Keywords
feeding behavior - dietary habits - ultraprocessed foods - pregnant - maternal nutrition
Palavras-chave
comportamento alimentar - hábitos alimentares - alimentos ultraprocessados - gestante
- nutrição materna
Introduction
Major changes have occurred in the dietary patterns of the population in recent years,
mainly in relation to the substitution of nutrient-rich in natura foods for industrialized foods with high energy density and low nutritional quality.[1] Factors such as the search for practicality and absence of time have led to various
social changes and changes in health and food consumption of the population.[2] Given the scenario of modification of food patterns and changes in the forms of
food and beverage processing, Monteiro et al. (2016)[3] proposed a new system for food classification. For this new classification, food
items were grouped according to the extent and purpose of processing. Titled NOVA, consumption items are classified into four categories: in natura or minimally-processed foods; processed culinary ingredients; processed foods and
ultraprocessed foods.[3] The presence of ultraprocessed foods in the food consumption of Brazilians has been
gradually expanding, with the need for a deep investigation of their impact on the
health of the population.[3]
[4] Although there is a trend of high intake of ultraprocessed foods at all stages of
the life cycle,[5]
[6] it is during gestation that women are more likely to change their pattern of food
consumption due to concern about their maternal responsiveness.[7] Thus, we analyzed the consumption of minimally-processed and ultraprocessed foods
in relation with sociodemographic factors, maternal habits, educational activities
and clinical history of pregnant women during prenatal care.
Methods
This is a cross-sectional, analytical and descriptive study, with data collected from
April to September 2010. The data of the present study are part of a larger study
titled “Evaluation of the Quality of Prenatal Care in the Metropolitan Region of Grande
Vitória, Espírito Santo, Brazil (RMGV-ES): Access and Integration of Health Services.”[8] The sample consisted of pregnant women living in the municipalities of the metropolitan
region of Grande Vitória, ES (MRGV-ES), hospitalized in establishments of the Unified
Health System (SUS, in the Portuguese acronym) due to childbirth. This research was
conducted in eight health facilities of the MRGV-ES, with these being contracted or
belonging to the SUS. To calculate the sample size, we used the sample size formula
to estimate a proportion of women studied. Through the data provided by the Information
System on Live Births (SINASC, in the Portuguese acronym), a total of 17,980 live
births were established in the public network or through the SUS, which number approximately
reflects the number of pregnant women residing in the MRGV-ES. The indicator “Percentage
of Live Births With by Seven or More Prenatal Care Consultations” was also considered,
as an approximation of the utilization of health services during pregnancy. Considering
a desired precision of 4%, drawing effect equal to 1.5 and significance level of 5%,
calculations resulted in the sample size of 850 women. The total was increased by
∼ 30% to consider possible losses or refusals. A pilot study was performed with 67
parturients (not included in the study) in one of the establishments where the main
study was conducted, for verification of the graphical presentations of the questionnaires,
as well as the test on the understanding of their items and the evaluation of the
average time of completion of the forms. All questions were tested by the Kappa, weighted
Kappa and McNemar tests. These analyses demonstrated their applicability in the population
studied, including questions about food consumption. The data were collected by a
team through a closed structured interview, a full copy of the pregnant woman's card,
and the retrieval of information from the medical records of the health facility where
the delivery was performed. The interviewers visited at least once a week all eight
maternity hospitals included in the study, approached women at the time after the
birth, when they verified the possibility of the interview and possession of the pregnant
woman's card. Women who did not have a pregnant woman's card were excluded, as well
as those who underwent prenatal (all or part) follow-up in the private subsystem;
pregnant women hospitalized with complications who did not have babies; and those
who had follow-up in the municipalities outside the MRGV-ES. The women who had cesarean
delivery had their data collected 12 hours after delivery, so that the anesthetic
effects of the surgical procedure would not interfere with the responses. The race/color
variable was determined by autoclassification as white, black, or brown (brown/mulatto).
