Background
Once regarded as a disease-specific to high-income countries, obesity is rising in low and middle-income countries too [
1], where are living almost 2 out of every 3 individuals with obesity worldwide [
2]. Obesity is a complex chronic disease, defined by World Health Organization (WHO) as “abnormal or excessive fat accumulation that presents a risk to health” [
3]. It has multiple determinants and consequences, currently affecting several population groups [
4,
5].
On a global level, over 50% of the 693 million individuals with obesity can be found in only 10 countries, including Brazil, which ranks fifth on this list [
2]. Between 2006 and 2019, the prevalence of obesity in Brazil rose from 11.8 to 20.3%, an average increase of 0.6% a year. This growth was observed nationwide, in both sexes and across all age groups and educational levels [
6].
Obesity stands out for being both a non-communicable disease (NCD) and a risk factor for other NCDs, such as arterial hypertension and diabetes mellitus [
4,
7‐
9]. Hypertension is about twice as prevalent in men and women with obesity as in those with a normal weight [
10]. In Brazil, it was estimated that approximately 40.0% of diabetes cases are attributed to obesity, especially among women [
11].
Besides being associated with high mortality, chronic NCDs can lead to disabilities [
12]. In Brazil, NCDs represent a major public health problem [
13]. Rates of hypertension (one of the most important risk factors for cardiovascular diseases) and diabetes are climbing every year, affecting 25.2 and 8.2% of Brazilian adults in 2020, respectively [
14].
Given the long-term nature of NCDs, they typically require constant health care, in a generally non-curative process of treatment, that requires routine preventive and therapeutic interventions. Consequently, permanent health system utilization is often necessary for patients with chronic diseases [
14].
Health service utilization represents the center of health system functioning, encompassing all direct (consultations, hospitalizations) or indirect (preventive and diagnostic exams) contact with health services [
15]. The use of these services is generally associated with a greater need for health care [
16], and stems from multiple interacting factors, including individual and contextual aspects, and those related to the type of care [
15]. In Brazil, the literature shows greater use of these services by women, individuals with chronic diseases, and patients with a higher number of comorbidities [
17‐
20].
With regard to obesity, despite its growing prevalence in practically all countries, with rates almost trebling over the last four decades [
21], this chronic disease appears to be yet invisible in primary health care in Brazil [
22]. In addition, there is a dearth of studies on the association between obesity and health service utilization, particularly in low- and middle-income countries. The majority of Brazilian studies investigating the factors related to the use of health services fail to include obesity [
16,
20,
23‐
25], while those addressing the condition have tended to center on estimating the cost of the illness [
26‐
29].
Therefore, the objective of the present study was to analyze the association between obesity and health system utilization (considering services related to hypertension and/or diabetes), based on representative data for the Brazilian population.
Results
The 2013 PNS interviewed 60,202 individuals aged ≥18 years. Of the original total, 800 individuals (1.3%) were excluded for missing weight and/or height data, giving a total sample of 59,402 participants for analysis. This population had a mean weight of 70.4 Kg (± 15.2 Kg), height of 1.63 m (± 9.7 cm), and mean BMI of 26.5 Kg/m2 (data not shown). Of the population assessed, 36.4% had overweight, and over a fifth (20.8%), obesity (data not shown).
The percentage distribution for the three BMI categories, according to sociodemographic characteristics and sex, is summarized in Table
