Abstract
Background
Unplanned pregnancy is a significant public health problem, especially in low- to middle-income countries. The objective of this study was to determine the neonatal outcomes associated with unplanned pregnancy in a public hospital in southern Peru.
Methods
A cross-sectional analytical study was conducted from June to August 2021 among 306 mothers and their newborns selected by convenience sampling. After obtaining informed consent, mothers were surveyed during their postpartum hospitalization about their pregnancy intentions. Unintended pregnancy was considered when it was mistimed or unwanted. Neonatal outcomes were assessed by reviewing medical records prior to discharge, evaluating the presence of low birth weight, insufficient birth weight, prematurity, respiratory distress syndrome, sepsis and neonatal mortality. Association was measured in terms of prevalence ratio (PR) and beta coefficient and respective 95% CIs, crude and adjusted for variables that obtained a value of p<0.20 in the crude model (maternal age, education and parity).
Results
The overall unplanned pregnancies rate was 65%, of which 40.5% were mistimed pregnancies and 24.5% unwanted pregnancies. Unplanned and mistimed pregnancies were significantly associated with insufficient birth weight [adjusted prevalence ratio (aPR)=2.14; 95% CI 1.01 to 4.56 and aPR=2.43; 95% CI 1.09 to 5.39, respectively] and unwanted pregnancies were significantly associated with preterm birth (aPR=3.49; 95% CI 1.01 to 12.11). Furthermore, adjusted analysis showed that birth weight and gestational age were lower in unplanned, mistimed and unwanted pregnancies.
Conclusions
Unplanned pregnancy was associated with lower birth weight and shorter gestational age of newborns. These data collected in a public hospital in a developing country may have significant implications today. If pregnancy intention is not included as a neonatal risk factor, insufficient birth weight and preterm birth could increase because a higher proportion of births will be unplanned.
Keywords: birth weight [source: mesh], infant, pregnancy, newborn, pregnancy, premature birth
Introduction
Unplanned pregnancy, a pregnancy that occurs when a woman desires no children or no more children (unwanted pregnancy), or which occurs earlier than desired (mistimed pregnancy), is a significant public health problem, especially in low- to middle-income countries and can result in an increased risk of adverse maternal and neonatal outcomes.1 Around 121 million unplanned pregnancies occur each year, representing around 48% of total pregnancies in women aged 15–49 y worldwide.2 Although the global unplanned pregnancy rate has decreased since 1990, progress in reducing unplanned pregnancies was less pronounced in developing regions (16%) compared with developed regions (30%).3 For example, Latin America was the second highest region in terms of unplanned pregnancy rate worldwide, that is, almost twice the unplanned pregnancy rate of Europe and North America, and the rate was only surpassed by Africa.2
It may seem self-evident that unplanned pregnancies would be associated with adverse neonatal outcomes, but the evidence base for this association is still controversial.4 Thus, two systematic reviews revealed that unplanned pregnancies were 1.41 times more likely to have a low birthweight newborn compared with planned pregnancies.5 In addition, there was a statistically significant increase in the odds of premature birth in unplanned (OR=1.31) and unwanted pregnancies (OR=1.50).6 Gharaee and Baradaran7 and Singh et al.8 found a significant correlation between unplanned pregnancy and lower birth weight for gestational age, as well as an 83% increased risk of neonatal mortality compared with planned pregnancies. Nevertheless, findings from a prospective cohort study showed no relation between unplanned pregnancies, low birth weight and neonatal death.9 Similarly, other research showed that pregnancy planning was not significantly associated with preterm birth or small-for-gestational-age newborns.10 We point out that almost all studies were conducted in the USA or European countries, meaning these findings may not be relevant to low- and middle-income countries. Furthermore, most studies have methodological limitations such as a cross-sectional design and possible recall bias as a consequence of the time elapsed between pregnancy and the timing of assessment.5
According to the Global Strategy for Women's, Children's and Adolescents’ Health proposed by the WHO, the reduction of unplanned pregnancies is fundamental to achieve the 2030 Sustainable Development Goals.11 In that context, the vision is to achieve a world in which every mother can enjoy a wanted and healthy pregnancy and childbirth, and every child not only survives but also reaches their full potential.12 Peru is one of the countries with the highest rates of unplanned pregnancies (52%), which has not decreased substantially in recent years.13 The Peruvian Ministry of Health declared that the leading cause of neonatal mortality was prematurity; moreover, there were 7.4 premature newborns per 100 births and the proportion of low birthweight newborns accounted for 30% of neonatal deaths in 2019.14 These proportions remain stable over time. Thus, the understanding of unplanned pregnancy, especially unwanted pregnancy, as a predisposing factor for adverse neonatal outcomes unveils a social problem that needs public health policymakers’ attention. The objective of this study was to determine the neonatal outcomes associated with unplanned pregnancy in a public hospital in southern Peru.
