Background: Metabolic syndrome (MetS) is a combination of risk factors for cardiovascular disease and type 2 diabetes mellitus. The prevalence of MetS worldwide is increasing. There is no study investigating the economic burden of MetS, especially in developing countries, on medication-related expenditure. The aim of this study was to investigate the association of medication-related expenditures with MetS and to explore how physical activity (PA) may influence this association. Methods: A total of 620 participants, 50 years or older, randomly selected in the city of Bauru, Brazil. Participants were followed from 2010 to 2014, and data on health care expenditure were collected annually. PA questionnaire was applied at baseline, 2 (2012), and 4 (2014) years later. Results: Mean age was 64.7 (95% confidence interval, 64.1–65.3). MetS was associated with higher medication expenditure related to diseases of the circulatory (P <.01) and endocrine (P <.01) systems. MetS explained 17.2% of medication-related expenditures, whereas PA slightly attenuated this association, explaining 1.1% of all health care costs. Conclusion: This study demonstrates that MetS has a significant burden on health care expenditures among adults, whereas PA seems to affect this phenomenon significantly, but in low magnitude.

Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors, including abdominal obesity, dyslipidemia, blood pressure, and elevated fasting glucose (impaired fasting glucose or type 2 diabetes mellitus). MetS is a predictor of cardiovascular disease and type 2 diabetes mellitus.1 Moreover, the presence of MetS is associated with cardiovascular disease and all-cause mortality, even after adjustments for potential confounders.2

There are 3 widely accepted definitions for MetS, set out by the World Health Organization,3 the National Cholesterol Education Program (NCEP-ATP III),4 and the International Diabetes Federation.5 Because of these varying definitions, the true prevalence of MetS is uncertain. In general, the prevalence of MetS varies between 34% and 39% in developed countries.6 Among developing nations, the prevalence is estimated to be between 16% and 32%.7 In Brazil, approximately 30% of adults are expected to have MetS.8

Aside from the challenge of reducing and preventing MetS through exercise,9,10 there is an economic aspect related to chronic diseases, especially in developing nations.11 Scholze et al12 estimated the economic burden of MetS at €24.4, €1.9, and €4.8 million in Germany, Spain, and Italy, respectively. In addition, this estimate is expected to rise by 59%, 179%, and 157% by 2020.12 Although there are few studies investigating health care expenditures directly related to MetS, several studies have investigated the economic burden of its components individually.1315

In the United States, the presence of hypertension added US$68.4 billion to annual all-cause medication costs in 2007.13 In addition, the estimated expenditure related to diabetes in 2012 was US$245 billion, 41% higher than the 2007 estimate.14 Regarding obesity, it is estimated that medical costs associated with obesity-related diseases will add US$48–66 billion a year to the health care costs by 2030.15

On the other hand, meeting the current physical activity (PA) guidelines is associated with lower health care expenditure in people with and without chronic disease.16 However, there is no study investigating the economic burden of MetS on health care expenditures, as well as how PA may influence this association, in developing countries. Therefore, the aim of this study was to investigate the association of medication-related expenditures in adults with MetS from the Brazilian National Health System (NHS). Moreover, we wanted to explore how the PA may influence this association.

Methods

Sample

The present study is part of an ongoing cohort study carried out with adults from the Brazilian NHS.1720 The analyses of this study were conducted using data collected from August 2010 to December 2014 in the city of Bauru, located in the central region of São Paulo, the most industrialized state in Brazil.

The Department of Health of Bauru (subordinated to the NHS) administrates primary care services in the city, which is composed of 17 basic health care units (BHUs). BHUs are small primary health care centers, in which a wide variety of health professionals (eg, general practitioners, gynecologists, obstetricians, psychiatrists, dentists, and nurses) offer health services of low complexity (eg, medical consultations, medicine prescription, and vaccinations) to the population of a specific region of the city. All services are free of charge and characterized as primary health care. More complex cases are directed to hospitals linked to the NHS.

Each BHU keeps records of all patients throughout the years and, based on these records and on sample size calculation,21 the researchers randomly selected 963 adults in 5 BHUs. The biggest BHU in each geographical region of the city (north, south, west, east, and downtown) was selected. Researchers contacted these adults by telephone to verify inclusion criteria: (1) aged >50 years, (2) registered for at least 1 year at the BHU, and (3) active health care service registration (at least 1 medical visit in the previous 6 mo). The present study only analyzed participants with data available for all time-points (2010, 2012, and 2014).

