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Multimorbidity patterns: obesity as the main modifiable risk factor in adult women in Southern Brazil

ABSTRACT

Objective:

To identify multimorbidity patterns in women in southern Brazil, and its relationship with sociodemographic, lifestyle characteristics, and nutritional status, as well as to explore the main independent risk factor for the identified patterns.

Subjects and methods:

This is a cross-sectional, population-based study with 1,128 women (20-69 years), southern Brazil. Chronic conditions were identified using the therapeutic and chemical anatomical classification of continuous use of medications. Multimorbidity was assessed as ≥2 or ≥3 chronic conditions to identify dyads and triads. Poisson regression was used to explore risk factors in the different adjustment models. As independent variables evaluated, in addition to sociodemographic characteristics, lifestyle variables were included: consumption of fruits and vegetables, physical activity, alcohol consumption, smoking and nutritional status.

Results:

Eleven dyads (frequencies between 2.0% and 6.4%) and three triads (frequencies between 1.9% and 2.1%) of morbidities were identified in the study. Aging was related to a higher prevalence of all patterns, and obesity was a risk factor for multimorbidity patterns that contained conditions related to the cardiovascular and metabolic system and mental health. After adjustment, obesity increased the probability of “hypertension + common mental disorders (CMD)” (PR 3.63; 95% CI 1.94-6.78) and “dyslipidemia + CMD” (PR 3.69; 95% CI 1.08-12.65) by more than three times.

Conclusion:

This study identified common and important diseases in the patterns, associated with a common risk factor, obesity, that must be addressed by public health policies to prevent multimorbidity.

Keywords
Multimorbidity; multiple chronic conditions; obesity; risk factor; health surveys

INTRODUCTION

Chronic health problems represent the dominant burden of health care today, especially because they are strongly related to population aging (11 World Health Organization (WHO). Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO; 2014.). Globally, chronic non-communicable diseases (CNCDs) have high rates of mortality and morbidity (22 Vos T, Abajobir AA, Abbafati C, Abbas KM, Abate KH, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1211-59.,33 Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736-88.), besides to being considered the greatest source of disease burden and responsible for the highest health costs (11 World Health Organization (WHO). Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO; 2014.). Furthermore, evidence has shown that, on a daily basis, the reality of people is even more complicated, since it is common for them to be affected by more than one chronic health condition, presenting itself as a public health problem of complex management, both in developed and developing countries (44 Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol Med Sci. 2016;71:205-14.).

Multimorbidity is characterized by coexistence of different chronic conditions in an individual without a single condition being considered as the main one, enhancing the idea of comprehensive care (55 World Health Organization (WHO). Multimorbidity. Technical Series on Safer Primary Care. Geneva: WHO; 2016;,66 Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: A systematic review of systematic reviews. Eur J Public Health. 2019;29:182-9.). Its operational definition differs in literature, with the occurrence of two or more chronic diseases being the most commonly used cutoff point, mainly in the adult population, in which the combination of more than two conditions is less prevalent (55 World Health Organization (WHO). Multimorbidity. Technical Series on Safer Primary Care. Geneva: WHO; 2016;77 Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.). Other approaches include counting the number of chronic conditions, clusters of pre-established diseases, and identification of multimorbidity patterns from the list of the most prevalent diseases (88 Willadsen TG, Bebe A, Køster-Rasmussen R, Jarbøl DE, Guassora AD, Waldorff FB, et al. The role of diseases, risk factors and symptoms in the definition of multimorbidity – a systematic review. Scand J Prim Health Care. 2016;34:112-21.1010 Ng SK, Tawiah R, Sawyer M, Scuffham P. Patterns of multimorbid health conditions: A systematic review of analytical methods and comparison analysis. Int J Epidemiol. 2018;47:1687-704.). Regarding its prevalence, studies have shown that multimorbidity affects the majority of the elderly population and women (1111 Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Prim. 2022;8(1):48.1313 Nunes BP, Batista SRR, Andrade FB de, Souza Junior PRB de, Lima-Costa MF, Facchini LA. Multimorbidade em indivíduos com 50 anos ou mais de idade: ELSI-Brasil. Rev Saude Publica [Internet]. 2018;52(Suppl 2).). A longitudinal study with a representative sample of Brazilian adults revealed that the prevalence of multimorbidity was 67.8% when considered ≥ 2 chronic conditions and 47.1% when considered ≥ 3 (1313 Nunes BP, Batista SRR, Andrade FB de, Souza Junior PRB de, Lima-Costa MF, Facchini LA. Multimorbidade em indivíduos com 50 anos ou mais de idade: ELSI-Brasil. Rev Saude Publica [Internet]. 2018;52(Suppl 2).).