In relation to age group, these were categorized as “ ≤ 19 years,” “between 20 and
34 years,” or “ ≥ 35 years,” and the conjugal situation was categorized as “lives
with partner” or “does not live with partner.” The variable maternal occupation was
classified as “paid work” for pregnant women who had some work with remuneration,
and “without paid work,” for those pregnant women who did not have paid work. In relation
to the variable head of the family, these were categorized as “own,” “companion,”
or “other.” Socioeconomic classification was determined based on the criterion of
Brazilian Economic Classification of the Brazilian Association of Research Companies.[9] The pregnant women were classified as class A/B, C1/C2, or D/E to improve the description
of the outcomes. The use of tobacco and alcohol during pregnancy was analyzed through
questions with direct answers (“yes” or “no”). It was questioned whether during the
prenatal period there was information about the advantages of healthy food and its
importance for the prevention of children's health problems. This variable was dichotomized
in “yes” or “no.” The data on prenatal medical care were analyzed considering the
number of prenatal consultations reported by the pregnant woman. This variable was
classified as having performed from “one to five” or “six or more” prenatal visits.
Fifteen food items registered through the questionnaire applied were evaluated. This
questionnaire was based on the Vigilance System for Risk Factors for Non-Communicable
Chronic Diseases of the Brazilian Ministry of Health (Vigitel, in the Portuguese acronym),
which annually evaluates the food habits in Brazilian capitals.[10] The frequency of reported intake was converted to daily frequency, so that all food
items had the same unit. The daily frequency values were multiplied by the portion
of each item consumed, thus revealing the number of servings consumed daily.[11] After the calculation of the weight of the frequency of consumption of each item,
the analyzed foods were inserted into groups established by the classification of
foods based on the extent and purpose of their processing by NOVA.[3] The foods were grouped according to the criteria proposed by Monteiro et al. (2016)[3] in the NOVA classification, which considers the characteristics of the purpose and extent of
the industrial processing they have undergone: in natura or minimaly-processed, processed
culinary ingredients, processed foods and ultraprocessed foods. For this study, the
foods that were classified as minimally-processed and ultraprocessed were used. The
Chi-square test was used to analyze the differences in proportions. The significance
level of α < 5% was adopted. The distribution of the consumption of minimally-processed
and ultraprocessed foods was evaluated in quartiles. The binary logistic regression
model was used to investigate the association between the independent variables and
the consumption of minimally-processed foods and consumption of ultraprocessed foods.
The variables included in the binary logistic regression analysis were selected according
to the statistical significance in the Chi-square test (p < 0.2). The lowest quartile (Q1) and the highest quartile (Q4) were analyzed for
the purpose of analyzing the extremes of consumption with the independent variables.
All statistical analyzes were performed in the IBM SPSS Statistics for Windows, version
22.0 (IBM Corp., Armonk, NY, USA). This study was conducted according to the guidelines
laid down in the Declaration of Helsinki, and all procedures involving human subjects
were approved by Research Ethics Committee of the Health Sciences Center of Universidade
Federal do Espírito Santo (grant number 3.060.797) and CAAE (grant number 99562818.0.0000.5060).
Written informed consent was obtained from all subjects.
Results
The sociodemographic variables, maternal habits, educational activity and clinical
history were analyzed according to the quartiles of minimally-processed food consumption
of 1,035 pregnant women ([Table 1]). The age group and the consumption of minimally-processed foods (p = 0.015) were associated, and it was observed that pregnant women aged ≤ 19 years
had lower consumption of minimally-processed foods (32.8%, n = 76) than the other age groups. Women ≥ 35 years of age presented higher consumption
of minimally-processed foods (32.5%, n = 26).