The study protocol was reviewed and approved by the Ethics Committee from São Paulo State University (UNESP), Brazil, and all participants provided written consent to participate in the study.

Health Care Expenditure

Health care expenditures were assessed annually from 2010 to 2014. The methodology for these assessments was described previously.22 Briefly, expenditures related to MetS and its components were estimated from medical records. The medical records keep a registry of laboratory tests, medical consultations, and medication dispensed. As we aimed to investigate the expenses related to medication use, only data from medication dispensed were used. In addition, categories of medications were created based on the International Classification of Diseases.23

Invoices obtained from BHUs were used to compute the dosage and market price of medication used by patients. All expenditures were computed in the Brazilian currency (Real) and converted to US$ considering the inflation of the period (2010–2014) to ensure comparability.

Metabolic Syndrome

The presence of MetS was assessed at baseline (2010), 2 (2012), and 4 (2014) years later. MetS was defined according to the criteria established by the NCEP-ATP III (3 or more of the following 5 risk factors): high blood pressure (≥130 mm Hg systolic or ≥85 mm Hg diastolic), central obesity (waist circumference >102 cm for men and >88 cm for women), high triglycerides (≥150 mg/dL), low high density lipoprotein cholesterol (<40 mg/dL for men and <50 mg/dL for women), and high fasting plasma glucose (≥100 mg/dL) or drug treatment for these conditions.

Blood pressure was assessed using a standard mercury brachial artery sphygmomanometer and stethoscope. Waist circumference was measured using a nonelastic metric tape, with the participant in standing position and at the maximum point of normal expiration. Biochemical levels (when available) of fasting glucose, triglycerides, and high density lipoprotein cholesterol or chronic condition diagnosis (diabetes, dyslipidemia, and hypertension) were obtained from medical records.24 All the data collection (blood pressure, waist circumference, and medical records analysis) were performed by trained researchers.

For the present analysis, categories according to the presence of MetS across time were created: (1) No MetS from 2010 to 2014, (2) MetS in 1 or 2 time-points, and (3) MetS in all time-points (since 2010).

Covariates

Anthropometric, sociodemographic, behavioral, and health covariates were considered for potential adjustments. Anthropometric variables were height, weight, and body mass index (BMI). Sociodemographic variables were sex (male or female), chronological age, and schooling. Behavioral variables used were smoking status (categorized as “yes” and “no”) and PA. PA was assessed through the Baecke questionnaire.25 The questionnaire comprises 16 questions scored on a 5-point Likert scale (never, seldom, sometimes, very often, and always), addressing 3 different PA domains: occupational (8 questions), sports participation (1 yes/no question, which is split up into another 3 questions when the participant pick up “yes” [intensity, weekly volume, and previous time of engagement]), and leisure time (7 questions). The Baecke questionnaire has a validated Brazilian Portuguese version.26,27 Health variables were the presence (categorized as “yes” and “no”) of other chronic diseases, such as arrhythmia, osteoporosis, and arthritis.

Statistical Procedures

Mean values, SDs, and 95% confidence intervals for numerical variables and percentages for categorical variables summarized the characteristics of the sample. Kruskal–Wallis test was used for comparison between medication expenditure across categories of MetS. Analysis of variance for repeated measures was used to assess changes in health care costs over the 4 years of follow-up. The assumption of sphericity has been tested using the Mauchly test, whereas the degrees of freedom were adjusted using the Greenhouse–Geisser approach when the model was not adequately fit. Analysis of variance model was simultaneously adjusted by all covariates (sex, age, years of formal education, smoking, and PA). Measures of effect size were expressed as eta-squared (η2) values. The level of significance was set at P < .05, and the software BioEstat 5.0 (Instituto Mamirauá, Tefé, Brazil) was used to perform all analyses.