The available literature is inconsistent with regard to identifying different combinations of chronic conditions and their magnitude. Certain multimorbidity patterns can have a different impact on the health of affected individuals, both in terms of quality of life and mortality, in addition to influencing the use of health services differently and, consequently, the resulting costs (99 Sinnige J, Braspenning J, Schellevis F, Stirbu-Wagner I, Westert G, Korevaar J. The prevalence of disease clusters in older adults with multiple chronic diseases - A systematic literature review. PLoS One. 2013;8(11):e79641.,1414 Violan C, Foguet-Boreu Q, Roso-Llorach A, Rodriguez-Blanco T, Pons-Vigues M, Pujol-Ribera E, et al. Burden of multimorbidity, socioeconomic status and use of health services across stages of life in urban areas: a cross-sectional study. BMC Public Health. 2014;14:530.1616 Picco L, Achilla E, Abdin E, Chong SA, Vaingankar JA, McCrone P, et al. Economic burden of multimorbidity among older adults: Impact on healthcare and societal costs. BMC Health Serv Res. 2016;16:1-12.). In Brazil, direct and indirect costs related to the group of cardiovascular diseases totaled R$ 37.1 billion in 2015, representing an estimated average cost of 0.7% of the Brazilian GDP (1717 Siqueira A de SE, De Siqueira-Filho AG, Land MGP. Análise do impacto econômico das doenças cardiovasculares nos últimos cinco anos no Brasil. Arq Bras Cardiol. 2017;109:39-46.). Furthermore, there are still gaps in the literature regarding the risk factors for multimorbidity patterns. Existing studies reveal that, in addition to the wide variety of multimorbidity patterns that can be found, these are mainly associated with different sociodemographic characteristics of the population and also according to the lifestyle characteristics of each individual (1515 Wang L, Palmer AJ, Cocker F, Sanderson K. Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: Implications of count versus cluster method for defining multimorbidity on HRQoL. Health Qual Life Outcomes. 2017;15:1-12.,1818 Jackson CA, Dobson AJ, Tooth LR, Mishra GD. Lifestyle and Socioeconomic Determinants of Multimorbidity Patterns among Mid-Aged Women: A Longitudinal Study. PLoS One. 2016;11:1-16.). In addition, obesity has an established role as a risk factor for several CNCDs, and studies have already shown its dose-response association with an increase in the number of chronic conditions (1919 Schmidt MI, Duncan BB, Azevedo G, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet. 2011;377:1949-61.,2020 Booth HP, Prevost AT, Gulliford MC. Impact of body mass index on prevalence of multimorbidity in primary care: cohort study. Fam Pract. 2014;31:38-43.). In this sense, the identification of modifiable risk factors involved in the multi-causality of multimorbidity patterns is essential for understanding the magnitude and transcendence of this problem in the population of adult women, in order to propose effective therapeutic and prevention strategies for this population.

Thus, this study aimed to identify multimorbidity patterns in women in southern Brazil, and its relationship with sociodemographic, lifestyle, and nutritional status characteristics, as well as to explore the main independent risk factor for the identified patterns.

SUBJECTS AND METHODS

Study design and population

This cross-sectional study included women aged 20-69 years living in the urban area of São Leopoldo, a municipality in southern Brazil. Data were collected in 2015 (from February to October), and all women in the age group of interest, except for pregnant women residing in the selected households, were eligible for the study. The larger project of which this study is a part, was submitted to and approved by the Ethics and Research Committee of the University of Vale do Rio dos Sinos (CAAE 30872914.6.0000.5344, Protocol 650.443), and all participants provided written informed consent.

The sample size calculation for the larger study was based on the prevalence of various outcomes related to women's health. A power of 80% was considered to identify a risk ratio of 2.0 between women with lower and higher education (not exposed – with 15 years or more of education) with a confidence level of 95%. Finally, 10% was added for eventual losses/refusals, and 15% was added to control for confounding factors in the data analysis, resulting in a total sample size of 1,281 women.

Sampling was probabilistic and performed at multiple stages. First, 371 census sectors in the urban area of São Leopoldo were listed and classified based on the highest average monthly nominal income for persons aged 10 years or older (with or without income). The sectors were numbered from 1 to 371, according to this classification. Subsequently, 45 census sectors were randomly selected. So, considering the average number of women per household (0.93), as well as the proportion of women in the age group of interest (32.1%), we estimate that 1,613 households would be visited. This number was divided into 45 census sectors, totaling 36 households in each sector (2121 Neutzling AL, Leite HM, Paniz VM, de Bairros FS, Dias da Costa JS, Olinto MTA. Association between common mental disorders, sleep quality, and menopausal symptoms: a population-based study in Southern Brazil. Menopause. 2020;27:463-72.). Posteriorly, was to draw randomly, in each conglomerate, the block and the corner to start the research. Households were selected from the defined point (corner indicated for the beginning), always toward the left of those facing the initial corner; the first house was selected for the study, skipping two houses, selecting the fourth house again, and so on, until 36 households per sector were completed (2222 Sturmer J, Franken DL, Ternus DL, Henn RL, Soares Dias-Da-Costa J, Olinto MTA, et al. Dietary patterns and prevalence of multimorbidity in women in southern Brazil. Menopause. 2022;29:1047-54.).

Trained interviewers collected data using a standardized and pretested questionnaire in a pilot study, which took place in a census sector not included among those selected for the study. Data quality control was carried out through a reduced questionnaire administered to 10% of the participants.

