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Conteúdo (article):

Prevalence, associated factors, and population attributable fraction of Diabetes Mellitus in adults in Brazilian capitals

Authors:
Dayana Pereira Faria. Faculdade de Enfermagem, Universidade Federal de Goiás, Goiânia, Goiás, Brazil. E-mail: dayana@discente.ufg.br. ORCID: https://orcid.org/0009-0000-4102-6619
Kamylla Guedes de Sena. Faculdade de Enfermagem, Universidade Federal de Goiás, Goiânia, Goiás, Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Goiás, Brazil. E-mail: kamyllasena@discente.ufg.br. ORCID: https://orcid.org/0000-0001-8345-9981
Rafael Alves Guimarães. Faculdade de Enfermagem, Universidade Federal de Goiás, Goiânia, Goiás, Brazil. Instituto de Patologia Tropical e Saúde Pública, Universidade Federal de Goiás, Goiânia, Goiás, Brazil. E-mail: rafaelalves@ufg.br. ORCID: https://orcid.org/0000-0001-5171-7958

Corresponding author
Rafael Alves Guimarães. Rua 235, Qd. 41, Lt.23, Setor Leste Universitário, Goiânia, Goiás, Brazil. CEP: 74340-025. E-mail: rafaelalves@ufg.br

Abstract
The objective of the study was to estimate the magnitude, associated factors, and risk-factor population attributable fractions (PAFs) for Diabetes Mellitus (DM) among adults living in Brazilian state capitals, according to sex. This was a cross-sectional study using data from a sample of 32,111 adults aged 18 years or older who participated in the 2019 National Health Survey. Poisson regression and PAFs estimation were used to assess the associated factors and their contributions to prevalent cases of DM, respectively. The prevalence of DM was 7.7% (7.2% in men and 8.0% in women). In the total sample, the Poisson model showed a direct association between DM and female sex, age > 60 years, no formal education, physical inactivity during leisure time, self-reported hypercholesterolemia and hypertension, overweight, and obesity. An inverse association was observed for binge drinking. Mixed-race and black race/skin colour, as well as leisure-time physical inactivity, were factors associated with DM only among women. The PAFs that contributed most to prevalent cases of DM, in both sexes, were age > 60 years, self-reported hypercholesterolemia and hypertension, overweight, and obesity. In conclusion, clinical factors had the highest PAFs for DM and should be the focus of prevention and control policies.
Keywords: Diabetes Mellitus; prevalence; associated factors.


Introduction
Diabetes Mellitus (DM) is a chronic noncommunicable disease (CNCD), highly prevalent worldwide and increasingly so in recent decades. It is considered a public health problem and is one of the main causes of death worldwide. In 2021, it accounted for 6.7 million deaths; a prevalence of 10.5% is projected to increase to 12.2% by 20451.
In Brazil, where DM levels are also high, they are associated with increasing morbidity, incapacity, mortality, deteriorating quality of life, and increased costs for health services, and society as a whole1. The prevalence of DM was 10.5% in 2021 and is forecast to increase to 23.2% by 20451. In 2021, estimates by Brazil’s system of surveillance of chronic disease risk and protective factors by telephone survey (Sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico, Vigitel) reported a prevalence of self-reported diabetes of 9.1% in adults (8.6% in men and 9.6% in women)2. In 2019, Brazil’s National Health Survey (Pesquisa Nacional de Saúde, PNS) estimated a prevalence of 7.7% (6.9% in men and 8.4% in women)3.
The heavy burden of DM is attributed mainly to the increasing prevalence of modifiable risk factors, such as physical inactivity, smoking, unhealthy diet, and overweight1-4. Other determinants, such as population ageing and unfavourable socioeconomic conditions have contributed to the high DM burden4-5. The magnitude of DM is aggravated by public health underfunding for health promotion and disease prevention, particularly in low- and middle-income countries6.
Although a number of determinants of DM have been described in the literature by way of regression models7-8-9, little is known about the contributions of risk factors, in terms of attributable fractions, at the population level10. One indicator that can be used for this assessment is the Population Attributable Fraction (PAF)11. A number of cohort studies have shown that Population Attributable Fractions (PAFs) for DM risk factors vary with the risk factor and study population, and include differences between the sexes. Investigations have shown that obesity, central obesity, hypertension, and physical inactivity are the risk factors with the highest PAFs, corroborating the influence of behavioural and biological factors on the DM burden12-13-10.
Although DM-related risk factors are known to differ by sex, few studies, especially population surveys, have reported these differences in Brazil. In addition, few studies have examined PAFs for DM risk factors in Brazil. Assessment of these aspects is an important tool for specifying actions for health promotion, diabetes prevention, health surveillance, and comprehensive care in support of Target 3.4 of the Sustainable Development Goals, which is to reduce premature mortality from non-communicable diseases by one-third by 203014. This information is also useful to guide the actions of Brazil’s Strategic Action Plan to Address Noncommunicable Diseases (NCDs) and Chronic Conditions, 2021−203015 in targeting interventions on risk factors with the highest PAFs, including different intervention approaches by sex.
Accordingly, the general aim of this study was to estimate, by sex, the magnitude, associated factors, and risk-factor PAFs for DM in adults in Brazilian state capitals.

