INTRODUCTION
Prenatal care is a fundamental intervention to improve maternal and neonatal health outcomes
1
. Numerous studies have demonstrated its association with a reduced incidence of adverse birth outcomes. Adequate prenatal care has been shown to significantly decrease the risks of preterm birth and low birth weight
2, 3
, as well as reduce the risk of neonatal mortality by 55%
4
.
Likewise, low utilization of prenatal care—either due to late initiation or an insufficient number of visits—has been associated with an increased risk of adverse maternal health behaviors and conditions, such as inadequate gestational weight gain, smoking during pregnancy, and absence of breastfeeding after delivery
5
. In resource-limited settings, restricted availability and low use of maternal health services contribute significantly to high maternal mortality, as reported in a study conducted in Ghana
6
.
Various factors are associated with inadequate prenatal care (PNC), including geographical and socioeconomic barriers. Women who depend on public transportation and require long travel times have been identified as more likely to receive inadequate prenatal care, which is associated with worse perinatal outcomes
7
. In low- and middle-income countries, access to and adherence to prenatal care are influenced by factors such as socioeconomic status, education, and regional poverty
8
. Other factors, such as low educational attainment, reduced household income, and smoking during pregnancy, have also been linked to inadequate use of prenatal care
6
.
The COVID-19 pandemic exacerbated many of these barriers, negatively impacting prenatal care and maternal and neonatal health outcomes. It has been reported that reduced prenatal visits, along with the implementation of potentially harmful care policies during the pandemic, contributed to an increase in maternal mental health issues and domestic violence, as well as negatively affected birth outcomes
9
.
It is essential to develop strategies that promote adherence to prenatal and postnatal care regimens
10
. While group prenatal care has shown significant benefits in reducing prematurity and low birth weight, there is still insufficient evidence to support the widespread implementation of other strategies
11
. Therefore, it is necessary to expand the study of factors that limit prenatal care utilization among pregnant women, considering their needs according to gestational stage and socioeconomic context
12
. This will enable the design of more precise and effective interventions to improve maternal and fetal health from the earliest stages of pregnancy.
For this reason, the objective of this study is to compare the factors associated with inadequate PNC during the first and second trimesters of pregnancy in women treated at the outpatient clinic of the Instituto Nacional Materno Perinatal (INMP), a referral hospital in Lima, Peru.
METHODOLOGY
Study design and setting. A retrospective, analytical, cross-sectional study was conducted among pregnant women seen at the outpatient clinics of the INMP in Lima, Peru. The INMP is a high-complexity public health institution specialized in maternal-perinatal medical-surgical care, with an annual record of approximately 22,000 births.
The outcome variable was inadequate PNC. PNC was considered adequate in the first trimester when the pregnant woman had at least one visit before 13 weeks of gestation, and adequate in the second trimester when she had at least two PNC visits between weeks 13 and 26 + 6 days, according to the recommendations of the World Health Organization (WHO)
13
and the Peruvian Ministry of Health
14
.
Population and sample. A total of 256 pregnant women with a completed second trimester (more than 26 weeks + 6 days of gestation) seen at the INMP outpatient clinics were included. Women were excluded if they did not have their PNC booklet at the time of the interview, had physical or mental limitations preventing them from answering the questionnaire, or refused to sign the informed consent. To determine the sample size, Fleiss’ formula with continuity correction for comparison of proportions was used. A reference value of 28% for positive unexposed and an expected prevalence ratio of 1.7 was considered based on previous literature
6
. A 95% confidence level and 80% statistical power were assumed. The initial calculation determined a sample size of 210 subjects, which was increased to account for a possible 20% refusal rate.
Variables and instruments. There was no primary exposure variable; rather, a thorough exploration of various factors potentially associated with inadequate PNC was conducted. A structured questionnaire was designed, including demographic questions (age, marital status, education level, and place of origin), gynecological-obstetric history (number of children, history of abortion, high-risk pregnancy, and planning of the current pregnancy), sociocultural factors (care by midwives in the community, type of transportation used to attend PNC, and travel time in hours), and perception of care received (waiting time for appointment assignment, waiting time on the day of the visit, and adequacy of information provided by health personnel).
To collect data, a targeted survey was administered focusing on factors related to non-attendance to PNC. The number of PNC visits was recorded by the principal investigator using a data collection form, which included the dates of PNC visits in the first and second trimesters, according to the documentation in the PNC booklet. PNC visits during the first trimester were those conducted between weeks 1 and 12 + 6 days, and second-trimester visits were those between weeks 13 and 26 + 6 days.