Table 1
Sociodemographic variables, maternal habits, educational ctivity and clinical history
according to the consumption of minimally-processed foods by pregnant women
|
Variable
|
Minimally-processed food consumption[*]
|
P[†]
|
Total
|
|
Q1
|
Q2
|
Q3
|
Q4
|
|
n
|
%
|
n
|
%
|
n
|
%
|
n
|
%
|
n
|
%
|
|
Age, y (n 1,035)
|
|
|
|
|
|
|
|
|
0.015
|
|
|
|
≤ 19
|
76
|
32.8
|
54
|
23.3
|
61
|
26.3
|
41
|
17.7
|
|
232
|
22.4
|
|
20–34
|
177
|
24.5
|
200
|
27.7
|
165
|
22.8
|
181
|
25.0
|
|
723
|
69.9
|
|
≥ 35
|
15
|
18.8
|
18
|
22.5
|
21
|
26.3
|
26
|
32.5
|
|
80
|
7.7
|
|
Race/color (n = 977)
|
|
|
|
|
|
|
|
|
0.096
|
|
|
|
White
|
21
|
15.7
|
33
|
24.6
|
41
|
30.6
|
39
|
29.1
|
|
134
|
13.7
|
|
Brown
|
188
|
27.5
|
180
|
26.3
|
156
|
22.8
|
160
|
23.4
|
|
684
|
70.0
|
|
Black
|
41
|
25.8
|
43
|
27.0
|
36
|
22.6
|
39
|
24.5
|
|
159
|
16.3
|
|
Marital status (n = 1,030)
|
|
|
|
|
|
|
|
0.294
|
|
|
|
Living with a partner
|
214
|
25.8
|
208
|
25.0
|
203
|
24.4
|
206
|
24.8
|
|
831
|
22.5
|
|
Not living with a partner
|
54
|
27.1
|
61
|
30.7
|
42
|
21.1
|
42
|
21.1
|
|
199
|
70.2
|
|
Maternal occupation (n = 1,034)
|
|
|
|
|
|
|
0.002
|
|
|
|
No paid work
|
212
|
28.6
|
197
|
26.6
|
171
|
23.1
|
160
|
21.6
|
|
740
|
71.6
|
|
Paid work
|
55
|
18.7
|
75
|
25.5
|
76
|
25.9
|
88
|
29.9
|
|
294
|
28.4
|
|
Householder (n 1028)
|
|
|
|
|
|
|
0.073
|
|
|
|
House owner
|
21
|
18.9
|
32
|
28.8
|
20
|
18.0
|
38
|
34.2
|
|
111
|
10.8
|
|
Partner's house
|
179
|
26.5
|
168
|
24.9
|
168
|
24.9
|
160
|
23.7
|
|
675
|
65.7
|
|
Others
|
65
|
26.9
|
70
|
28.9
|
57
|
23.6
|
50
|
20.7
|
|
242
|
23.5
|
|
Socioeconomic classification (n = 913)
|
|
|
|
|
|
0.994
|
|
|
|
D/E
|
73
|
25.5
|
74
|
25.8
|
69
|
24.1
|
70
|
24.5
|
|
286
|
31.3
|
|
C1/C2
|
152
|
26.2
|
157
|
27.0
|
139
|
23.9
|
133
|
22.9
|
|
581
|
63.6
|
|
A/B
|
11
|
23.9
|
14
|
30.4
|
10
|
21.7
|
11
|
23.9
|
|
46
|
5.0
|
|
Information on prenatal diet (n = 1,012)
|
|
|
< 0.001
|
|
|
|
No
|
161
|
32.7%
|
128
|
26.0%
|
98
|
19.9%
|
106
|
21.5%
|
|
493
|
48.7
|
|
Yes
|
94
|
18.1%
|
137
|
26.4%
|
147
|
28.3%
|
141
|
27.2%
|
|
519
|
51.3
|
|
Alcohol use during pregnancy (n = 1,034)
|
|
|
|
0.215
|
|
|
|
No
|
238
|
25,5
|
239
|
25.6
|
229
|
24.5
|
228
|
24.4
|
|
934
|
90.3
|
|
Yes
|
30
|
30.0
|
32
|
32.0
|
18
|
18.0
|
20
|
20.0
|
|
100
|
9.7
|
|
Smoking during pregnancy (n = 1,026)
|
|
|
|
|
< 0.001
|
|
|
|
No
|
211
|
23.5
|
241
|
26.9
|
212
|
23.7
|
232
|
25.9
|
|
896
|
87.3
|
|
Yes
|
53
|
40.8
|
29
|
22.3
|
33
|
25.4
|
15
|
11.5
|
|
130
|
12.7
|
|
Prenatal consultations (n = 948)
|
|
|
|
|
|
0.134
|
|
|
|
≤ 5
|
83
|
34.2
|
67
|
27.5
|
61
|
27.1
|
59
|
25.0
|
|
270
|
28.5
|
|
> 6
|
160
|
65.8
|
177
|
72.5
|
164
|
72.9
|
177
|
75.0
|
|
678
|
71.5
|
|
Parity[‡] (n 997)
|
|
|
|
|
0.991
|
|
|
|
1
|
106
|
26.9
|
102
|
25.9
|
94
|
23.9
|
92
|
23.4
|
|
394
|
39.5
|
|
2
|
76
|
25.6
|
76
|
25.6
|
71
|
23.9
|
74
|
24.9
|
|
297
|
29.8
|
|
3 or more
|
74
|
24.2
|
80
|
26.1
|
75
|
24.5
|
77
|
25.2
|
|
306
|
30.7
|
Abbreviations: Q1, first quartile; Q2, second quartile; Q3, third quartile; Q4, fourth
quartile.