Methods
Design and study area
An analytical cross-sectional study was conducted at Hospital Hipólito Unanue de Tacna (HHUT), which is the only hospital of the Ministry of Health in Tacna, Peru. Furthermore, this secondary care hospital attends approximately 70% of births among rural and urban mothers.
Population and sample
Participants were recruited from a population of 850 women whose deliveries were attended at HHUT services during June–August 2021. The sample size included 265 women and their newborns selected by convenience sampling, calculated with a confidence level of 95%, a margin error of 5% and an expected frequency of 50%. Mothers aged 15-49 y with newborns weighing ≥500 g were included. Multiple pregnancies and pregnancies resulting from sexual violence were excluded. The manuscript was written following the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology.15
Exposure and outcome measures
The selection of variables was carried out by an extensive literature review of pre-existing studies and was reviewed by a panel of four experts from the Neonatology & Pediatrics and Gynecology & Obstetrics department.
We measured our exposure (unplanned pregnancy) with the following question: ‘When you got pregnant, did you want to get pregnant at that time?, did you want to wait longer?, or did you want no (more) children?’ Family violence was evaluated with three questions to define if a woman had experienced psychological, physical and/or sexual violence from an intimate partner. The questions for these two variables were obtained from the ‘Individual Questionnaire’ of the Demographic and Family Health Survey (ENDES) 2020, available online in the Reproduction and Domestic Violence section, respectively.16
The maternal characteristics included were age (adolescent: 15–19 y, adequate age: 20–34 y, advanced age: ≥35 y), educational degree (primary, secondary, higher), marital status (single, cohabitant, married), economic dependence (unemployed or employed), nutritional status (underweight: <18.5 kg/m2, normal weight: 18.5–24.9 kg/m2, overweight: 25.0–29.9 kg/m2, obesity: ≥30 kg/m2), use of contraceptive (yes or no), type of contraceptive (hormonal, barrier, intrauterine device, natural), parity (nulliparous, multiparous, grand multiparous), prenatal care (absence, inadequate: 1–5, adequate: ≥6), maternal morbidity (yes or no), maternal pathology (anemia, urinary tract infection, premature rupture of membranes, hypertensive disorders) and type of delivery (cesarean or vaginal). The neonatal characteristics included were sex (woman or man), birth weight (low weight: <2500 g, insufficient weight: 2500–2999 g, adequate weight: 3000–3999 g, macrosomia: ≥4000 g), gestational age (preterm: <37 birth weeks, term: 37–41 6/7 birth weeks, post-term: ≥42 birth weeks), birth weight for gestational age (small for gestational age: <10th percentile, adequate for gestational age: 10th–90th percentile, large for gestational age: >90th percentile), 1-min Apgar score (<7 or 7–10 points), 5-min Apgar score (<7 or 7–10 points), neonatal morbidity (yes or no), neonatal pathology (hyperbilirubinemia, sepsis, hypoglycemia, respiratory distress syndrome, congenital malformation, asphyxia) and neonatal mortality (yes or no). In addition, we included two numerical variables: gestational age in weeks and birth weight in grams.
Data collection
Answers to questions about the intention of her current pregnancy, the use of contraceptive methods and the presence of family violence, were collected in a postpartum interview in-person. Immediately after the interview, medical-record reviewers abstracted data referring to maternal characteristics, neonatal characteristics, neonatal morbidity and neonatal mortality from hospital records. When there were missing data, the medical records books from the delivery room or neonatology service, and the perinatal record cards of the mother's pregnancy control attached to the maternal medical record, were reviewed. To avoid poor data quality, all of this was carried out before the mother and newborn were discharged. Data collection was carried out by two authors, both of whom were healthcare professionals with clinical and research experience.
Statistical analysis
We estimated the unplanned pregnancy rate and the corresponding estimates for the mistimed pregnancy and unwanted pregnancy rates. In addition, variables were described using absolute and relative frequencies, and means and SDs. The χ2 test or Fisher's exact test was used to compare the proportion of independent variables in each type of unplanned pregnancy. The measure of association for the categorical variables was the prevalence ratio (PR) and for the numerical variables it was the beta coefficient (β), with their respective 95% CIs. Furthermore, those variables that obtained a value of p<0.20 in the crude model and were associated with unplanned pregnancy in the literature found were considered to obtain the adjusted model. All statistical analyses were performed using the Stata v. 16.0 program (StataCorp, College Station, TX, USA).
Ethical aspects
The Universidad Privada de Tacna’s ethics committee approved the research protocol. Then a document was presented to the HHUT’s Executive Director, who provided permission to carry out the investigation in this hospital. Verbal and signed informed consent were obtained from each participant. This included an explanation of the objectives of the investigation, as well as the rights of the participants (anonymity and the right to refrain from participating as considered appropriate), also preserving the confidentiality and rights of women and their newborns.