1. Nationally, 16.8% of men had obesity, versus 24.3% of women.
Table 1
Distribution of population by BMI, sociodemographic characteristics, and sex (n = 59,402). PNS, Brazil, 2013
BRAZIL | 44.5 | 43.4–45.6 | 38.8 | 37.7–39.8 | 16.8 | 15.9–17.6 | 42.0 | 41.0–43.0 | 33.7 | 32.8–34.5 | 24.3 | 23.5–25.1 |
Macroregion |
North | 45.8 | 43.4–48.2 | 39.3 | 36.9–41.7 | 14.9 | 13.2–16.6 | 46.8 | 44.9–48.7 | 33.3 | 31.5–35.1 | 19.8 | 18.3–21.4 |
Northeast | 49.2 | 47.3–51.0 | 36.8 | 35.1–38.5 | 14.0 | 12.8–15.3 | 44.5 | 42.9–46.1 | 34.1 | 32.6–35.5 | 21.4 | 20.1–22.7 |
Midwest | 42.9 | 40.7–45.1 | 38.3 | 35.8–40.7 | 18.8 | 17.2–20.5 | 43.3 | 41.5–45.0 | 32.0 | 30.3–33.8 | 24.7 | 23.1–26.3 |
Southeast | 43.3 | 41.2–45.3 | 39.4 | 37.5–41.2 | 17.4 | 15.8–19.0 | 40.2 | 38.5–41.9 | 33.7 | 32.1–35.2 | 26.1 | 24.6–27.6 |
South | 39.7 | 37.5–42.0 | 40.4 | 37.8–43.0 | 19.9 | 17.9–21.9 | 39.8 | 37.3–42.4 | 33.8 | 31.8–35.9 | 26.3 | 24.2–28.5 |
Residence area |
Urban | 42.6 | 41.3–43.8 | 39.6 | 38.5–40.8 | 17.8 | 16.8–18.8 | 41.7 | 40.6–42.8 | 33.6 | 32.6–34.5 | 24.7 | 23.8–25.6 |
Rural | 55.3 | 53.0–57.6 | 33.7 | 31.8–35.6 | 11.0 | 9.4–12.5 | 44.2 | 41.7–46.6 | 34.1 | 32.0–36.3 | 21.7 | 19.8–23.6 |
Age (years) |
18–29 | 60.0 | 58.0–62.0 | 29.4 | 27.5–31.3 | 10.6 | 9.1–12.0 | 62.5 | 60.6–64.5 | 23.4 | 21.7–25.0 | 14.1 | 12.6–15.6 |
30–39 | 40.9 | 38.6–43.2 | 42.0 | 39.8–44.3 | 17.1 | 15.6–18.5 | 42.4 | 40.6–44.3 | 33.9 | 32.0–35.7 | 23.7 | 22.0–25.4 |
40–49 | 36.0 | 33.8–38.2 | 43.2 | 41.0–45.4 | 20.8 | 18.8–22.8 | 33.3 | 31.2–35.4 | 38.6 | 36.6–40.6 | 28.1 | 26.2–30.0 |
50–59 | 34.6 | 31.8–37.3 | 44.2 | 41.3–47.0 | 21.3 | 18.9–23.6 | 29.4 | 27.4–31.4 | 37.8 | 35.6–40.1 | 32.7 | 30.3–35.1 |
≥ 60 | 42.3 | 39.9–44.7 | 39.7 | 37.2–42.3 | 17.9 | 15.7–20.2 | 34.0 | 32.0–36.1 | 38.4 | 36.4–40.4 | 27.6 | 25.7–29.4 |
Race/skin color |
White | 40.2 | 38.6–41.8 | 40.6 | 39.0–42.2 | 19.1 | 17.8–20.5 | 41.8 | 40.4–43.2 | 33.3 | 32.0–34.6 | 24.9 | 23.7–26.0 |
Black | 46.8 | 43.3–50.2 | 35.7 | 32.4–38.9 | 17.6 | 14.6–20.5 | 41.2 | 38.1–44.2 | 30.5 | 27.8–33.3 | 28.3 | 25.6–31.0 |
Yellow | 57.6 | 47.4–67.9 | 35.3 | 25.4–45.2 | 7.0 | 2.8–11.2 | 53.5 | 43.6–63.4 | 31.4 | 22.6–40.1 | 15.1 | 9.29–20.9 |
Mixed-race | 48.3 | 46.7–49.9 | 37.4 | 36.0–38.9 | 14.2 | 13.1–15.3 | 42.2 | 40.8–43.5 | 34.8 | 33.4–36.1 | 23.0 | 21.9–24.2 |
Indigenous | 47.8 | 35.4–60.3 | 36.9 | 24.9–48.8 | 15.3 | 6.6–24.0 | 40.8 | 30.2–51.4 | 35.4 | 25.0–45.8 | 23.8 | 14.7–32.9 |
Education |
No formal schooling or incomplete primary | 47.8 | 46.1–49.5 | 37.1 | 35.5–38.7 | 15.0 | 13.9–16.2 | 35.0 | 33.6–36.4 | 36.4 | 34.9–37.8 | 28.6 | 27.3–30.0 |
Complete primary or incomplete secondary | 50.8 | 48.1–53.5 | 33.5 | 31.1–36.0 | 15.7 | 13.5–17.9 | 42.1 | 39.6–44.7 | 33.8 | 31.3–36.2 | 24.1 | 22.0–26.1 |
