Chronic noncommunicable diseases (NCDs) are one of the major challenges faced by governments and global health systems. These diseases account for approximately 71% of global mortality, especially cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes (1). NCDs are responsible for 41 million deaths annually, most in low- and middle-income countries (1).

Multimorbidity is the coexistence of two or more chronic diseases/conditions in an individual (2); however, some studies have used other definitions, such as ≥3, ≥4, and ≥ 5 (3). The most common chronic diseases used to define multimorbidity are cardiovascular diseases, hypertension, diabetes, and dyslipidemias (3). Previous systematic reviews identified the prevalence of multimorbidity from 33.1 to 43.0% (3–5). A meta-analysis found a pooled multimorbidity prevalence of 42.4% in adults (3). Multimorbid individuals have a higher probability of hospitalization, disability, and death than those with a single chronic disease/condition (6, 7). Multimorbidity is also related to declining physical function (8), worse quality of life (9), and higher socioeconomic costs for countries, individuals, and families (10).

In Brazil, multimorbidity is a serious challenge for public health systems. A recent systematic review reported a substantial variation in the estimated prevalence of multimorbidity from 16.8 to 93.4% (5). National-level assessment using data from the Brazil National Household Sample Survey and the National Health Survey (NHS) showed an increase in multimorbidity prevalence from 17.1 to 21.0% between 1998 and 2013 (8).

Multiple factors are associated with multimorbidity. Systematic reviews have shown a higher prevalence in older populations, women (3), and people with lower socioeconomic status and education (11). Several NCD risk factors, including cigarette smoking, alcohol use, decreased physical activity, and unhealthy diet, are associated with various diseases used to calculate multimorbidity (12). Prior studies have reported a positive association between multimorbidity and smoking (current or former smoker) and physical inactivity (13–15). However, healthy eating indicators are controversial in the literature. Data show the association of protection (16), risk (17, 18), and no association (19, 20). Thus, it remains unclear which risk factors most influence multimorbidity.

Despite the existing literature, the epidemiology and factors associated with multimorbidity are still poorly understood, especially in developing countries like Brazil (3). Furthermore, few studies have explored the prevalence according to sociodemographic and lifestyle variables. Many also do not include potential interactions in regression models, such as between race/skin color, education, and geographic region. Investigations of associated factors stratified by sex are also limited. Studies indicate that factors related to multimorbidity may differ between sexes (11, 21).

Most previous studies have found a higher prevalence of multimorbidity among women (4, 19, 21, 22), but not all studies have reported this (23–25). In almost all countries of the world, women have a higher life expectancy than men (26), they are more often affected by a series of non-fatal chronic diseases that negatively affect their quality of life and daily physical capacity (27). Thus, studies on multimorbidity require greater knowledge about its prevalence, the diseases involved and their relationship with possible gender differences. In Brazil, the prevalence of multimorbidity by gender has not been well documented in large populations.

Identifying multimorbidity risk factors stratified by sex is essential to effectively establish specific strategies for disease prevention, health promotion, and integrated care, particularly in a scenario of increased demand for health services due to population aging (28). The analysis of multimorbidity patterns can contribute to the planning of interventions to achieve Sustainable Development Goal 3 (Health and Wellbeing) and monitoring the Global Action Plan for the Prevention and Control of NCDs (29) and Strategic Plan for Tackle Chronic Diseases and Noncommunicable Diseases of the Brazilian Ministry of Health (30). Thus, this study aims to estimate the prevalence and analyze the factors associated with multimorbidity in Brazilian adults according to sex.

Using a nationally representative sample, we found that multimorbidity was prevalent in the Brazilian adult population. Alarmingly, one in four Brazilian adults had multimorbidity. Overall, multimorbidity was more prevalent among women, the older people, residents of the South and Southeast regions, urban area residents, former smokers, current smokers, physically inactive, overweight, and obese individuals. In turn, individuals with complete high school/incomplete higher education showed a lower prevalence of multimorbidity than those with higher education. The associations between education and multimorbidity differed between sexes. In men, multimorbidity was inversely associated with the strata of complete middle school/incomplete high school and complete high school/incomplete higher education, while in women, the association was not significant. The South and Midwest regions were associated with a higher prevalence in women, while leisure-time physical inactivity remained associated only in men. The recommended fruit and vegetable consumption was inversely related to multimorbidity for the total sample and both sexes.

