Socioeconomic inequalities in abdominal obesity among Peruvian adults

Marioli Y Farro-Maldonado; Glenda Gutiérrez-Pérez; Akram Hernández-Vásquez; Antonio Barrenechea-Pulache; Marilina Santero; Carlos Rojas-Roque; Diego Azañedo

doi:10.1371/journal.pone.0254365

. 2021 Jul 21;16(7):e0254365. doi: 10.1371/journal.pone.0254365

Socioeconomic inequalities in abdominal obesity among Peruvian adults

Marioli Y Farro-Maldonado ¹, Glenda Gutiérrez-Pérez ¹, Akram Hernández-Vásquez ², Antonio Barrenechea-Pulache ^1,^*, Marilina Santero ³, Carlos Rojas-Roque ⁴, Diego Azañedo ⁵

Editor: Isil Ergin⁶

PMCID: PMC8294571 PMID: 34288938

Abstract

Objectives

Abdominal obesity (AO) has become a public health issue due to its impact on health, society and the economy. The relationship between socioeconomic disparities and the prevalence of AO has yet to be studied in Peru. Thus, our aim was to analyze the socioeconomic inequalities in AO distribution defined using the International Diabetes Federation (IDF) cut-off points in Peruvian adults in 2018–2019.

Methods

This was a cross-sectional study using data from the 2018–2019 Demographic and Family Health Survey (ENDES) of Peru. We analyzed a representative sample of 62,138 adults over 18 years of age of both sexes from urban and rural areas. Subjects were grouped into quintiles of the wealth to calculate a concentration curve and the Erreygers Concentration Index (ECI) in order to measure the inequality of AO distribution. Finally, we performed a decomposition analysis to evaluate the major determinants of inequalities.

Results

The prevalence of AO among Peruvian adults was 73.8%, being higher among women than men (85.1% and 61.1% respectively, p < 0.001). Socioeconomic inequality in AO was more prominent among men (ECI = 0.342, standard error (SE) = 0.0065 vs. ECI = 0.082, SE = 0.0043). The factors that contributed most to inequality in the prevalence of AO for both sexes were having the highest wealth index (men 37.2%, women 45.6%, p < 0.001), a higher education (men 34.4%, women 41.4%, p < 0.001) and living in an urban setting (men 22.0%, women 57.5%, p < 0.001).

Conclusions

In Peru the wealthy concentrate a greater percentage of AO. The inequality gap is greater among men, although AO is more prevalent among women. The variables that most contributed to inequality were the wealth index, educational level and area of residence. There is a need for effective individual and community interventions to reduce these inequalities.

Introduction

Over the last decades, the high prevalence of abdominal obesity (AO) has become a global public health issue with great social and economic impact. AO is a risk factor for multiple non-communicable diseases such as diabetes, cardiovascular disease, cancer [1] and has recently been identified as a predisposing factor for severe forms of COVID-19 [2]. This represents a high cost of treatment and its complications will exceed 5% of the annual health budget for the next 30 years [3]. In high-income countries such as the United States and Portugal, the prevalence of AO reaches 57.2% [4] and 50.5% [5] respectively, being even higher in upper-middle-income countries such as Mexico where it reaches 74.0% [6]. Using the International Diabetes Federation (IDF) cut-off points, the prevalence of AO in Peru in 2013 was 64.1% [7]. This disease threatens to overload the economic and resolutive capacity of health systems, particularly in Latin American countries in which budgets assigned to health are very limited, being of around 7.9% of the Gross Domestic Product (GDP) in 2018 compared to 16.9% in the United States [8].

The World Health Organization (WHO) recommends using the body mass index (BMI) to define obesity [9]. However, this parameter does not discriminate between muscle mass and lean mass [10]. The recommended BMI values were obtained in a Caucasian population in which the average height and body fat distribution is different from that found in other countries and may therefore underestimate the prevalence of overweight and obesity [11]. One study reported that 1 out of every 3 individuals with "normal weight" determined with the BMI had AO [12].Taking these results into account, waist circumference and the concept of AO is now being adopted in various studies [13, 14]. Waist circumference is easily applicable and correlated with the presence of visceral fat measured in tomographic studies [15]; it is recognized as a more accurate indicator of cardiovascular disease than the BMI [13]. Although in Peru, the Demographic and Family Health Survey (ENDES, acronym in Spanish) measures waist circumference, which is recognized by the Ministry of Health as part of the nutritional anthropometric assessment of adults, national reports and most local studies still consider the BMI as the main measure of obesity [16]. Nonetheless, this is controversial due to the low average height of the Peruvian population, being 165 cm for men and 153 cm for women [17].

Many researchers have reported socioeconomic variables influencing inequalities in the distribution of AO. In Indonesia, for example, the main determinants associated with inequalities were wealth status, occupational class, and educational level [18]. Currently, even with the information available, the influence of socioeconomic factors on the prevalence of AO in Peru has not been explored.

Therefore, this article aimed to analyze the socioeconomic inequalities in AO distribution, using the International Diabetes Federation (IDF) cut-off points for South and Central America in Peruvian adults using information from the 2018–2019 ENDES. The results of this study will help to better identify populations at risk of developing complications associated with AO and may be useful as a baseline for the elaboration of health policies aimed at the prevention of AO.

Materials and methods

Study population and design

Peru is a country divided into 24 sub-national administrative units, known as “administrative regions” and 1 constitutional province. The territorial area is 1,285,215.60 km2 and borders Ecuador, Colombia, Brazil, Bolivia and Chile. The total population in 2019 was 32,131,400 million people, being the 7th most populated country in America [19]. Peru can be divided into three natural regions: the coast, which concentrates 58% of the national population and many of the most developed cities including Lima, the capital [20]; the jungle, which is difficult to access due to the rugged terrain of the Amazon and whose population has insufficient access to basic services; and the highlands, the Andean area which presents the highest level of monetary poverty in the country. According to The World Bank the economy of Perú belongs to the upper middle income (gross national income per capita between $4,046 and $12,535) [21]. In 2018, 5.2% of the GDP was invested in health, being one of the lowest compared to other South American countries such as Colombia, Chile and Brazil [8].

This was cross-sectional study that used data from the 2018–2019 ENDES carried out by the National Institute of Statistics and Informatics (INEI, acronym in Spanish) of Peru. The ENDES is an annual survey, the objective of which is to obtain up-to-date information about the demographic dynamics and health condition of mothers, children younger than 5, and people older than 15 years of age who reside in Peru. It uses a two-stage, balanced, stratified and probabilistic sample, which is representative at national, administrative region and natural region levels. Each year studied had a sample size of 36,760 households, of which one individual 15 years of age or older was included in the survey. We used information compiled from both the household and the health questionnaires to carry out a secondary analysis to determine the prevalence and inequalities in the distribution of AO in adults. All measurements made during the survey were carried out by trained staff. Details about the procedures and measurement of waist circumference have been published by the INEI and can be found in the ENDES datasheet [22]. The sampling techniques and estimation of weighting factors can be consulted in the technical report [23].

In our study the units of analysis were individuals ≥ 18 years old residing in the selected sample of urban or rural households. We included 31,553 and 30,585 individuals from the 2018 and 2019 datasets, respectively. Both datasets were pooled to increase the power (n = 62,138).

Variables

In our study, AO defined as a waist circumference ≥ 90 cm for men and ≥ 80 cm for women was the dependent variable. These cut-off points were recommended by the IDF for use in South and Central America [24]. The wealth index was the independent variable. This is a measure of household wealth constructed using the principal component analysis method which considers the availability of goods, services and housing characteristics [25]. In addition, to characterize the population, we grouped the wealth index into 5 quintiles (the first quintile being that with the lowest level of well-being and quintile five indicating the highest). AO was also analyzed according to the following population characteristics: 1) age group: 18–29 years, 30–59 years, 60 years or more; 2) marital status: never married, married or cohabiting and separated/divorced or widowed; 3) educational level: no formal school, primary, secondary, higher; 4) chronic disease: yes, no [reporting of at least one chronic condition including: hypertension, diabetes mellitus or depression]; 5) smoker: yes, no [having smoked during the last 30 days]; 6) area of residence: urban or rural; 7) altitude above sea level of the housing conglomerate: 0–499 (meters above sea level), 500–1499 m.a.s.l., 1500–2999 m.a.s.l., and 3000 or more; and 8) natural region: jungle, mountain range, rest of coast, and Metropolitan Lima.

Statistical analysis

We used weighted frequencies and their 95% confidence intervals to describe the socioeconomic characteristics of the study population and the prevalence of AO. Waist circumference was described using means and standard error (SE). The prevalence of AO was standardized according to the ages of the reference population indicated by the WHO [26]. Age standardization produces an age-adjusted prevalence, which is a weighted average, for each of the populations to be compared. Thus, standardization better represents the relative age distribution of the population. The prevalence of AO and the mean waist circumference were described according to sex.

To measure the socioeconomic inequality in the distribution of AO across the population, subjects were grouped into quintiles of wealth to calculate the concentration curve and the concentration index (CI). This curve has been used in other health indicators to describe the gradient related to socioeconomic inequality [27, 28] On the X-axis we plotted the cumulative percentage of the sample, ranked by the wealth index, and on the Y-axis we plotted the cumulative percentage of AO according to sex. A curve above the line of equality indicates a greater concentration of AO among the poor and vice versa. CI is a common method to measure income related inequality in health [27]. CI shows the covariance of the AO and the fractional rank of income distribution as:

C I = \frac{2}{μ} {c o v}_{w} [y_{i t} R_{i}^{t}]

[a]

where i is an individual, y_i is AO (yes/no), μ is the mean of AO and R is the fractional rank in the income distribution. CI represents the concentration curve as a single number by summarizing the inequality weights at different points in the income distribution. It ranges between −1 and +1: negative values indicate that AO is concentrated among the poorest individuals, the same is true for the opposite results. One shortfall of the CI is that in the scenario of data contamination, the index is sensitive to extreme values at one or both tails of the distribution [29]. However, its main advantages are that it reflects the socioeconomic dimension to inequalities in health and the experiences of the entire population. The CI is sensitive to changes in the distribution of the population across socioeconomic groups, and, therefore, has been widely used to measure inequality within health economics.

Since AO is a binary variable, CI has the limitation that when the mean increases, the range of the possible values of the CI shrinks, tending to zero as the mean tends to one [31]. To solve this disadvantage, Errerygers introduced the Erreygers concentration index (ECI), a concentration index more compatible with a binary dependent variable [30, 31]. Mathematically, ECI can be expressed as:

E [h] = \frac{4 μ}{[b_{n} - a_{n}]} C [h]

[b]

where C[h] represents the standard CI, μ is the mean of AO in population and b_n and a_n are the upper and lower limits of AO. Following the Van Doorslaer methodology [32], decomposition analysis was performed to assess how much the independent variables contribute separately to socioeconomic inequality in AO. The decomposition was performed based on generalized linear models (GLM). In comparison to other approaches such as probit estimations or the ordinary least squares, GLM has shown to be the best choice when decomposing inequalities using a binary outcome [33]. This study decomposes the inequality of abdominal obesity using the following equation.

E C I = 4 * (Σ_{k} (β_{k}^{m} \underline{x} k) {C I}_{k} + {G C I}_{ε}

[c]

Where ECI Is the Erreygers concentration index,xk is the mean of the explanatory variables included in the decomposition (the socioeconomic and demographic factors), $β_{k}^{m}$ is the partial effect (dy/dk) evaluated at the sample means, CI_k is the mean of the concentration index, and GCI_ε is the generalized concentration index of the stochastic term of error. Eq [c] reflects that an explanatory variable contributes to the inequality in AO when this variable is correlated with AO and is not equally distributed across the wealth index. The contribution of the explanatory variable to the inequality in AO depends on the absolute value of the partial effect and the unequal distribution of the explanatory variable with respect to household income per-capita. A positive sign of the partial effect means that the explanatory variable contributes to an increase in the inequality observed, and vice versa [27]. All analyses were performed using Stata version 14.2.