Dependent variable

Twenty-six chronic conditions were identified in the study population, with the most prevalents being: arterial hypertension, common mental disorders (CMDs), acid-related digestive disorders, dyslipidemia, thyroid diseases, diabetes mellitus (DM), circulatory disorders, and chronic pain (2323 Franken DL, Olinto MTA, Dias-da-Costa JS, Bairros FS De, Paniz VMV. Lifestyle, high Body Mass Index, and markers of socioeconomic conditions associated with multimorbidity in women. Rev Bras Epidemiol. 2022;25.). Information about their presence was obtained from the identification of the continuous use of medicines prescribed by a doctor, reported by the women at the time of the interview. The Anatomical Therapeutic Chemical (ATC) classification (2424 World Health Organization Collaborating Centre (WHOCC). World Health Organization Collaborating Centre for Drug Statistics Methodology. Anatomical Therapeutic Chemical (ATC) classification index with Defined Daily Doses (DDD's). Geneva: WHOCC; 2004.) was used to classify drugs according to the organ or system in which they operated, thus serving as a proxy for the identification of chronic conditions (2323 Franken DL, Olinto MTA, Dias-da-Costa JS, Bairros FS De, Paniz VMV. Lifestyle, high Body Mass Index, and markers of socioeconomic conditions associated with multimorbidity in women. Rev Bras Epidemiol. 2022;25.,2525 Huber CA, Szucs TD, Rapold R, Reich O. Identifying patients with chronic conditions using pharmacy data in Switzerland: An updated mapping approach to the classification of medications. BMC Public Health. 2013;13:1-10.). When the same drug had more than one indication of use, the participant's information on the reason why the medicine was prescribed was considered. We excluded drugs reported as being occasional use from the outcome assessment. Multimorbidity was categorized for participants with two or three or more chronic conditions (55 World Health Organization (WHO). Multimorbidity. Technical Series on Safer Primary Care. Geneva: WHO; 2016;77 Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.), and the different combinations of these defined the patterns as dyads and triads of chronic conditions.

Exposures

The evaluated exposures were:

  1. Sociodemographic: age collected in years (categorized every 10 years), self-reported skin color (white, black, indigenous, yellow and brown), marital status (not having a partner and having a partner), education in years (≥11 years; eight to 10 years; five to seven years; ≤4 years), household income per capita in quartiles (the first quartile, ≤R$ 525.30; the second quartile, >R$ 525.30-R$ 869.00; the third quartile, >R$ 869.00-R$ 1547.00; and the fourth quartile, >R$ 1547.00) economic class (A/B; C; D/E) according to the economic classification criteria proposed by the Brazilian Association of Research Companies (2626 Associação Brasileira de Empresas de Pesquisa (ABEP). Critério de Classificação Econômica do Brasil. São Paulo (SP); 2014. p. 1-5.), and occupation (employed and unemployed);

  2. Lifestyle: smoking (non-smoker, former smoker, current smoker), alcohol consumption based on frequency, type of drink, and quantity ingested, considering the risk of alcohol consumption for women and the cutoff point of 15 g of ethanol/d (2727 Barroso WKS, Rodrigues CIS, Bortolotto LA, Mota-Gomes MA, Brandão AA, Feitosa AD de M, et al. Diretrizes Brasileiras de Hipertensão Arterial – 2020. Arq Bras Cardiol. 2021;116:516-658.) (no/yes), physical activity (active and inactive), and consumption of fruits and vegetables (adequate and inadequate). Participants were considered physically active when they reached at least 150 minutes of weekly physical activity, as verified by the International Physical Activity Questionnaire short version (2828 Matsudo S, Araújo T, Marsudo V, Andrade D, Andrade E, Oliveira L. Questionário Internacional de Atividade Física (IPAQ). Estudo de validade e reprodutibilidade no Brasil. Rev Bras Atividade Física Saúde. 2001;6:5-18.). The consumption of fruits and vegetables was considered adequate when it was ≥ five times/day (2929 Stables GJ, Subar AF, Patterson BH, Dodd K, Heimendinger J, Van Duyn MAS, et al. Changes in vegetable and fruit consumption and awareness among US adults: Results of the 1991 and 1997 5 A Day for Better Health Program surveys. J Am Diet Assoc. 2002;102:809-17.).

  3. Nutritional status: defined by the body mass index (BMI), which is given by the measurement of weight in kilograms divided by the square of the height in meters, and was classified according to criteria from the World Health Organization (WHO): ≤24.9 kg/m² = Eutrophy; ≥25.0 −29.9 kg/m² = Overweight; ≥30.0 kg/m² = Obesity (3030 World Health Organization (WHO). Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894.). For the assessment of nutritional status, the body weight of each participant was measured using a portable analog scale with a precision of 100 g. Height was measured using a portable stadiometer with a precision of 1 mm. Both measurements were performed in duplicate and the respective mean values were considered.

Statistical analyses

From the sample of 1,128 women, power calculations of the present study were performed using Stata 12.0 (StataCorp LP, College Station, TX, USA) software using the “sampsi” command. There was a power of 80% for associations with a prevalence ratio (PR) of 1.7 or greater and a confidence level of 95%.

Descriptive analyses were performed, using absolute and relative frequencies and the respective confidence intervals (95% CI), to establish multimorbidity patterns, investigating the most prevalent chronic condition dyads and triads, in addition to the prevalence of patterns among the population with multimorbidity, and their frequency in relation to age and nutritional status of women. Multivariable analysis was performed in two stages. Initially, considering that it was a complex sample, Poisson regression with robust variance was used to understand the relationship between the exposure variables and the identified multimorbidity patterns, and crude and adjusted analyses were performed for all dyads with a prevalence ≥ 2% considering three levels of determination (3131 Victora CG, Huttly SR, Fuchs SC, Olinto MT. The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epidemiol. 1997;26:224-7.). The first level included sociodemographic variables, the intermediate level included lifestyle variables, and the third-level variable was nutritional status. Subsequently, considering the possible confounding factors (p ≤ 0.20), obesity exposure was tested as an independent risk factor for the occurrence of multimorbidity patterns (with a prevalence ≥ 2%) through different adjustment models: Model I, with no adjustment; Model II with adjustment for sociodemographic variables; and Model III, with adjustment for sociodemographic and lifestyle variables. A significance level of 5% was considered for all the analyses.