Results
We interviewed 310 postpartum women, of whom four were excluded, therefore 306 mothers and their newborns (98.7%) were finally included (Figure 1).
Figure 1.
Sample selection flowchart.
In our study, 65% of pregnancies were unplanned (Figure 2). The mean age of the participants was 29 (SD=6.7) y. The maternal characteristics examined, namely, age, educational level, marital status, contraceptive use and parity, were significantly associated with the intention of pregnancy (p<0.05) (Table 1). There was no statistically significant association between the characteristics of the newborn and the intention of pregnancy (Table 2).
Figure 2.
Frequency of mistimed, unwanted and planned pregnancies.
Table 1.
Maternal characteristics of mistimed, unwanted and planned pregnancies
| Unwanted | Mistimed | Planned | ||
|---|---|---|---|---|
| n=75 | n=124 | n=107 | ||
| Maternal characteristics | n (%) | n (%) | n (%) | p |
| Age in y (SD) | 31.4 (6.6) | 26.6 (6.4) | 30.3 (6.3) | |
| Age | ||||
| Adolescent | 2 (2.7) | 21 (16.9) | 2 (1.9) | 0.000 |
| Adequate age | 47 (62.7) | 85 (68.5) | 77 (72.0) | |
| Advanced age | 26 (34.7) | 18 (14.5) | 28 (26.2) | |
| Educational degree | ||||
| Primary | 11 (14.7) | 4 (3.2) | 4 (3.7) | 0.004 |
| Secondary | 52 (69.3) | 91 (73.4) | 65 (60.7) | |
| Higher | 12 (16.0) | 29 (23.4) | 38 (35.5) | |
| Marital status | ||||
| Single | 5 (6.7) | 37 (29.8) | 20 (18.7) | 0.002 |
| Cohabitant | 58 (77.3) | 78 (62.9) | 74 (69.2) | |
| Married | 12 (16.0) | 9 (7.3) | 13 (12.1) | |
| Economic dependence | ||||
| Unemployed | 62 (82.7) | 94 (75.8) | 80 (74.8) | 0.414 |
| Employed | 13 (17.3) | 30 (24.2) | 27 (25.2) | |
| Family violence | ||||
| Yes | 11 (14.7) | 17 (13.7) | 8 (7.5) | 0.228 |
| No | 64 (85.3) | 107 (86.3) | 99 (92.5) | |
| Type of violence | ||||
| Psychological | 11 (14.7) | 16 (12.9) | 7 (6.5) | 0.163 |
| Physical | 2 (2.7) | 7 (5.6) | 3 (2.8) | 0.561 |
| Sexual | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Nutritional status | ||||
| Underweight | 0 (0.0) | 3 (2.4) | 1 (0.9) | 0.071 |
| Normal weight | 14 (18.7) | 43 (34.7) | 33 (30.8) | |
| Overweight | 32 (42.7) | 49 (39.5) | 41 (38.3) | |
| Obesity | 29 (38.7) | 28 (22.6) | 32 (29.9) | |
| Use of contraceptive | ||||
| Yes | 53 (70.7) | 65 (52.4) | 20 (18.7) | 0.000 |
| No | 22 (29.3) | 59 (47.6) | 87 (81.3) | |
| Type of contraceptive | ||||
| Hormonal | 35 (46.7) | 40 (32.3) | 14 (13.1) | 0.683 |
| Barrier | 9 (12.0) | 15 (12.1) | 4 (3.7) | |
| IUD | 2 (2.7) | 1 (0.8) | 1 (0.9) | |
| Natural | 7 (9.3) | 9 (7.3) | 1 (0.9) | |
| Parity | ||||
| Nulliparous | 7 (9.3) | 61 (49.2) | 42 (39.3) | 0.000 |
| Multiparous | 60 (80.0) | 63 (50.8) | 65 (60.7) | |
| Grand multiparous | 8 (10.7) | 0 (0.0) | 0 (0.0) | |
| Prenatal care | ||||
| Absence | 1 (1.3) | 1 (0.8) | 1 (0.9) | 0.422 |
| Inadequate | 17 (22.7) | 27 (21.8) | 15 (14.0) | |
| Adequate | 57 (76.0) | 96 (77.4) | 91(85.1) | |
| Maternal morbidity | ||||
| Yes | 29 (38.7) | 40 (32.3) | 28 (26.2) | 0.201 |
| No | 46 (61.3) | 84 (67.7) | 79 (73.8) | |
| Maternal pathology | ||||
| Anemia | 19 (25.3) | 33 (26.6) | 22 (20.6) | 0.544 |
| UTI | 9 (12.0) | 14 (11.3) | 11 (10.3) | 0.933 |
| PRM | 7 (9.3) | 4 (3.2) | 4 (3.7) | 0.148 |
| Hypertensive disorders | 1 (1.3) | 7 (5.6) | 8 (7.5) | 0.180 |
| Type of delivery | ||||
| Cesarean | 38 (50.7) | 75 (60.5) | 63 (58.9) | 0.374 |
| Vaginal | 37 (49.3) | 49 (39.5) | 44 (41.1) |
Abbreviations: IUD, intrauterine device; PRM, premature rupture of membranes; UTI, urinary tract infection.