Complete secondary or incomplete higher | 42.0 | 40.1–43.9 | 40.4 | 38.5–42.2 | 17.6 | 16.1–19.1 | 47.0 | 45.4–48.6 | 31.3 | 29.8–32.8 | 21.7 | 20.3–23.1 |
Complete higher or above | 30.6 | 27.8–33.4 | 47.3 | 44.3–50.4 | 22.1 | 19.5–24.7 | 49.2 | 46.5–51.8 | 31.8 | 29.5–34.0 | 19.1 | 17.0–21.1 |
For both sexes, the Southern region of the country accounted for the greatest number of individuals with obesity and dwellers in urban areas. The obesity rate increased with age, particularly from 30 years and older, where the prevalence of obesity was double among those aged 50–59 years, compared the 18–29 years age group. Regarding rates among older adults, 27.6% of women and 17.9% of men were with obesity. Men with obesity had a greater probability of being white, and progressively higher obesity rates were observed with increasing education level. For women, obesity was more frequent in black individuals and those with no formal schooling or with incomplete primary (Table
1).
The prevalence of individuals diagnosed with hypertension and/or diabetes in the 59.402 participants surveyed, together with rates of health service utilization for the two diseases, are given in Table
2. Overall, 21.8% had hypertension, 6.8% had diabetes and 3.9% had both diseases (data not shown). Of the hypertension group, 38.1% were with overweight and 36.3% with obesity, whereas in the diabetes group, 38.2% were with overweight and 37.0% with obesity (data not shown).
Table 2
Type of health service used by disease and sex (n = 59,402). PNS, Brazil, 2013
Prevalence of disease diagnosed | SAH | 18.7 | 17.9–19.4 | 24.7 | 24.0–25.5 |
DM | 5.9 | 5.4–6.5 | 7.5 | 7.0–8.0 |
SAH and DM | 3.1 | 2.7–3.5 | 4.7 | 4.3–5.1 |
Routine visits to doctor or health service | SAH | 9.9 | 9.3–10.5 | 15.5 | 14.8–16.2 |
DM | 3.4 | 3.0–3.8 | 5.0 | 4.6–5.4 |
SAH and DM | 3.7 | 3.3–4.2 | 5.7 | 5.3–6.2 |
Prescribed exams done | SAH | 12.4 | 11.8–13.1 | 17.4 | 16.7–18.0 |
DM | 4.3 | 3.8–4.7 | 5.4 | 5.0–5.8 |
SAH and DM | 4.6 | 4.1–5.0 | 6.0 | 5.6–6.4 |
Referral to specialista | SAH | 9.1 | 8.5–9.7 | 12.0 | 11.3–12.6 |
DM | 2.6 | 2.3–3.0 | 3.2 | 2.8–3.5 |
SAH and DM | 3.2 | 2.8–3.6 | 4.0 | 3.7–4.4 |
Hospitalization for disease or related complication | SAH | 2.3 | 2.0–2.6 | 3.7 | 3.3–4.0 |
DM | 0.8 | 0.6–1.0 | 0.9 | 0.7–1.0 |
SAH and DM | 1.1 | 0.9–1.3 | 1.8 | 1.6–2.1 |
Rates of the two diseases were higher in women, as were figures for all outcomes investigated, particularly for the variables “routine visits to doctor or health service for hypertension” (15.5% versus 9.9% in men) and “exams done due hypertension” (17.4% versus 12.4% in men) (Table
2).