RESULTS

We interviewed 1,128 women, and 153 (11.94%) were classified as losses and refusals. The women were characterized in terms of their sociodemographic profile, lifestyle, and nutritional status (Table 1). Their mean age was 43.4 years (SD = 13.4) and mean schooling time was 9.8 years (SD = 10.8). There was a predominance of white women (74.5%), of those who lived with a partner (63.8%), who belonged to economic class C (53.1%), and who were employed (58.1%). Half of the population had a family income per capita of up to R$ 869.00, and 41.7% had 11 or more years of study. Regarding lifestyle characteristics, most reported inadequate food consumption of fruits and vegetables (56.3%), physical inactivity (85.6%), consumption of alcoholic beverages (66.8%), and not smoking (58.6%). Approximately one-third of the participants had obesity (32.9%).

Table 1
Sample characteristics and prevalence of 0-1 chronic condition, 2 chronic conditions and ≥3 chronic conditions according to sociodemographic and lifestyle characteristics. São Leopoldo, RS, Brazil, 2015 (n = 1,128)

The average of chronic conditions in the population studied was 0.9 (SD = 1.3), and among those who had some chronic morbidity, it was 1.9 (SD = 1.2). The individuals who presented zero to one, two, and three or more morbidities represented 78.3%, 9.5%, and 12.2%, respectively of the studied population. As can be seen in Table 2, eleven multimorbidity patterns were present in more than 2% of the study population, with the dyad “hypertension + dyslipidemia” being present in 6.4% of the general population studied and in almost 30% of the population with multimorbidity. Among the individuals with multimorbidity, chronic conditions with higher proportions were hypertension (71.4%), dyslipidemia (37.6%), and CMD (35.9%). It was observed that among the most prevalent chronic condition dyads, most were combined with hypertension. Other conditions that combined more frequently were dyslipidemia, CMD, acid-related digestive disorders (medicines for the alimentary tract, such as antacids and antispasmodics), DM, and circulatory disorders, which also comprised the most prevalent chronic condition triad, with “hypertension + dyslipidemia + circulatory disorders” present in about 2% of the study population. In addition, it was observed that among 11 dyads of the most prevalent conditions, five were constituted by conditions of the group that could be characterized by cardiometabolic conditions, including conditions such as hypertension, dyslipidemia, DM, and circulatory disorders.

Table 2
Chronic conditions, dyads and triads of chronic conditions most prevalent in the sample of women. São Leopoldo, RS, Brazil, 2015

In Figure 1, graph “a” shows the number of chronic conditions according to the age groups of the study population, while graphs “b” and “c” show the prevalence of dyads of chronic conditions according to age and BMI, respectively. Graph “a” shows an abrupt and marked increase of four or more chronic conditions between 40 and 50 years of age, and multimorbidity reaching 10% of women between 30 and 39 years old. Graph “b,” on the other hand, shows an increase in the prevalence of all dyads of morbidities as age increases, although dyads containing CMD show some stabilization after 50 years of age. The dyad “hypertension + dyslipidemia” presents the most accentuated increase after 40 years of age. Regarding nutritional status, all multimorbidity patterns increase as the BMI categories increase, with greater magnitude for the dyad “hypertension + dyslipidemia” (graph c).

Figure 1
a) Number of chronic conditions in the sample of women according to age groups. b) Prevalence of dyads of chronic conditions in the sample of women according to age groups. c) Prevalence of dyads of chronic conditions in the sample of women according to nutritional status categories. São Leopoldo, RS, Brazil, 2015. (n = 1,128).

Table 3 shows that obesity is strongly associated with multimorbidity patterns involving the presence of conditions in the group of cardiovascular and metabolic diseases, such as hypertension, dyslipidemia, DM, and diseases related to mental health, such as CMD, as well as chronic pain. After adjustment in multivariable analysis, it was observed that obesity increased the probability of occurrence of “hypertension + CMD” and “dyslipidemia + CMD” in women in the sample by more than three times.

Table 3
Association between obesity and dyads of chronic conditions according to the different adjustment models. São Leopoldo, RS, Brazil, 2015. (n = 1,128)

DISCUSSION

This study investigated the multimorbidity patterns in a representative sample of women aged 20-69 years and identified, among the most prevalent, eleven dyads and three triads of chronic morbidities, with hypertension and dyslipidemia being the conditions most present in the different patterns identified. The increase in age was related to the increase in the prevalence of all observed patterns, and although our hypothesis regarding lifestyle variables has not been confirmed, obesity was identified as the main modifiable risk factor for multimorbidity patterns that contained conditions related to the cardiovascular and metabolic system as well as mental health, in which their presence more than doubled the likelihood of these patterns occurring.