Table 2.
Neonatal characteristics of mistimed, unwanted and planned pregnancies
| Unwanted | Mistimed | Planned | ||
|---|---|---|---|---|
| n=75 | n=124 | n=107 | ||
| Neonatal characteristics | n (%) | n (%) | n (%) | p |
| Sex | ||||
| Woman | 34 (45.3) | 62 (50.0) | 41 (38.3) | 0.332 |
| Man | 41 (54.7) | 62 (50.0) | 66 (61.7) | |
| Birth weight | ||||
| Low weight | 5 (6.7) | 6 (4.8) | 4 (3.7) | 0.353 |
| Insufficient weight | 10 (13.3) | 20 (16.1) | 8 (7.5) | |
| Adequate weight | 48 (64.0) | 84 (67.7) | 76 (71.0) | |
| Macrosomia | 12 (16.0) | 14 (11.3) | 19 (17.8) | |
| Gestational age | ||||
| Preterm | 8 (10.7) | 9 (7.3) | 5 (4.7) | 0.305 |
| Term | 67 (89.3) | 115 (92.7) | 102 (95.3) | |
| Post-term | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Birth weight for gestational age | ||||
| Small for gestational age | 1 (1.3) | 5 (4.0) | 4 (3.7) | 0.451 |
| Adequate for gestational age | 64 (85.3) | 107 (86.3) | 85 (79.4) | |
| Large for gestational age | 10 (13.3) | 12 (9.7) | 18 (16.8) | |
| 1-min Apgar score | ||||
| <7 | 2 (2.7) | 4 (3.2) | 0 (0.0) | 0.165 |
| 7 to 10 | 73 (97.3) | 120 (96.8) | 107 (100.0) | |
| 5-min Apgar score | ||||
| <7 | 2 (2.7) | 1 (0.8) | 0 (0.0) | 0.257 |
| 7 to 10 | 73 (97.3) | 123 (99.2) | 107 (100.0) | |
| Neonatal morbidity | ||||
| Yes | 21 (28.0) | 44 (35.5) | 32 (29.9) | 0.484 |
| No | 54 (72.0) | 80 (64.5) | 75 (70.1) | |
| Neonatal pathology | ||||
| Hyperbilirubinemia | 4 (5.3) | 21 (16.9) | 13 (12.1) | 0.055 |
| Sepsis | 3 (4.0) | 5 (4.0) | 2 (1.9) | 0.608 |
| Hypoglycemia | 2 (2.7) | 2 (1.6) | 1 (0.9) | 0.735 |
| Respiratory distress syndrome | 1 (1.3) | 0 (0.0) | 2 (1.9) | 0.347 |
| Congenital malformation | 0 (0.0) | 3 (2.4) | 0 (0.0) | 0.185 |
| Asphyxia | 1 (1.3) | 1 (0.8) | 0 (0.0) | 0.716 |
| Neonatal mortality | ||||
| Yes | 2 (2.7) | 1 (0.8) | 0 (0.0) | 0.257 |
| No | 73 (97.3) | 123 (99.2) | 106 (100.0) |
The adjusted model suggested that newborns of unplanned pregnancies were 2.14, 1.63 and 2.14 times more likely to be born with insufficient birth weight, low birth weight and premature compared with newborns of planned pregnancies, respectively. Moreover, babies of mistimed and unwanted pregnancies were 2.43 and 3.49 times more likely to be born with insufficient birth weight [adjusted prevalence ratio (aPR)=2.43; 95% CI 1.09 to 5.39] and premature (aPR=3.49; 95% CI 1.01 to 12.11) compared with babies of planned pregnancies, respectively (Table 3).
Table 3.