* The distribution of the consumption of foods was evaluated in quartiles.
† The differences between quartiles were tested by the Chi-square test.
‡ Number of parturitions.
There was an association between maternal occupation (p = 0.002) and consumption of minimally-processed foods, in which it was possible to
observe that pregnant women without paid work had a lower consumption of minimally-processed
(28.6%, n = 212). On the other hand, women with paid work had a higher consumption of minimally-processed
foods (29.9%, n = 88).
A statistical difference was identified in pregnant women who received information
on healthy eating during prenatal care (p < 0.001) in relation to the consumption of minimally-processed foods, since women
who did not receive information about food presented lower consumption of minimally-processed
foods (32.7%, n = 161).
It was observed that pregnant women who smoked during pregnancy had lower consumption
of minimally-processed foods (p < 0.001).
Regarding the analysis of sociodemographic variables, maternal habits, educational
activity, and clinical history according to consumption of ultraprocessed foods ([Table 2]), there was a statistically significant association between age group (p < 0.001) and the consumption of ultraprocessed foods, with higher consumption of
ultraprocessed foods among pregnant women age ≤ 19 years, and lower consumption of
ultraprocessed foods among women in the age group of ≥ 35 years.
Table 2
Sociodemographic variables, maternal habits, educational activity and clinical history
according to consumption of ultraprocessed foods by pregnant women
|
Variable
|
Ultraprocessed food consumption∗
|
|
P[†]
|
Total
|
|
Q1
|
Q2
|
Q3
|
Q4
|
|
n
|
%
|
n
|
%
|
n
|
%
|
n
|
%
|
n
|
%
|
|
Age, y (n = 1,035)
|
|
|
|
|
|
|
|
< 0.001
|
|
|
|
≤ 19
|
36
|
15,5
|
55
|
23.7
|
59
|
25.4
|
82
|
35.3
|
|
232
|
22.4
|
|
20–34
|
199
|
27.5
|
185
|
25.6
|
173
|
23.9
|
166
|
23.0
|
|
723
|
69.9
|
|
≥ 35
|
32
|
40.0
|
27
|
33.8
|
15
|
18.8
|
6
|
7.5
|
|
80
|
7.7
|
|
Race/color (n = 977)
|
|
|
|
|
|
|
0.270
|
|
|
|
White
|
38
|
28.4
|
34
|
25.4
|
30
|
22.4
|
32
|
23.9
|
|
134
|
13.7
|
|
Brown
|
182
|
26.6
|
167
|
24.4
|
168
|
24.6
|
167
|
24.4
|
|
684
|
70.0
|
|
Black
|
34
|
21.4
|
50
|
31.4
|
29
|
18.2
|
46
|
28.9
|
|
159
|
16.3
|
|
Marital status (n = 1,030)
|
|
|
|
|
|
|
0.631
|
|
|
|
Living with a partner
|
209
|
25.2
|
219
|
26.4
|
201
|
24.2
|
202
|
24.3
|
|
831
|
80.7
|
|
Not living with a partner
|
56
|
28.1
|
45
|
22.6
|
46
|
23.1
|
52
|
26.1
|
|
199
|
19.3
|
|
Maternal occupation (n = 1,034)
|
|
|
|
|
0.023
|
|
|
|
No paid work
|
177
|
23.9
|
190
|
25.7
|
174
|
23.5
|
199
|
26.9
|
|
740
|
71.6
|
|
Paid work
|
90
|
30.6
|
77
|
26.2
|
72
|
24.5
|
55
|
18.7
|
|
294
|
28.4
|
|
Householder (n = 1,028)
|
|
|
|
|
|
|
0.004
|
|
|
|
Own
|
36
|
32.4
|
22
|
19.8
|
24
|
21.6
|
29
|
26.1
|
|
111
|
10.8
|
