Artigos

INTRODUCTION
Chronic diseases cause important health and economic burdens on society, as they often require medical interventions and lead to limitations in daily activities (1). The burden of chronic diseases and exposure to their risk factors are increasing worldwide, which are particularly alarming in low- and middle-income countries, where health systems may not be prepared due to limited capacity and low national health spending (1-3).
Multimorbidity can be defined as the coexistence of multiple chronic conditions in a single individual, and it is associated with worse clinical outcomes, lower quality of life, disability, and frailty, as well as increased drug and health service utilization (4). Over one-third of the global population has multimorbidity, which is higher in South America, where the prevalence is 46% (1).
Multimorbidity treatment is complex and requires health system- and patient-centered approaches that include access to pharmacological therapies and health care services, shared decision-making, self-management, and integrative care (5). It causes heavy financial burdens on health systems and society, with increased hospitalization, care transition, primary care, dental care, emergency department use, and hospitalization costs (6, 7). Multimorbidity also results in a treatment burden on patients, which can negatively affect health-related quality of life (8).
The Brazilian Amazon is a region where important inequalities in income, access to basic infrastructure, and availability of health care services and professionals exist (9, 10). These barriers are derived mainly from policies focused on the exploitation of natural resources and the neglect of local needs (9, 10). The co-occurrence of multiple health conditions and social, economic, political, and environmental factors that exacerbate this burden results in a syndemic model of health in the region (9). Manaus is characterized by significant social inequities in health care, including unequal utilization of health services and access to medicines, in addition to a high prevalence of mental illnesses, such as depressive and anxiety symptoms (11-14).
The unique features of this region may add complexities to the illness process and thus prompted the initiation of this study, which aimed to assess the prevalence of chronic diseases and the factors associated with multimorbidity in a city in the Brazilian Amazon.

METHODS
Study design and setting
This was a cross-sectional population-based study carried out between April and June 2019, and it included adults (?18 years old) living in Manaus, Amazonas. This study is part of a major survey that assessed the use of health care services in this city (15).
Manaus, the capital of the state of Amazonas, is located in the North Region of Brazil and had 2,063,547 inhabitants in 2022, representing more than 50% of the state’s population (16).
Participants and sample size
A three-phase probabilistic sampling method stratified by sex and age was used to select participants: census tracts (random), households (systematic), and individuals (random) (15). The sample size was calculated considering a prevalence of health service utilization of 20%, confidence levels of 95%, absolute precision of 2%, and population estimates of 2,106,322 adults in 2018 (15). Thus, the study planned to include a total of 2,300 participants.
Variables and data sources
The primary outcomes were the mean number of chronic diseases and the prevalence of multimorbidity and multimorbidity treatment. The presence of chronic diseases was assessed by the following question: “Has any physician given you a diagnosis of the following diseases in the past 12 months?” Fourteen chronic diseases were listed, as follows: hypertension, diabetes, high cholesterol, heart disease (e.g., myocardial infarction, angina, heart failure), stroke, asthma, arthritis, chronic back pain, depression, mental disorders (e.g., schizophrenia, bipolar disorder, psychosis, obsessive?compulsive disorder), lung disease (e.g., pulmonary emphysema, chronic bronchitis or chronic obstructive pulmonary disease), cancer, chronic renal failure, and any other long-term condition (duration of ?6 months). The list of chronic diseases that was used in this study was based on the National Health Survey in Brazil (17). Multimorbidity was defined as the self-reporting of ?2 chronic diseases (4). For each chronic disease to which the participants responded “yes”, the following question was asked: “Have you received any treatment (including medicines, health care services, physical therapy, diet or physical activity) to treat this condition in the past 12 months?” Multimorbidity treatment was considered if participants with multimorbidity received treatments for all of their self-reported chronic conditions individually, which was used as a proxy to estimate access to treatments among those with multimorbidity. For each self-reported chronic disease, we also investigated the level of limitations these conditions caused in daily activities via a Likert scale ranging from 1 to 5 (18): 1 (very mild limitations), 2 (mild), 3 (moderate), 4 (severe), or 5 (very severe). This scale was used with the aim of estimating the impact of chronic diseases on the quality of life in this population. The degree to which these chronic conditions limited daily activities was stratified by the proportion of participants who responded ‘1’ or ‘2’ (mild limitations), ‘3’ (moderate limitations), or ‘4’ and ‘5’ (severe limitations). The proportion of participants with multimorbidity who received treatments for all their conditions was calculated as the ratio between treatment and multimorbidity multiplied by 100%. The number of chronic diseases was calculated by the sum of self-reported chronic conditions among those with multimorbidity.
Independent variables included sex (men, women), age group (18–24, 25–34, 35–44, 45–59, and ?60 years old), ethnicity/skin color (white and Asian, nonwhite [black, brown and indigenous]), social class (A/B, C, D/E, where A refers to the wealthiest and E to the poorest according to the Brazilian Economic Criteria (19)), educational level (higher education or above, high school, elementary school, less than elementary school), partnership status (with partner, without partner), occupation (formal job [formal employment relationship that guarantees labor rights and social benefits], informal job [autonomous economic activity without social security or contractual relationship with an employer], retired, student, unemployed, and housewife).