Additionally, a sub-analysis of the prevalence of OA according to the sociodemographic characteristics of the population and the administrative region of residence was performed using the cut-off points recommended by the guidelines of the Third Adult Treatment Panel (ATP III) [34] and the Latin American Consortium of Studies in Obesity (LASO) [35] in which AO was defined as >102 cm in men, > 88 cm in women; and ≥ 97 cm in men, ≥ 94 cm in women, respectively.

Ethical considerations

The Institutional Research Ethics Committee of the Universidad Científica del Sur (registration code: 560-2020-PRE15) approved the execution of this study.

Results

Population characteristics

After applying inclusion (adults over 18 years with waist circumference measurement) and exclusion criteria (individuals <18 years, pregnant, incomplete data), a total of 31,553 and 30,585 individuals were included from the 2018 and 2019 datasets, respectively (Fig 1). We included a total of 26,789 men and 35,349 women in the analysis.

Table 1 describes the socioeconomic characteristics of the population. More than half of the men and women ranged between 30–59 years of age and most were married or cohabiting. About 37.0% of men and 33.0% of women had a higher education. Most participants lived in the urban area (80.6%), a similar percentage of men and women lived in this area (80.3% and 80.9%, respectively, p = 0.092). Chronic diseases were more prevalently among men than women (24.6% vs. 20.8%, p < 0.001). Likewise, smoking was more prevalent among men than women (18.9% vs. 4.5%, p < 0.001) (Table 1).

Table 1. Socioeconomic characteristics of a Peruvian adult population.

	Overall		Men		Women
Characteristics	n	% (95% CI)	n	% (95% CI)	n	% (95% CI)	p-value^a
Sample size	62138	100	26789	100	35349	100
Age groups, years
18–29	17261	27.2 (26.7–27.8)	6559	27.8 (27.0–28.7)	10702	26.7 (26.0–27.4)	0.038
30–59	35046	54.6 (54.0–55.2)	15726	54.5 (53.6–55.5)	19320	54.6 (53.8–55.4)
60 or more	9831	18.2 (17.7–18.7)	4504	17.6 (16.9–18.4)	5327	18.7 (18.0–19.4)
Marital status
Never married	8518	17.2 (16.7–17.7)	4316	19.9 (19.1–20.7)	4202	14.6 (14.0–15.3)	<0.001
Married/Cohabiting	43392	66.1 (65.4–66.7)	19850	70.1 (69.2–71.0)	23542	62.2 (61.3–63.0)
Separated/Divorced/Widowed	10228	16.8 (16.3–17.3)	2623	10 (9.5–10.6)	7605	23.2 (22.5–24.0)
Education level
No formal school	3306	4 (3.8–4.3)	537	1.5 (1.3–1.7)	2769	6.5 (6.1–6.8)	<0.001
Primary	15698	20.6 (20.2–21.1)	6358	18.1 (17.4–18.7)	9340	23.1 (22.4–23.7)
Secondary	24696	40.1 (39.5–40.8)	11636	43.4 (42.5–44.4)	13060	36.9 (36.1–37.7)
Higher	18438	35.2 (34.6–35.9)	8258	37 (36.0–38.0)	10180	33.5 (32.7–34.4)
Wealth Index
Poorest	19854	18.5 (18.1–18.9)	8732	18.6 (18.0–19.2)	11122	18.4 (17.8–18.9)	0.064
Poorer	15551	20.8 (20.3–21.4)	6726	21.4 (20.6–22.2)	8825	20.3 (19.7–21.0)
Middle	11384	20.8 (20.2–21.3)	4775	20.5 (19.8–21.3)	6609	21 (20.3–21.7)
Richer	8738	20.1 (19.5–20.7)	3749	20.3 (19.5–21.2)	4989	19.9 (19.2–20.7)
Richest	6611	19.9 (19.2–20.5)	2807	19.2 (18.3–20.2)	3804	20.5 (19.7–21.3)
Natural regions
Jungle	14302	12.1 (11.6–12.5)	6318	12.5 (12.0–13.1)	7984	11.6 (11.1–12.1)	0.021
Mountain Range	22962	24.9 (24.2–25.6)	9661	24.3 (23.5–25.2)	13301	25.5 (24.7–26.3)
Rest of Coast	17705	25.7 (25.0–26.3)	7610	25.5 (24.7–26.3)	10095	25.8 (25.1–26.5)
Metropolitan Lima	7169	37.4 (36.7–38.1)	3200	37.7 (36.6–38.8)	3969	37.1 (36.2–38.0)
Area of residence
Rural	21656	19.4 (19.0–19.8)	9700	19.7 (19.2–20.3)	11956	19.1 (18.6–19.5)	0.092
Urban	40482	80.6 (80.2–81.0)	17089	80.3 (79.7–80.8)	23393	80.9 (80.5–81.4)
Altitude (meters above sea level)
0–499	30259	65.7 (64.8–66.6)	13182	66.2 (65.1–67.2)	17077	65.3 (64.3–66.2)	0.043
500–1499	7256	8 (7.3–8.8)	3202	8.1 (7.3–8.9)	4054	7.9 (7.1–8.7)
1500–2999	9663	11.7 (11.1–12.3)	4145	11.6 (10.9–12.3)	5518	11.7 (11.1–12.4)
3000 or more	14960	14.6 (14.1–15.2)	6260	14.1 (13.5–14.9)	8700	15.1 (14.5–15.8)
Chronic disease
No	50284	77.4 (76.8–77.9)	20870	75.4 (74.5–76.2)	29414	79.2 (78.5–79.9)	<0.001
Yes	11854	22.6 (22.1–23.2)	5919	24.6 (23.8–25.5)	5935	20.8 (20.1–21.5)
Smoker^b
No	55764	88.5 (88.0–88.9)	21546	81.1 (80.3–81.8)	34218	95.5 (95.1–95.9)	<0.001
Yes	6374	11.5 (11.1–12.0)	5243	18.9 (18.2–19.7)	1131	4.5 (4.1–4.9)

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Weight specifications included the expansion factor and the ENDES sample specifications.

CI: confidence interval

^aP-value for chi2 test of difference between men and women

^bSmoked during the previous 30 days

Mean waist circumference and prevalence of AO

The mean waist circumference was higher among men than women (93.5 cm vs. 92.3 cm, respectively, p < 0.001). Among men the mean waist circumference increased linearly across the categories of variables so that those with the highest wealth index, aged 60 or more, and with a higher education had the largest waist circumference. Meanwhile, among women the mean waist circumference tended to be higher among the middle categories; for example, women with a middle wealth index, those aged 30–59, and women with primary education had the highest waist circumference. In the case of chronic disease and altitude, in both sexes, individuals that reported having a chronic disease and who lived between 0–499 m.a.s.l. had the highest mean waist circumference (Table 2).

Table 2. Mean waist circumference and prevalence of abdominal obesity in men and women by socioeconomic characteristics.

	Mean waist circumference (SE), cms				Prevalence of abdominal obesity (%)
Characteristics	Men	P-value^a	Women	P-value^a	Men	P-value^b	Women	P-value^b
Age-standardized sample^d	93.5 (0.10)		92.3 (0.09)	<0.001^c	61.1 (60.3–61.9)		85.1 (84.6–85.7)	<0.001^c
Age groups, years
18–29	87.0 (0.21)	<0.001	86.6 (0.16)	<0.001	36.1 (34.4–37.9)	<0.001	70.8 (69.4–72.2)	<0.001
30–59	96.0 (0.14)		94.7 (0.13)		70.9 (69.9–71.9)		92.1 (91.5–92.7)
60 or more	96.4 (0.24)		94.4 (0.25)		72.3 (70.3–74.2)		87.2 (85.9–88.3)
Marital status
Never married	87.3 (0.27)	<0.001	86.4 (0.29)	<0.001	37.4 (35.2–39.7)	<0.001	66.6 (64.4–68.8)	<0.001
Married/Cohabiting	95.2 (0.12)		93.5 (0.12)		67.5 (66.5–68.4)		89.2 (88.6–89.7)
Separated/Divorced/Widowed	95.0 (0.35)		93.5 (0.21)		67.0 (64.2–69.7)		87.6 (86.5–88.6)
Education level
No formal schooling	89.1 (0.70)	<0.001	89.9 (0.39)	<0.001	45.1 (38.9–51.4)	<0.001	76.2 (73.8–78.4)	<0.001
Primary	91.8 (0.21)		94.4 (0.18)		54.4 (52.4–56.3)		88.6 (87.7–89.4)
Secondary	92.7 (0.18)		93.0 (0.16)		58.1 (56.7–59.5)		86.5 (85.5–87.3)
Higher	95.7 (0.19)		91.0 (0.18)		69.5 (68.1–70.9)		84.1 (82.9–85.2)
Wealth Index
Poorest	87.4 (0.14)	<0.001	88.6 (0.17)	<0.001	36.6 (35.1–38.1)	<0.001	76.0 (74.9–77.2)	<0.001
Poorer	91.7 (0.21)		92.9 (0.19)		54.4 (52.6–56.2)		87.1 (86.0–88.2)
Middle	93.8 (0.25)		93.9 (0.22)		63.8 (61.7–65.8)		87.2 (86.0–88.4)
Richer	96.4 (0.25)		93.6 (0.25)		72.4 (70.3–74.4)		87.5 (86.0–88.8)
Richest	98.5 (0.31)		92.9 (0.28)		79.4 (77.4–81.3)		88.6 (87.0–90.0)
Natural regions
Jungle	90.6 (0.18)	<0.001	90.7 (0.16)	<0.001	50.4 (48.7–52.0)	<0.001	83.2 (82.2–84.2)	<0.001
Mountain Range	90.2 (0.16)		90.5 (0.16)		48.0 (46.5–49.5)		80.3 (79.3–81.2)
Rest of Coast	95.0 (0.18)		94.0 (0.15)		67.1 (65.7–68.5)		89.0 (88.1–89.8)
Metropolitan Lima	95.8 (0.25)		93.3 (0.22)		70.0 (68.1–71.8)		87.3 (86.1–88.5)
Area
Rural	88.3 (0.15)	<0.001	89.4 (0.17)	<0.001	40.0 (38.5–41.5)	<0.001	78.0 (76.9–79.0)	<0.001
Urban	94.9 (0.13)		93.2 (0.12)		66.7 (65.7–67.8)		87.3 (86.6–87.9)
Altitude (meters above sea level)
0–499	95.1 (0.16)	<0.001	93.3 (0.14)	<0.001	67.2 (66.0–68.4)	<0.001	87.6 (86.8–88.3)	<0.001
500–1499	92.2 (0.33)		92.2 (0.28)		56.3 (53.4–59.1)		85.8 (84.2–87.3)
1500–2999	91.4 (0.24)		91.7 (0.23)		52.3 (50.1–54.4)		83.4 (82.2–84.7)
3000 or more	89.3 (0.20)		89.6 (0.20)		45.0 (43.1–46.9)		77.9 (76.6–79.2)
Chronic disease
No	91.9 (0.12)	<0.001	91.1 (0.11)	<0.001	55.8 (54.8–56.8)	<0.001	83.5 (82.8–84.2)	<0.001
Yes	98.8 (0.23)		97.6 (0.23)		78.9 (77.3–80.4)		93.1 (92.1–93.9)
Smoker^e
No	93.5 (0.12)	0.331	92.4 (0.10)	0.002	61.7 (60.7–62.7)	0.387	85.5 (84.9–86.1)	0.906
Yes	93.8 (0.29)		94.2 (0.57)		60.6 (58.4–62.7)		85.7 (82.5–88.3)

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Weight specifications included the expansion factor and the ENDES sample specifications.

SE: standard error

Men (n = 26,789)

Women (n = 35,349)

^aP-value for ANOVA test

^bP-value for chi2 test

^cBetween men and women

^dBy WHO Population

^eSmoked during the previous 30 days

Regarding the prevalence of AO, women presented a higher prevalence than men both in general (85.1% vs. 61.1%, p < 0.001) and across all other independent variables. For women the highest prevalence of AO was concentrated among those from 30–59 years of age. Among individuals 18–29 years of age, we found a difference of 34.7 percentage points between the two sexes, favoring women. According to educational level, the prevalence of AO in men increased linearly with the level of education, while in women AO was concentrated among those with a primary level of education, maintaining a prevalence above 70% across the different educational levels. In both sexes the prevalence of AO was higher in metropolitan Lima and the rest of the coast; being 70.0% and 67.1% respectively in men and remaining above 80% across all the natural regions in women. We observed a lower prevalence of AO in men without chronic diseases compared to those who had comorbidities (55.8% vs. 78.9%, p < 0.001), while in women the rate of AO was high in both groups (83.5% vs. 93.1%, p < 0.001) (Table 2).

