, even in patients with a normal lipid profile.25

The limitations of this study include the definition of the outcome in which blood glucose measurements were not used. Nevertheless, the question asked in the instrument we used has been used in other epidemiological studies.6 Moreover, this study detected that only two women who were taking medication for DM did not report having DM, confirming the sensitivity of the question, and as such they were not recategorized. Another limitation was the impossibility of distinguishing whether the women had type 1 or type 2 DM. It is known, however, that type 2 DM prevalence among people aged over 20 is much higher.27 The proportion of losses and refusals was higher than expected but nevertheless the sample size needed to established associations with DM was achieved. It should be highlighted that the study sample was representative of São Leopoldo (except for the 20-29 age group) and that the study was conducted so as to faithfully follow data collection techniques.

The study was conducted rigorously and its main findings support some suggestions with regard to addressing DM. The disease was found to be more associated with a poorer economic situation, thus reinforcing the need to build public policies aimed at reducing poverty and promoting healthier living conditions, especially in the case of individuals aged 50 or over. Specific intervention strategies can effectively address the burden imposed by chronic noncommunicable diseases, such as DM. Such interventions include measures involving the population to reduce tobacco, alcohol and salt consumption, greater awareness about healthy lifestyles, increased special taxation and greater regulation of products that are harmful to health.28 Health-related behaviors reflect social determinants that require incentives, involving diet changes, such as reducing consumption of red meat and sugar, and increasing consumption of healthy food, such as nuts, fruit and vegetables,29 as well as formulating and implementing multisectoral policies to encourage physical activity.30

Similarly, the association of diabetes mellitus with obesity, arterial hypertension, common mental disorders and dyslipidemia made evident the question of multimorbidity and its challenges to a comprehensive approach to health, building new clinical and therapeutic paradigms.


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Associate Editor: Doroteia Aparecida Höfelmann –

*This study received financial support from the National Council for Scientific and Technological Development (CNPQ) / Ministry of Science, Technology, Innovation and Communication (MCTIC): Process No. 457235/2014-4 (Call for Proposals MCTI/CNPQ/UNIVERSAL 14/2014).

Received: November 02, 2019; Accepted: March 04, 2020

Correspondence: Juvenal Soares Dias-da-Costa – Universidade do Vale do Rio dos Sinos (Unisinos), Programa de Pós-Graduação em Saúde Coletiva, Av. Unisinos, no. 950, São Leopoldo, RS, Brazil. Postcode: 93022-000 E-mail:

Authors’ contributions

Dias-da-Costa JS, Silocchi CS, Schwendler SC, Morimoto T, Mottin VHM, Paniz VMV, Bairros FS and Olinto MTA contributed to the conception of the study, review of the analysis, critical review and final approval of the manuscript. Dias-da-Costa JS, Silocchi CS, Schwendler SC, Morimoto T, Mottin VHM contributed to data analysis and interpretation, as well as writing the draft versions of the manuscripts. All the authors have approved the final version and are responsible for all aspects of this work, including guaranteeing its accuracy and integrity.

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