Procedures. Pregnant women were contacted during their appointments at the INMP outpatient clinics. After explaining the study objectives and obtaining informed consent, the structured survey was applied individually in a private setting. Subsequently, the principal investigator reviewed each participant’s PNC booklet to record the number of visits made during the first and second trimesters.
Statistical analysis
In the descriptive analysis, frequencies and percentages were used for qualitative variables, while measures of central tendency (mean) and dispersion (standard deviation) were calculated for quantitative variables. For hypothesis testing of qualitative variables, the chi-square test was used, considering a p-value <0.05 as statistically significant.
The strength of association was estimated using a Poisson regression model, adjusted to control for confounding variables through multiple regression analysis. Results were reported in terms of prevalence ratios (PR) and 95% confidence intervals (CI), with statistical significance set at p<0.05.
Ethical considerations
The study was approved by the Institutional Research Ethics Committee of the INMP under letter No. 091-2023-CIEI/INMP. Informed consent was obtained from all participants prior to administering the survey, and confidentiality of data and adherence to ethical principles of research involving human subjects were ensured.
RESULTS
A total of 256 pregnant women with more than 26 weeks + 6 days of gestation were surveyed. Of these, 114 had adequate PNC and 142 did not in the first trimester, while in the second trimester, 211 had adequate PNC and 45 did not.
Demographic and gynecological-obstetric characteristics
Significant differences in inadequate PNC during the first trimester were found in relation to marital status (p=0.043), education level (p<0.001), place of birth (p=0.005), and age range (p=0.002). In the second trimester, significant differences were observed regarding place of birth (p=0.027), number of children (p=0.007), and history of abortion (p=0.043)
(Table 1).
Social and institutional characteristics
In the first trimester, inadequate PNC attendance showed significant differences with the following factors: high-risk pregnancy (p<0.001), planned pregnancy (p<0.001), usual prenatal consultation with midwives (p<0.001), type of transportation (p=0.002), and waiting time for appointment assignment (p<0.001). In the second trimester, significant differences were found regarding high-risk pregnancy (p=0.019), waiting time for appointment assignment (p<0.001), waiting time on the day of the visit (p<0.001), and adequacy of information provided by health personnel (p<0.001)
(Table 2).
Table 1. Distribution of demographic and gynecological characteristics of pregnant women according to adequate attendance to prenatal care in the first and second trimester at INMP during 2023.
|
Inadequate attendance in 1st trimester |
Inadequate attendance in 2nd trimester |
Total |
No |
Yes |
p-value |
No |
Yes |
p-value |
N=139 |
N=139 |
N=211 |
N=45 |
N=256 |
Marital status |
Married or cohabiting |
126 (90.6%) | 96 (82.1%) | 0.043 |
187 (88.6%) | 35 (77.8%) | 0.052 |
222 (86.7%) |
Single |
|
24 (11.4%) | 10 (22.2%) | |
34 (13.3%) |
Education level |
Higher education (complete/incomplete) |
62 (44.6%) | 17 (14.5%) | <0.001 |
70 (33.2%) | 9 (20.0%) | 0.082 |
79 (30.9%) |
Primary and secondary |
77 (55.4%) | 100 (85.5%) | |
141 (66.8%) | 36 (80.0%) | |
177 (69.1%) |
Place of birth |
Lima |
88 (63.3%) | 64 (54.7%) | 0.005 |
118 (55.9%) | 34 (75.6%) | 0.027 |
152 (59.4%) |
Province |
39 (28.1%) | 51 (43.6%) | |
82 (38.9%) | 8 (17.8%) | |
90 (35.2%) |
Foreign country |
12 (8.6%) | 2 (1.7%) | |
11 (5.2%) | 3 (6.7%) | |
14 (5.5%) |
Number of pregnancies |
2 or fewer |
95 (68.3%) | 89 (76.1%) | 0.170 |
159 (75.4%) | 25 (55.6%) | 0.007 |
184 (71.9%) |
3 or more |
44 (31.7%) | 28 (23.9%) | |
52 (24.6%) | 20 (44.4%) | |
72 (28.1%) |
Age range |
Under 30 years |
78 (56.1%) | 87 (74.4%) | 0.002 |
132 (62.6%) | 33 (73.3%) | 0.170 |
165 (64.5%) |
30 to 35 years |
61 (43.9%) | 30 (25.6%) | |
79 (37.4%) | 12 (26.7%) | |
91 (35.5%) |
History of abortion |
No |
100 (71.9%) | 90 (76.9%) | 0.360 |
162 (76.8%) | 28 (62.2%) | 0.043 |
190 (74.2%) |
Yes |
39 (28.1%) | 27 (23.1%) | |
49 (23.2%) | 17 (37.8%) | |
66 (25.8%) |
Table 2. Distribution of social and institutional characteristics of pregnant women according to adequate attendance to prenatal care in the first and second trimester at INMP during 2023.