Methods
Design
This cross-sectional analytical study used the 2019 PNS as its data source. The PNS is a population- and household-based survey by the Ministry of Health in partnership with Brazil’s official bureau of statistics, the Instituto Brasileiro de Geografia e Estatística (IBGE). Its purpose is to produce national data to characterise health conditions in Brazil’s population, including the magnitude and determinants of CNCDs16.

Setting
The 2019 PNS was conducted in 27 states in five of Brazil’s major regions, which constituted the study settings16.

Population
The PNS target population comprised individuals aged 15 years or more residing in permanent private households (PPHs). PPHs are those built exclusively as housing. Households in special areas, such as indigenous villages, barracks, hospitals, long-stay institutions, and so on, were excluded16.

Sampling and sample
The PNS was conducted in clusters in three stages. The first involved primary sampling units comprising census tracts or sets of tracts. The second stage involved sampling households, termed secondary sampling units. The third stage selected residents aged 15 years or more in each household, from the list of residents at the time of interview. The selected residents were interviewed as tertiary sampling units. Sampling at all stages was simple and random16.
The PNS sample size was specified based on the precision desired in estimating main CNCD risk and protection factors, disease prevalences, health service access, and other indicators. The indicators’ mean values and variances were also considered, as well as a 20% non-response rate16.
The sample initially estimated was 108,525 households. However, data were collected from 94,114 households (a non-response rate of 6.4%). A total of 88,090 adults aged 15 years or more were interviewed. The questionnaire comprised questions on sociodemographic particulars, CNCD risk and protection factors, prior NCD diagnoses, and other variables16.

Variables
Dependent variable
The main indicator examined was the prevalence (%) of self-reported DM, which – categorised dichotomously as “0” (No) and “1” (Yes) – was also used as the dependent variable. The numerator of the indicator was the number of adults interviewed who responded affirmatively to the question “Has a doctor ever given you a diagnosis of diabetes?” and the denominator was the total of adults aged 18 years or more interviewed in the Brazilian state capitals. Women who reported gestational DM were excluded from the numerator17.

Independent variables
The following independent variables were used:
– Sex: male or female;
– Age group (years): categorised as 18–39 years, 40–59 years, or ≥ 60 years18;
– Race/skin colour (self-reported): categorised as white, black, mixed-race, or others18, the latter including the categories Asian and Native American, due to the small numbers observed in the sample;
– Schooling: none/incomplete lower secondary, complete lower secondary/incomplete upper secondary, complete upper secondary/incomplete higher, and complete higher or more18;
– Family income (in minimum wages): categorised as ≤ 1, 2–3, 4–5, or > 518. The minimum wage was considered to be the amount of the basic wage in Brazil in the study month of reference: R$ 998.00 in 2019, R$ 1,039 in January 2020 and R$ 1,045 in February and March 202019-20;
– Region of residence: North, Northeast, Southeast, South, or Midwest;
– Area of residence: rural or urban;
– Smoking: categorised as non-smoker, former smoker, or smoker18.
– Binge drinking: No or yes; defined, for both sexes, as drinking at least five doses on the same occasion in the past 30 days21;
– Eating ultra-processed foods (UPFs): No or yes; defined as eating from five or more groups of industrialised foods on the day before the study, including: chocolate-flavoured drinks or flavoured yoghurts; packaged snacks or biscuits; sweet or cream-filled biscuits or packaged cakes; hotdog sausages, sausages, mortadella, or ham and so on22;
– Recommended intake of fruit and vegetables: No or yes; defined as eating at least 25 portions of fruit (including juice) and vegetables per week. The sum of these portions is equivalent to a daily intake of approximately five portions of fruit and vegetables22;
– Excessive intake of soft drinks or artificial juices: No or yes; defined as the intake of soft drinks or artificial juices at least five days a week22;
– Leisure-time physical activity: active or inactive; the former being individuals who reported practising at least 150 minutes of light or moderate intensity physical activity per week or 75 minutes of vigorous intensity activity per week23;
– Self-reported hypercholesterolemia: No or yes18;
– Self-reported arterial hypertension (AH): No or yes18;
– Nutritional status: classified by body mass index (BMI, in kg/m2), as recommended by the World Health Organisation24. BMI was calculated from reported weight and height, using the variables weight (kg) divided by height squared. Individuals were classified as underweight (BMI < 18.5 kg/m2); eutrophic (BMI from 18.5 to 24.9 kg/m2), overweight (BMI from 25.0-29.9 kg/m2), and obese (BMI ≥ 30 kg/m2)24.