The data were obtained from face-to-face interviews via the software SurveyToGo (Dooblo Ltd., Israel), and answers were recorded via electronic devices (Intel TabPhone 710 Pro). Experienced interviewers were hired and trained before data collection. Responses were recorded and submitted to the research database via the internet.
Bias
Pilot data collection was conducted in the major survey with 150 participants, who were included in the final sample, to assess their understanding of the questionnaire. Phone audits were carried out in 20% of the interviews to confirm data credibility. The interviews were georeferenced by the electronic devices, and the audio was partially recorded to ensure reliability of the data.
Statistical analysis
We used descriptive statistics to characterize the sample and calculate the absolute and relative frequencies of multimorbidity.
Poisson regression with robust variance was used to calculate the prevalence ratios (PRs) for multimorbidity for each independent variable with 95% confidence intervals (95% CIs). Significant variables with p<0.20 in the unadjusted analyses (sex, age group, social class, educational level, and occupation) were included in the final adjusted regressions. The Wald test was used to analyze the significance of variables with multiple categories, with p<0.05 considered statistically significant. Multicollinearity was investigated by the variance inflation factor (VIF); variables with a VIF>10 were removed from the analyses.
All the analyses were conducted via Stata 14.2, and the complex sampling design of the survey (svy command) was considered.
Ethics
This study was approved by the Research Ethics Committee of the Federal University of Amazonas (approval letter No. 3.102.942 from 28 December 2018). All the participants signed a written informed consent before the interview.

RESULTS
After the sampling process, 5,769 households were approached; 2,523 were closed or empty. Among the 3,246 households with adults, 80 had ineligible individuals, and 845 refused to participate in the survey. In total, 2,321 participants were included in the study. Most of them were women (52.2%), nonwhite (89.9%), were in the middle class (social class C: 53.6%), had completed high school (50.4%), and had partners (55.9%). The minority were elderly individuals (?60 years old, 11.6%) and were retired (7.2%) (Table 1).
The mean number of chronic diseases among those with multimorbidity was 2.99±1.27. The prevalence of multimorbidity was 30.6% (95% CI 28.7–32.4%) (Table 1). Multimorbidity treatment was obtained by 28.8% (95% CI 25.3–32.0%) of individuals with multimorbidity.
Among people with multimorbidity, the chronic diseases with the highest self-reported prevalence were back pain (30.5%; 95% CI 28.6–32.4%), hypertension (19.8%; 95% CI 18.2–21.5%), and high cholesterol (19.7%; 95% CI 18.0–21.3%), which were also the conditions for which the highest proportion of patients received treatment: hypertension (14.1%; 95% CI 12.7–15.5%), high cholesterol (13.2%; 95% CI 11.8–14.6%), and back pain (9.7%; 95% CI 8.5–10.9%). The diseases that caused the most severe limitations on daily activities were other mental illnesses (36.6%; 95% CI 22.4–53.5%), other chronic diseases (32.1%; 95% CI 24.5–40.7%), and renal disease (29.1%; 95% CI 13.7–51.5%), whereas those that caused the mildest limitations on daily activities were lung disease (78.3%; 95% CI 67.8–86.0%), cancer (77.4%; 95% CI 62.1–87.7%), and diabetes (77.5%; 95% CI 70.4–83.3%) (Table 2).
The adjusted regressions revealed that multimorbidity was greater in women (PR=1.46; 95% CI 1.28–1.66), older people (35–44 years: PR=2.11; 95% CI 1.57–2.84; 45–59 years: PR=3.38; 95% CI 2.54–4.49; ?60 years: PR=3.49; 95% CI 2.57–4.74), and retired individuals (PR=1.41; 95% CI 1.13–1.75) (Table 3). None of the variables presented a VIF>10; thus, multicollinearity was discarded.
The prevalence of multimorbidity was greater in both older men and women who were retired than in those with formal jobs (Figure 1).

DISCUSSION
Multimorbidity affected approximately 3 in 10 residents of Manaus, who had, on average, three chronic diseases. A similar proportion of individuals with multimorbidity received treatment for all their self-reported conditions. The most prevalent conditions were back pain, hypertension, and high cholesterol, whereas the conditions that caused the greatest degree of limitations on daily activities were mental illnesses, other less common long-term diseases, and renal disease. The prevalence of multimorbidity was greater in women, older people, and retired individuals.
The cross-sectional design of this study does not allow the assessment of causality, but may provide insights into the causal effects of exposure on disease prevalence (20). The sample size calculations relied on previous estimates of health service utilization, meaning that this analysis was not specifically powered to investigate chronic diseases, multimorbidity, or multimorbidity treatment. Selection bias was minimized because of the probabilistic sampling method that was used. As chronic diseases were self-reported, recall bias was possible, which could have led to an underestimation of the prevalence of multimorbidity. The assessment of chronic diseases was limited to the previous 12 months; individuals with diagnoses for more than one year might have not been considered, resulting in underestimation of the results. Multimorbidity treatment was limited to individuals with multimorbidity who received treatment for all of their conditions; those who received treatment for most of their diseases, but not all, were not considered. This outcome was also used as a proxy for access to treatments, but access to pharmacological treatment is a complex and multidimensional concept that considers four dimensions: availability, geographic accessibility, acceptability, and affordability (21). The study did not confirm whether participants adhered to long-term treatments such as physical exercise and diet, which may have resulted in information bias.