Association between unplanned pregnancy and neonatal outcomes
| Crude model | Adjusted model* | |||||
|---|---|---|---|---|---|---|
| Neonatal outcomes | PR | 95% CI | p | aPR | 95% CI | p |
| Unplanned vs planned pregnancy | ||||||
| Low weight | 1.48 | 0.48 to 4.54 | 0.494 | 1.63 | 0.54 to 4.95 | 0.389 |
| Insufficient weight | 2.05 | 0.98 to 4.32 | 0.058 | 2.14 | 1.01 to 4.56 | 0.049 |
| Preterm | 1.83 | 0.69 to 4.83 | 0.223 | 2.18 | 0.87 to 5.45 | 0.096 |
| Small for gestational age | 0.81 | 0.23 to 2.80 | 0.735 | 1.06 | 0.26 to 4.35 | 0.932 |
| Neonatal morbidity | 1.09 | 0.77 to 1.55 | 0.624 | 1.18 | 0.82 to 1.69 | 0.368 |
| Respiratory distress syndrome | 0.27 | 0.02 to 2.94 | 0.282 | 0.45 | 0.06 to 3.28 | 0.434 |
| Sepsis | 2.15 | 0.46 to 9.97 | 0.328 | 2.00 | 0.38 to 10.51 | 0.413 |
| Mistimed vs planned pregnancy | ||||||
| Low weight | 1.29 | 0.37 to 4.48 | 0.684 | 1.66 | 0.53 to 5.20 | 0.384 |
| Insufficient weight | 2.18 | 1.00 to 4.75 | 0.049 | 2.43 | 1.09 to 5.39 | 0.029 |
| Preterm | 1.55 | 0.54 to 4.50 | 0.418 | 1.80 | 0.67 to 4.81 | 0.240 |
| Small for gestational age | 1.08 | 0.30 to 3.93 | 0.909 | 1.26 | 0.29 to 5.48 | 0.755 |
| Neonatal morbidity | 1.19 | 0.81 to 1.73 | 0.372 | 1.22 | 0.83 to 1.79 | 0.321 |
| Respiratory distress syndrome | 2.15 | 0.43 to 10.93 | 0.353 | 2.65 | 0.32 to 22.07 | 0.368 |
| Unwanted vs planned pregnancy | ||||||
| Low weight | 1.78 | 0.49 to 6.44 | 0.378 | 1.91 | 0.30 to 12.30 | 0.496 |
| Insufficient weight | 1.84 | 0.76 to 4.44 | 0.175 | 1.80 | 0.66 to 4.88 | 0.248 |
| Preterm | 2.28 | 0.77 to 6.73 | 0.134 | 3.49 | 1.01 to 12.11 | 0.049 |
| Small for gestational age | 0.36 | 0.04 to 3.15 | 0.353 | 0.40 | 0.04 to 3.92 | 0.434 |
| Neonatal morbidity | 0.94 | 0.59 to 1.49 | 0.782 | 1.12 | 0.65 to 1.93 | 0.674 |
| Respiratory distress syndrome | 0.71 | 0.07 to 7.78 | 0.782 | 4.20 | 0.58 to 30.26 | 0.155 |
| Sepsis | 2.14 | 0.36 to 12.55 | 0.399 | 2.84 | 0.42 to 19.09 | 0.284 |
*Adjusted for maternal age, educational degree and parity.
Birth weight was significantly lower in the newborns of unplanned (β=−207.64; 95% CI −357.70 to −57.59), mistimed (β=−211.53; 95% CI −365.59 to −57.47) and unwanted (β=−259.97; 95% CI −483.63 to −36.32) pregnancies compared with those of planned pregnancies. In addition, gestational age was significantly lower in babies of unplanned (β=0.63; 95% CI −1.12 to −0.15), mistimed (β=−0.60; 95% CI −1.03 to −0.17) and unwanted (β=0.86; 95% CI −1.60 to −0.13) pregnancies compared with those of planned pregnancies (Table 4).
Table 4.
Association between unplanned pregnancy, birth weight and gestational age
| Crude model | Adjusted model* | |||||
|---|---|---|---|---|---|---|
| Neonatal outcomes | β | 95% CI | p | aβ | 95% CI | p |
| Unplanned vs planned pregnancy | ||||||
| Birth weight | −166.22 | −308.71 to −23.72 | 0.022 | −207.64 | −357.70 to −57.59 | 0.007 |
| Gestational age | −0.53 | −0.99 to −0.07 | 0.025 | −0.63 | −1.12 to −0.15 | 0.011 |
| Mistimed vs planned pregnancy | ||||||
| Birth weight | −169.45 | −318.78 to −20.13 | 0.026 | −211.53 | −365.59 to −57.47 | 0.007 |
| Gestational age | −0.45 | −0.86 to −0.03 | 0.037 | −0.60 | −1.03 to −0.17 | 0.007 |
| Unwanted vs planned pregnancy | ||||||
| Birth weight | −160.87 | −345.04 to −23.31 | 0.087 | −259.97 | −483.63 to −36.32 | 0.023 |
| Gestational age | −0.66 | −1.27 to −0.05 | 0.033 | −0.86 | −1.60 to −0.13 | 0.021 |
*Adjusted for maternal age, educational degree and parity.