|
Companion
|
187
|
27.7
|
183
|
27.1
|
146
|
21.6
|
159
|
23.6
|
|
675
|
65.7
|
|
Others
|
41
|
16.9
|
62
|
25.6
|
73
|
30.2
|
66
|
27.3
|
|
242
|
23.5
|
|
Socioeconomic classification (n = 913)
|
|
|
|
|
|
0.695
|
|
|
|
D/E
|
75
|
26.2
|
82
|
28.7
|
64
|
22.4
|
65
|
22.7
|
|
286
|
31.3
|
|
C1/C2
|
153
|
26.3
|
147
|
25.3
|
136
|
23.4
|
145
|
25.0
|
|
581
|
63.6
|
|
A/B
|
9
|
19.6
|
11
|
23.9
|
15
|
32.6
|
11
|
23.9
|
|
46
|
5.0
|
|
Information on prenatal diet (n = 1,012)
|
|
|
0.534
|
|
|
|
No
|
135
|
27.4
|
133
|
27.0
|
114
|
23.1
|
111
|
22.5
|
|
493
|
48.7
|
|
Yes
|
128
|
24.7
|
132
|
25.4
|
125
|
24.1
|
134
|
25.8
|
|
519
|
51.3
|
|
Alcohol use during pregnancy (n = 1,034)
|
|
|
|
|
0.066
|
|
|
|
No
|
248
|
26.6
|
247
|
26.4
|
216
|
23.1
|
223
|
23.9
|
|
934
|
90.3
|
|
Yes
|
19
|
19.0
|
20
|
20.0
|
31
|
31.0
|
30
|
30.0
|
|
100
|
9.7
|
|
Smoking during pregnancy (n = 1,026)
|
|
|
|
|
|
< 0.001
|
|
|
|
No
|
246
|
27.5
|
235
|
26.2
|
215
|
24.0
|
200
|
22.3
|
|
896
|
87.3
|
|
Yes
|
19
|
14.6
|
31
|
23.8
|
31
|
23.8
|
49
|
37.7
|
|
130
|
12.7
|
|
Prenatal consultations (n = 948)
|
|
|
|
|
|
|
0.590
|
|
|
|
≤ 5
|
67
|
27.0
|
68
|
27.0
|
69
|
32.1
|
66
|
28.3
|
|
270
|
28.5
|
|
> 6
|
181
|
73.0
|
184
|
73.0
|
146
|
67.9
|
167
|
71.7
|
|
678
|
71.5
|
|
Parity[‡] (n 997)
|
|
|
|
|
|
0.161
|
|
|
|
1
|
93
|
23.6
|
104
|
26.4
|
94
|
23.9
|
103
|
26.1
|
|
394
|
39.5
|
|
2
|
66
|
22.2
|
77
|
25.9
|
80
|
26.9
|
74
|
24.9
|
|
297
|
29.8
|
|
3 or more
|
96
|
31.3
|
77
|
25.1
|
64
|
20.9
|
69
|
22.5
|
|
306
|
30.7
|
Abbreviations: Q1, first quartile; Q2, second quartile; Q3, third quartile; Q4, fourth
quartile.
* The distribution of the consumption of foods was evaluated in quartiles.
† The differences between quartiles were tested by the chi-square test.
‡ number of parturitions.
When evaluating the variables maternal occupation and head of the family, it was possible
to identify an association with ultraprocessed food consumption, showing that women
with paid work had lower ultraprocessed foods consumption, and women with unpaid work
had higher consumption of ultraprocessed foods (p = 0.023). The pregnant women who were head of the family showed lower consumption
of ultraprocessed foods (p = 0.004).
Women who smoked during pregnancy (37.7%, n = 49) had a statistically significant association with the highest quartile of ultraprocessed
food consumption (p < 0.001).
Binary logistic regression analyses showed that pregnant women ≤ 19 years of age were
2.9 times more likely to consume ultraprocessed foods (odds ratio [OR] 2.950, confidence
interval [CI] 95% 1.683–5.168, p < 0.001), while those ≥ 35 years of age had less chance to consume them (OR 0.265,
95% CI 0.105–0.666, p = 0.005). Smoking during pregnancy increased the chance of consumption of ultraprocessed
foods by 2.2 times (OR 2.247, 95% CI 1.202–4.199, p = 0.011) ([Table 3]).