Regarding the relationship between BMI category and health service utilization, a gradient was observed, with commensurately higher rates of use of all services among subjects with overweight and obesity, compared with under/normal weight individuals. This pattern was evident for both sexes but was stronger among women. Overall, 8.3% (95%CI 7.5–9.1) of women of under/normal weight reported routine visits to the doctor or health service for hypertension, compared to 25.1% (95%CI 23.6–26.7) of women with obesity. Hospital admission for diabetes was the only outcome that showed no statistically significant difference for BMI category, in both sexes (Table
3).
Table 3
Type of health service used by disease, sex and BMI category (n = 59,402). PNS, Brazil, 2013
Routine visits to doctor or health service | SAH | 5.9 | 5.2–6.6 | 10.7 | 10.0–11.8 | 18.6 | 16.5–20.7 | 8.3 | 7.5–9.1 | 16.8 | 15.6–18.1 | 25.1 | 23.6–26.7 |
DM | 2.2 | 1.7–2.6 | 3.6 | 2.8–4.3 | 6.6 | 5.3–7.8 | 2.5 | 2.1–2.9 | 5.7 | 4.9–6.4 | 8.1 | 7.1–9.0 |
SAH and DM | 2.2 | 1.8–2.8 | 3.9 | 3.2–4.8 | 7.2 | 6.0–8.6 | 2.7 | 2.3–3.2 | 6.3 | 5.5–7.1 | 9.7 | 8.6–10.8 |
Exams done | SAH | 7.8 | 7.0–8.6 | 12.8 | 11.7–13.9 | 23.7 | 21.5–25.9 | 9.4 | 8.6–10.3 | 19.1 | 17.8–20.4 | 27.6 | 26.0–29.3 |
DM | 2.8 | 2.2–3.3 | 4.4 | 3.6–5.2 | 8.0 | 6.6–9.3 | 2.8 | 2.3–3.2 | 6.1 | 5.3–6.8 | 8.6 | 7.6–9.6 |
SAH and DM | 2.9 | 2.4–3.5 | 4.6 | 3.9–5.5 | 8.7 | 7.4–10.2 | 3.0 | 2.6–3.5 | 6.8 | 6.0–7.6 | 9.6 | 8.6–10.7 |
Referral to specialistc | SAH | 5.4 | 4.7–6.1 | 9.8 | 8.8–10.9 | 17.3 | 15.3–19.3 | 6.4 | 5.6–7.2 | 13.7 | 12.6–14.8 | 18.4 | 17.1–19.8 |
DM | 1.7 | 1.2–2.1 | 2.8 | 2.2–3.4 | 4.9 | 3.8–6.0 | 1.4 | 1.1–1.7 | 3.6 | 3.0–4.2 | 5.4 | 4.6–6.2 |
SAH and DM | 2.0 | 1.6–2.6 | 3.2 | 2.6–4.0 | 6.0 | 4.9–7.3 | 1.7 | 1.3–2.0 | 4.7 | 4.1–5.5 | 6.9 | 6.1–7.8 |
Hospitalization | SAH | 1.4 | 1.1–1.8 | 2.3 | 1.7–2.8 | 4.6 | 3.4–5.8 | 1.9 | 1.5–2.2 | 3.8 | 3.2–4.4 | 6.4 | 5.5–7.2 |
DM | 0.7 | 0.4–0.9 | 0.7 | 0.4–1.0 | 1.4 | 0.8–2.0 | 0.6 | 0.4–0.7 | 0.9 | 0.6–1.2 | 1.4 | 0.9–1.8 |
SAH and DM | 0.8 | 0.5–1.1 | 0.9 | 0.7–1.3 | 2.3 | 1.6–3.2 | 0.8 | 0.6–1.0 | 2.0 | 1.6–2.5 | 3.2 | 2.5–4.0 |
The results of the multiple regression model revealed that higher prevalence ratios were associated with individuals with overweight and obesity, where this also held true for all outcomes measured and for both sexes. After adjusting for sociodemographic variables, the strength of association was attenuated for all outcomes. However, adjusted Prevalence Ratios (aPRs) still showed around double (or greater) the rates of health service use among subjects with obesity versus under/normal weight individuals (Table
4).