|
Inadequate attendance in 1st trimester |
Inadequate attendance in 2nd trimester |
Total |
No |
Yes |
p-value |
No |
Yes |
p-value |
N=114 |
N=142 |
N=211 |
N=45 |
N=256 |
High-risk pregnancy |
No |
68 (59.6%) | 96 (67.6%) | <0.001 |
142 (67.3%) | 22 (48.9%) | 0.019 |
164 (64.1%) |
Yes |
46 (40.4%) | 46 (32.4%) | |
69 (32.7%) | 23 (51.1%) | |
92 (35.9%) |
Planned pregnancy |
No |
74 (53.2%) | 90 (76.9%) | <0.001 |
140 (66.4%) | 36 (80.0%) | 0.073 |
176 (68.8%) |
Yes |
65 (46.8%) | 27 (23.1%) | |
71 (33.6%) | 9 (20.0%) | |
80 (31.3%) |
PNC usually conducted by midwives in her community |
No |
76 (54.7%) | 100 (85.5%) | <0.001 |
162 (76.8%) | 37 (82.2%) | 0.430 |
199 (77.7%) |
Yes |
63 (45.3%) | 17 (14.5%) | |
49 (23.2%) | 8 (17.8%) | |
57 (22.3%) |
Type of transportation |
Own vehicle |
131 (94.2%) | 68 (58.1%) | 0.002 |
23 (10.9%) | 2 (4.4%) | 0.140 |
25 (9.8%) |
Public transport |
8 (5.8%) | 49 (41.9%) | |
148 (70.1%) | 38 (84.4%) | |
186 (72.7%) |
Taxi |
20 (14.4%) | 5 (4.3%) | |
40 (19.0%) | 5 (11.1%) | |
45 (17.6%) |
Distance from hospital |
Less than 1 hour |
89 (64.0%) | 97 (82.9%) | 0.580 |
139 (65.9%) | 27 (60.0%) | 0.450 |
166 (64.8%) |
1 hour or more |
30 (21.6%) | 15 (12.8%) | |
72 (34.1%) | 18 (40.0%) | |
90 (35.2%) |
Long wait to get appointment |
No |
88 (63.3%) | 78 (66.7%) | <0.001 |
168 (79.6%) | 21 (46.7%) | <0.001 |
189 (73.8%) |
Yes |
51 (36.7%) | 39 (33.3%) | |
43 (20.4%) | 24 (53.3%) | |
67 (26.2%) |
Long wait on the appointment day |
No |
116 (83.5%) | 73 (62.4%) | 0.860 |
160 (75.8%) | 18 (40.0%) | <0.001 |
178 (69.5%) |
Yes |
23 (16.5%) | 44 (37.6%) | |
51 (24.2%) | 27 (60.0%) | |
78 (30.5%) |
Sufficient information |
No |
96 (69.6%) | 78 (70.5%) | 0.530 |
186 (88.2%) | 26 (57.8%) | <0.001 |
212 (82.8%) |
Yes |
43 (30.9%) | 33 (29.9%) | |
25 (11.8%) | 19 (42.2%) | |
44 (17.2%) |
Source: INMP
Multivariate regression analysis
In the multivariate regression model for inadequate PNC attendance during the first and second trimesters, the following variables were included: marital status, education level, place of birth, gestational age, age range, high-risk pregnancy, planned pregnancy, usual PNC with midwives, type of transportation, and distance from residence to the hospital.
In the first trimester, single women had a higher risk of inadequate attendance (aPR = 1.55; p = 0.004), as did those with primary or secondary education (aPR = 1.77; p = 0.019). Pregnant women with high-risk pregnancies showed a lower risk of inadequate attendance (aPR = 0.55; p < 0.001). In addition, usual prenatal consultation with midwives was associated with a higher risk (aPR = 2.00; p < 0.001), as was living one hour or more away from the hospital (aPR = 1.45; p = 0.027) (Table 3).
In the second trimester, single women also had a higher risk of inadequate attendance (aPR = 1.95; p = 0.059), while those with primary or secondary education presented a significantly higher risk (aPR = 2.19; p = 0.026). Women born in provinces showed a lower risk of inadequate attendance (aPR = 0.34; p = 0.011), whereas those with three or more children had a higher risk (aPR = 2.69; p = 0.001) (Table 3).
traduce esto
Table 3. Crude and adjusted relative risk of sociodemographic and personal characteristics of pregnant women according to adequate attendance to prenatal check-ups during the first and second trimester of pregnancy at INMP in 2023.