Our findings are in line with the findings of the National Health Survey (PNS), 2013 (3232 Carvalho JN de, Roncalli ÂG, Cancela M de C, Souza DLB de. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One. 2017;12:e0174322.), constituted by a representative sample of the Brazilian adult population in which, among the 14 conditions evaluated, “hypertension + dyslipidemia” was the most prevalent dyad, while the most prevalent triad was “hypertension + dyslipidemia + DM,” which was also among the three most frequent conditions found in our study. However, the PNS showed that, among individuals with multimorbidity, 13.6% had “hypertension + dyslipidemia,” whereas in our population of women with multimorbidity, the prevalence of this combination was higher (29.4%). Methodological differences make comparability between studies difficult, and may explain, at least in part, the lower prevalence found in the PNS, since this cross-sectional survey included men and women aged ≥ 18 years from all regions of the country. It should be noted that the highest prevalence of multimorbidity was found in women and in the southern states of the country (3232 Carvalho JN de, Roncalli ÂG, Cancela M de C, Souza DLB de. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One. 2017;12:e0174322.).

Our study showed that the occurrence of multimorbidity patterns became more expressive around the age of 45, consistent with a study carried out in Spain that included the presence of 146 chronic conditions in patients from primary health care. It was also observed that hypertension and dyslipidemia were among the most prevalent conditions in individuals with multimorbidity and that, from the age of 45 years, this was the most common combination (1414 Violan C, Foguet-Boreu Q, Roso-Llorach A, Rodriguez-Blanco T, Pons-Vigues M, Pujol-Ribera E, et al. Burden of multimorbidity, socioeconomic status and use of health services across stages of life in urban areas: a cross-sectional study. BMC Public Health. 2014;14:530.).

This research revealed that almost half of the dyads of morbidities were constituted by conditions belonging to the cardiovascular and endocrine systems, in agreement with the literature, which showed cardiometabolic pattern among the most prevalent, and included combinations of conditions such as hypertension, dyslipidemia, DM, heart diseases, and obesity (44 Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol Med Sci. 2016;71:205-14.,77 Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.,3333 Nunes BP, Chiavegatto Filho ADP, Pati S, Cruz Teixeira DS, Flores TR, Camargo-Figuera FA, et al. Contextual and individual inequalities of multimorbidity in Brazilian adults: A cross-sectional national-based study. BMJ Open. 2017;7.,3434 Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014;67:254-66.). In PNS 2013, the cardiometabolic pattern, composed of high blood pressure, acute myocardial infarction, angina, heart failure, stroke, dyslipidemia, DM, and arthritis/rheumatism was among the patterns identified that explained 92% of the total variance in principal component analysis (3333 Nunes BP, Chiavegatto Filho ADP, Pati S, Cruz Teixeira DS, Flores TR, Camargo-Figuera FA, et al. Contextual and individual inequalities of multimorbidity in Brazilian adults: A cross-sectional national-based study. BMJ Open. 2017;7.). This is explained by the fact that cardiovascular and metabolic diseases, such as DM, share both pathophysiological pathways and risk factors (3535 Saeed A, Kampangkaew J, Nambi V. Prevention of Cardiovascular Disease in Women. Methodist Debakey Cardiovasc J. 2017;13:185-92.,3636 Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol. 2018;15:230-40.), which is why it is plausible that they coexist, in addition to the fact that the accumulation of their risk factors over time contributes to the increase in prevalence with age.

This study also showed that CMD was present in 36% of women with multimorbidity, which is consistent with the literature showing that mental health conditions often coexist with other diseases (77 Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.,3737 Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37-43.3939 Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA. Multimorbidity prevalence and patterns across socioeconomic determinants: a cross-sectional survey. BMC Public Health. 2012;12:201.). Furthermore, it was observed that among the most prevalent dyads of conditions, CMD appeared in combination with conditions such as hypertension and dyslipidemia, similar to that observed in the study by Bobo and cols. (2016). Nunes and cols. (2016) also found the dyad “hypertension + depression” among the most prevalent in the adult population, in a study conducted in southern Brazil (77 Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.). It has been shown in literature that a bidirectionality and dose-response association exists in the relationship between psychiatric disorders, such as depression, and conditions that are part of the metabolic syndrome, such as dyslipidemia and hypertension, in which interactions occur between multiple factors, including iatrogenic effects of psychotropic drugs, propensity to unhealthy lifestyles, and genetic and pathophysiological vulnerability, which explains our findings, in which these conditions combine with each other (4040 Penninx BWJH, Lange SMM. Metabolic syndrome in psychiatric patients: Overview, mechanisms, and implications. Dialogues Clin Neurosci. 2018;20:63-73.).

Our findings pointed out obesity as the main independent risk factor for most of the identified multimorbidity patterns, which included combinations of cardiovascular diseases, and among those with DMor CMD. In Australia, a cohort of women aged 45-50 years also found that obesity increased the chances of having a cardiometabolic pattern by almost 2.5 times, which was characterized by cardiovascular disease, DM, and glucose intolerance (1818 Jackson CA, Dobson AJ, Tooth LR, Mishra GD. Lifestyle and Socioeconomic Determinants of Multimorbidity Patterns among Mid-Aged Women: A Longitudinal Study. PLoS One. 2016;11:1-16.). PNS 2013 data, including men and women, showed four multimorbidity patterns with different characteristics from our findings, but they were also associated with obesity: cardiometabolic/cancer, mental/occupational, musculoskeletal, and respiratory patterns (4141 Carvalho JN De, Cancela MDC, Souza DLB de. Lifestyle factors and high body mass index are associated with different multimorbidity clusters in the Brazilian population. PLoS One. 2018;13:1-15.).