Table 4
Association of obesity and type of service by sex and disease diagnosed (n = 59,402). PNS, Brazil, 2013
Routine visits to doctor or health service | SAH | 1.79 | 1.52–2.11 | 3.12 | 2.65–3.68 | 2.01 | 1.78–2.28 | 3.00 | 2.66–3.38 | 1.44 | 1.23–1.68 | 2.34 | 1.99–2.74 | 1.45 | 1.29–1.63 | 2.03 | 1.81–2.26 |
DM | 1.65 | 1.22–2.22 | 3.04 | 2.26–4.09 | 2.25 | 1.82–2.78 | 3.20 | 2.62–3.90 | 1.30 | 0.98–1.72 | 2.19 | 1.64–2.93 | 1.62 | 1.32–1.99 | 2.14 | 1.75–2.61 |
SAH and DM | 1.75 | 1.33–2.30 | 3.20 | 2.41–4.27 | 2.33 | 1.90–2.86 | 3.59 | 2.98–4.32 | 1.38 | 1.06–1.79 | 2.32 | 1.75–3.07 | 1.66 | 1.36–2.03 | 2.36 | 1.96–2.84 |
Exams done | SAH | 1.63 | 1.42–1.88 | 3.02 | 2.63–3.48 | 2.01 | 1.79–2.25 | 2.90 | 2.59–3.24 | 1.32 | 1.16–1.50 | 2.28 | 1.99–2.60 | 1.47 | 1.32–1.63 | 2.02 | 1.82–2.24 |
DM | 1.61 | 1.24–2.08 | 2.90 | 2.22–3.79 | 2.16 | 1.77–2.62 | 3.06 | 2.53–3.69 | 1.27 | 0.99–1.62 | 2.10 | 1.62–2.72 | 1.56 | 1.28–1.89 | 2.06 | 1.70–2.48 |
SAH and DM | 1.59 | 1.25–2.04 | 2.99 | 2.32–3.86 | 2.23 | 1.84–2.70 | 3.14 | 2.63–3.76 | 1.26 | 1.00–1.59 | 2.17 | 1.70–2.78 | 1.59 | 1.32–1.91 | 2.09 | 1.75–2.49 |
Referral to specialistd | SAH | 1.82 | 1.53–2.16 | 3.19 | 2.68–3.79 | 2.12 | 1.84–2.46 | 2.85 | 2.47–3.30 | 1.43 | 1.22–1.67 | 2.33 | 1.98–2.75 | 1.55 | 1.35–1.77 | 1.95 | 1.70–2.23 |
DM | 1.66 | 1.18–2.35 | 2.92 | 2.07–4.11 | 2.61 | 1.96–3.47 | 3.87 | 2.94–5.09 | 1.24 | 0.89–1.73 | 2.01 | 1.44–2.81 | 1.86 | 1.40–2.48 | 2.56 | 1.94–3.38 |
SAH and DM | 1.58 | 1.19–2.09 | 2.91 | 2.14–3.94 | 2.85 | 2.21–3.67 | 4.14 | 3.24–5.30 | 1.19 | 0.90–1.56 | 2.01 | 1.49–2.71 | 2.02 | 1.57–2.59 | 2.70 | 2.11–3.46 |
Hospitalization | SAH | 1.60 | 1.13–2.26 | 3.21 | 2.31–4.45 | 2.02 | 1.56–2.60 | 3.37 | 2.65–4.28 | 1.35 | 0.96–1.88 | 2.55 | 1.81–3.61 | 1.48 | 1.15–1.89 | 2.34 | 1.85–2.95 |
DM | 1.04 | 0.60–1.81 | 2.11 | 1.17–3.80 | 1.60 | 0.99–2.57 | 2.42 | 1.52–3.85 | 0.95 | 0.57–1.60 | 1.81 | 1.01–3.25 | 1.17 | 0.73–1.87 | 1.66 | 1.04–2.63 |
SAH and DM | 1.24 | 0.76–2.03 | 2.99 | 1.83–4.89 | 2.52 | 1.79–3.56 | 4.04 | 2.90–5.63 | 1.10 | 0.70–1.75 | 2.48 | 1.54–3.99 | 1.80 | 1.28–2.52 | 2.66 | 1.92–3.69 |
Comparing the health services assessed, on adjusted models, men with obesity had higher PR (Prevalence Ratios), especially regarding health service use for hypertension, particularly hospital admission (aPR = 2.55; 95%CI 1.81–3.61). In women with obesity, higher PR were found regarding health service use for diabetes, particularly referrals or consultations with specialists (aPR = 2.56; 95%CI 1.94–3.38) (Table
4).