Inequality in the distribution of abdominal obesity

The concentration curves for AO in men and women were both below the line of equity which shows that the cumulative percentage of AO was concentrated among the wealthiest individuals. The pro-rich orientation was more marked in men than in women. The ECI for AO in men was 0.342 (SE 0.0065) being 0.082 (SE 0.0043) in women. When we decomposed the inequality in AO, we found that the major contributors were the wealth index (men 37.2%, women 45.6%), education level (men 34.4%, women 41.4%) and living in an urban setting (men 22%, women 57.5%) (Fig 2) (Table 3).

Table 3. Decomposition of concentration indices for men and women.

	Men				Women
Variable	Elasticity	ECIs	Contribution	%	Elasticity	ECIs	Contribution	%
Age groups, years
18–29	Base	Base	Base	Base	Base	Base	Base	Base
30–59	0.490	0.053	0.026	7.7	0.308	0.056	0.017	20.9
60 or more	0.164	0.003	0.001	0.1	0.067	0.021	0.001	1.7
Marital status
Never married	Base	Base	Base	Base	Base	Base	Base	Base
Married/Cohabiting	0.475	-0.012	-0.005	-1.6	0.314	-0.078	-0.024	-29.8
Separated/Divorced/Widowed	0.050	-0.032	-0.002	-0.5	0.087	-0.009	-0.001	-1.0
Education level
No formal school	Base	Base	Base	Base	Base	Base	Base	Base
Primary	0.059	-0.336	-0.020	-5.8	0.069	-0.307	-0.021	-26.0
Secondary	0.183	-0.160	-0.029	-8.6	0.115	-0.049	-0.006	-6.8
Higher	0.221	0.533	0.118	34.4	0.067	0.506	0.034	41.4
Wealth Index
Poorest	Base	Base	Base	Base	Base	Base	Base	Base
Poorer	0.090	-0.354	-0.032	-9.3	0.055	-0.349	-0.019	-23.4
Middle	0.139	0.003	0.000	0.1	0.048	-0.014	-0.001	-0.8
Richer	0.177	0.335	0.059	17.4	0.049	0.312	0.015	18.7
Richest	0.205	0.621	0.127	37.2	0.058	0.651	0.037	45.6
Natural regions
Jungle	Base	Base	Base	Base	Base	Base	Base	Base
Mountain Range	-0.008	-0.320	0.003	0.8	0.020	-0.362	-0.007	-8.7
Rest of Coast	0.058	0.040	0.002	0.7	0.040	0.044	0.002	2.1
Metropolitan Lima	0.039	0.496	0.020	5.7	0.019	0.518	0.010	12.1
Area
Rural	Base	Base	Base	Base	Base	Base	Base	Base
Urban	0.134	0.562	0.075	22.0	0.086	0.552	0.047	57.5
Altitude (meters above sea level)
0–499	Base	Base	Base	Base	Base	Base	Base	Base
500–1499	-0.002	-0.072	0.000	0.0	0.000	-0.061	0.000	0.0
1500–2999	-0.008	-0.099	0.001	0.2	-0.006	-0.098	0.001	0.7
3000 or more	-0.026	-0.238	0.006	1.8	-0.028	-0.281	0.008	9.7
Chronic disease
No	Base	Base	Base	Base	Base	Base	Base	Base
Yes	0.145	0.087	0.013	3.7	0.062	0.050	0.003	3.7
Smoker^a
No	Base	Base	Base	Base	Base	Base	Base	Base
Yes	0.011	0.029	0.000	0.1	0.003	0.061	0.000	0.2
Residual			-0.021				-0.280

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Weight specifications included the expansion factor and the ENDES sample specifications.

^aHaving smoked during the previous 30 days

Prevalence of AO using different cut-off points

Using the IDF cut-off points, the overall prevalence of AO in this study was 73.8%. When using the ATP III and LASO cut-off points, the percentage of AO decreased to 43.6% and 40.6%, respectively (S1 Table). For all cut-off points, the coast region concentrated the administrative regions with the highest prevalence of AO, which were: Tumbes, Lima, Callao, Moquegua, Tacna, Arequipa, and Ica. It should be mentioned that Moquegua and Tacna, in particular, maintained a AO prevalence greater than 50% even with the more flexible cut-off points (S2 Table).

Discussion

Our study examines the socioeconomic inequalities in AO and their determinants among Peruvian adults. We found that AO is more concentrated among the wealthiest individuals, with higher inequality being found in men. The major contributors of inequality were the wealth index, higher education and living in an urban setting.

We found that the wealth index was the main driver of inequality in AO. This finding is consistent with studies in other upper-middle-income countries such as Indonesia [18] and China [36]. In addition, we found a higher prevalence of AO in coastal cities. This result may be due to the fact that coastal cities are the wealthiest compared to jungle and mountain regions. The incidence of monetary poverty in the coast, jungle and mountain are 13.8%, 25.8% and 29.3% respectively [37]. The annual GDP per capita in 2017 for the coast, the mountain and the jungle were 9,764, 3,780 and 2,442 US dollars [38]. The prevalence of AO in coastal cities may be higher because people with a high level of well-being, especially those in an urban environment, are more likely to engage in unhealthy routine behaviors such as sedentarism and consumption of high caloric food [39], in addition, these people are exposed to a great amount of advertising of caloric dense food [40]. Furthermore, the wealthiest individuals tend to suffer ’technological sedentarism’ due to greater access to their own form of motorized transport and greater quantities of office work which promote less physical activity [41]. In short, our country faces a series of economic and nutritional changes that promote an increase of AO, which seems to be more pronounced in the coast.

We found a higher prevalence of AO among more educated individuals. This coincides with other studies including a systematic review of 91 countries including Peru, in which people with a higher education in less developed countries tended to be more obese [42, 43], while in more developed countries such as China the opposite is true [44]. A Peruvian study found that the population with a higher education level was 1.5 times less likely to perform physical activity [45], which could be due to the close association with office jobs, in which individuals remain seated for long hours. Indeed, a study in France reported that approximately 7.5 hours a day and 37.5 hours a week are spent sitting in front of a computer [46]. Other factors such as a greater workload and extended hours could limit access to a more balanced diet prepared at home, leading to greater demand for high-calorie fast food [47], favoring the development of obesity. Therefore, it is important to promote the adoption of healthy lifestyles across all educational levels, providing information about AO and its consequences.

Another relevant finding was that residents of both sexes in urban areas had higher AO. In Perú, the population in urban areas is 1.9 times more likely to have lower physical activity when compared to that living in rural areas [45]. In the United States it has been reported that occupational physical activity in metropolitan areas has been reduced by about 120 kcal/day [48]. Furthermore, the supply of vehicles in the capital of Peru is 175.8 vehicles per 1000 inhabitants, while in the jungle and mountains, such as Loreto and Puno, the rate only reaches 5.24 and 33.37 vehicles per 1000 inhabitants, respectively [49]. Roads in rural areas are usually very rough with insufficient access ways, and residents of this area are thereby conditioned to do more physical activity as part of their daily commute [50]. Access to communication technologies and the consequent time spent in front of a screen could be another contributor to this observation: 55.9% of homes in Metropolitan Lima have cable TV while in rural areas this is available in only 11.7% of homes. Likewise, only 4.6% of rural homes have access to internet while in Metropolitan Lima it reaches 58.7% [51]. Gaps in access to these technologies can therefore favor sedentarism in urban areas.

Finally, we observed a higher prevalence of AO in women, which is consistent with other studies [18, 36, 52]. The prevalence of AO was greatest among women belonging to the 30–59 age group, which might be explained by the decline in regular exercise and the use of diets over time [18]. Of note was the large proportion of women aged 18–29 years of age with AO compared to their male counterparts. In Peru, the prevalence of fertility in women between 20–34 years of age exceeds 60% in both rural and urban areas [53], and this can cause progressive weight gain associated with pregnancy. In Latin America, the participation of women in the labor market is still lagging compared to other Organization for Economic Co-operation and Development (OECD) countries [54]. Even among individuals with higher education it is estimated that 86.2% of men in Perú are employed vs. 74.5% of women. This percentage drops to 67.8% among women with a primary education but remains around 82.2% for their male counterparts. Likewise, the average monthly salary of men of 1846 PEN (487.69 USD) is higher than the 1322 PEN (349.26 USD) earned by women [55]. Therefore, Peruvian women may be less motivated to pursue their professional career path due to inequalities in the workforce and a lack of monetary incentive; in particular women with a primary education would most likely be housewives or unemployed, thereby being less physically active [56]. The persistence of traditional gender roles, in which the male is usually engaged in physically demanding work [57], contributes to women being more obese in rural areas, being widely accepted among women because of its association with pregnancy and child-bearing [58]. Due to social beliefs in countries such as Indonesia and Perú [59], obesity might be seen as a symbol of good economic status [18]. All this could contribute to women more frequently presenting AO, and thus, it is important to develop interventions aimed at these gender disparities, considering the social and economic context of Peru.

Among the limitations of this study, due to it being a cross sectional, secondary analysis, we were unable to establish causal relationships. However, ENDES is the most representative national demographic health survey, providing updated information on the country’s demographic situation. The second limitation is that there are no established cut-off values for determining AO using waist circumference for the Peruvian population. This may bias the results because the definitions used for estimations are not adapted to the population and context in Peru. In an attempt to solve this, we estimated the prevalence of AO with different cut-off points such as those of ATP III and LASO. Despite the potential limitations, the results have important implications for equity and health policy.

Conclusion

The socioeconomic characteristics of the population impact the distribution of AO in Peru. This inequality is more prominent among men although AO is more prevalent among women. A higher wealth index, higher levels of education and living in urban areas were the main contributors to AO inequality. Peru is going through an economic and nutritional transition that is generating a change in the development and distribution of AO. Understanding this problem could serve as the basis for the creation of health programs focused on reducing inequality gaps among the population at greatest risk.

Supporting information

S1 Table. Prevalence of abdominal obesity according to socioeconomic characteristics and based on the Third Adult Treatment Panel and the Latin American Consortium of Studies in Obesity cut-off points.

(DOCX)

Click here for additional data file.^{(23.9KB, docx)}

S2 Table. Prevalence of abdominal obesity in men and women according to the administrative region of residence (ENDES 2018–2019) and based on IDF, the guidelines of the Third Adult Treatment Panel (ATP III) and the Latin American Consortium of Studies in Obesity (LASO).

(DOCX)

Click here for additional data file.^{(20.7KB, docx)}

Acknowledgments

We thank Donna Pringle for reviewing the language and style of the manuscript.

Data Availability

The database is freely accessible from the National Institute of Statistics and Informatics (INEI) website (http://iinei.inei.gob.pe/microdatos/). The information can be obtained by entering the survey query tab and selecting the ENDES 2018-2019; data is obtained from modules #64, #65 and #414.

Funding Statement

The authors received no specific funding for this work.