Results

Follow-up data from 2010 to 2014 were available for a total of 620 participants (166 men [26.8%] and 454 women [73.2%]; P < .01). At baseline, mean age was 64.7 (95% confidence interval, 64.1–65.3) years. Prevalence of MetS from 2010 to 2012 was 26.1% and 30%, respectively (P = .02), and from 2010 to 2014 was 26.1% and 30.1%, respectively (P = .04). Participants with MetS since baseline presented higher weight (85.9 [15.1]; P < .01) and BMI (34.5 [5.3]; P < .01). In addition, participants with MetS since baseline were more frequently identified as overweight/obese from 2010 to 2014 (2.9 [0.3]; P < .01) and presented lower PA levels (18.8 [3.6]; P < .01) (Table 1).

Table 1

Characteristics of Participants According to MetS Groups

No MetS (n = 366)MetS in 1 or 2 time-points (n = 154)MetS since baseline (n = 100)P value
Baseline
 Age, y65.1 (8.9)64.3 (8.5)64.1 (7.9).47
 Weight, kg67.2 (12.9)80.6 (14.7)a85.9 (15.1)a,b<.01
 Height, m1.56 (0.14)1.57 (0.08)1.57 (0.09).60
 BMI, kg/m227.2 (4.1)32.5 (5.8)a34.5 (5.3)a,b<.01
 Economic status (score)18.3 (5.6)18.6 (6.1)18.7 (5.5).74
 Physical activity (score)7.1 (1.4)6.8 (1.6)6.9 (1.6).05
 BMI (overweight/obesity)72.7%95.5%99%<.01
 Smoking (yes)10.7%14.3%8.1%.46
 Arrhythmia (yes)9.8%12.3%12.1%.42
 Osteoporosis (yes)21.6%25.3%22.1%.70
 Arthritis (yes)33.6%46.8%42.1%.02
Longitudinal
 BMIc2.0 (1.2)2.7 (0.6)a2.9 (0.3)a<.01
 Physical activity (score ∑2010–2014)19.7 (3.6)18.6 (3.9)a18.8 (3.6)a<.01

Abbreviations: BMI, body mass index; MetS, metabolic syndrome. Note: Values are mean (SD).

aDifferent from “No MetS” group. bDifferent from “MetS in 1 or 2 time-points” group.

cNumber of assessments that participants were classified as overweight/obese from 2010 to 2014.

Table 2 shows the medication expenditure according to MetS groups. Participants with MetS since baseline presented higher expenditure related to medication for diseases of the circulatory system (US$ 42.4 [62–693]; P < .01) and for endocrine, nutritional, and metabolic diseases (US$ 136.1 [36–2627]; P < .01). Total expenditure related to medication use from 2010 to 2014 was also higher among participants with MetS since baseline (US$ 241.4 [46–2772]; P < .01).

Table 2

Medication-Related Expenditure According to MetS Groups From 2010 to 2014

No MetS (n = 366)MetS in 1 or 2 time-points (n = 154)MetS since baseline (n = 100)
Medication (ICD)US$ Median (IR)US$ Median (IR)US$ Median (IR)P value*
Diseases of the circulatory system (I00-I99)18.74 (750,50)32.44 (606,50)a42.47 (693,62)a,b<.01
Diseases of the musculoskeletal system and connective tissue (M00-M99)0.00 (2,91)0.00 (257,96)0.00 (105,66).64
Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism (D00-D99)0.00 (0,00)0.00 (0,00)0.00 (0,00).70
Diseases of the nervous system (G00-G99)0.00 (0,00)0.00 (0,00)0.00 (30,41).20
Diseases of the respiratory system (J00-J99)0.00 (0,00)0.00 (0,00)0.00 (0,33).78
Endocrine, nutritional, and metabolic diseases (E00-E99)17.02 (1029,99)56.17 (1424,95)a136.13 (2627,36)a,b<.01
Total61.62 (3401,69)128.26 (1465,46)a241.36 (2772,46)a,b<.01
Total percentage changec+108%+291%

Abbreviations: ICD, International Classification of Diseases; IR, interquartile range; MetS, metabolic syndrome.

aDifferent from “No MetS” group. b Different from “MetS in 1 or 2 time-points” group. cIn relation to “No MetS” group.

*Kruskal–Wallis test.

Expenditures related to medication use from 2010 to 2014 were significantly impacted by the presence of MetS (η2 = 0.171 [17.1%]; P < .01). Moreover, the impact of MetS was slightly attenuated after adjustments, especially by PA, which was responsible for 1.1% of the variance observed on expenditure trend from 2010 to 2014 (Figure 1).