Individuals diagnosed with both diseases tended to present an increased risk for using practically all types of services investigated. Higher PR was observed among women with obesity, mainly in relation to being referred to a specialist (aPR = 2.70; 95%CI 2.11–3.46) and hospitalization (aPR = 2.66; 95%CI 1.92–3.69) (Table
4).
Further analyses were carried out using BMI stratified into six categories, namely: underweight, normal weight, overweight, obesity grade I, obesity grade II and obesity grade III. Given there was no statistically significant difference for obesity grade, despite the pattern of greater service use with increasing BMI, the pooled form of the variable was retained. This lack of difference is likely explained by the low proportion of individuals with higher grades of obesity contained in the sample. These results can be found in the
supplementary material.
Discussion
The present study found greater health service utilization by individuals with overweight and obesity, of both sexes, compared to under/normal-weight individuals. Individuals with hypertension or diabetes may need to use health services to a lesser or greater degree, and overweight and obesity influenced the extent of this utilization. The presence of obesity doubled the utilization of the health services investigated. One explanation for this higher use is that obesity may worsen the clinical condition of individuals with hypertension and/or diabetes, leading to the need for greater, or more frequent, health care. Another hypothesis is that, since obesity is a risk factor for other diseases, individuals with obesity needed to use health services more often after developing other NCDs, such as hypertension and diabetes.
These findings corroborate results seen in some high-income countries, such as Ireland [
41], the USA (United States of America) [
42], and Canada [
43]. In Ireland, a representative study of the middle-aged and older adult population found that all obesity categories were associated with a higher number of GP visits [
41]. In the USA, an eight-year retrospective cohort study of young and middle-aged adults revealed that obesity was associated with a higher rate of outpatient consultations, ER visits, and hospitalizations. Individuals with obesity had a two-fold greater risk of visiting the emergency room than normal-weight subjects, and weight gain over time was also associated with a higher risk of emergency room use [
42]. In a five-year study, Canadians with a BMI ≥ 35 Kg/m
2 made more GP visits than their normal-weight counterparts. Results of the study highlighted the burden of obesity, particularly in primary health care [
43].
The present study identified an association between obesity and greater health service utilization in Brazil, a middle-income country. In addition, this increased use by individuals with obesity was found across all three levels of healthcare.
Greater health service utilization raises health costs, both in the private and public systems, particularly for higher-cost services such as hospital admissions, exams, and consultations with specialists [
26,
28,
41,
42,
44]. Moreover, high demand for less accessible services, such as those requiring specialized staff and those that have limited hospital beds, can overload the health system, precluding care delivery or reducing the duration or quality of the service provided, since there will be much demand for the same service [
45].
The study results showed higher health service utilization by women. The literature shows, among other factors, that culturally women have a greater tendency to seek healthcare services, compared to men [
17,
24,
26,
46,
47].
Besides the fact that caring for one’s health is not regarded as a male pursuit, given that most primary and secondary healthcare services are not available during night-time hours or weekends, men, who still make up the majority of the formal workforce [
48], may experience more difficulty accessing these services [
24,
47]. Although not explained solely by access, it remains a key determinant of health service utilization [
15]. Furthermore, the shortcomings of public services with regard to care, where users often face long waiting times and do not always have their health needs resolved in a single session, may pose another barrier to utilization by men. On top of these factors, men more often display a mindset of somehow “being unsusceptible to illness” [
24,
47], reflecting a poorer perception of their need for healthcare, which itself may stem from lower use of these services [
16].
Women also had the highest rates of hypertension and diabetes. This finding may suggest that, due to their greater propensity to seek healthcare services, women undergo more diagnostic exams and hence have a clearer picture of their true health situation [
17]. Women also made greater use of services for follow-up treatment of the diseases (routine visits to doctor or health services use to realize exams). More rigorous disease follow-up suggests greater control and prevention of complications related to the disease [
34].