References

1.Hu L, Huang X, You C, Li J, Hong K, Li P, et al. Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China. PLoS One. 2017;12(9):e0183934. doi: 10.1371/journal.pone.0183934 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Favre G, Legueult K, Pradier C, Raffaelli C, Ichai C, Iannelli A, et al. Visceral fat is associated to the severity of COVID-19. Metabolism. 2021;115(154440):154440. doi: 10.1016/j.metabol.2020.154440 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.OECD. Heavy Burden of Obesity: The Economics of Prevention. OECD. 2019. 10.1787/67450d67-en. [DOI]
4.Caspard H, Jabbour S, Hammar N, Fenici P, Sheehan JJ, Kosiborod M. Recent trends in the prevalence of type 2 diabetes and the association with abdominal obesity lead to growing health disparities in the USA: An analysis of the NHANES surveys from 1999 to 2014. Diabetes Obes Metab. 2018;20(3):667–71. doi: 10.1111/dom.13143 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Oliveira A, Araújo J, Severo M, Correia D, Ramos E, Torres D, et al. Prevalence of general and abdominal obesity in Portugal: comprehensive results from the National Food, nutrition and physical activity survey 2015–2016. BMC Public Health. 2018;18(1):614. doi: 10.1186/s12889-018-5480-z [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Barquera S, Campos-Nonato I, Hernández-Barrera L, Flores M, Durazo-Arvizu R, Kanter R, et al. Obesity and central adiposity in Mexican adults: results from the Mexican National Health and Nutrition Survey 2006. Salud Publica Mex. 2009;51 Suppl 4:S595–603. doi: 10.1590/s0036-36342009001000014 [DOI] [PubMed] [Google Scholar]
7.Pajuelo-Ramírez J, Torres-Aparcana H, Agüero-Zamora R, Quispe AM. Altitude and its inverse association with abdominal obesity in an Andean country: a cross-sectional study. F1000Res. 2019;8:1738. doi: 10.12688/f1000research.20707.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.The World Bank. Current health expenditure (% of GDP) [Internet]. 2018 [cited 2021 May 9]. Available from: https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS
9.World Health Organization. Obesity and overweight. World Health Organization. 2020. [cited 2021 May 7]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
10.Sinaga M, Yemane T, Tegene E, Lidstrom D, Belachew T. Performance of newly developed body mass index cut-off for diagnosing obesity among Ethiopian adults. J Physiol Anthropol. 2019;38(1):14. doi: 10.1186/s40101-019-0205-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Federación Latinoamericana de Sociedades de Obesidad. [II Latin American Congress on Obesity 2017]. Federación Latinoamericana de Sociedades de Obesidad. 2017. Available from: https://bibliotecavirtual.insnsb.gob.pe/ii-consenso-latinoamericano-de-obesidad-2017/
12.Owolabi EO, Ter Goon D, Adeniyi OV. Central obesity and normal-weight central obesity among adults attending healthcare facilities in Buffalo City Metropolitan Municipality, South Africa: a cross-sectional study. J Health Popul Nutr. 2017;36(1):54. doi: 10.1186/s41043-017-0133-x [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Lukács A, Horváth E, Máté Z, Szabó A, Virág K, Papp M, et al. Abdominal obesity increases metabolic risk factors in non-obese adults: a Hungarian cross-sectional study. BMC Public Health. 2019;19(1):1533. doi: 10.1186/s12889-019-7839-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Tarqui-Mamani C, Alvarez-Dongo D, Espinoza-Oriundo P. Cardiovascular risk according to abdominal circumference in Peruvians. An Fac Med. 2017;78(3):287. [Google Scholar]
15.Onat A, Avci GS, Barlan MM, Uyarel H, Uzunlar B, Sansoy V. Measures of abdominal obesity assessed for visceral adiposity and relation to coronary risk. Int J Obes Relat Metab Disord. 2004;28(8):1018–25. doi: 10.1038/sj.ijo.0802695 [DOI] [PubMed] [Google Scholar]
16.Instituto Nacional de Estadística e Informática. [Peru: Noncommunicable and Communicable Diseases, 2019]. Instituto Nacional de Estadística e Informática. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1734/
17.Pennisi E. High altitude may have driven short stature in Peruvians. Science. 2018;360(6390):696 doi: 10.1126/science.360.6390.696 [DOI] [PubMed] [Google Scholar]
18.Pujilestari CU, Nyström L, Norberg M, Weinehall L, Hakimi M, Ng N. Socioeconomic inequality in abdominal obesity among older people in Purworejo District, Central Java, Indonesia—a decomposition analysis approach. Int J Equity Health. 2017;16(1):214. doi: 10.1186/s12939-017-0708-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Instituto Nacional de Estadística e Informática.[State of the Peruvian population. 2019]. Peru: National Institute of Statistics and Informatics. Instituto Nacional de Estadística e Informática. 2019. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1671/libro.pdf
20.Instituto Nacional de Estadística e Informática. [Peru: Final Results of the 2017 National Census]. Instituto Nacional de Estadística e Informática. 2018. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1544/
21.The World Bank. World Bank Country and Lending Groups. The World Bank. 2021. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
22.Instituto Nacional de Estadística e Informática. [Interviewer’s manual, Demographic and Family Health Survey, 2018.] Instituto Nacional de Estadística e Informática. 2018. Available from: https://proyectos.inei.gob.pe/endes/2018/documentos_2018/MANUAL_DE_LA_ENTREVISTADORA_2018_ENERO.pdf
23.Instituto Nacional de Estadística e Informática. [Datasheet, Demographic and family health survey, 2019] Instituto Nacional de Estadística e Informática. 2019. Available from: https://proyectos.inei.gob.pe/endes/2019/documentos_2019/FICHA_TECNICA_ENDES%202019.pdf
24.International Diabetes Federation. Consensus Worldwide Definition of the Metabolic Syndrome. International Diabetes Federation. 2006. Last Update 2020. Available from: https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome
25.Rutstein SO, Johnson K. The DHS Wealth Index. DHS Comp Reports No 6. 2004;1–71.
26.Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. World Health Organization; 2001. [Google Scholar]
27.O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. The World Bank; 2008. [Google Scholar]
28.Wagstaff A, Van Doorslaer E. Measuring inequalities in health in the presence of multiple-category morbidity indicators. Health Econ. 1994;3(4):281–9. doi: 10.1002/hec.4730030409 [DOI] [PubMed] [Google Scholar]
29.Giorgi GM, Gigliarano C. The Gini concentration index: A review of the inference literature: The Gini concentration index. J Econ Surv. 2017;31(4):1130–1148. [Google Scholar]
30.Wagstaff A. The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 2005;14(4):429–32. doi: 10.1002/hec.953 [DOI] [PubMed] [Google Scholar]
31.O’Donnell O, O’Neill S, Van Ourti T, Walsh B. conindex: Estimation of concentration indices. Stata J. 2016;16(1):112–38. [PMC free article] [PubMed] [Google Scholar]
32.Koolman X, van Doorslaer E. On the interpretation of a concentration index of inequality. Health Econ. 2004;13(7):649–56. doi: 10.1002/hec.884 [DOI] [PubMed] [Google Scholar]
33.Yiengprugsawan V, Lim LL, Carmichael GA, Dear KB, Sleigh AC. Decomposing socioeconomic inequality for binary health outcomes: an improved estimation that does not vary by choice of reference group. BMC Res Notes. 2010;3(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. doi: 10.1001/jama.285.19.2486 [DOI] [PubMed] [Google Scholar]
35.Herrera VM, Casas JP, Miranda JJ, Perel P, Pichardo R, González A, et al. Interethnic differences in the accuracy of anthropometric indicators of obesity in screening for high risk of coronary heart disease. Int J Obes. 2009;33(5):568–76. doi: 10.1038/ijo.2009.35 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Zhao P, Gu X, Qian D, Yang F. Socioeconomic disparities in abdominal obesity over the life course in China. Int J Equity Health. 2018;17(1):96 doi: 10.1186/s12939-018-0809-x [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Instituto Nacional de Estadística e Informática. [Evolution of monetary poverty 2008–2019]. Instituto Nacional de Estadística e Informática; 2020. Available from: https://www.inei.gob.pe/media/cifras_de_pobreza/informe_pobreza2019.pdf
38.Seminario B. Zegarra M. Palomino L. [Evolution of departmental GDP and analysis of regional inequality in Peru: 1795–2017.] Documento de trabajo del BID N° IDB-WP-1016. Inter-American Development Bank. 2019. Available from: https://publications.iadb.org/publications/spanish/document/Evoluci%C3%B3n_del_PIB_departamental_y_an%C3%A1lisis_de_la_desigualdad_regional_en_el_Per%C3%BA_1795-2017_es.pdf
39.García CM. Association of globalization in its different dimensions with overweight and obesity: an analysis in 10 Latin American and Caribbean countries. Salud Publica Mex. 2019;61(2):174–83. doi: 10.21149/8886 [DOI] [PubMed] [Google Scholar]
40.Ministerio de Salud. [A fat problem: Overweight and Obesity in Peru]. Ministerio de Salud. 2012. Available from: https://www.gob.pe/institucion/minsa/informes-publicaciones/321813-un-gordo-problema-sobrepeso-y-obesidad-en-el-peru
41.Wang Y, Wang L, Xue H, Qu W. A Review of the Growth of the Fast Food Industry in China and Its Potential Impact on Obesity. Int J Environ Res Public Health. 2016;13(11):1112. doi: 10.3390/ijerph13111112 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Cohen AK, Rai M, Rehkopf DH, Abrams B. Educational attainment and obesity: a systematic review. Obes Rev. 2013;14(12):989–1005. doi: 10.1111/obr.12062 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Zhang H, Xu H, Song F, Xu W, Pallard-Borg S, Qi X. Relation of socioeconomic status to overweight and obesity: a large population-based study of Chinese adults. Ann Hum Biol. 2017;44(6):495–501. doi: 10.1080/03014460.2017.1328072 [DOI] [PubMed] [Google Scholar]
44.Liao C, Gao W, Cao W, Lv J, Yu C, Wang S, et al. Association of Educational Level and Marital Status With Obesity: A Study of Chinese Twins. Twin Res Hum Genet. 2018;21(2):126–35. doi: 10.1017/thg.2018.8 [DOI] [PubMed] [Google Scholar]
45.Tarqui C, Alvarez D, Espinoza P. [Prevalence and factors associated with low physical activity in the Peruvian population]. Nutr. clín. diet. hosp. 2017; 37(4):108–115 [Google Scholar]
46.Genin PM, Dessenne P, Finaud J, Pereira B, Dutheil F, Thivel D, et al. Effect of Work-Related Sedentary Time on Overall Health Profile in Active vs. Inactive Office Workers. Front Public Health. 2018;6:279. doi: 10.3389/fpubh.2018.00279 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Clohessy S, Walasek L, Meyer C. Factors influencing employees’ eating behaviours in the office-based workplace: A systematic review. Obes Rev. 2019;20(12):1771–80. doi: 10.1111/obr.12920 [DOI] [PubMed] [Google Scholar]
48.Church TS, Thomas DM, Tudor-Locke C, Katzmarzyk PT, Earnest CP, Rodarte RQ, et al. Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One. 2011;6(5):e19657. doi: 10.1371/journal.pone.0019657 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Ministerio del Ambiente. [National Environmental Information System. Indicator: Vehicles per thousand inhabitants]. Ministerio del Ambiente. 2016. Available from: https://sinia.minam.gob.pe/indicador/966
50.Day K. Physical environment correlates of physical activity in developing countries: A review. J Phys Act Health. 2018;15(4):303–14. doi: 10.1123/jpah.2017-0184 [DOI] [PubMed] [Google Scholar]
51.Instituto Nacional de Estadística e Informática. [Statistics on information and communication technologies in households. Quarterly: Enero-February-March 2020]. Instituto Nacional de Estadística e Informática. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/boletin_tics.pdf
52.Pajuelo-Ramírez J, Torres Aparcana L, Agüero Zamora R, Bernui Leo I. [Overweight, obesity and abdominal obesity in the adult population of Peru]. An Fac Med. 2019;80(1):21–7. [Google Scholar]
53.Instituto Nacional de Estadística e Informática. [Peru—Demographic and Family Health Survey ENDES 2019]. Instituto Nacional de Estadística e Informática; 2020. Available from https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Endes2019/
54.Marchionni M, Gasparini L, Edo M. [Gender gaps in Latin America. A state of situation]. CAF. 2019. Available from: http://scioteca.caf.com/handle/123456789/1401
55.Instituto Nacional de Estadistica e Informatica. [Technical Report: Statistics with a gender perspective—Trimester: October-November-December 2019] [Internet]. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/01-informe-tecnico-n01_estadisticas-genero_oct-nov-dic2019.PDF
56.Jan Mohamed HJB, Mitra AK, Zainuddin LRM, Leng SK, Wan Muda WM. Women are at a higher risk of metabolic syndrome in rural Malaysia. Women Health. 2013;53(4):335–48. doi: 10.1080/03630242.2013.788120 [DOI] [PubMed] [Google Scholar]
57.Yu S, Xing L, Du Z, Tian Y, Jing L, Yan H, et al. Prevalence of Obesity and Associated Risk Factors and Cardiometabolic Comorbidities in Rural Northeast China. BioMed Res Int. 2019;2019:6509083. doi: 10.1155/2019/6509083 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Kanter R, Caballero B. Global gender disparities in obesity: a review. Adv Nutr. 2012;3(4):491–8. doi: 10.3945/an.112.002063 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Sifuentes- León E, Rivas-Díaz L. [Obesity and Overweight in Beliefs and Attitudes of Residents an Urban Community from the Sociology of Health]. Investigaciones sociales. 2019; 22(41):261–277. [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0254365.r001

Decision Letter 0

Isil Ergin

9 Mar 2021

PONE-D-20-35836

Socioeconomic inequalities in abdominal obesity among Peruvian adults

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Reviewer #1: The statistical analysis is based primarily on the Lorenze curve and the ECI with appropriate decomposition. The authors have concluded that, of the socioeconomic characteristics of the population, the variables which impact the inequality of the distribution of AO in Peru are the wealth index, education level and area of residence. The major comparison seems to be between the sexes. This appears in a univariate format for all the results.