Figure 1
Figure 1

—Total medication expenditure from 2010 to 2014 according to groups of MetS. Note: ANOVA model adjusted by sex (P = .93 and η2 = 0.01%), age (P = .61 and η2 = 0.01%), formal education (P = .21 and η2 = 0.03%), smoking (P = .38 and η2 = 0.01%), and physical activity (P = .01 and η2 = 1.1%). ANOVA indicates analysis of variance; CI, confidence interval; MetS, metabolic syndrome.

Citation: Journal of Physical Activity and Health 16, 10; 10.1123/jpah.2018-0609

Discussion

The main findings of the present study were that participants with MetS since baseline presented higher expenditures related to medication use for diseases of the circulatory system and for endocrine, nutritional, and metabolic diseases than participants without MetS. In addition, PA seems to slightly influence this association.

The prevalence of MetS in our study was 26.1% and 30.1% at baseline and last follow-up, respectively. This result is in accordance with previous findings.28,29 The prevalence of MetS in the US increased from 32.9% in 2003–2004 to 34.7% in 2011–2012.28 The increased prevalence observed over time may be partially explained by the age of our sample (≥50 y old).

Changes in the endocrine system, lifestyle, and body composition are expected to occur with age.30 Decreased sex hormones may lead to central fat redistribution, resulting in an increase in abdominal adipose tissue.31,32 In addition, it is well established that abdominal adiposity plays an important role in insulin resistance, and the age-related change in insulin sensitivity is likely due to an increase in adiposity.33 The reduced levels of PA and increased time spent in sedentary activities, a common pattern in aging,34,35 may also influence energy expenditure, thus, leading to an increase in adipose tissue.

It is already known that the prevalence of MetS increases with age.28 Moreover, the association of higher medical expenditures with chronic diseases is not entirely new in the literature. Wang et al13 found that adults with hypertension were more likely to have higher medication expenditure than normotensive adults. People with diabetes have greater medical expenditures when compared with those without the disease.36 Moreover, higher prescription drug expenditure was found among obese adults.37,38 These data regarding chronic disease-related expenditure, especially from developing countries, provide information on isolated diseases. To the best of our knowledge, this is the first study investigating the economic burden related to medication expenditure of MetS in adults from developing settings.

As the pathophysiology of MetS addresses a combination of different conditions, such as abdominal obesity and risk factors for hypertension and diabetes, different drugs might be used for controlling each of this condition, thus, increasing the expenditure related to medication use. Moreover, the increased expenses related to health care may be related to an oversupply of medication39 and lower levels of PA.21,22,40

In our study, PA did not eliminate the impact of MetS on medication expenses, just slightly attenuated it. Previous studies have found similar results. Chevan et al41 investigated the short-term health care expenditure associated with PA. They found that PA may have little effect on general expenses, such as health services and drugs. On the other hand, there is some evidence that leisure PA might be associated with lower medical expenditure42 and increasing PA may reduce health care expenditures.43 Given the health benefits related to regular engagement in PA,44 it should be encouraged in the primary care system to both mitigate costs and promote the improvement of other health aspects.

In this study, PA explained only 1.1% of the health care expenditure observed over time. However, being considered physically active was inversely associated with number of diagnosis of MetS. Previous findings have shown similar results. Katzmarzyk et al45 estimated that 2.6% of the total health care costs are attributable to physical inactivity, while a recent study found this proportion to be a little bit higher, 11.1%.43 These differences in estimates may be explained by the large variation in methodologies, health systems, PA measurement, adjustments for covariates, type of costs, and time frame.46 Thus, using standard methods and the best global data available, an estimate of 0.64% was calculated as the global proportion of total health care costs attributable to physical inactivity.47

Several limitations of the present study need to be highlighted. The diagnose of MetS used in our study is not the only one present in the literature. Our results may be underestimated or overestimated based on the MetS criteria used. However, the criteria used in our study are widely accepted.1 Another limitation is the presence of undetected comorbid illness (preexisting condition), such as musculoskeletal pain, arthritis, and depression, which might influence prescription and use of medication.