Table 3
Binary logistic regression analysis between consumption of ultraprocessed foods and
associated variables in pregnant women
|
Variable
|
Ultraprocessed food consumption
|
|
Gross values
|
Adjusted values
|
|
P[a]
|
OR[*]
|
CI
|
P[a]
|
OR[*]
|
CI
|
|
LL 95%
|
UL 95%
|
LL 95%
|
UL 95%
|
|
Age, y
|
|
|
|
|
|
|
|
|
|
20–34
|
|
1
|
|
|
|
1
|
|
|
|
≤ 19
|
< 0.001
|
2.731
|
1.754
|
4.251
|
< 0.001
|
2.95
|
1.683
|
5.168
|
|
≥ 35
|
0.001
|
0.225
|
0.092
|
0.551
|
0.005
|
0.265
|
0.105
|
0.666
|
|
Maternal occupation
|
|
|
|
|
|
|
|
|
No paid work
|
|
1
|
|
|
|
1
|
|
|
|
Paid work
|
0.002
|
0.544
|
0.367
|
0.804
|
0.225
|
0.762
|
0.491
|
1.182
|
|
Householder
|
|
|
|
|
|
|
|
Own
|
|
1
|
|
|
|
1
|
|
|
|
Companion
|
0.842
|
1.056
|
0.62
|
1.798
|
0.98
|
0.992
|
0.543
|
1.814
|
|
Others
|
0.03
|
1.998
|
1.069
|
3.735
|
0.243
|
1.543
|
0.745
|
3.194
|
|
Alcohol use during pregnancy
|
|
|
|
|
|
No
|
|
1
|
|
|
|
1
|
|
|
|
Yes
|
0.067
|
1.756
|
0.9613
|
3.2074
|
0.298
|
1.480
|
0.708
|
3.092
|
|
Smoking during pregnancy
|
|
|
|
|
|
|
|
|
No
|
|
1
|
|
|
|
1
|
|
|
|
Yes
|
< 0.001
|
3.1721
|
1.809
|
5.5622
|
0.011
|
2.247
|
1.202
|
4.199
|
|
Parity
|
|
|
|
|
|
|
|
1
|
|
1
|
|
|
|
1
|
|
|
|
2
|
0.956
|
1.012
|
0.656
|
1.563
|
0.089
|
1.548
|
0.935
|
2.563
|
|
3 or more
|
0.042
|
0.649
|
0.427
|
0.985
|
0.562
|
1.170
|
0.689
|
1.987
|
Abbreviations: CI, confidence interval; LL, lower limit; OR, odds ratio; UL, upper
limit.
a Statistical significance ≤ 0.2% in the Chi-square test were included in the analysis.
* ORs were adjusted to the other variables using binary logistic regression with 95%CIs.
As for the consumption of minimally-processed foods ([Table 4]), pregnant women aged ≤ 19 years were 45.3% less likely than pregnant women aged
20 to 34 to consume these foods (OR 0.547, 95% CI 0.324–0.923, p = 0.024). Brown-skinned women were 57.8% less likely to consume minimally-processed
foods than white-skinned ones (OR 0.466, 95% CI 0.226–0.803, p = 0.008).
Table 4
Binary logistic regression analysis between consumption of minimally-processed foods
and associated variables in pregnant women
|
Variable
|
Minimally-processed food consumption
|
|
Gross values
|
Adjusted values
|
|
P
[a]
|
OR[*]
|
CI
|
P
[a]
|
OR[*]
|
CI
|
|
LL 95%
|
UL 95%
|
LL 95%
|
UL 95%
|
|
Age, y
|
|
|
|
|
|
|
|
|
20–34
|
1
|
|
|
|
1
|
|
|
|
≤ 19
|
0.004
|
0.528
|
0.342
|
0.813
|
0.024
|
0.547
|
0.324
|
0.923
|
|
≥ 35
|
0.122
|
1.695
|
0.869
|
3.307
|
0.388
|
1.411
|
0.646
|
3.081
|
|
Race/color
|
|
|
|
|
|
|
|
|
White
|
|
1
|
|
|
|
1
|
|
|
|
Brown
|
0.007
|
0.458
|
0.259
|
0.811
|
0.008
|
0.426
|
0.226
|
0.803
|
|
Black
|
0.057
|
0.512
|
0.257
|
1.019
|
0.053
|
0.469
|
0.217
|
1.011
|
|
Marital status
|
|
|
|
|
|
|
|
Living with a partner
|
|
1
|
|
|
|
1
|
|
|
|
Not living with a partner
|
0.349
|
0.808
|
0.517
|
1.263
|
0.273
|
0.749
|
0.447
|
1.256
|
|
Maternal occupation
|
|
|
|
|
|
No paid work
|
|
1
|
|
|
|
1
|
|
|
|
Paid work
|
< 0.001
|
2.12
|
1.429
|
3.145
|
0.281
|
1.295
|
0.809
|
2.072
|
|
Householder
|
|
|
|
|
|
|
Own
|
|
1
|
|
|
|
1
|
|
|
|
Companion
|
0.016
|
0.494
|
0.278
|
0.877
|
0.017
|
0.432
|
0.217
|
0.859
|
|
Others
|
0.01
|
0.425
|
0.222
|
0.813
|
0.125
|
0.544
|
0.25
|
1.184
|
|
Information on prenatal feeding
|
|
|
|
|
|
No
|
|
1
|
|
|
|
1
|
|
|
|
Yes
|
< 0.001
|
2.278
|
1.593
|
3.259
|
< 0.001
|
2.177
|
1.455
|
3.257
|
|
Smoking during pregnancy
|
|
|
|
|
|
|
|
No
|
|
1
|
|
|
|
1
|
|
|
|
Yes
|
< 0.001
|
0.2574
|
0.1409
|
0.4703
|
< 0.001
|
0.269
|
0.132
|
0.545
|
|
Prenatal consultations
|
|
|
|
|
|
|
|
|
<5
|
|
1
|
|
|
|
1
|
|
|
|
>6
|
0.0287
|
1.5562
|
1.047
|
2.3132
|
0.1684
|
1.3771
|
0.8734
|
2.1713
|
Abbreviations: CI, confidence interval; LL, lower limit; OR, odds ratio; UL, upper
limit.
a Statistical significance ≤ 0.2% in the Chi-square test were included in the analysis.