The prevalence found in this study was similar to that estimated in low- and middle-income countries (29.7%), according to a meta-analysis involving 1,180,111 individuals from 57 countries (4). The prevalence ratio of multimorbidity is higher in high-income countries compared to low- and middle-income countries, possibly due to differences in diagnostic and data management systems between countries (4).

The results were also higher than those found in the Brazilian adult population in 2013 (23.6%), indicating an increase in multimorbidity burden in Brazil (43). This increment was reported in different countries (44, 45). The rise in multimorbidity is related to demographic, nutritional, and epidemiological transition, besides other determinants such as urbanization (46).

Regardless of sex, we observed increasing multimorbidity with age. This association is well described in the literature (3, 4). Different mechanisms may explain this relationship. First, due to the demographic transition that is responsible for the increase in the number of older people (47). Second, changes inherent to aging predispose to the development of chronic diseases/conditions (48). Several physiological changes, for example, are associated with senescence, such as increased body fat, especially in women, which often leads to a higher prevalence of overweight and obesity; decrease in fat-free mass and reduced capacity of the glucose uptake system, contributing to insulin resistance and diabetes; and reduced cardiac output, which can lead to increased cardiovascular disease risk (49). Other mechanisms have also been associated with a higher incidence of chronic diseases/conditions (49). Third, owing to the accumulation of risk factors. Longevity is related to longer exposure to these covariates, such as smoking status, unhealthy diet, alcohol consumption, and physical inactivity (50). Therefore, as the older people population grows, an increasing prevalence of multimorbidity is expected, with a potential overload of health care systems (12). Despite this, our results indicated that multimorbidity is not restricted to elderly. Indeed, younger people showed a high prevalence, consistent with previous observations (19, 23, 43).

Women had a higher prevalence of multimorbidity than men, corroborating other studies (4, 16, 51). One of the most consistent explanations for this result is a possible residual confounding due to higher consultation rates in women, leading to higher rates of chronic disease/condition diagnosis, regardless of age (52). In fact, in this investigation, women had a higher prevalence of multimorbidity than men in all age strata. Another explanation would be related to survival bias, as men have a lower life expectancy (53). Thus, the burden of chronic diseases/conditions in women tends to increase.

The lowest prevalence of multimorbidity was observed among participants with complete high school/incomplete higher education compared to people with higher education in the study population, which is in agreement with previous findings (54–56). A meta-analysis reported that low education level was associated with a 64% increased odds of multimorbidity (11). Considering only low- and middle-income countries, such as Brazil, earlier studies have shown inconsistencies, with a positive association (19, 57, 58), negative association (32, 59), or no significant difference between education and multimorbidity (60). Behavioral aspects are probably related to this result. The prevalence of risk factors differs by educational level; smoking, unhealthy eating habits, and obesity are more frequent among less educated individuals. In turn, leisure-time physical activity and the recommended fruit and vegetable consumption are higher among people with higher education (34). For example, a study carried out with Chinese residents showed that participants with a high level of education had positive knowledge, attitudes and behaviors related to diet that contributed to the prevention of hypertension (61).

Our findings suggest different associations between education and multimorbidity by sex. Intermediate educational levels (complete middle school/incomplete high school and complete high school/incomplete higher education) were a protective factor for multimorbidity among men, while this association was not verified among women. Previous investigations demonstrated relevant similarities (55, 62). However, a systematic review reported that most works found associations for sex-matched samples, thereby limiting the scope of evidence stratified by sex (11). The relationship between education and multimorbidity by sex can be explained, besides other factors, by differences in the magnitude between low education and the most prevalent chronic diseases in the study (55). For example, additional analysis showed that the strength of the association between low education (no education/incomplete middle education) and diseases, such as diabetes (PR_women: 4.22 versus PR_men: 1.69), hypertension (PR_women: 2.65 versus PR_men: 1.41), and high cholesterol (PR_women: 1.73 versus PR_men: 0.77), is higher in women compared to men (data not shown in the results).