The analysis of health service utilization for hypertension revealed a higher risk among the group of men with obesity for regular visits to doctor and for exams, compared to the utilization of these same services by women with obesity. In addition to obesity being associated with an increased risk of using all health services investigated, these findings reveal that gender also influenced the use of two of the four types of services investigated. In general, the risk of complications related to arterial hypertension was higher among men [
49], where presenting obesity is expected to worsen the clinical condition of these individuals. This scenario, together with less frequent follow-up (and consequently less control) of the disease, would normally increase the need for the use of these services.
The cross-sectional nature of the present study does not allow inferring whether obesity preceded the diseases (given obesity is a risk factor for them), or whether weight gain (and consequently increased BMI) occurred after diagnosis of the diseases investigated. Nevertheless, in the present study, the association between the occurrence of obesity and greater health service utilization was clear.
This study has some limitations. It was not possible to distinguish between the two types of diabetes (types 1 and 2). Given that type 2 diabetes is more strongly linked with lifestyle, this type can better explain an association with obesity. However, because type 2 diabetes is more prevalent than type 1 [
50], most individuals with this sub-diagnosis probably had the type 2.
Regarding race/skin color, two categories are composed of small numbers of individuals (indigenous and yellow), so it is difficult to conclude about them due to low precision. However, we chose to keep with these categories because we understand that is relevant to present descriptive data on this population, recognize their existence, and reflect the need for specific surveys related to these groups.
About the question: “routine visits to the doctor or health service for the disease”, it was not possible to quantify the number of visits, rendering the variable subject to interpretation, i.e. the concept of “routine” could have been interpreted differently by respondents.
In addition, this was a household-based study in which data on the presence of diseases and use of health services were reported by patients as opposed to being drawn from medical records, where this self-reporting may have led to under or overestimations. However, there is growing the use of self-reported information on morbidity in regular health surveys, despite its limitations, owing to the faster data collection and publication afforded. These factors, together with the inherently lower costs, make this approach useful and timely for health surveillance actions [
35]. Also, the PNS did not collect data on health service access and we focused only on services related to hypertension and diabetes since only for those would be possible to assess all levels of healthcare (primary, secondary, tertiary).
It is also important to mention that, despite the data from the PNS-2019 being available, the survey had measured data only for a small subsample and self-referred data for all individuals. The present work analyzed the data from the PNS-2013 because, in this survey, the weight and height data were measured by trained professionals for the whole population, which eliminates reporting bias. As the objective of the present study was to analyze the association between obesity and the use of health services, and not to describe the prevalence, it is believed that the magnitude of the association is not influenced by this temporal difference.
Lastly, previous studies suggest that increased health service utilization by individuals with overweight or obesity is only partly explained by chronic health conditions [
51,
52]. Thus, the findings of the present study may be underestimated, as the use of health services directly related to obesity was not investigated.
The underestimation of health services use by individuals with obesity may also be associated with social stigma in this group. A systematic review of observational studies on this issue found that 19.2–41.8% of individuals with obesity had been subject to discrimination [
53], including healthcare-related discrimination, which can make them use less health services than they really need.
Therefore, the present findings likely represent a lower rate of use of these services than actually required by individuals with obesity. In addition, only two NCDs associated with obesity were assessed, while the literature shows obesity to be associated with many other NCDs [
4,
7,
9,
54]. In addition, although in the present study no significant difference was found in the utilization of health services between individuals with an isolated or simultaneous diagnosis of hypertension and diabetes, it is known that the presence of multimorbidity may interfere with this utilization [
19], which is a relevant point to be considered, especially for studies that assess a greater number of obesity-related diseases.
Taken together, the results showed that individuals with obesity made greater use of health services than their under/normal-weight counterparts, for both low and high-complexity services. Results of population-based studies, such as the present investigation, can significantly contribute to the planning, formulation, and management of health policies, particularly in the public sphere. Future studies on this subject should include data on access to health services and investigate health service utilization associated with the direct and indirect costs of obesity.
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