In relation to cardiometabolic diseases, the role of obesity can be explained because its occurrence requires functional adaptations of the systems, due to its implication in inflammation and vascular homeostasis, in addition to mediation with other cardiometabolic conditions among themselves (4242 Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98-107.), thus complicating the management and prognosis of these diseases (4343 Mandviwala T, Khalid U, Deswal A. Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Curr Atheroscler Rep. 2016;18.). Regarding mental health diseases, evidence has shown an association between obesity and psychiatric disorders, such as anxiety and, mainly, depression (4444 Rajan TM, Menon V. Psychiatric disorders and obesity: A review of association studies. J Postgrad Med. 2017;63:182-90.,4545 Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18-33.), treating it as a bidirectional relationship, intertwined by a series of biological, psychological, and even behavioral mechanisms, which result in the fact that the presence of one condition increases the chance of the other occurring (4545 Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18-33.). This in turn would explain our results, since obesity appears as a risk factor for cardiovascular conditions in combination with CMD.

For some authors, obesity is considered a chronic condition in the investigation of multimorbidity (44 Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol Med Sci. 2016;71:205-14.,3939 Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA. Multimorbidity prevalence and patterns across socioeconomic determinants: a cross-sectional survey. BMC Public Health. 2012;12:201.). According to some others, it is considered a risk factor for determining chronic conditions (1818 Jackson CA, Dobson AJ, Tooth LR, Mishra GD. Lifestyle and Socioeconomic Determinants of Multimorbidity Patterns among Mid-Aged Women: A Longitudinal Study. PLoS One. 2016;11:1-16.,4141 Carvalho JN De, Cancela MDC, Souza DLB de. Lifestyle factors and high body mass index are associated with different multimorbidity clusters in the Brazilian population. PLoS One. 2018;13:1-15.). Our study treated obesity as a proximal determinant of different multimorbidity patterns and, in line with previous studies; it revealed a higher probability of multimorbidity among women with obesity compared to those without obesity, regardless of sociodemographic and behavioral variables.

Some aspects must be considered when interpreting our findings. Initially, the identification of chronic morbidities through ATC determined that these conditions were already established, since they had an indication for pharmacological treatment. However, the initial treatment indicated for many chronic conditions is non-pharmacological, such as the adoption of healthy habits. In this study, as women who were already using medicines were included, it is plausible to think that the isolated non-pharmacological treatment was not enough, suggesting the severity of the disease, and therefore, the indispensability of the medicines. Moreover, information on medicines use was reported, so our measure of chronic conditions may be underestimated, indicating that the prevalence of multimorbidity could be even higher. That's because it was not possible to identify chronic conditions in cases in which the woman was not using medicines at the time of the interview due to lack of access to treatment. But, in the literature it is already well established that the lack of access to pharmacological treatment is less than 5% (4646 Tavares NUL, Luiza VL, Oliveira MA, Costa KS, Mengue SS, Arrais PSD, et al. Free access to medicines for the treatment of chronic diseases in Brazil. Rev Saude Publica. 2016;50:1-10.). Yet, considering that we investigated the current use of medicines, this strategy did not allow captured diagnosed chronic conditions in non-pharmacological treatment. Our study is a cross-sectional design, and, therefore, the temporality bias must be considered. To minimize this bias, we used different adjustment models, with three levels of determination, to evaluate the association of potential risk factors on the occurrence of multimorbidity patterns. In addition, all adjustments in the multivariable models considered the collinearity between the independent variables, as well as the potential confounding factors associated with exposure and outcome (p ≤ 0.20) for each multimorbidity pattern analyzed, as a way of better understanding multicausality. Moreover, this study is population-based with a representative sample, which increases the possibility to generalize the results to the population of women in the investigated age group.

Finally, the study investigated young women and revealed the presence of common and important diseases in the identified dyads and triads patterns, whose low prevalence indicated an opportunity for prevention. These patterns were associated with a common risk factor, obesity, a global epidemic whose management is already a major public health challenge (4747 Di Cesare M, Bentham J, Stevens GA, Zhou B, Danaei G, Lu Y, et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387:1377-96.). The obesity epidemic has worsened with the pandemic of the coronavirus (COVID-19), in which social distance and self-quarantine that were defended in order to prevent further spread of the virus resulted in increase in body weight among other side effects (4848 Zachary Z, Forbes B, Lopez B, Pedersen G, Welty J, Deyo A, et al. Self-quarantine and weight gain related risk factors during the COVID-19 pandemic. Obes Res Clin Pract. 2020;14:210-6.). The scenario is even worse when evidence has already been shown about the negative impact of the covid-19 pandemic in relation to the occurrence of depressive symptoms and/or anxiety (4949 Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain Behav Immun. 2020;89:531-42.), at the same time that the prevalence of hypertension and other cardiovascular diseases has increased in the population (3636 Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol. 2018;15:230-40.,5050 Lobo LAC, Canuto R, Dias-da-Costa JS, Pattussi MP. Tendência temporal da prevalência de hipertensão arterial sistêmica no Brasil. Cad Saude Publica. 2017;33.). Furthermore, the increase in the prevalence of patterns is strongly related to aging, and although the adult population is still the largest population group (5151 United Nations. Department of Economic and Social Affairs, Population Division: World Population Prospects 2019, Volume II: Demographic Profiles. 2019;II:1-5.), it is possible to speculate that, in the current scenario, a good portion of this population will have multimorbidity even before reaching old age.