We found a higher prevalence of multimorbidity among urban residents compared to rural residents, regardless of sex, which is in accordance with previous reports (51, 63, 64). People in urban areas have different behavior patterns and lifestyles than those in rural areas. Urban residents, for example, have a higher consumption of energy-dense foods, rich in sugar, trans fats, and sodium (65), as they are more exposed to ultra-processed foods and marketing strategies of food companies (66). These ultra-processed foods require less preparation time, hence meeting the needs of urban populations who work outside the home and may have limited time or resources to cook (67). Furthermore, scientific literature reports that urban residents spend less time on moderate and vigorous-intensity physical activity, and consume more alcohol, increasing the likelihood of developing multiple chronic diseases/conditions (68).

The five regions of Brazil showed inequalities in the prevalence of multimorbidity. The South and Southeast regions had higher prevalence, similar to that previously reported (63, 69, 70). No significant associations were observed in men. Conversely, women living in the South and Midwest regions had a higher multimorbidity prevalence than the others, corroborating a study conducted with a similar population (71). These outcomes probably reflect socioeconomic disparities in access to health services between sexes and regions. For example, states in the South and Southeast regions, which include the cities of São Paulo and Rio de Janeiro, have a higher percentage of urban population (72), higher level of socioeconomic development (73), are more industrialized, with better infrastructure (74) and have greater access to health services (75) compared to the North and Northeast regions. These aspects can facilitate access to the diagnosis of chronic diseases/conditions (76). In Brazil, the North (1.0/1,000 inhabitants) and Northeast (1.2/1,000 inhabitants) regions have the lowest ratio of doctors per 1,000 inhabitants in comparison to the Southeast (2.6/1,000 inhabitants) and South (2.0/1,000 inhabitants) (76). Another explanation for this result is the difference in demographic and epidemiological transition processes between the Brazilian regions. Data from the Global Burden of Disease Study 2016 showed that the transition from communicable, maternal, neonatal, and nutritional diseases to NCDs is more advanced in the South and Southeast regions, in addition to the higher life expectancy in these regions compared to the others (77).

Our findings revealed that smoking (former and current smokers) was positively associated with a higher prevalence of multimorbidity, regardless of sex, confirming existing literature (13–15). The greatest magnitude of association was observed in former smokers, which is in line with other investigations (78, 79). The cross-sectional nature of this study does not allow to establish causality between smoking cessation and multimorbidity. However, longitudinal studies have shown that a former history of smoking and current smoking increase the risk of multimorbidity (80, 81). Smoking is associated with the occurrence of multiple diseases. Recent research indicated that smoking was associated with 470 diseases, such as lung cancer, COPD, acute myocardial infarction, ischemic stroke, and ischemic heart disease (82). Cigarette smoke contains more than 7,000 chemical substances, of which approximately 70 are carcinogens. For instance, inhaling these chemicals leads to changes in airway inflammation, resulting in abnormal processes that can cause serious smoking-related illnesses, such as cancer and COPD (83, 84).

We observed that leisure-time physical inactivity was associated with multimorbidity in the total sample, as found in other studies (13–15). In the analysis stratified by sex, this association was verified only in men, corroborating previous investigations (85, 86). Another research estimated that physical inactivity is a primary cause of 35 chronic diseases/conditions that affect different body systems (87). Physical activity confers benefits for several health outcomes, such as improved physical fitness (cardiorespiratory and muscular fitness), cardiometabolic health (blood pressure, dyslipidemia, and insulin resistance), bone health, mental health, and reduced adiposity (88).