There are several variables involved. However, the analysis lacks sophistication. There may be some age adjustments involved. However, there are no obvious multivariate models presented or the investigators have failed to adequately explain any multivariate or confounding relationships if they exist. This certainly is a major limitation of this cross sectional presentation. The manuscript should be re-assessed statistically.

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Attachment

Submitted filename: PONE-D-20-35836.docx

Click here for additional data file.^{(25.1KB, docx)}

PLoS One. 2021 Jul 21;16(7):e0254365. doi: 10.1371/journal.pone.0254365.r002

Author response to Decision Letter 0

11 May 2021

May 10th, 2021

Dear Isil Ergin, Assoc. Prof.

Academic Editor

PLOS ONE

Ref: Submission [PONE-D-20-35836]

Title: "Socioeconomic inequalities in abdominal obesity among Peruvian adults."

We thank the reviewers and the Editor-in-Chief for their helpful comments and suggestions provided for our manuscript. All comments and suggestions have been addressed in the new revised version of the manuscript. All the comments and changes are described below according to the revision process.

Journal requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at: https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf

Answer: Thank you for bringing this to our attention. We have reviewed the journal style requirements and have changed the formatting of the tables, the reference style and file naming accordingly.

Answer: Thank you for bringing this to our attention. We have added a data sharing statement section in materials and methods. Likewise, we have updated the data availability statement in our cover letter modeling it after what is published in other articles found in PLos One that used the ENDES database [1,2].

1. Accinelli RA, Leon-Abarca JA. Age and altitude of residence determine anemia prevalence in Peruvian 6 to 35 months old children. PLoS One [Internet]. 2020 Jan 1 [cited 2021 May 5];15(1):e0226846. Available from: https://doi.org/10.1371/journal.pone.0226846

2. Chambergo-Michilot D, Rebatta-Acuña A, Delgado-Flores CJ, Toro-Huamanchumo CJ. Socioeconomic determinants of hypertension and prehypertension in Peru: Evidence from the peruvian demographic and health survey. PLoS One [Internet]. 2021 Jan 1 [cited 2021 May 5];16(1 January):e0245730. Available from: https://doi.org/10.1371/journal.pone.0245730

Data sharing statement:

“The database used in this study is open access and available on the INEI website at http://iinei.inei.gob.pe/microdatos/.”

In our cover letter it now reads:

“Data Availability statement

3. We note that Figure S2 in your submission contains map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

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Answer: Thank you for bringing this to our attention. We have decided to replace the figures for an additional table (to be considered as supplementary material) in order to avoid any potential copyright infringements and provide more specific information about each sub-national administrative unit. This table specifies the prevalence of abdominal obesity in men and women according to the region of residence based on IDF, ATP III and LASO cut-off points.

Changes have been made to the final paragraph of the results section, incorporating a general interpretation of the results of this new table.

It now reads: “Using the IDF cut-off points, the overall prevalence of AO in this study was 73.8%. When using the ATP III and LASO cut-off points, the percentage of AO decreased to 43.6% and 40.6%, respectively (S1 Table). For all cut-off points, the coast region concentrated the administrative regions with the highest prevalence of AO, which were: Tumbes, Lima, Callao, Moquegua, Tacna, Arequipa, and Ica. It should be mentioned that Moquegua and Tacna, in particular, maintained an AO prevalence greater than 50% even with the more flexible cut-off points (S2 Table).”

Comments and suggestions made by the Editor

1. The use of “influence” or similar terminologies of effect should be reevaluated as this is a cross sectional study design.

Answer: Thank you for your comment. We agree with the reviewer that since our study design is cross sectional we cannot establish causal relationships. We have modified the sentence in materials and methods.

The second paragraph of study population and design now reads:

“..We used information compiled from both the household and the health questionnaires to carry out a secondary analysis to determine the prevalence and inequalities in the distribution of AO in adults. ...”

2. Rewording of the aim should be considered as it does not include an evaluation of the cut offs.

Answer: We agree with your comment and have included the cut-off points in the Abstract and Introduction.

The abstract now reads “...Thus, our aim was to analyze the socioeconomic inequalities in AO distribution defined using the International Diabetes Federation (IDF) cut-off points in Peruvian adults in 2018-2019.”

The last paragraph of the introduction now reads: “Therefore, this article aimed to analyze the socioeconomic inequalities in AO distribution, using the International Diabetes Federation (IDF) cut-off points for South and Central America [7] in Peruvian adults using information from the 2018-2019 ENDES. …”

3. Regarding the use of Concentration Index to evaluate the inequalities, please define what advantages and disadvantages it has for such evaluation.

Answer: Thanks for this comment. We have added further details about the disadvantages and advantages of using the concentration index In the methods section.

The following text was added to the second paragraph of the Statistical Analysis subsection:

“One shortfall of the CI is that in the scenario of data contamination, the index is sensitive to extreme values at one or both tails of the distribution [29]. However, its main advantages are that it reflects the socioeconomic dimension to inequalities in health and the experiences of the entire population. The CI is sensitive to changes in the distribution of the population across socioeconomic groups, and, therefore, has been widely used to measure inequality within health economics.”

We have added a reference:

29. Giorgi GM, Gigliarano C. The Gini concentration index: A review of the inference literature: The Gini concentration index. J Econ Surv. 2017;31(4):1130–1148.

4. The additions recommended for a clearer view on sampling, data presentation and statistical analysis should be dealt in detail.

Answer. We appreciate this comment. We have carefully reviewed the details regarding sampling, data presentation and statistical analysis and have modified the corresponding section to make it more specific. We have added a section describing the population characteristics, inclusion and exclusion criteria and a flowchart of the study population included.

5. Language editing needed.

Answer: We thank the editor for bringing this to our attention, we have edited the text with the aid of a native speaker so as to solve any language errors. This has been referenced in the acknowledgments section.

Comments and suggestions made by the Reviewer 1

1. The statistical analysis is based primarily on the Lorenze curve and the ECI with appropriate decomposition. The authors have concluded that, of the socioeconomic characteristics of the population, the variables which impact the inequality of the distribution of AO in Peru are the wealth index, education level and area of residence. The major comparison seems to be between the sexes. This appears in a univariate format for all the results. There are several variables involved. However, the analysis lacks sophistication. There may be some age adjustments involved. However, there are no obvious multivariate models presented or the investigators have failed to adequately explain any multivariate or confounding relationships if they exist. This certainly is a major limitation of this cross sectional presentation. The manuscript should be re-assessed statistically.

Answer: We thank the reviewer for this comment. As we noted, in the manuscript the results of Table 3 appear as a univariate analysis and in the methods sections there is no evidence that a multivariable analysis was performed .However, by checking our analysis and the codes developed for the decomposition, we confirm that our decomposition analysis was done using the generalized linear models (GLM) approach. In comparison to other approaches such as the probit estimations or the ordinary least squares, GLM has shown to be the best choice when decomposing inequalities using a binary outcome [1]. In addition, the decomposition analysis was adjusted for the socioeconomic (wealth index) and demographic variables (age, marital status, educational level (no formal school/primary/secondary/higher), presence of chronic disease (yes/no), smoker (yes/no), area of residence (urban/rural), altitude above sea level of the housing conglomerate, natural region (jungle/mountain range/rest of coast/Metropolitan Lima) included in the analysis. To state the methods of the decomposition analysis more clearly, we have expanded our description and detailed the econometric equation estimated. All the methodology was based on the book of O’Donnell and colleagues [2]. The book deals with analyzing health equity using household survey data.

References

1. Yiengprugsawan V, Lim LL, Carmichael GA, Dear KB, Sleigh AC. Decomposing socioeconomic inequality for binary health outcomes: an improved estimation that does not vary by choice of reference group. BMC Res Notes (2010) 3:57. doi:10.1186/1756-0500-3-57.

2. O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. The World Bank, Washington, D.C; 2008. 234p

In the subsection Statistical Analysis of the manuscript, we have added the following:

“The decomposition was performed based on generalized linear models (GLM). In comparison to other approaches such as probit estimations or the ordinary least squares, GLM has shown to be the best choice when decomposing inequalities using a binary outcome [33]. This study decomposes the inequality of abdominal obesity using the following equation:

Where ECI is the Erreygers concentration index, XX is the mean of the explanatory variables included in the decomposition (the socioeconomic and demographic factors), XX is the partial effect evaluated at the sample means, XX is the mean of the concentration index, and XX is the generalized concentration index of the stochastic term of error. Equation [c] reflects that an explanatory variable contributes to the inequality in AO when this variable is correlated with AO and is not equally distributed across the wealth index. The contribution of the explanatory variable to the inequality in AO depends on the absolute value of the partial effect and the unequal distribution of the explanatory variable with respect to household income per-capita. A positive sign of the partial effect means that the explanatory variable contributes to an increase in the inequality observed, and vice versa [27]. All analyses were performed using Stata version 14.2.”

One reference has been added:

33. Yiengprugsawan V, Lim LL, Carmichael GA, Dear KB, Sleigh AC. Decomposing socioeconomic inequality for binary health outcomes: an improved estimation that does not vary by choice of reference group. BMC Res Notes. 2010;3(1):57.

Comments and suggestions made by the Reviewer 2

Overall comments.

1. For every study cited in this manuscript, I suggest adding detailed information on where the study was conducted and/or the study population (i.e., age, sex, etc.).

Answer. Thank you for your suggestion. We have added information regarding the studies cited, whenever we deem it pertinent to be specific. Changes have been made throughout the introduction and discussion sections. Specifically, information has been added regarding the population under study (age and country of origin if information regarding the latter had not been mentioned previously). Adding sex details was only considered if any of the cited studies only included men or women exclusively.

2. Many sentences (especially in the discussion section) that based on the reference was not explained properly. Please add the detail on the reference studies in the sentence to help readers understand.

Answer. Thank you for pointing this out. We have revisited the introduction and discussion sections to add details in the sentences that are based on the referenced papers. Likewise, we have added further information to the discussion section.

3. I noticed some long sentences (e.g., Line 270-274) which are difficult to follow. Please re-phrase the long sentences and make it short and clear.

Answer. Thank you for the observation. We have rephrased this sentence.

It now reads:

“... A Peruvian study found that the population with a higher education level was 1.5 times less likely to perform physical activity [45], which could be due to the close association with office jobs, in which individuals remain seated for long hours. Indeed, a study in France reported that approximately 7.5 hours a day and 37.5 hours a week are spent sitting in front of a computer [46].”

Detailed comments.

1. The sentence in the abstracts section should be revised to make it short but clear, especially the methods part.

Answer. Thank you for the suggestion, we have shortened and rewritten the abstract sections for better understanding.

The methods section of the abstract now reads:

“This was a cross-sectional study using data from the 2018-2019 Demographic and Family Health Survey (ENDES) of Peru. We analyzed a representative sample of 62,138 adults over 18 years of age of both sexes from urban and rural areas. Subjects were grouped into quintiles of the wealth to calculate a concentration curve and the Erreygers Concentration Index (ECI) in order to measure the inequality of AO distribution. Finally, we performed a decomposition analysis to evaluate the major determinants of inequalities.”

2. Line 26, 38. AO and ECI was used first here, but no information what AO and ECI abbreviation stands for in the abstracts section.