Factors |
Inadequate attendance in first trimester |
Inadequate attendance in second trimester |
cRR |
p-value |
aRR |
p-value |
cRR |
p-value |
aRR |
p-value |
95% CI |
95% CI |
95% CI |
95% CI |
Marital status |
Married or cohabiting |
Ref. 1.43 (1.05 - 1.94) |
0.0022 |
1.55 (1.15 - 2.10) |
0.004 |
Ref. 1.87 (1.02 - 3.41) |
0.043 |
1.95 (0.98 - 3.90) |
0.059 |
Single |
Education level: Primary and secondary |
Higher education (complete or incomplete) |
Ref. 2.63 (1.69 - 4.08) |
<0.001 |
1.77 (1.10 - 2.85) |
0.019 |
Ref. 1.79 (0.90 - 3.53) |
0.096 |
2.19 (1.10 - 4.36) |
0.026 |
Primary and secondary |
Place of birth |
Lima |
Ref. |
|
Ref. |
|
Province |
1.35 |
0.025 |
0.77 |
0.107 |
0.40 |
0.013 |
0.34 |
0.011 |
(1.04 - 1.75) |
|
(0.57 - 1.06) |
|
(0.19 - 0.82) |
|
(0.15 - 0.78) |
|
Foreign country |
0.34 |
0.103 |
0.45 |
0.208 |
0.96 |
0.936 |
0.98 |
0.971 |
(0.093 - 1.24) |
|
(0.13 - 1.55) |
|
(0.34 - 2.73) |
|
(0.33 - 2.89) |
|
Number of pregnancies |
Less than 2 |
Ref. |
|
Ref. |
|
3 or more |
0.80 |
0.190 |
1.29 |
0.114 |
2.04 |
0.007 |
2.69 |
0.001 |
(0.58 - 1.11) |
|
(0.94 - 1.77) |
|
(1.21 - 3.45) |
|
(1.48 - 4.92) |
|
Age range |
Under 30 years |
Ref. |
|
Ref. |
|
Between 30 and 35 years |
0.63 |
0.004 |
0.92 |
0.626 |
0.66 |
0.181 |
0.56 |
0.089 |
(0.45 - 0.87) |
|
(0.67 - 1.27) |
|
(0.36 - 1.21) |
|
(0.29 - 1.09) |
|
(0,45 - 0,87) | | (0,67 - 1,27) | | (0,36 - 1,21) | | (0,29 - 1,09) | |
Risky pregnancy |
No |
Ref. |
|
Ref. |
|
Sí |
0,54 |
<0,001 |
0,55 |
<0,001 |
1,86 |
0,021 |
1,40 |
0,235 |
(0,38 - 0,76) | | (0,39 - 0,78) | | (1,10 - 3,16) | | (0,81 - 2,43) | |
Planned pregnancy |
No |
Ref. |
|
Ref. |
|
Yes | 0,37 | <0,001 | 0,56 | 0,017 | 0,55 | 0,086 | 0,64 | 0,258 |
(0,24 - 0,58) | | (0,35 - 0,91) | | (0,28 - 1,09) | | (0,29 – 1,40) | |
Regular CPN with midwives | No |
Ref. |
|
Ref. |
|
Yes | 2.52 | <0.001 | 2.00 | <0.001 | 0.76 | 0.435 | 0.96 | 0.913 |
(2,02 - 3,14) | | (1,49 - 2,68) | | (0,37 - 1,53) | | (0,45 - 2,06) | |
Type of transport |
Own |
Ref. |
|
Ref. |
|
Public | 2.60 | 0.018 | 1.77 | 0.178 | 2.55 | 0.177 | 1.96 | 0.383 |
(1.18 - 5.79) | | (0.77 - 4.08) | | (0.65 - 9.97) | | (0.43 - 8.91) | |
Taxi | 1.67 | 0.260 | 1.26 | 0.606 | 1.39 | 0.681 | 1.11 | 0.899 |
(0,69 - 4,05) | | (0,52 - 3,05) | | (0,29 - 6,67) | | (0,24 - 5,20) | |
Living distance from hospital |
Less than 1 hour |
Ref. |
|
Ref. |
|
1 hour or more | 0.92 | 0.580 | 1.45 | 0.027 | 1.23 | 0.453 | 1.07 | 0.821 |
(0.69 - 1.23) | | (1.04 - 2.01) | | (0.72 - 2.11) | | (0.60 - 1.93) | |
Wait a long time for appointment assignment | No |
Ref. |
|
Ref. |
|
Yes | 1.70 | <0.001 | | | 3.22 | <0.001 | | |
(1,32 - 2,18) | | | | (1,92 - 5,40) | | | |
Wait a long time for attention on the scheduled day | No |
Ref. |
|
Ref. |
|
Yes | 0.97 | 0.861 | | | 3.42 | <0.001 | | |
(0.73 - 1.31) | | | | (2.01 - 5.84) | | | |
Sufficient information | No |
Ref. |
|
Ref. |
|
Yes | 1.12 | 0.517 | | | 3.52 | <0.001 | | |
(0.80 - 1.56) | | | | (2.15 - 5.78) | | | |
Fuente: INMP
DISCUSSION
In 32 low-income countries, key determinants of prenatal care utilization were identified, suggesting the need to integrate safe maternity programs with social development strategies
8
. In Brazil, a study in the northeast of the country found high levels of prenatal care attendance, although one-third of pregnant women showed inadequate utilization due to socioeconomic and demographic factors, such as maternal age, education level, and lack of a partner
19
. In Ethiopia, socioeconomic inequality at the start of prenatal care was associated with wealth, education level, and region of residence, highlighting the need for targeted interventions
20
.