* ORs were adjusted to the other variables using binary logistic regression with 95%CIs.
Pregnant women who had the family headed by their partners were 56.8% less likely
to consume minimally-processed foods than pregnant women who were the head of the
family (OR 0.432, 95% CI 0.217–0.859, p = 0.017). However, women who received information on healthy food during prenatal
care presented 2.17 times more chances to consume minimally-processed foods (OR 2.177,
95% CI 1.455–3.257, p = 0.001).
Discussion
The present study allowed identifying the factors associated with the consumption
of ultraprocessed and minimally-processed foods. The data show that the consumption
of ultraprocessed products diverged between the age groups, demonstrating a lower
consumption of ultraprocessed foods among older women. This finding was similar to
that in the study by Alves-Santos et al.[12] in which older women showed a lower intake of ultraprocessed foods than the younger
ones, indicating the age factor as strongly associated with the consumption of minimally-processed
foods. It is still consistent with the findings by McGowan and McAuliffe,[13] in which older women tend to adhere to a “healthy conscience” pattern, consisting
mainly of higher intakes of whole-grain breads, fruits, vegetables, low-fat milk,
and white meat, among others. This relationship can also be observed in Brazilian
households, considering the 2008 to 2009 family budgets survey, conducted with individuals
who were 10 years of age or older, which showed the consumption of ultraprocessed
products tends to decrease as age increases.[14]
However, younger pregnant women presented high consumption of ultraprocessed foods,
in agreement with the results already presented in the adolescent population of the
city of Rio de Janeiro, in which pregnant women presented a high frequency of consumption
of soft drinks, pizza, potato chips, and salty snacks, indicating food habit that
is harmful to health.[15] The association between high consumption of ultraprocessed products and age groups
can also be found in non-pregnant women, in which adolescents consumed and spent more
to purchase ultraprocessed foods, compromising the quality of their diet.[16]
The present study showed that maternal habits may interfere with food consumption.
Women who reported having smoked during pregnancy were associated with higher consumption
of ultraprocessed foods. Knowing that maternal smoking has a negative impact on the
health of the mother and is related to a series of risk behaviors because it is associated
with a greater chance of maternal and fetal intercurrences,[17]
[18] it is important to offer pregnant women information about the effects of substances,
which, in turn, will contribute to benefit obstetric outcomes.[18] The impacts of maternal smoking on pregnancy outcomes are well documented and include
predisposition to gestational diabetes,[19] higher mortality and morbidity,[20] and increased risk of preterm birth, and the consequences of cigarette smoking during
pregnancy may accompany the baby throughout life.[21] In addition, fetuses exposed to cigarette smoke during pregnancy tend to have a
higher body mass index (BMI) in adulthood.[22]
Other adverse outcomes associated with maternal smoking include congenital malformations,[23]
[24] low birth weight[21]
[22] and restriction of intrauterine growth,[20]
[25] suggesting that any newborn with significant exposure to smoking during gestation
is likely to be adversely affected to some degree due to the chemical and toxic additives
of tobacco.[26] The present study has shown that pregnant women who presented a maternal habit of
not smoking during the gestational period had a better habit of food consumption.