The prevalence of multimorbidity reported in our study was slightly higher than that reported in a previous survey conducted in the Manaus Metropolitan Region, with 4,001 participants in 2015 (29%) (22). The national estimates obtained from the Brazilian National Health Survey from 2019, which included 88,531 adults, reported a similar prevalence (23).
People with multimorbidity had an average of 2.99 chronic diseases, but only 28.8% received treatment for all of their conditions. The limited access to multimorbidity treatment might be a reflection of the many existing challenges in low- and middle-income settings, such as resource and infrastructure constraints, low health system financing and continuity of care, and health system models designed to treat individual conditions that require multiple visits to different health care providers and specialties, discouraging patients from seeking care for their multiple conditions (24, 25). Multimorbidity may cause treatment burdens on patients, as the need to take and manage multiple medications, use health care services, monitor health, and change lifestyle behaviors can be overwhelming and could lead to low treatment adherence (26). In Brazil, the increasing degree of inequalities affects the unmet needs for health care services and medications, especially in poorer regions of the country, such as the Amazon, which may exacerbate the difficulties experienced by individuals with multimorbidity in receiving proper treatment (27). In a previous study carried out in 2016 with 8,347 Brazilian residents aged 50 years or older, multimorbidity was associated with higher catastrophic health expenditures, particularly among individuals with worse socioeconomic conditions (28). Another study conducted in São Paulo city with 3,184 adult individuals in 2015 revealed that multimorbidity was more common in people who reported higher health expenditures in the preceding month, with higher usage of health care services (29). A cross-sectional study conducted in the United Kingdom in 2019 with 835 elderly individuals with multimorbidity reported that financial difficulty, a greater number of long-term conditions, and limited health literacy were associated with a greater treatment burden on patients (30).
Similar to our study, the 2019 Brazilian National Health Survey reported hypertension, chronic back problems, and hypercholesterolemia as the most prevalent diseases among those with multimorbidity (23). The 2013 edition of the same survey also revealed that other mental illnesses and other long-term chronic conditions were the ones that caused the most severe limitations on daily activities, in addition to cerebrovascular accidents. In contrast to our results, renal disease was not suggested as a limiting condition in this study (31).
We found that women and older people experienced multimorbidity more frequently. Global estimates from a systematic review with a meta-analysis of population-based studies with a total sample of 15.4 million individuals also revealed that multimorbidity was more prevalent in females than in males (39.4% versus 32.8%, respectively) and in elderly individuals than in adults aged ?30 years (51.0% versus 44.4%) (1). A panel of nationally representative cross-sectional studies conducted in Brazil from 1998 to 2019 with 877,032 participants reported similar results; the prevalence of multimorbidity was 1.7 times higher in women than in men and nearly 20 times higher in elderly individuals than in those aged 18–29 years (32). Gender differences in health-seeking behavior may explain our findings, as women tend to care for their health, self-report diseases, and use health care services more than men do because of social and cultural influences (33-35). In Manaus, assessments of both the 2015 and 2019 (present study) surveys yielded 5,800 participants and revealed that women more frequently visited doctors than men did in the region (12). Risk factors for chronic diseases, such as mental illnesses, affect more women than men, which may also explain these results (36). Older age is a known factor associated with multimorbidity, as aging increases the risk of developing and diagnosing chronic diseases (37). Special considerations are needed for this age group, as multimorbidity is associated with higher catastrophic health expenditures and mortality rates among the elderly (38, 39).
Multimorbidity was greater in retired people than in those working in formal jobs, which is consistent with the higher risk of multimorbidity in retired individuals than in those actively employed in several labor activities, as observed in a cohort study carried out with 28,523 adult residents of the United Kingdom between 2018 and 2020 (40). A plausible explanation is that retired people are usually elderly and are already at greater risk of multimorbidity. Individuals with multimorbidity may also be prone to exiting paid employment due to disabilities and early retirement as a result of their disease (41).
In this study, multimorbidity was not associated with socioeconomic variables, such as social class, ethnicity, or educational level, in contrast to previous studies that suggest a key role of social determinants in the development of multimorbidity (42). Survival bias may explain this finding, since poorer individuals have lower survival rates, even though they are possibly more affected by chronic diseases. Another plausible reason would be information bias due to a lack of access to health care services and diagnoses for chronic conditions compared with those from higher strata (12).
In conclusion, approximately three of ten adults in Manaus have multimorbidity, and these adults presented, on average, three chronic conditions; of those, 29% received treatment for all of their diseases. Multimorbidity was more prevalent in women, older people, and retired individuals. The findings of this study may contribute to the identification of risk factors for multimorbidity and highlight potential deficiencies in access to treatments for chronic diseases in the region, which can support discussions of public policies that target vulnerable populations.
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