  • Financial Support: 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). The funders had no role in study design, data collection and analysis, decision to publish and the preparation or approval of the article.

Acknowledgments:

D.L.F. received a scholarship from Brazilian Federal Agency for Support and Evaluation of Graduated Education (CAPES). M.T.A.O. received research productivity grants from the Brazilian Council for Scientific and Technological Development - CNPq (process n. 307257/2013-4 e 307175/2017-0). J.S.D.C. received research productivity grants from CNPq (process n. 310595/2018-0). We also thank to the National Council for Scientific and Technological Development for the support that they are providing for development of this study.

REFERENCES

  • 1
    World Health Organization (WHO). Global Status Report on Noncommunicable Diseases 2014. Geneva: WHO; 2014.
  • 2
    Vos T, Abajobir AA, Abbafati C, Abbas KM, Abate KH, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1211-59.
  • 3
    Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736-88.
  • 4
    Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. J Gerontol Med Sci. 2016;71:205-14.
  • 5
    World Health Organization (WHO). Multimorbidity. Technical Series on Safer Primary Care. Geneva: WHO; 2016;
  • 6
    Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: A systematic review of systematic reviews. Eur J Public Health. 2019;29:182-9.
  • 7
    Nunes BP, Camargo-Figuera, Guttier M, Oliveira PD de, Munhoz TN, Matijasevich A, et al. Multimorbidity in adults from a southern Brazilian city: occurrence and patterns. Int J Public Health. 2016;61:1013-20.
  • 8
    Willadsen TG, Bebe A, Køster-Rasmussen R, Jarbøl DE, Guassora AD, Waldorff FB, et al. The role of diseases, risk factors and symptoms in the definition of multimorbidity – a systematic review. Scand J Prim Health Care. 2016;34:112-21.
  • 9
    Sinnige J, Braspenning J, Schellevis F, Stirbu-Wagner I, Westert G, Korevaar J. The prevalence of disease clusters in older adults with multiple chronic diseases - A systematic literature review. PLoS One. 2013;8(11):e79641.
  • 10
    Ng SK, Tawiah R, Sawyer M, Scuffham P. Patterns of multimorbid health conditions: A systematic review of analytical methods and comparison analysis. Int J Epidemiol. 2018;47:1687-704.
  • 11
    Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Prim. 2022;8(1):48.
  • 12
    Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. J Comorbidity. 2019;9:2235042X1987093.
  • 13
    Nunes BP, Batista SRR, Andrade FB de, Souza Junior PRB de, Lima-Costa MF, Facchini LA. Multimorbidade em indivíduos com 50 anos ou mais de idade: ELSI-Brasil. Rev Saude Publica [Internet]. 2018;52(Suppl 2).
  • 14
    Violan C, Foguet-Boreu Q, Roso-Llorach A, Rodriguez-Blanco T, Pons-Vigues M, Pujol-Ribera E, et al. Burden of multimorbidity, socioeconomic status and use of health services across stages of life in urban areas: a cross-sectional study. BMC Public Health. 2014;14:530.
  • 15
    Wang L, Palmer AJ, Cocker F, Sanderson K. Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: Implications of count versus cluster method for defining multimorbidity on HRQoL. Health Qual Life Outcomes. 2017;15:1-12.
  • 16
    Picco L, Achilla E, Abdin E, Chong SA, Vaingankar JA, McCrone P, et al. Economic burden of multimorbidity among older adults: Impact on healthcare and societal costs. BMC Health Serv Res. 2016;16:1-12.
  • 17
    Siqueira A de SE, De Siqueira-Filho AG, Land MGP. Análise do impacto econômico das doenças cardiovasculares nos últimos cinco anos no Brasil. Arq Bras Cardiol. 2017;109:39-46.
  • 18
    Jackson CA, Dobson AJ, Tooth LR, Mishra GD. Lifestyle and Socioeconomic Determinants of Multimorbidity Patterns among Mid-Aged Women: A Longitudinal Study. PLoS One. 2016;11:1-16.
  • 19
    Schmidt MI, Duncan BB, Azevedo G, Menezes AM, Monteiro CA, Barreto SM, et al. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet. 2011;377:1949-61.
  • 20
    Booth HP, Prevost AT, Gulliford MC. Impact of body mass index on prevalence of multimorbidity in primary care: cohort study. Fam Pract. 2014;31:38-43.
  • 21
    Neutzling AL, Leite HM, Paniz VM, de Bairros FS, Dias da Costa JS, Olinto MTA. Association between common mental disorders, sleep quality, and menopausal symptoms: a population-based study in Southern Brazil. Menopause. 2020;27:463-72.
  • 22
    Sturmer J, Franken DL, Ternus DL, Henn RL, Soares Dias-Da-Costa J, Olinto MTA, et al. Dietary patterns and prevalence of multimorbidity in women in southern Brazil. Menopause. 2022;29:1047-54.
  • 23
    Franken DL, Olinto MTA, Dias-da-Costa JS, Bairros FS De, Paniz VMV. Lifestyle, high Body Mass Index, and markers of socioeconomic conditions associated with multimorbidity in women. Rev Bras Epidemiol. 2022;25.