In light of that, women who quit smoking before or during pregnancy can substantially
reduce risks for themselves and their children.[27]
Sociodemographic factors were associated with the consumption of minimally-processed
foods, specifically race/skin color and being head of household. In a study of American
women who were in the third trimester of pregnancy, it was shown that Black women
tended to have higher caloric intake, consuming foods rich in fats, carbohydrates,
and sugar and poor in important nutrients, such as folate and fiber.[28]
The head of household association and the consumption of healthier foods may demonstrate
social inequalities, since female-headed households are largely associated with situations
of economic vulnerability, in which women often end up in domestic activities and
care of the offspring, which, in turn, results in greater difficulties to guarantee
the subsistence of their own family, already with part-time affected generates dependency
in low-paid jobs.[29]
Receiving information about healthy food during prenatal care was highlighted among
women who had the highest consumption of minimally-processed foods, as recommended
in the pregnant woman's booklet, which states that to have a healthy pregnancy the
pregnant woman should seek to have a diet rich in natural foods and poor in processed
foods, in favor of maternal well-being, growth, and adequate training of the fetus.[30]
Participants who did not receive information about healthy eating during the prenatal
period had less chance of having healthier eating habits. Considering that such educational
activities depend on health professionals, the challenge seems to be to understand
the reasons why these behaviors are not being fully available to the target population.[31] The educator role played by the health professional during prenatal consultations
is essential, since pregnant women should be instructed to adopt improvements or maintenance
of healthy eating practices, making changes and/or creating new habits and, thus promoting
a pregnancy free of future intercurrences.[32] With these data, prenatal monitoring is identified as an important source of information
that may contribute to excellent results in food consumption during pregnancy.
In the study done by Barros et al.[15] it was verified that the adolescent pregnant women who received information about
healthy eating during the prenatal period had better results in nutrient consumption.
Verbeke and De Bourdeaudhuij[33] analyzed the eating behavior and nutritional choices of 148 pregnant women and 130
non-pregnant women and observed that pregnant women who followed nutritional recommendations
made better eating choices than non-pregnant women. This corroborates the importance
of prenatal follow-up, recognized as a relevant stage in the collection and perception
of information, effectively contributing to improvements in the results of food consumption
and gestation.[15]
The limitations of the present study are related to the possibility of occurrence
of recall bias, because the data was obtained through an interview, but this limitation
may have been minimized by investigating the pregnant woman's medical chart and/or
card. Still, the method used to collect data on eating habits may have involved underestimation;
however, it made it possible to assess the consumption of the main food items evaluated
in national surveys. Nevertheless, despite the limitations pointed out, the data presented
here reflect the reality of the public and/or contracted establishments that performed
the deliveries by the SUS of the MRGV-ES, and the design adopted enables assuming
that the conclusions can be used in similar contexts.
It is also highlighted that it is the first study to investigate the association between
maternal variables and educational activity received during prenatal care with ultraprocessed
and minimally-processed consumption and, differently from other studies that analyzed
isolated foods, in this study we used a classification that groups the foods according
to purpose and extent of processing.[3]
Although this classification was recent and in the present study the fourth quartile
was used to define the high consumption of a certain group, it allowed to classify
in a satisfactory way those foods that belonged to the group that presented high amounts
of sugars, saturated fat, trans fat, and low amount of fiber, characteristics that
the literature shows belong to the ultraprocessed foods, which intensifies the risks
for various diseases.[1]
[3]
[34]
[35]
[36]
Conclusion
The results of the present study suggest that being a pregnant adolescent and having
smoked during pregnancy influence the increase in the consumption of ultraprocessed
foods, which, consequently, can have an impact on the quality of the food choices
of pregnant women.
The data also indicate that being an older pregnant woman, not being of brown skin
color, being the head of the household, not having smoked cigarettes during pregnancy
and having received information about healthy eating during prenatal care contribute
to the better consumption of minimally-processed or in natura foods, thus offering better eating habits. It is necessary to evaluate the food consumption
through these food groups, as it allows to identify the vulnerability of the population
to food excesses, and thus to adapt and propose intervention measures that guarantee
maternal and baby health. The present results point out that there is a need for the
implementation of intervention measures in health service establishments with the
objective of providing educational information and promoting healthy eating for mothers.