Answer: We thank the reviewer for bringing this to our attention. We added the full name in the cited sections.

3. The ‘Methods’ part in abstracts need detailed information on where the study conducted, who is the study subjects and their age range.

Answer: Thank you for your comment. As mentioned above we have rewritten the abstract and detailed the country of study (Peru) and the age of the population studied (over 18 years old).

4. Line 30-31: “We analysed a representative sample of adults residing in urban/rural households (31 553 and 30 585 from the 2018 and 2019 dataset respectively).”

a. Adults age? Men and women?

b. Urban/rural household in Peru? Is it urban AND rural? Or is it urban OR rural?

c. 31 553 and 30 585 is the total number of individuals or household? Perhaps you should add a comma in all the number to make it easier to read (31,553 and 30,585)

Answer: We agree with your suggestion and have modified this section to better characterize our study population.

It now reads: “This was a cross-sectional study using data from the 2018-2019 Demographic and Family Health Survey (ENDES) of Peru. We analyzed a representative sample of 62,138 adults over 18 years of age of both sexes from urban and rural areas.”

5. Line 43. The conclusion should be clearer. E.g., Abdominal obesity in Peru was more prevalent among women. Socioeconomic inequality in abdominal obesity exists among Peruvian favouring the advantaged group. The inequality gap is less prominent among women, showing obesity being more common among the poor. Etc.

Answer: Thank you for the observation. We have modified the conclusion section of the abstract to make it more specific.

It now reads: “In Peru the wealthy concentrate a greater percentage of AO. The inequality gap is greater among men, yet AO is more prevalent among women. The variables that most contribute to inequality were the wealth index, education level and area of residence. There is a need for effective individual and community interventions to reduce these inequalities.”

6. Line 59: Which developing countries does the author refer to? Need a Reference to support this sentence.

Answer: Thank you for bringing this to our attention, we have modified the cited sentence and added a reference to clarify the message.

It now reads: “This disease threatens to overload the economic and resolutive capacity of health systems, particularly in Latin American countries in which budgets assigned to health are very limited, being of around 7.9% of the Gross Domestic Product (GDP) in 2018 compared to 16.9% in the United States [8].”

We have added 1 reference:

8. The World Bank. Current health expenditure (% of GDP) [Internet]. 2018 [cited 2021 May 9]. Available from: https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS

7. I suggest adding a paragraph on information about the study settings (in the introduction or methods section), i.e., Total population, number of cities in Peru, the economy classification by The World Bank, the population health, etc. This brief background information of Peru will increase the reader’s understanding of the study subject and the contexts.

Answer. We appreciate the reviewer's comment. We added a paragraph in the beginning of the study population and design section to better characterize Peru and its population.

It now reads: “Peru is a country divided into 24 sub-national administrative units, known as “administrative regions” and 1 constitutional province. The territorial area is 1,285,215.60 km2 and borders Ecuador, Colombia, Brazil, Bolivia and Chile. The total population in 2019 was 32,131,400 million people, being the 7th most populated country in Latin America [19]. Peru can be divided into three natural regions: the coast, which concentrates 58% of the national population and many of the most developed cities including Lima, the capital [20]; the jungle, which is difficult to access due to the rugged terrain of the Amazon and whose population has insufficient access to basic services; and the highlands, the Andean area which presents the highest level of monetary poverty in the country. According to The World Bank the economy of Peru belongs to the upper middle income (gross national income per capita between $4,046 and $12,535) [21]. In 2018, 5.2% of the GDP was invested in health, being one of the lowest compared to other South American countries such as Colombia, Chile and Brazil [8].”

Notes. 3 references were added in the manuscript:

19. Instituto Nacional de Estadística e Informática.[State of the Peruvian population. 2019]. Peru: National Institute of Statistics and Informatics. Instituto Nacional de Estadística e Informática. 2019. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1671/libro.pdf

20. Instituto Nacional de Estadística e Informática. [Peru: Final Results of the 2017 National Census]. Instituto Nacional de Estadística e Informática. 2018. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1544/

21. The World Bank. World Bank Country and Lending Groups. The World Bank. 2021. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

8. Line 104. ‘36 760’ is the number of individuals or household?

Answer.

We appreciate the reviewer's comment, the ENDES utilizes a two step sample, the second step of which is at the household level from here at least 1 individual 15 years and older is included for the survey. We have modified the second paragraph of the study population and design to better reflect this.

It now reads: “... It uses a two-stage, balanced, stratified and probabilistic sample, which is representative at national, administrative region and natural region levels. Each year studied had a sample size of 36,760 households, of which one individual 15 years of age or older was included in the survey. We used information compiled from both the household and the health questionnaires to carry out a secondary analysis to determine the prevalence and inequalities in the distribution of AO in adults. ...”

9. Line 105-106. Would the authors describe what is the inclusion and exclusion criteria? How many data extracted in the beginning? How many individuals are excluded?

Answer: We thank the reviewer for bringing this to our attention. Following the STROBE guidelines we have added a population characteristics subsection at the beginning of results and a simple flowchart to aid the reader in understanding our study sample.

It now reads:

“After applying inclusion (adults over 18 years with waist circumference measurement) and exclusion criteria (individuals <18 years, pregnant, incomplete data), a total of 31,553 and 30,585 individuals were included from the 2018 and 2019 datasets, respectively (Fig 1). We included a total of 26, 789 men and 35, 349 women in the analysis.”

10. Line 103-107. The explanation of the sampling procedure was very short. Please add a simple flowchart on how the study population generated from ENDES datasets.

Answer. Thank you for your observations. We have added a flowchart in the results section to better explain the process of how we selected our study population.

11. Line 140-141. “To measure the socioeconomic inequality in the distribution of AO across the population grouped in wealth quintiles,”

Please rephrase the sentence here to make it easier to understand.

Answer. Thank you for bringing this to our attention. The sentence has been modified for better understanding.

Now it reads: “To measure the socioeconomic inequality in the distribution of AO across the population, subjects were grouped into quintiles of wealth to calculate the concentration curve and the concentration index (CI).”

12. Table 1, 2 and 3. Typo in Age groups: ‘60 o more’.

I think it is better to maintain the same number of digits across all tables, whether it is 2 digits or 3 digits.

Answer. Thank you for bringing this to our attention. We have reviewed the instructions for authors and there is no specification as to the number of digits required for tables. Regardless, we have reviewed previous publications on PLoS One and found that authors usually use 1 digit for percentages as can be referenced below:

2. Id DJB, Id CPN, Mcgloughlin S, Pilcher D, Sarode V V, Gatward JJ. Preparation for airway management in Australia and New Zealand ICUs during the COVID -19 pandemic. 2021;1–10. Available from: http://dx.doi.org/10.1371/journal.pone.0251523

13. Table 1. No information on smoking in table 1, however, it is mentioned in the footnote (Line 193).

Answer. Thanks for the observation. We apologize for this. There was an error at the time of entry of the tables, due to it being inserted as an excel sheet. We have substituted these for conventional tables integrated in the MS word application. All the variables included are now clearly visible.

14. Line 194. I suggest dividing the section ‘Mean waist circumference and prevalence of AO’ into two paragraphs. The first paragraph talked about mean waist circumference and the second paragraph about AO prevalence.

Answer. Thank for your observation. We separated the text into two separate paragraphs as suggested.

The first paragraph now reads: “ The mean waist circumference was higher among men than women (93.5 cm vs. 92.3 cm, respectively, p < 0.001). [...] In the case of chronic disease and altitude in both sexes, those that reported having a chronic disease and who lived between 0-499 m.a.s.l. had the highest mean waist circumference (Table 2).”

The second paragraph now reads: “Regarding the prevalence of AO, women presented a higher prevalence than men both in general (85.1% vs. 61.1%, p < 0.001) and across all other independent variables. For women the highest prevalence of AO was concentrated among those from 30-59 years of age [...] We observed a lower prevalence of AO in men without chronic diseases compared to those who had comorbidities (55.8% vs. 78.9%, p < 0.001), while in women the rate of AO was high in both groups (83.5% vs. 93.1%, p < 0.001) (Table 2).

15. Line 195-199. “In both groups, the mean waist circumference was higher in wealthy, older individuals, those who were separated, divorced or widowed, had a higher education, those living in lower altitudes, and subjects who self-reported chronic disease (p < 0.001).

Please re-check this sentence to match the actual value in Table 1. I can see that some information in this sentence is incorrect.

Answer: Thank you for bringing this to our attention, we believe the reviewer refers to table 2 which states the mean waist circumference found among the participants in our study. We have modified the paragraph in order to more accurately reflect our results.

It now reads: “Among men the mean waist circumference increased linearly across the categories of variables so that those with the highest wealth index, aged 60 or more, and with a higher education had the largest waist circumference. Meanwhile, among women the mean waist circumference tended to be higher among the middle categories; for example, women with a middle wealth index, those aged 30-59, and women with primary education had the highest waist circumference. In the case of chronic disease and altitude, in both sexes, individuals that reported having a chronic disease and who lived between 0-499 m.a.s.l. had the highest mean waist circumference (Table 2).”

16. Line 199-201. “The prevalence of AO among women had small variations across all the independent variables, and prevalence was consistently higher than men (85.1% vs. 61.1%, p < 0.001).”

Perhaps the author should re-phrase this sentence so it will be easier to understand by the reader.

Answer. We agree with your observation and have modified the sentence to state: “Regarding the prevalence of AO, women presented a higher prevalence than men both in general (85.1% vs. 61.1%, p < 0.001) and across all other independent variables. ”

17. Line 203-204. “By educational level, the prevalence of AO in men was positively linked with the level of education,”

What does the author meant by ‘positively linked’ in this sentence?

Answer. Thank you for bringing this to our attention. We refer to the fact that as education level increases so does the prevalence of AO. We have changed the sentence to make our point clearer: “According to educational level, the prevalence of AO in men increased linearly with the level of education ... ”

18. Line 240. “According to the IDF, at a national level, the prevalence of AO was 73.8%. When using the ATP III and LASO cut-off points, the percentage of AO decreased to 43.6% and 40.6% respectively (S1 Table).”

This sentence needs additional information e.g.: “According to the IDF cut-off points, the overall prevalence of AO in this study was 73.8%. When using the ATP III and LASO cut-off points, the percentage of AO decreased to 43.6% and 40.6% respectively (S1 Table)”

Answer. We appreciate the reviewer's comment and have added additional information to make the text clearer using your suggestion: “Using the IDF cut-off points, the overall prevalence of AO in this study was 73.8%...”

19. Line 250. “Having a greater wealth index, a higher education and living in an urban setting were the major independent determinants of inequality and were positively associated with its prevalence.”

What does the author meant by ‘were positively associated with its prevalence’? Was it meant these variables (greater wealth index, a higher education and living in an urban setting) positively associated with abdominal obesity prevalence? I do not think this manuscript studied the association between the variables and abdominal obesity prevalence.

Answer. Thanks for this comment. We agree with the reviewer that measuring the association between the mentioned variables and abdominal obesity was not part of our objective. With this in mind, we have reviewed the whole manuscript and have rewritten the indicated sentence and others with similar characteristics, being more specific regarding our study aims and the analyses performed. For instance, in the first paragraph of the discussion section, we have added the following sentence:

“The major contributors of inequality were the wealth index, higher education and living in an urban setting.”

20. Line 260. Please elaborate what kind of ‘unhealthy routine behaviours’ does the author meant here.

Answer. We have added the following information: “...unhealthy routine behaviors, such as sedentarism and consumption of high caloric food..”. The explanation of these behaviors in the aforementioned paragraph has not been expanded, because they are explained in the following paragraphs of the discussion section.

21. Line 261. Please also elaborate on what is the ‘ultra-processed food’.

Answer. Thank you for the observation. Ultra-processed food is a category of high caloric food, which passes through multiple processes usually including the addition of many ingredients such as salt, sugar and oil as defined by the NOVA classification proposed by the Food and Agriculture Organization of the United Nations [1]. Examples of this food are soft drinks, chocolate, ice-cream, chips, hotdogs, fries, etc. In order to avoid confusion, we have modified the term in the sentence cited.