  • 24
    World Health Organization Collaborating Centre (WHOCC). World Health Organization Collaborating Centre for Drug Statistics Methodology. Anatomical Therapeutic Chemical (ATC) classification index with Defined Daily Doses (DDD's). Geneva: WHOCC; 2004.
  • 25
    Huber CA, Szucs TD, Rapold R, Reich O. Identifying patients with chronic conditions using pharmacy data in Switzerland: An updated mapping approach to the classification of medications. BMC Public Health. 2013;13:1-10.
  • 26
    Associação Brasileira de Empresas de Pesquisa (ABEP). Critério de Classificação Econômica do Brasil. São Paulo (SP); 2014. p. 1-5.
  • 27
    Barroso WKS, Rodrigues CIS, Bortolotto LA, Mota-Gomes MA, Brandão AA, Feitosa AD de M, et al. Diretrizes Brasileiras de Hipertensão Arterial – 2020. Arq Bras Cardiol. 2021;116:516-658.
  • 28
    Matsudo S, Araújo T, Marsudo V, Andrade D, Andrade E, Oliveira L. Questionário Internacional de Atividade Física (IPAQ). Estudo de validade e reprodutibilidade no Brasil. Rev Bras Atividade Física Saúde. 2001;6:5-18.
  • 29
    Stables GJ, Subar AF, Patterson BH, Dodd K, Heimendinger J, Van Duyn MAS, et al. Changes in vegetable and fruit consumption and awareness among US adults: Results of the 1991 and 1997 5 A Day for Better Health Program surveys. J Am Diet Assoc. 2002;102:809-17.
  • 30
    World Health Organization (WHO). Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894.
  • 31
    Victora CG, Huttly SR, Fuchs SC, Olinto MT. The role of conceptual frameworks in epidemiological analysis: a hierarchical approach. Int J Epidemiol. 1997;26:224-7.
  • 32
    Carvalho JN de, Roncalli ÂG, Cancela M de C, Souza DLB de. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One. 2017;12:e0174322.
  • 33
    Nunes BP, Chiavegatto Filho ADP, Pati S, Cruz Teixeira DS, Flores TR, Camargo-Figuera FA, et al. Contextual and individual inequalities of multimorbidity in Brazilian adults: A cross-sectional national-based study. BMJ Open. 2017;7.
  • 34
    Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014;67:254-66.
  • 35
    Saeed A, Kampangkaew J, Nambi V. Prevention of Cardiovascular Disease in Women. Methodist Debakey Cardiovasc J. 2017;13:185-92.
  • 36
    Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol. 2018;15:230-40.
  • 37
    Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37-43.
  • 38
    Bobo WV, Yawn BP, St Sauver JL, Grossardt BR, Boyd CM, Rocca WA. Prevalence of Combined Somatic and Mental Health Multimorbidity: Patterns by Age, Sex, and Race/Ethnicity. Journals Gerontol A Biol Sci Med Sci. 2016;71:1483-91.
  • 39
    Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA. Multimorbidity prevalence and patterns across socioeconomic determinants: a cross-sectional survey. BMC Public Health. 2012;12:201.
  • 40
    Penninx BWJH, Lange SMM. Metabolic syndrome in psychiatric patients: Overview, mechanisms, and implications. Dialogues Clin Neurosci. 2018;20:63-73.
  • 41
    Carvalho JN De, Cancela MDC, Souza DLB de. Lifestyle factors and high body mass index are associated with different multimorbidity clusters in the Brazilian population. PLoS One. 2018;13:1-15.
  • 42
    Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98-107.
  • 43
    Mandviwala T, Khalid U, Deswal A. Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Curr Atheroscler Rep. 2016;18.
  • 44
    Rajan TM, Menon V. Psychiatric disorders and obesity: A review of association studies. J Postgrad Med. 2017;63:182-90.
  • 45
    Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18-33.
  • 46
    Tavares NUL, Luiza VL, Oliveira MA, Costa KS, Mengue SS, Arrais PSD, et al. Free access to medicines for the treatment of chronic diseases in Brazil. Rev Saude Publica. 2016;50:1-10.
  • 47
    Di Cesare M, Bentham J, Stevens GA, Zhou B, Danaei G, Lu Y, et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387:1377-96.
  • 48
    Zachary Z, Forbes B, Lopez B, Pedersen G, Welty J, Deyo A, et al. Self-quarantine and weight gain related risk factors during the COVID-19 pandemic. Obes Res Clin Pract. 2020;14:210-6.
  • 49
    Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: Systematic review of the current evidence. Brain Behav Immun. 2020;89:531-42.
  • 50
    Lobo LAC, Canuto R, Dias-da-Costa JS, Pattussi MP. Tendência temporal da prevalência de hipertensão arterial sistêmica no Brasil. Cad Saude Publica. 2017;33.
  • 51
    United Nations. Department of Economic and Social Affairs, Population Division: World Population Prospects 2019, Volume II: Demographic Profiles. 2019;II:1-5.

Publication Dates

  • Publication in this collection
    05 June 2023
  • Date of issue
    2023

History

  • Received
    22 Sept 2022
  • Accepted
    13 Jan 2023
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