We found an inverse association between recommended fruit and vegetable consumption and multimorbidity, regardless of sex. Evidence suggests that high consumption of fruits and vegetables is a protective factor for several NCDs, such as obesity (89), ischemic stroke (90), and diabetes (91), contrary to our findings. This can be explained by the following factors: (i) information on the recommended fruit and vegetable consumption is self-reported in the NHS. Although this indicator provided satisfactory validity (92–94), response bias may have occurred; also, the data refer to the day before the interview, not reflecting the relationship between lifetime consumption and multimorbidity; (ii) people with multimorbidity tend to adopt healthier eating habits (17); and (iii) additional analysis (not shown in the results) demonstrated that the recommended fruit and vegetable consumption was twice as high in the older people compared to young people who, in turn, had a prevalence of multimorbidity almost seven times lower. This inverse association between healthy eating indicators and age may reflect a greater concern with health among older people, as described in the literature (20, 65, 95–98). Additionally, given the disagreements about the positive relationship between multimorbidity and recommended fruit and vegetable consumption (15, 16, 18, 19, 99), further in-depth investigations are required, especially using Food-Frequency Questionnaires. However, studies using data from the NHS 2013 (17, 18) showed similar results to ours.

The associations between overweight and multimorbidity are well known (18, 81, 100, 101), as found in this work. Obesity can lead to dyslipidemia, systemic inflammation, increased blood pressure, insulin resistance, and other metabolic changes, which may be common pathways for the development of cardiovascular disease and diabetes (102).

The results of this study indicate that the low educational level of the population and physical inactivity are determining factors for multimorbidity. Emphasizing the importance of actions that reduce health inequalities and promote the practice of physical activity. In addition, we showed that the older adults and women are vulnerable groups for the occurrence of multimorbidity. Therefore, tailored interventions for these groups are needed to prevent a scenario with an even higher prevalence of multimorbidity and its burden on the health system in the future.

This study has some limitations. First, the cross-sectional nature does not allow to establish causality between the independent variables and multimorbidity, as there may be reverse causality. Thus, longitudinal studies are needed. Second, data on chronic diseases/conditions were self-reported, considered less accurate than objective measures, and susceptible to memory and response bias. Therefore, some morbidities may be underestimated. Third, the definition of multimorbidity was based on the number of chronic diseases/conditions without considering severity. Ultimately, the analysis was based on a list of only 14 morbidities and the interviewee’s report of a medical diagnosis, which has a strong relationship with access to health services and may reduce the frequency of multimorbidity among the socioeconomically disadvantaged population. Moreover, mental disorders were grouped, and anxiety, the most prevalent mental illness in Brazil (103), was not analyzed. However, our investigation has several strengths, such as the complex sampling design, which makes it representative of the Brazilian adult population. Also, we examined the association of multimorbidity with sociodemographic and lifestyle factors stratified by sex. The results are valuable for future evidence-based recommendations, such as gender-specific prevention strategies to address multimorbidity. Importantly, our findings can be used to provide a deeper understanding of the association between sociodemographic and lifestyle determinants and multimorbidity in developing countries, considering the knowledge gap in the literature.

In conclusion, the higher prevalence of multimorbidity was associated with sociodemographic characteristics, including older age, female sex, living in the South and Southeast regions of Brazil, living in urban areas, in addition to unhealthy lifestyles, such as smoking and physical inactivity. Overweight and obesity also increased the prevalence of multimorbidity. Intriguingly, the recommended fruit and vegetable consumption demonstrated an inverse association. Compared with participants with higher educational levels, those with intermediate education (complete high school/incomplete higher education) had a lower prevalence of multimorbidity. In men and women, the association between education and multimorbidity showed differences, as well as for physical activity.

Publicly available datasets were analyzed in this study. This data can be found: Brazilian Institute of Geography and Statistics with the Ministry of Health/Brazil (https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=2101764).

The studies involving human participants were reviewed and approved by the Research Ethics Committee of the National Health Council under protocol number 3,529,376/2019. The patients/participants provided their written informed consent to participate in this study.

RG contributed to the conception and design of the study. CCP and RG organized the database and performed the statistical analysis. CCP wrote the first draft of the manuscript. CFP and SB wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

This study was funded by the Ministry of Health of Brazil.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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