1. Monteiro C.A, Cannon G, Lawrence M, Costa M.L, Pereira P. Ultra-processed foods,

diet quality, and health using the NOVA classification system. Food and Agriculture Organization of the United Nations. Rome; 2019.

Now it reads: “...in addition, these people are exposed to a great amount of advertising of caloric dense food”

22. Line 268, 270, 273, 281. Please mention in which country is the cited studies were conducted? This will help the reader understand the context.

Answer We agree with the reviewer on this point. We have added the countries according to the bibliography to support the manuscript and make it easier for readers to understand.

23. Line 280. “The population in urban areas is 1.9 times more likely to have lower physical activity when compared to that living in rural areas [42].” If this is not the findings of this study, please add detailed information on where is the reference study was conducted etc.

Answer. Thank you for the observation, as mentioned above we have specified the countries according to the bibliography.

24. Line 281. “It has been reported that occupational physical activity in metropolitan areas has been reduced by about 120 kcal/day [45].” Where it is reported? Who reported it?

Answer. We thank the reviewer for bringing this to our attention. We found this information in the journal “Nutrición Hospitalaria”; however, this study did not have these data as their findings. Therefore, we have modified this reference to one reporting that in the last 50 years Americans have decreased physical activity and that the public health measures implemented are still not insufficient to stop this trend.

Reference 45 is now number 48:

● Church TS, Thomas DM, Tudor-Locke C, Katzmarzyk PT, Earnest CP, Rodarte RQ, et al. Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One. 2011;6(5):e19657.

25. Line 287-289. “Roads in rural areas are usually very rough with insufficient access ways, thus residents of this area are conditioned to do more physical activity as part of their daily commute [47]”

This sentence cites WHO report on ageing and health, is that correct?

Answer. We appreciate the reviewer's comment. This was a mistake and the reference mentioned has been replaced by the correct one.

Reference 47 is now number 50:

● Day K. Physical environment correlates of physical activity in developing countries: A review. J Phys Act Health. 2018;15(4):303–14.

26. Line 296-298. “This was concentrated in the 30-59 age group, a fact that could be explained both by the hormonal component typical of menopause and postmenopause [50],”

What does ‘This’ refer to? I think it is better to rephrase to “Among women, abdominal obesity was….”.

I also do not agree to connect this age group to menopause or post menopause. How does the author explain those women in the age group 60 or more having lower prevalence of AO? On the opposites, in the next sentences, the author mentioned about the fertility rate in the same age group.

Answer. We thank the reviewer for bringing this to our attention. By “this” we refer to the higher prevalence of AO in women. We have changed the first paragraph to make this clearer and reference 50 about menopause and postmenopause has been deleted. Regarding the fertility rate, we mean that young women (18-29 years old), who have more AO, compared to their male counterparts, could be explained by the higher fertility rate in the group of 20-34 years old in Perú.

It now reads: “The prevalence of AO was greatest among women belonging to the 30-59 age group, which might be explained by the decline in regular exercise and the use of diets over time [18].”

27. Line 200-300. “We must acknowledge the large proportion of young women with AO compared to their male counterparts”.

Do ‘young women’ here refer to women in the age group 18-29 years old?

Answer. Thank you for bringing this to our attention. Yes, by “Young women“ we mean the age group from 18 to 29 years old, in whom the prevalence of AO was 70.8% compared to males presenting a prevalence of 36.1%, with a 34.7 percentage point difference. We have modified the cited text to clarify.

It now reads: “Of note was the large proportion of women aged 18-29 years of age with AO compared to their male counterparts. ”

28. Line 301. “In Peru, women between 20-34 years of age reach the highest levels of fertility, exceeding 60% in both rural and urban areas [51],”

What is exceeding 60%? What is this number refer to?

Answer. Thank you for bringing this to our attention, we meant to stress the fact that women in this age group have the highest prevalence of fertility both in rural and urban areas. We have modified the sentence.

It now reads: “In Peru, the prevalence of fertility in women between 20-34 years of age exceeds 60% in both rural and urban areas [53]”

29. Line 302-308. Table 1 showed that the majority of women in this study were having secondary (37%) and higher (33.5%) education level. Which means the participation of Peruvian women in the labour market may be higher. It somehow contradicts the two sentences here. How do you explain this?

Answer. Despite a large proportion of residents of both sexes in Peru being able to obtain secondary and higher education, the rate of participation of women in the workforce was approximately 16 percentage points less than that of men in 2018 [1]. Even among those with higher education it is estimated that 86.2% of men are employed vs. 74.5% of women. Likewise, the average monthly salary of men (1846 PEN (487.69 USD)) is higher than that obtained by women (1322 PEN (349.26 USD)) [2]. These differences might induce women to leave behind their professional aspirations in favor of raising their children and tending to other household chores.

1. INEI. [Peru: Gender Gaps 2016. Progress towards equality between women and men]. Inst Nac Estadística e Informática. 2016;

2.Instituto Nacional de Estadistica e Informatica. [Technical Report: Statistics with a gender perspective - Trimester: October-November-December 2019] [Internet]. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/01-informe-tecnico-n01_estadisticas-genero_oct-nov-dic2019.PDF

To further illustrate this point we have added a few sentences in the referenced paragraph: “Even among individuals with higher a education it is estimated that 86.2% of men in Perú are employed vs. 74.5% of women. This percentage drops to 67.8% among women with a primary education but remains around 82.2% for their male counterparts. Likewise, the average monthly salary of men of 1846 PEN (487.69 USD) is higher than the 1322 PEN (349.26 USD) earned by women with [55]. Therefore, Peruvian women may be less motivated to pursue their professional career path due to inequalities in the workforce and a lack of monetary incentive;... ”

We have added one reference:

55. Instituto Nacional de Estadistica e Informatica. [Technical Report: Statistics with a gender perspective - Trimester: October-November-December 2019] [Internet]. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/01-informe-tecnico-n01_estadisticas-genero_oct-nov-dic2019.PDF

30. Line 308-309. “Finally, due to social beliefs in some contexts, obesity could be seen as a symbol of good economic status”, Which contexts? Does this social belief also exist in Peru?

Answer. In the social context of Indonesia cultural factors and social beliefs might influence the burden of obesity. Indeed, like this country, the same social trend may occur in Peru. An example of this is a study conducted in an urban Peruvian population in Lima - Peru which found that obesity was a source of pride because it is perceived to be associated with a better economic position in society.

One reference has been added as number 59 in the text:

59. Sifuentes- León E, Rivas-Díaz L. [ Obesity and Overweight in Beliefs and Attitudes of Residents an Urban Community From the Sociology of Health]. Investigaciones sociales. 2019; 22(41):261-277.

31. Line 308-311. “Finally, due to social beliefs in some contexts, obesity could be seen as a symbol of good economic status [19], becoming widely accepted among women because of its association with pregnancy and child-bearing [55].”

This sentence should be divided into two sentences, as it talked about obesity from a different point i.e., obesity as a symbol of good economic status and obesity association with pregnancy and childbearing.

Answer. We agree with your observation. The two sentences were separated according to the suggestion and the order of the sentences has also been changed for better linkage.

It now reads: “... being widely accepted among women because of its association with pregnancy and child-bearing [58]. Due to social beliefs in countries such as Indonesia and Perú [59], obesity might be seen as a symbol of good economic status [18]. ...”

32. Line 325. “We found that the socioeconomic characteristics of the population impact the distribution of AO in Peru”.

The author can delete ‘We found that’ in this conclusion section.

The socioeconomic inequality in AO exists, but it is less prominent among women.

Answer. We agree with your observation and have changed the sentence to make it clearer.

It now reads: “The socioeconomic characteristics of the population impact the distribution of AO in Peru. This inequality is more prominent among men although AO is more prevalent among women. …”

33. Reference.

Perhaps the author should update some outdated references to more recent publications.

Several references were not in English, and no translation made available in the Reference section. Some references were not complete and not cited according to Vancouver style.

Answer. We have reviewed our references and updated those related to associations made between abdominal obesity and various risk and sociodemographic factors where pertinent. References in Spanish have been translated to English by the authors. We must emphasize that some of the older references pertain to various economic and statistical measures and analyses that were published over a decade ago and updated data is limited.

The authors.

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PLoS One. doi: 10.1371/journal.pone.0254365.r003

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Isil Ergin

25 Jun 2021

Socioeconomic inequalities in abdominal obesity among Peruvian adults

PONE-D-20-35836R1

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Isil Ergin

30 Jun 2021

PONE-D-20-35836R1

Socioeconomic inequalities in abdominal obesity among Peruvian adults

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Data Availability Statement

[pone.0254365.ref001] 1.Hu L, Huang X, You C, Li J, Hong K, Li P, et al. Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China. PLoS One. 2017;12(9):e0183934. doi: 10.1371/journal.pone.0183934 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref002] 2.Favre G, Legueult K, Pradier C, Raffaelli C, Ichai C, Iannelli A, et al. Visceral fat is associated to the severity of COVID-19. Metabolism. 2021;115(154440):154440. doi: 10.1016/j.metabol.2020.154440 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref003] 3.OECD. Heavy Burden of Obesity: The Economics of Prevention. OECD. 2019. 10.1787/67450d67-en. [DOI]

[pone.0254365.ref004] 4.Caspard H, Jabbour S, Hammar N, Fenici P, Sheehan JJ, Kosiborod M. Recent trends in the prevalence of type 2 diabetes and the association with abdominal obesity lead to growing health disparities in the USA: An analysis of the NHANES surveys from 1999 to 2014. Diabetes Obes Metab. 2018;20(3):667–71. doi: 10.1111/dom.13143 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref005] 5.Oliveira A, Araújo J, Severo M, Correia D, Ramos E, Torres D, et al. Prevalence of general and abdominal obesity in Portugal: comprehensive results from the National Food, nutrition and physical activity survey 2015–2016. BMC Public Health. 2018;18(1):614. doi: 10.1186/s12889-018-5480-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref006] 6.Barquera S, Campos-Nonato I, Hernández-Barrera L, Flores M, Durazo-Arvizu R, Kanter R, et al. Obesity and central adiposity in Mexican adults: results from the Mexican National Health and Nutrition Survey 2006. Salud Publica Mex. 2009;51 Suppl 4:S595–603. doi: 10.1590/s0036-36342009001000014 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref007] 7.Pajuelo-Ramírez J, Torres-Aparcana H, Agüero-Zamora R, Quispe AM. Altitude and its inverse association with abdominal obesity in an Andean country: a cross-sectional study. F1000Res. 2019;8:1738. doi: 10.12688/f1000research.20707.2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref008] 8.The World Bank. Current health expenditure (% of GDP) [Internet]. 2018 [cited 2021 May 9]. Available from: https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS

[pone.0254365.ref009] 9.World Health Organization. Obesity and overweight. World Health Organization. 2020. [cited 2021 May 7]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight

[pone.0254365.ref010] 10.Sinaga M, Yemane T, Tegene E, Lidstrom D, Belachew T. Performance of newly developed body mass index cut-off for diagnosing obesity among Ethiopian adults. J Physiol Anthropol. 2019;38(1):14. doi: 10.1186/s40101-019-0205-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref011] 11.Federación Latinoamericana de Sociedades de Obesidad. [II Latin American Congress on Obesity 2017]. Federación Latinoamericana de Sociedades de Obesidad. 2017. Available from: https://bibliotecavirtual.insnsb.gob.pe/ii-consenso-latinoamericano-de-obesidad-2017/

[pone.0254365.ref012] 12.Owolabi EO, Ter Goon D, Adeniyi OV. Central obesity and normal-weight central obesity among adults attending healthcare facilities in Buffalo City Metropolitan Municipality, South Africa: a cross-sectional study. J Health Popul Nutr. 2017;36(1):54. doi: 10.1186/s41043-017-0133-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref013] 13.Lukács A, Horváth E, Máté Z, Szabó A, Virág K, Papp M, et al. Abdominal obesity increases metabolic risk factors in non-obese adults: a Hungarian cross-sectional study. BMC Public Health. 2019;19(1):1533. doi: 10.1186/s12889-019-7839-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref014] 14.Tarqui-Mamani C, Alvarez-Dongo D, Espinoza-Oriundo P. Cardiovascular risk according to abdominal circumference in Peruvians. An Fac Med. 2017;78(3):287. [Google Scholar]