Discussion
Unplanned pregnancy
More than one-half (65%) of pregnancies in our study were unplanned, which is higher than the estimated global, Latin American and Peruvian prevalence of unplanned pregnancies (48%, 63% and 52%, respectively).2,13 Also, the proportion of these unplanned pregnancies was higher than the proportion in other countries such as Singapore (44%), India (38.5%), Bangladesh (30.5%), USA (30%), Nepal (25%), Iran (19.8%), the UK (16.2%) and Belgium (2%).8,17–23 Therefore, the frequency of unplanned pregnancies in our study is the highest rate reported in the literature of the last 10 y. This finding could be explained based on two principles. First, the evidence on the subject mostly comes from developed countries where women have fewer educational, social and economic barriers to exercising their reproductive autonomy and guaranteeing their sexual and reproductive health. Bearak et al. found that unintended pregnancy rates were generally higher in settings where abortion was legally restricted; furthermore, among countries where abortion is legal, almost 70% of all unplanned pregnancies end in abortion.2 Therefore, most pregnancies ending in birth are considered planned, which veils the true percentage of unplanned pregnancies in these developed countries. Second, our research was developed approximately 1 y after the start of the COVID-19 pandemic; thus, our results could be a reflection of the consequences of quarantine; this means in a context with restrictions on free movement and an indirect reduction in access to healthcare services, especially family planning services that were considered non-essential.24 Recent evidence predicted that a decline in contraceptive use by women in low- and middle-income countries would be around 80%.25
Neonatal outcomes
An important finding of this research is the lower birth weight associated with unplanned pregnancy; while low birth weight was not found as a neonatal outcome as in many studies, an association was found with insufficient birth weight (2500 to 2999 g). The literature on such an association is non-existent. Even although unplanned pregnancy has historically been associated with low birth weight, we decided to include the group of insufficient birth weight newborns because Tacna has the lowest proportion of low birthweight newborns at national level (4%); consequently this fact could bias our results a priori.26 Moreover, the prevalence of insufficient birth weight was 10.77% in Tacna (2001–2010) and it was shown that these newborns behaved similarly to low birthweight newborns in terms of morbidity and mortality.27 Studies showed that the number of insufficient birth weight newborns was four to eight times higher than those with low birth weight and its analysis is necessary in Tacna.28,29 It is clear that investigators focused on low birth weight, but another indicator to consider is insufficient birth weight, which is underexplored as a public health problem and there are limited data. While it is not yet understood how pregnancy intention affects birth weight, it could be explained by mothers’ feelings about having an unplanned pregnancy with conscious or unconscious neglect of their health, late start of prenatal care or less weight gain during pregnancy, and all these factors may contribute to their newborns’ weight.30 As well, available evidence supports that women reporting unplanned conception experience depressive symptoms, anxiety and high stress levels, and could lead to insufficient birth weight babies.31
According to our adjusted analysis, newborns of unwanted pregnancies were 3.49 times more likely to be premature compared with newborns of planned pregnancies. Likewise, Shah et al. obtained similar results in a meta-analysis of adjusted data that revealed higher odds of preterm delivery in unwanted pregnancies, but not for unplanned or mistimed pregnancies.6 Notwithstanding, two longitudinal studies in the USA and Mexico showed the opposite, that pregnancy intention was not significantly associated with prematurity.10,32 A large narrative review on the effects of stress in preterm labor concluded that the strongest predictor of prematurity was pregnancy-specific anxiety.33 In addition, Rose et al. demonstrated in their meta-analysis a significant association between maternal prenatal anxiety and preterm birth with an OR=1.70.34 A possible explanation for our result is the link between psychosocial stress in an unwanted pregnancy and elevation of corticotropin-releasing hormone, because it was shown that higher levels of this hormone increased the risk of premature delivery.35,36
Strengths and limitations
The strengths of our study include the use of medical records to capture robust measures for neonatal outcomes, rather than using less reliable retrospective survey responses. Likewise, we decided to categorize the variable ‘unplanned pregnancy’ in mistimed and unwanted pregnancy, which enriches our findings because it is postulated that unwanted pregnancy is the real risk factor for adverse neonatal outcomes. Finally, to the best of our knowledge, our analysis provides for the first time measured birth weight and gestational age as quantitative variables, being consistent with the methodological recommendation that variables originally numerical in nature should not become qualitative variables because they lead to less precise statistical results.37
Nevertheless, there are some limitations to our investigation. First, the assessment of self-report pregnancy intention was performed after their children were born, a time when they may be stressed due to the birth experience; therefore, memories of the pregnancy intention are conflated with feelings about childbirth and subjected to errors due to recall bias. Additionally, the participants were able to select response options that were socially most appropriate; we hypothesize that this could lead to an overestimation of planned pregnancies and bias the association between adverse outcomes and unplanned pregnancy. The same could happen with the measurement of intimate partner violence. Second, the cross-sectional study design cannot be used to infer causality between the variables of interest because a temporal sequence cannot be established. Third, although we used a representative sample of newborns, the results of this study cannot be generalized to other contexts. Despite these limitations, we consider that the findings obtained in our study can provide an overview of this situation and its association with neonatal outcomes.