This study has strengths, such as the random selection of the sample and the quality in the measurement of direct medication-related expenditures. This is the first longitudinal study, especially in a developing country, to investigate the association of MetS with medication-related expenditure. Public health efforts on prevention of MetS and PA promotion may help to reduce expenditure related to medication in the public health system.

In summary, our study provides evidence that MetS has a significant burden over health care expenditures related to medication use in adults, whereas PA seems to affect this phenomenon significantly, but in low magnitude.

Acknowledgments

The authors acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES), the São Paulo Research Foundation (FAPESP [#2015/17777-3, #2016/11140-6 and #2018/01744-7]), the Health Secretary of Bauru, and the health professionals of primary care units. This work was not funded by an external source.

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    Milanovic Z, Jorgić B, Trajković N, Sporis G, Pantelić S, James N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging. 2013;8:549. PubMed ID: 23723694 doi:10.2147/CIA.S44112

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    Chen C-C, Blank RH, Cheng S-H. Medication supply, healthcare outcomes and healthcare expenses: longitudinal analyses of patients with type 2 diabetes and hypertension. Health Policy. 2014;117(3):374–381. doi:10.1016/j.healthpol.2014.04.002

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    Turi BC, Codogno JS, Fernandes RA, Monteiro HL. Caminhada e gastos com saúde em adultos usuários do sistema público de saúde brasileiro: estudo transversal retrospectivo. Cien Saude Colet. 2015;20(11):3561–3568. PubMed ID: 26602733 doi:10.1590/1413-812320152011.00092015

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    Carlson SA, Fulton JE, Pratt M, Yang Z, Adams EK. Inadequate physical activity and health care expenditures in the United States. Prog Cardiovasc Dis. 2015;57(4):315–323. PubMed ID: 25559060 doi:10.1016/j.pcad.2014.08.002

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    Katzmarzyk PT, Janssen I. The economic costs associated with physical inactivity and obesity in Canada: an update. Can J Appl Physiol. 2004;29(1):90–115. PubMed ID: 15001807 doi:10.1139/h04-008

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    Ding D, Kolbe-Alexander T, Nguyen B, Katzmarzyk PT, Pratt M, Lawson KD. The economic burden of physical inactivity: a systematic review and critical appraisal. Br J Sports Med. 2017;51(19):1392–1409. PubMed ID: 28446455 doi:10.1136/bjsports-2016-097385

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    Ding D, Lawson KD, Kolbe-Alexander TL, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–1324. PubMed ID: 27475266 doi:10.1016/S0140-6736(16)30383-X

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If the inline PDF is not rendering correctly, you can download the PDF file here.

Lemes is with the Department of Physical Therapy, São Paulo State University (UNESP), Presidente Prudente, São Paulo, Brazil. Fernandes, Codogno, de Morais, and Koyama are with the Department of Physical Education, São Paulo State University (UNESP), Presidente Prudente, São Paulo, Brazil. Turi-Lynch is with the Department of Physical Education and Exercise Science, Lander University, Greenwood, SC, USA. Monteiro is with the Department of Physical Education, São Paulo State University (UNESP), Bauru, São Paulo, Brazil.

Lemes (itolemes@hotmail.com) is corresponding author.
  • View in gallery

    —Total medication expenditure from 2010 to 2014 according to groups of MetS. Note: ANOVA model adjusted by sex (P = .93 and η2 = 0.01%), age (P = .61 and η2 = 0.01%), formal education (P = .21 and η2 = 0.03%), smoking (P = .38 and η2 = 0.01%), and physical activity (P = .01 and η2 = 1.1%). ANOVA indicates analysis of variance; CI, confidence interval; MetS, metabolic syndrome.

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    Chen C-C, Blank RH, Cheng S-H. Medication supply, healthcare outcomes and healthcare expenses: longitudinal analyses of patients with type 2 diabetes and hypertension. Health Policy. 2014;117(3):374–381. doi:10.1016/j.healthpol.2014.04.002

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    Chevan J, Roberts DE. No short-term savings in health care expenditures for physically active adults. Prev Med. 2014;63:1–5. doi:10.1016/j.ypmed.2014.02.020

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    Ding D, Lawson KD, Kolbe-Alexander TL, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388(10051):1311–1324. PubMed ID: 27475266 doi:10.1016/S0140-6736(16)30383-X

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