[pone.0254365.ref015] 15.Onat A, Avci GS, Barlan MM, Uyarel H, Uzunlar B, Sansoy V. Measures of abdominal obesity assessed for visceral adiposity and relation to coronary risk. Int J Obes Relat Metab Disord. 2004;28(8):1018–25. doi: 10.1038/sj.ijo.0802695 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref016] 16.Instituto Nacional de Estadística e Informática. [Peru: Noncommunicable and Communicable Diseases, 2019]. Instituto Nacional de Estadística e Informática. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1734/

[pone.0254365.ref017] 17.Pennisi E. High altitude may have driven short stature in Peruvians. Science. 2018;360(6390):696 doi: 10.1126/science.360.6390.696 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref018] 18.Pujilestari CU, Nyström L, Norberg M, Weinehall L, Hakimi M, Ng N. Socioeconomic inequality in abdominal obesity among older people in Purworejo District, Central Java, Indonesia—a decomposition analysis approach. Int J Equity Health. 2017;16(1):214. doi: 10.1186/s12939-017-0708-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref019] 19.Instituto Nacional de Estadística e Informática.[State of the Peruvian population. 2019]. Peru: National Institute of Statistics and Informatics. Instituto Nacional de Estadística e Informática. 2019. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1671/libro.pdf

[pone.0254365.ref020] 20.Instituto Nacional de Estadística e Informática. [Peru: Final Results of the 2017 National Census]. Instituto Nacional de Estadística e Informática. 2018. Available from: https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1544/

[pone.0254365.ref021] 21.The World Bank. World Bank Country and Lending Groups. The World Bank. 2021. Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

[pone.0254365.ref022] 22.Instituto Nacional de Estadística e Informática. [Interviewer’s manual, Demographic and Family Health Survey, 2018.] Instituto Nacional de Estadística e Informática. 2018. Available from: https://proyectos.inei.gob.pe/endes/2018/documentos_2018/MANUAL_DE_LA_ENTREVISTADORA_2018_ENERO.pdf

[pone.0254365.ref023] 23.Instituto Nacional de Estadística e Informática. [Datasheet, Demographic and family health survey, 2019] Instituto Nacional de Estadística e Informática. 2019. Available from: https://proyectos.inei.gob.pe/endes/2019/documentos_2019/FICHA_TECNICA_ENDES%202019.pdf

[pone.0254365.ref024] 24.International Diabetes Federation. Consensus Worldwide Definition of the Metabolic Syndrome. International Diabetes Federation. 2006. Last Update 2020. Available from: https://www.idf.org/e-library/consensus-statements/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome

[pone.0254365.ref025] 25.Rutstein SO, Johnson K. The DHS Wealth Index. DHS Comp Reports No 6. 2004;1–71.

[pone.0254365.ref026] 26.Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. World Health Organization; 2001. [Google Scholar]

[pone.0254365.ref027] 27.O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation. The World Bank; 2008. [Google Scholar]

[pone.0254365.ref028] 28.Wagstaff A, Van Doorslaer E. Measuring inequalities in health in the presence of multiple-category morbidity indicators. Health Econ. 1994;3(4):281–9. doi: 10.1002/hec.4730030409 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref029] 29.Giorgi GM, Gigliarano C. The Gini concentration index: A review of the inference literature: The Gini concentration index. J Econ Surv. 2017;31(4):1130–1148. [Google Scholar]

[pone.0254365.ref030] 30.Wagstaff A. The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 2005;14(4):429–32. doi: 10.1002/hec.953 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref031] 31.O’Donnell O, O’Neill S, Van Ourti T, Walsh B. conindex: Estimation of concentration indices. Stata J. 2016;16(1):112–38. [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref032] 32.Koolman X, van Doorslaer E. On the interpretation of a concentration index of inequality. Health Econ. 2004;13(7):649–56. doi: 10.1002/hec.884 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref033] 33.Yiengprugsawan V, Lim LL, Carmichael GA, Dear KB, Sleigh AC. Decomposing socioeconomic inequality for binary health outcomes: an improved estimation that does not vary by choice of reference group. BMC Res Notes. 2010;3(1):57. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref034] 34.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. doi: 10.1001/jama.285.19.2486 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref035] 35.Herrera VM, Casas JP, Miranda JJ, Perel P, Pichardo R, González A, et al. Interethnic differences in the accuracy of anthropometric indicators of obesity in screening for high risk of coronary heart disease. Int J Obes. 2009;33(5):568–76. doi: 10.1038/ijo.2009.35 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref036] 36.Zhao P, Gu X, Qian D, Yang F. Socioeconomic disparities in abdominal obesity over the life course in China. Int J Equity Health. 2018;17(1):96 doi: 10.1186/s12939-018-0809-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref037] 37.Instituto Nacional de Estadística e Informática. [Evolution of monetary poverty 2008–2019]. Instituto Nacional de Estadística e Informática; 2020. Available from: https://www.inei.gob.pe/media/cifras_de_pobreza/informe_pobreza2019.pdf

[pone.0254365.ref038] 38.Seminario B. Zegarra M. Palomino L. [Evolution of departmental GDP and analysis of regional inequality in Peru: 1795–2017.] Documento de trabajo del BID N° IDB-WP-1016. Inter-American Development Bank. 2019. Available from: https://publications.iadb.org/publications/spanish/document/Evoluci%C3%B3n_del_PIB_departamental_y_an%C3%A1lisis_de_la_desigualdad_regional_en_el_Per%C3%BA_1795-2017_es.pdf

[pone.0254365.ref039] 39.García CM. Association of globalization in its different dimensions with overweight and obesity: an analysis in 10 Latin American and Caribbean countries. Salud Publica Mex. 2019;61(2):174–83. doi: 10.21149/8886 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref040] 40.Ministerio de Salud. [A fat problem: Overweight and Obesity in Peru]. Ministerio de Salud. 2012. Available from: https://www.gob.pe/institucion/minsa/informes-publicaciones/321813-un-gordo-problema-sobrepeso-y-obesidad-en-el-peru

[pone.0254365.ref041] 41.Wang Y, Wang L, Xue H, Qu W. A Review of the Growth of the Fast Food Industry in China and Its Potential Impact on Obesity. Int J Environ Res Public Health. 2016;13(11):1112. doi: 10.3390/ijerph13111112 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref042] 42.Cohen AK, Rai M, Rehkopf DH, Abrams B. Educational attainment and obesity: a systematic review. Obes Rev. 2013;14(12):989–1005. doi: 10.1111/obr.12062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref043] 43.Zhang H, Xu H, Song F, Xu W, Pallard-Borg S, Qi X. Relation of socioeconomic status to overweight and obesity: a large population-based study of Chinese adults. Ann Hum Biol. 2017;44(6):495–501. doi: 10.1080/03014460.2017.1328072 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref044] 44.Liao C, Gao W, Cao W, Lv J, Yu C, Wang S, et al. Association of Educational Level and Marital Status With Obesity: A Study of Chinese Twins. Twin Res Hum Genet. 2018;21(2):126–35. doi: 10.1017/thg.2018.8 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref045] 45.Tarqui C, Alvarez D, Espinoza P. [Prevalence and factors associated with low physical activity in the Peruvian population]. Nutr. clín. diet. hosp. 2017; 37(4):108–115 [Google Scholar]

[pone.0254365.ref046] 46.Genin PM, Dessenne P, Finaud J, Pereira B, Dutheil F, Thivel D, et al. Effect of Work-Related Sedentary Time on Overall Health Profile in Active vs. Inactive Office Workers. Front Public Health. 2018;6:279. doi: 10.3389/fpubh.2018.00279 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref047] 47.Clohessy S, Walasek L, Meyer C. Factors influencing employees’ eating behaviours in the office-based workplace: A systematic review. Obes Rev. 2019;20(12):1771–80. doi: 10.1111/obr.12920 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref048] 48.Church TS, Thomas DM, Tudor-Locke C, Katzmarzyk PT, Earnest CP, Rodarte RQ, et al. Trends over 5 decades in U.S. occupation-related physical activity and their associations with obesity. PLoS One. 2011;6(5):e19657. doi: 10.1371/journal.pone.0019657 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref049] 49.Ministerio del Ambiente. [National Environmental Information System. Indicator: Vehicles per thousand inhabitants]. Ministerio del Ambiente. 2016. Available from: https://sinia.minam.gob.pe/indicador/966

[pone.0254365.ref050] 50.Day K. Physical environment correlates of physical activity in developing countries: A review. J Phys Act Health. 2018;15(4):303–14. doi: 10.1123/jpah.2017-0184 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref051] 51.Instituto Nacional de Estadística e Informática. [Statistics on information and communication technologies in households. Quarterly: Enero-February-March 2020]. Instituto Nacional de Estadística e Informática. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/boletin_tics.pdf

[pone.0254365.ref052] 52.Pajuelo-Ramírez J, Torres Aparcana L, Agüero Zamora R, Bernui Leo I. [Overweight, obesity and abdominal obesity in the adult population of Peru]. An Fac Med. 2019;80(1):21–7. [Google Scholar]

[pone.0254365.ref053] 53.Instituto Nacional de Estadística e Informática. [Peru—Demographic and Family Health Survey ENDES 2019]. Instituto Nacional de Estadística e Informática; 2020. Available from https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Endes2019/

[pone.0254365.ref054] 54.Marchionni M, Gasparini L, Edo M. [Gender gaps in Latin America. A state of situation]. CAF. 2019. Available from: http://scioteca.caf.com/handle/123456789/1401

[pone.0254365.ref055] 55.Instituto Nacional de Estadistica e Informatica. [Technical Report: Statistics with a gender perspective—Trimester: October-November-December 2019] [Internet]. 2020. Available from: https://www.inei.gob.pe/media/MenuRecursivo/boletines/01-informe-tecnico-n01_estadisticas-genero_oct-nov-dic2019.PDF

[pone.0254365.ref056] 56.Jan Mohamed HJB, Mitra AK, Zainuddin LRM, Leng SK, Wan Muda WM. Women are at a higher risk of metabolic syndrome in rural Malaysia. Women Health. 2013;53(4):335–48. doi: 10.1080/03630242.2013.788120 [DOI] [PubMed] [Google Scholar]

[pone.0254365.ref057] 57.Yu S, Xing L, Du Z, Tian Y, Jing L, Yan H, et al. Prevalence of Obesity and Associated Risk Factors and Cardiometabolic Comorbidities in Rural Northeast China. BioMed Res Int. 2019;2019:6509083. doi: 10.1155/2019/6509083 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref058] 58.Kanter R, Caballero B. Global gender disparities in obesity: a review. Adv Nutr. 2012;3(4):491–8. doi: 10.3945/an.112.002063 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0254365.ref059] 59.Sifuentes- León E, Rivas-Díaz L. [Obesity and Overweight in Beliefs and Attitudes of Residents an Urban Community from the Sociology of Health]. Investigaciones sociales. 2019; 22(41):261–277. [Google Scholar]

PERMALINK

Socioeconomic inequalities in abdominal obesity among Peruvian adults

Marioli Y Farro-Maldonado

Glenda Gutiérrez-Pérez

Akram Hernández-Vásquez

Antonio Barrenechea-Pulache

Marilina Santero

Carlos Rojas-Roque

Diego Azañedo

Roles

Abstract

Objectives

Methods

Results

Conclusions

Introduction

Materials and methods

Study population and design

Variables

Statistical analysis

Ethical considerations

Results

Population characteristics

Fig 1. Flowchart of the study population included from the 2018–2019 ENDES.

Table 1. Socioeconomic characteristics of a Peruvian adult population.

Mean waist circumference and prevalence of AO

Table 2. Mean waist circumference and prevalence of abdominal obesity in men and women by socioeconomic characteristics.

Inequality in the distribution of abdominal obesity

Fig 2. Study abdominal obesity concentration curves in men and women.

Table 3. Decomposition of concentration indices for men and women.

Prevalence of AO using different cut-off points

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Isil Ergin

Roles

Author response to Decision Letter 0

Decision Letter 1

Isil Ergin

Roles

Acceptance letter

Isil Ergin

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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