Conclusions
Approximately seven of 10 pregnancies were unplanned in our study. Compared with newborns from planned pregnancies, newborns from unplanned and unwanted pregnancies were 2.14 and 3.49 times more likely to be born with insufficient birth weight and to be premature, respectively. In addition, babies after unwanted pregnancies weighed 260 g less and were born 6 d earlier compared with births from planned pregnancies. These results should be useful for healthcare providers to identify this group of newborns as a high-risk group in need of additional care and support. Otherwise, these findings may have important implications in the present and future. If the intention of pregnancy is not included as a neonatal risk factor, insufficient birth weight and preterm birth may increase because a higher proportion of births will be unplanned.
Contributor Information
Diana Manuela Ticona, Universid ad Privada de Tacna, Faculty of Health Sciences, 23003, Tacna, Peru.
Diana Huanco, Universidad Nacional Jorge Basadre Grohmann, Faculty of Health Sciences, 23003, Tacna, Peru.
Manuel Benedicto Ticona-Rendón, Universidad Nacional Jorge Basadre Grohmann, Faculty of Health Sciences, 23003, Tacna, Peru.
Authors’ contributions
DMT and DH conceived the study; DMT and DH designed the study protocol; DMT, DH and MBTR analyzed and interpreted data; DMT, DH and MBTR drafted the manuscript; DMT, DH and MBTR critically revised the manuscript for intellectual content. All the authors read and approved the final version of the manuscript. DMT, DH and MBTR are guarantors of the paper.
Acknowledgements
The authors thank Carolina Perca for reviewing the language and style of the work.
Funding
None.
Competing interests
None declared.
Ethical approval
The study was conducted in accordance with the Declaration of Helsinki and approved by the Universidad Privada de Tacna's ethics committee (protocol code 057-FACSA-UI, 16 April 2021). Informed consent was obtained from all subjects involved in the study.
Data availability
None.
References
- 1. Khan MN, Harris ML, Shifti DMet al. Effects of unintended pregnancy on maternal healthcare services utilization in low- and lower-middle-income countries: Systematic review and meta-analysis. Int J Public Health . 2019;64:743–54. [DOI] [PubMed] [Google Scholar]
- 2. Bearak J, Popinchalk A, Ganatra Bet al. Unintended pregnancy and abortion by income, region, and the legal status of abortion: Estimates from a comprehensive model for 1990–2019. Lancet Glob Health . 2020;8:e1152–61. [DOI] [PubMed] [Google Scholar]
- 3. Bearak J, Popinchalk A, Alkema Let al. Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: Estimates from a Bayesian hierarchical model. Lancet Glob Health . 2018;6:e380–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Sedgh G, Singh S, Hussain R.. Intended and unintended pregnancies worldwide in 2012 and recent trends. Stud Fam Plann . 2014;45:301–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Hall JA, Benton L, Copas Aet al. Pregnancy intention and pregnancy outcome: Systematic review and meta-analysis. Matern Child Health J. 2017;21:670–704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Shah PS, Balkhair T, Ohlsson Aet al. Intention to become pregnant and low birth weight and preterm birth: A systematic review. Matern Child Health J. 2011;15:205–16. [DOI] [PubMed] [Google Scholar]
- 7. Gharaee M, Baradaran HR.. Consequences of unintended pregnancy on mother and fetus and newborn in North-East of Iran. J Matern Fetal Neonatal Med. 2018;33:876–9. [DOI] [PubMed] [Google Scholar]
- 8. Singh A, Singh A, Mahapatra B.. The consequences of unintended pregnancy for maternal and child health in rural India: Evidence from prospective data. Matern Child Health J . 2013;17:493–500. [DOI] [PubMed] [Google Scholar]
- 9. Hall JA, Barrett G, Copas Aet al. Reassessing pregnancy intention and its relation to maternal, perinatal and neonatal outcomes in a low-income setting: A cohort study. PLoS One . 2018;13:e0205487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Gariepy AM, Lundsberg LS, Stolar Met al. Are pregnancy planning and timing associated with preterm or small for gestational age births? Fertil Steril. 2015;104:1484–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Kuruvilla S, Bustreo F, Kuo Tet al. The global strategy for women's, children's and adolescents’ health (2016–2030): A roadmap based on evidence and country experience. Bull World Health Organ. 2016;94:398–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Frost L, Hinton R, Pratt BAet al. Using multistakeholder dialogues to assess policies, programmes and progress for women's, children's and adolescents’ health. Bull World Health Organ. 2016;94:393–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Instituto Nacional de Estadística e Informática . Encuesta Demográfica y de Salud Familiar 2020. Peru, 2021. [Google Scholar]