Analysis and statistical modelling
First, descriptive analysis of the characteristics of the total sample was performed, by sex, using measures of relative frequency (%) and respective 95% confidence intervals (95%CIs). The Pearson chi-square (χ2) test, corrected for the study design, was used to ascertain statistical differences by sex.
Poisson bivariate and multiple regression analyses were then performed to examine for associations between self-reported DM and the independent variables. Variables with p-value < 0.20 in the bivariate analysis were retained in the Poisson multiple regression using the forced entry method. The final models were reported as adjusted prevalence ratios (APRs) and respective 95%CIs. The models’ statistical significance was established by the Wald chi-square test; p-values < 0.05 were considered statistically significant.
PAFs were calculated for each DM-related risk factor. In cross-sectional studies, this measure represents the proportion of prevalent cases of a disease that could be avoided if the risk factor in question were eliminated. The following formula was used: PAF = (p_e (APR-1))/(p_e (APR-1)+1),11, where PAF = Population Attributable Fraction; p_e = proportion of the population exposed to the risk factor of interest; and APR = Adjusted Prevalence Ratio obtained from the multiple regression model.
All analyses were disaggregated by sex and followed the same method of forced entry into the Poisson regression model. The analyses were performed using the “survey” package for complex samples of STATA (StataCorp LLC, version 16.0, College Station, TX, USA).

Ethical considerations
The 2019 PNS was approved by Brazil’s national research ethics committee (Comissão Nacional de Ética em Pesquisa, CONEP), in opinion No. 3.529.376 of 2019. All participants signed a declaration of free and informed consent.