A survival analysis conducted in 57 low- and middle-income countries demonstrated that prenatal care attendance is associated with lower neonatal mortality, underscoring the importance of promoting more prenatal visits
4
. Furthermore, the validation of a model of adherence to prenatal recommendations in the United States emphasized the relevance of shared decision-making and cultural competence to improve adherence to prenatal check-ups
21
.
The impact of the COVID-19 pandemic on maternal and perinatal health highlighted the lack of planning for continuity of prenatal care, reflected in a concerning reduction in prenatal visits and an increase in maternal mental health issues. These findings underscore the need to implement adaptive strategies and allocate additional resources to ensure the continuity of prenatal care during crisis situations
9
. In this context, a systematic review of interventions to improve prenatal care adherence in sub-Saharan Africa concluded that current strategies have limited effectiveness, which highlights the need for innovative approaches
10
. Similarly, a study in the United Kingdom found that ethnic and socioeconomic inequalities affect the timely initiation of prenatal care, emphasizing the importance of addressing these disparities to improve maternal outcomes
22
.
Prenatal care attendance remains a challenge influenced by various sociodemographic, economic, and cultural factors. The evidence shows that the lack of economic resources, distance to health centers, and lack of time continue to be significant barriers to adequate attendance to prenatal check-ups
23
. Additionally, the impact of factors such as maternal education and social stability has been widely documented
24
, highlighting the need to strengthen educational programs and access to medical services from a comprehensive perspective. Interventions targeted at at-risk populations, such as the implementation of behavioral incentives and patient navigation, have shown promise in increasing adherence to prenatal care
25
. Furthermore, the development of innovative prenatal care models, such as group care and the use of technology for remote consultations, may help reduce access gaps
26
. Since access to prenatal care directly impacts the reduction of maternal and neonatal complications
27
, it is recommended to implement public policies that ensure equity in access to these services. Moreover, integrating a biopsychosocial approach in perinatal care would allow for more effective addressing of the individual needs of pregnant women, promoting better maternal and neonatal health outcomes
28
. Finally, to reduce disparities in prenatal care attendance, collaboration between governments, communities, and healthcare professionals is essential to develop sustainable and culturally sensitive strategies that encourage greater adherence to prenatal check-ups
29
.
This study presents some limitations. As a retrospective analytical cross-sectional design, it does not allow for establishing causal relationships between the analyzed variables. Furthermore, the information obtained through surveys may be subject to recall or response biases, which could affect the accuracy of some data. Another limitation is that the study was conducted at a single reference center, which may limit the generalizability of the findings to other contexts with different socioeconomic characteristics and access to healthcare. Finally, other potential determinants of prenatal care attendance, such as family support or cultural barriers, were not assessed, and these could influence the results.
CONCLUSION
This study demonstrates that factors associated with inadequate PNC attendance vary between the first and second trimesters of pregnancy. While being single and having a lower education level were risk factors in both trimesters, other variables, such as high-risk pregnancy, habitual consultations with midwives, and distance from the hospital, primarily influenced the first trimester, while the number of children and place of birth had an impact in the second trimester. These findings underscore the importance of designing differentiated strategies according to the gestational stage, considering sociodemographic, cultural, and healthcare access factors to improve PNC coverage and adherence. Implementing policies that reduce geographical and economic barriers, as well as strengthening prenatal education, could contribute to greater adherence to check-ups and, consequently, improve maternal and neonatal outcomes.