- 14. Centro Nacional de Epidemiología, Prevención y Control de Enfermedades . Boletín Epidemiológico Del Perú 2019. Peru, 2019. [Google Scholar]
- 15. Von Elm E, Altman DG, Egger Met al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J Clin Epidemiol . 2008;61:344–9. [DOI] [PubMed] [Google Scholar]
- 16. Instituto Nacional de Estadística e Informática . Encuesta Demográfica y de Salud Familiar 2020: Documento Metodológico. Peru, 2020. [Google Scholar]
- 17. Rahman M, Nasrin SO, Rahman Met al. Maternal pregnancy intention and its association with low birthweight and pregnancy complications in Bangladesh: Findings from a hospital-based study. Int Health . 2019;11:447–54. [DOI] [PubMed] [Google Scholar]
- 18. Goossens J, Van Den Branden Y, Van der Sluys Let al. The prevalence of unplanned pregnancy ending in birth, associated factors, and health outcomes. Hum Reprod . 2016;31:2821–33. [DOI] [PubMed] [Google Scholar]
- 19. Cheng TS, Loy SL, Cheung YBet al. Demographic characteristics, health behaviors before and during pregnancy, and pregnancy and birth outcomes in mothers with different pregnancy planning status. Prev Sci. 2016;17:960–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kost K, Lindberg L.. Pregnancy intentions, maternal behaviors, and infant health: Investigating relationships with new measures and propensity score analysis. Demography. 2015;52:83–111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Singh A, Singh A, Thapa S.. Adverse consequences of unintended pregnancy for maternal and child health in Nepal. Asia Pac J Public Health . 2015;27:NP1481–91. [DOI] [PubMed] [Google Scholar]
- 22. Omani-Samani R, Ranjbaran M, Mohammadi Met al. Impact of unintended pregnancy on maternal and neonatal outcomes. J Obstet Gynaecol India . 2019;69:136–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wellings K, Jones KG, Mercer CHet al. The prevalence of unplanned pregnancy and associated factors in Britain: Findings from the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3). Lancet. 2013;382:1807–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kumar N. COVID 19 era: A beginning of upsurge in unwanted pregnancies, unmet need for contraception and other women related issues. Eur J Contracept Reprod Health Care . 2020;25:323–5. [DOI] [PubMed] [Google Scholar]
- 25. Marie Stopes International. Methodology for calculating impact of COVID-19 . UK, 2020. [Google Scholar]
- 26. Instituto Nacional de Estadística e Informática . Perú: Nacidos Vivos y Nacidas Vivas Con Bajo Peso 2015-2018. Peru, 2020. [Google Scholar]
- 27. Ticona-Rendón M, Huanco-Apaza D.. Factores de riesgo del peso insuficiente al nacer en el Hospital Hipólito Unanue de Tacna, 2001 - 2010. Rev Per Ginecol Obstet. 2012;58:169–76. [Google Scholar]
- 28. Viana K de J, Taddei JA, de ACet al. Peso ao nascer de crianças brasileiras menores de dois anos. Cad Saúde Pública . 2013;29:349–56. [DOI] [PubMed] [Google Scholar]
- 29. Estrada-Restrepo A, Restrepo-Mesa SL, Feria NDCCet al. Maternal factors associated with birth weight in term infants, Colombia, 2002-2011. Cad Saude Publica. 2016;32:e00133215. [DOI] [PubMed] [Google Scholar]
- 30. Rahman MM. Is unwanted birth associated with child malnutrition in Bangladesh? Int Perspect Sex Reprod Health . 2015;41:80–8. [DOI] [PubMed] [Google Scholar]
- 31. Biaggi A, Conroy S, Pawlby Set al. Identifying the women at risk of antenatal anxiety and depression: A systematic review. J Affect Disord. 2016;191:62–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Barrios-Prieto E, Corona-Gutiérrez AA, Romo-Huerta HPE.. Resultados perinatales del embarazo no deseado. Rev Med MD . 2013;4.5:227–32. [Google Scholar]
- 33. Shapiro GD, Fraser WD, Frasch MGet al. Psychosocial stress in pregnancy and preterm birth: Associations and mechanisms. J Perinat Med . 2013;41:631–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Rose MS, Pana G, Premji S.. Prenatal maternal anxiety as a risk factor for preterm birth and the effects of heterogeneity on this relationship: A systematic review and meta-analysis. Biomed Res Int . 2016;2016:8312158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Levine TA, Alderdice FA, Grunau REet al. Prenatal stress and hemodynamics in pregnancy: A systematic review. Arch Womens Ment Health . 2016;19:721–39. [DOI] [PubMed] [Google Scholar]
- 36. Ruiz RJ, Gennaro S, O'Connor Cet al. CRH as a predictor of preterm birth in minority women. Biol Res Nurs. 2016;18:316–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. DeCoster J, Iselin A-MR, Gallucci M.. A conceptual and empirical examination of justifications for dichotomization. Psychol Methods. 2009;14:349–66. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
None.