Results
This study comprised 32,111 individuals aged 18 years or more (18,122 women and 13,989 men). The sample characteristics, by sex, are summarised in Table 1.
The prevalence of self-reported DM was 7.7% (95%CI: 7.5−8.1) and was similar between men and women: 7.2% (95%CI: 6.5–7.9) in men and 8.0% (95%CI: 7.5−8.7) in women.
After adjustment, the multiple regression model for the total sample showed a 1.21 times higher prevalence of self-reported DM among women (APR: 1.21; 95%CI: 1.08−1.36) than among men. The prevalence of self-reported DM displayed a positive gradient with increasing age, with greater magnitude among older adults (APR: 8.74; 95%CI: 6.29−11.85). The prevalence of DM also increased statistically with decreasing schooling. In the lowest group (none/incomplete lower secondary), prevalence was 39% higher (APR: 1.39; 95%CI: 1.13−1.70) than among those with complete higher education or more. Prevalence of self-reported DM was also greater in individuals from the Northeast region (APR: 1.17; 95%CI: 1.00−1.38), the Southeast (APR: 1.19; 95%CI: 1.01−1.41), and the Midwest (APR: 1.20; 95%CI; 1.01−1.43) compared with the North region. It was also greater in individuals who were inactive in leisure time (APR: 1.19; 95%CI: 1.05−1.36), those with hypercholesterolemia (APR: 1.93; 95%CI: 1.73−2.14), AH (APR: 2.17; 95%CI: 1.89−2.49), overweight (APR: 1.45; 95%CI: 1.27−1.66), and obesity (APR: 1.95; 95%CI: 1.70−2.24). Meanwhile, the prevalence of self-reported DM found was 27% lower (APR: 0.73; 95%CI: 0.61−0.88) in individuals who engaged in binge drinking than in those who did not (Table 2).
In the male subgroup, the multiple regression model showed that age group, schooling, income, hypercholesterolemia, AH, overweight, and obesity were positively associated with DM, while binge drinking was inversely associated (Table 3).
Among women, positive associations were observed between DM and age group, race/skin colour, region of residence, physical inactivity, hypercholesterolemia, overweight, and obesity. Similarly to men, alcohol intake was inversely associated with DM in this group (Table 4).
The PAFs for the risk factors that contributed most to prevalent cases, in both sexes, were arterial hypertension (PAF: 53.9%), hypercholesterolemia (PAF: 48.2%), overweight (PAF: 42.2%) and obesity (PAF: 48.7%). Binge drinking showed a negative PAF (-37.0%). These values were similar among men and women. Leisure-time physical inactivity showed a PAF of 14.0% among the women only (Table 5).
Discussion
The study found a high prevalence of self-reported DM in adults of both sexes in Brazilian state capitals. Factors related to increased prevalence included being female, advanced age, low level of schooling, residing in the Northeast, Southeast or Midwest regions, physical inactivity, AH, hypercholesterolemia, overweight, and obesity. When disaggregated by sex, the analysis found, in women only, that increased prevalence of DM was associated with mixed-race and black race/skin colour, and physical inactivity. On the other hand, in men alone, a low level of schooling and income of 4−5 minimum wages were associated with higher prevalence. In both sexes, binge drinking was a protective factor. In both sexes, the largest PAFs for factors associated with DM were self-reported hypertension, self-reported hypercholesterolemia, overweight and obesity.
The results showed the prevalence of self-reported DM to be 7.7%. That value was higher than that found by the 2013 PNS population-based household study (6.5%)25, but the same as that found in the sample of Brazil’s state capitals and non-state capital cities in 2019 (7.7%)3. Comparisons with the 2013 figures suggest a steady increase in the prevalence of DM in adults in Brazil, resulting from an increase in risk factors, particularly obesity26. Also in 2019, the Vigitel showed a 7.4% prevalence of self-reported DM among adults in Brazilian state capitals27, similar to what was found in this study.
This study found an association between being female and DM, similar to the findings of population-based surveys conducted in Brazil using measured or laboratory data28-29. Nonetheless, this association is not a consensus in the literature. Some studies have shown higher prevalence of DM in men than in women and these divergences may result from differences in methodology and the distribution of risk factors between the sexes30-31. The higher prevalence of DM in women may be attributed partly to their greater use of health services, resulting in greater opportunities for diagnosis32. The decline in oestrogen and progesterone production associated with menopause increases insulin resistance, which may play a significant role in this disparity33. Also, physical inactivity over the course of life is more frequent in women than in men, as is obesity2-30-34.
Increasing age was found to be associated with prevalence of DM, independently of sex. This finding corroborates previous studies6-18. Mechanisms such as increased central obesity, reduced insulin secretion and insulin resistance during the ageing process, as well as behavioural risk factors accumulated over the life course, such as physical inactivity and unhealthy diet, explain the greater magnitude of DM among older individuals than younger ones35. Another factor relevant to the higher prevalence of DM with advancing age is the increase in opportunities for diagnosis, because health services normally recommend screening for this disease in persons aged 45 years or older36.
An association was found between DM and mixed-race and black race/skin colour in women, but not in men. Previous studies not disaggregated by sex have shown an association between mixed-race and black race/skin colour and self-reported DM in the general population, revealing lasting, generalised disparities in health by race/colour37-38. A previous study of older adults in Brazil showed that DM did not vary by race/colour in men, but did in women39. Although the evidence disaggregated by sex for the association between race/skin colour and DM is limited, studies have shown that individuals of black and mixed race/colour are more likely to have DM than whites. Also, black individuals diagnosed with DM are less likely to achieve clinical quality of life standards and more likely to suffer complications from DM37-40. This may be explained by the black and mixed-race population’s lack of access to health services – and those services’ lack of accessibility – resulting from significant socioeconomic inequalities and lower levels of schooling, which contribute to these individuals’ having fewer opportunities for diagnosis. These disparities are even more evident in women than in men37, which would explain the association found in this study.
On the other hand, we found an association between low level of schooling and DM in the overall sample and in men, but not in women. This association, disaggregated by sex, has been found in other Brazilian studies6-38-28. Meanwhile, a study in 29 developing countries showed that the risk of DM increased with rising levels of schooling, as compared with individuals with no formal education41. Another study also found that DM was 62.0% more prevalent in individuals with more schooling9. Thus, this association is not a consensus in the literature. Individuals with higher levels of schooling have greater economic power and a higher likelihood of being diagnosed. A low level of schooling is associated with limitations in access to and accessibility of health services, as well as lower levels of knowledge about DM risk factors, which increases the likelihood of DM42. Another mechanism is differential exposure, as the distribution of overweight and obesity differs among levels of schooling. Individuals with lower levels of schooling display greater exposure to overweight and obesity. That the association was found in men, but not in women, can be explained by the potential mechanism of differential exposure, in which the prevalence of overweight was greater in men than in women.
The relationship between physical inactivity and DM is clearly established in the literature43. Evidence shows that physical inactivity reduces insulin sensitivity, causing a progressive loss of beta cells and reduced glucose tolerance, which can cause DM to develop43. In this study, physical inactivity was found to be positively associated with DM in the overall sample and in women, but not in men. One possible explanation for this finding is that this behaviour is more prevalent in women at all stages of their lives than in men, which heightens the association2. Physical inactivity was, in fact, more prevalent among the women than the men in this study.
An association was found between self-reported hypertension and self-reported hypercholesterolemia and DM. This finding has been reported commonly in previous studies, together with the physiopathogenic mechanisms already reported44-18. Lastly, overweight and obesity were associated with DM in this study, as found in other studies18-45. High BMI raises the risk of DM, as a result of the insulin resistance mechanism which, when triggered by obesity, is normally associated with markers of chronic low-grade systemic inflammation. In that condition, pro-inflammatory molecules are liberated into the bloodstream and in specific tissues, inducing a process of inflammation, known as metabolic inflammation, which impairs beta-cell function, raising the risk of insulin resistance and increasing glucose production35.
The relationship between DM and binge drinking is complex and influenced by several factors, especially dose-response. Studies have shown that low to moderate alcohol intake can have beneficial effects on DM prevention, because it can improve insulin sensitivity and raise levels of HDL cholesterol, which has anti-inflammatory properties46. On the other hand, there is evidence that excessive or abusive consumption of alcohol is associated with increased risk of developing DM, because of its potential to induce weight gain and insulin resistance47. This study showed that prevalence of DM was greater in individuals who did not binge drink. However, the question used was limited to investigating behaviour in the previous 30 days and did not assess other factors, such as the exact dose consumed nor the type of beverage, which may have contributed to the association. Caution is thus required when interpreting this result and alcohol should not be considered a protective factor for DM, given the complexity and variety of influences on the relationship.
The largest PAFs found were for arterial hypertension, self-reported hypercholesterolemia, overweight and obesity. A number of studies have shown that PAFs for DM risk factors vary by factor and by study population. Studies have shown that BMI ≥ 30 Kg/m2 results in a PAF ranging from 32.7% to 45.3%48. Central obesity results in a PAF ranging from 43.0% to 57.6%12. Other studies have found a PAF of 26% for AH, 19.0% for moderate alcohol consumption10, a PAF ranging from 5.6% to 8.7% for sugar sweetened beverages49 and a PAF of 30.0% for following a Western dietary pattern10. The PAF for physical inactivity ranged from 13.0% to 29.0%50. One previous study showed that the combination of smoking, drinking and overweight contributed to a 53.0% PAF for DM risk factors, corroborating evidence that behavioural risk factors have a significant impact on the incidence of this disease13. The findings of this study are consistent with previous research, which has shown that biological factors contribute most to the burden of DM.
There are certain limitations to the study. As it is cross-sectional, it does not permit temporal relations to be established between independent and dependent variables and, accordingly, does not permit causal inferences as to risk or protective factors. Also noteworthy are possible reverse causality bias or lifestyle changes resulting from the disease or guidance from health personnel. Another limitation is the study’s use of self-reported data, which may be subject to memory and response bias. The use of self-reported DM also depends on health service access for diagnosis and may be under-reported. Nonetheless, self-reported DM has 59.0% sensitivity as compared with laboratory diagnosis by glycated haemoglobin51. The study also did not distinguish between type 1 and type 2 diabetes. Also, certain sociodemographic groups, for example, the indigenous and yellow population, were under-represented in the sample. Lastly, certain variables, such as family history of DM, were also not investigated.
In conclusion, a high prevalence of self-reported DM was estimated in adults in Brazilian state capitals. It was found to be associated with being female, of more advanced age, having a low level of schooling, being physically inactive in leisure, having arterial hypertension, hypercholesterolemia, and being overweight or obese. Mixed-race and black race/skin colour , as well as physical inactivity, were associated with DM in the women, but not in the men. Low level of schooling was associated with DM in the men only. Self-reported AH and hypercholesterolemia, overweight and obesity were the factors with the largest PAFs in both sexes. These findings point to a need for interventions to prevent DM, especially in the subgroups most affected and exposed to risk factors with the largest PAFs, as well as for study designs personalised by sex. It is recommended that specific policies be implemented for different subgroups, including men, younger adults, women of mixed-race and black race/skin colour, and individuals with other chronic conditions, together with interventions directed to each segment of the population. These strategies can contribute to achieving the goals of the Global Action Plan for the Prevention and Control of Noncommunicable Diseases52 and the Strategic Action Plan to Tackle CNCDs in Brazil, 2021-2030, so as to reduce premature mortality from NCDs15.

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