Abstract
Background: Low birth weight (LBW), defined as less than 2,500 grams, remains a significant public health concern globally, with multiple maternal factors influencing neonatal outcomes. Specific Background: Although LBW neonates can survive and thrive, inadequate maternal knowledge about its risk factors can contribute to preventable cases. Knowledge Gap: Limited studies in Iraq assess maternal awareness regarding LBW causes, especially in the context of sociodemographic variables. Aims: This study aimed to evaluate mothers’ knowledge of LBW risk factors during pregnancy at the Maternal and Pediatric Hospital in Diwaniyah, Iraq. Results: Among 64 mothers aged 18–45, most demonstrated fair knowledge (mean = 1.92) regarding LBW risk factors. Notably, 40.6% exhibited good knowledge on daily activities, but 35.9% had low awareness of medication-related risks. Age showed a significant correlation with knowledge (p = 0.011), while academic achievement and economic status did not. Novelty: This study uniquely identifies age—not education or income—as a significant determinant of maternal knowledge on LBW, challenging traditional assumptions about health literacy. Implications: Findings underscore the need for targeted, age-sensitive educational interventions to improve maternal awareness and reduce LBW incidence, regardless of socioeconomic status or educational background.
Highlighhts:
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Low birth weight linked to maternal knowledge gaps.
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Assess mothers’ awareness of LBW risk factors.
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Most mothers had fair knowledge; age influenced awareness.
Keywords: Assessment, Mothers, Knowledge, Risk Factors, Pregnancy, Low Birth Weight
Introduction
Babies born weighing less than five pounds, eight ounces (2,500 grams) are considered low birth weight. A newborn typically weighs eight pounds. Despite their size, a low-birth-weight infant may be healthy, but they may also experience some severe health issues. [1].
LBW can be brought on by either preterm birth (low gestational age at birth, usually less than 37 weeks), short stature for gestational age (slow prenatal growth rate), or a combination of the two. Young ages, numerous pregnancies, prior LBW babies, poor nutrition, heart disease or hypertension, untreated celiac disease, substance use disorder, heavy alcohol use, and inadequate prenatal care are often risk factors for low birth weight in mothers. Additionally, pre-labor membrane rupture may be the cause [2-5]. Exposure to lead, smoking, and other forms of air pollution are examples of environmental risk factors. [5].
Intrauterine growth restriction (IUGR) is another disorder that can result in low birth weight. When a baby does not grow properly throughout pregnancy, this happens. The mother's health, the baby's health, or issues with the placenta could be the cause. [6].
Risk factor of LBW; Low birth weight (LBW), defined as a weight of less than 5.5 pounds (2,500 grams) at birth, can occur due to various risk factors, which can be categorized into maternal, fetal, and environmental factors. Here are some of the key risk factors Maternal Factors; Poor Nutrition; Inadequate nutrition during pregnancy can lead to LBW , Teenage Pregnancy: Young mothers may not have fully developed bodies to support a healthy pregnancy , Advanced Maternal Age; Women over 35 may face higher risks of complications , Chronic Health Conditions ; Conditions such as diabetes, hypertension, or heart disease can impact fetal growth ,Substance Abuse: Smoking, alcohol consumption, and illicit drug use can adversely affect fetal development, Infections; Certain infections during pregnancy can lead to LBW, Low Pre-pregnancy Weight; Women who are underweight before pregnancy are at higher risk , Fetal Factors;multiple Pregnancies; Twins or higher-order multiples often have lower birth weights, Genetic Factors; Genetic predispositions can influence fetal growth and development , Intrauterine Growth Restriction (IUGR); Conditions that restrict growth can lead to LBW, Fetal health may be impacted by environmental factors, such as exposure to environmental toxins and pollutants, Low Socioeconomic Status: Limited access to healthcare and nutrition can increase risks , Stress: High levels of stress during pregnancy can affect fetal growth .[7]
A number of nursing illnesses can be linked to low birth weight (LBW). Making sure your baby is growing healthily is one of the primary goals of routine prenatal examinations. There are various methods to assess your baby's size throughout pregnancy. You can monitor the baby's growth by tracking your consistent weight increase. Fundal height is another method, here are some common nursing diagnoses related to LBW: [8] Knowledge Deficit; Related to the parents' lack of understanding about care for a low-birth-weight infant. Each diagnosis should be tailored to the specific needs of the infant and the family, with appropriate interventions and goals established [9, 10].
Methods
A quantitative descriptive study design is used to assess mothers’ knowledge about risk factors during pregnancy that cause low birth weight in newborns at maternal and Pediatric Hospital in Diwaniyah. The study's time frame began in November 2024 and ended in March 2025. Prior to actually collecting data, formal administrative consent is requested from the Al-Diwaniyah Teaching Hospital to conduct the study after receiving approval from the College of Nursing at the University of Al-Qadisiyah. To obtain their participation and agreement in order to gather information for emergency department nurses.
Fifteen randomly selected patients who were in the hospital during the morning shift participated in a pilot study to assess the study instrument's dependability. From January 7 to January 11, 2025, the pilot study was carried out. The fifteen patient participants underwent testing and retesting. Purposive, or non-probability, sampling was chosen in order to collect precise data. From (64) women at the maternal and pediatric hospital in Diwaniyah, we analyzed the mother's Knowledge About Risk Factors During Pregnancy That cause low Birth Weight in Newborn for 64 mothers aged from 18 to 45, through the duration from December 2024 to March 2025 at Maternal and Pediatric Hospital in Diwaniyah the patients' medical conditions were arranged according to the age and various questions.
A panel of specialists with over five years of professional experience in their respective industries evaluated the multiple-choice questions to assess their content validity. Data were gathered using a structured interviewing technique and an assessment tool created by the researchers. The researchers used a paper questionnaire in two hospitals in AL-Dewaniyah, using the Arabic version of the questionnaire, and conducted interviews with all participants who were part of the study sample using the same questionnaire. The questionnaire was used from January 10, 2025, to February 10, 2025. It takes about fifteen to twenty minutes for each participant to finish the questionnaire.
Demographic Data | Variables | Frequency | Percent |
Age | 18 – 25 | 30 | 46.9 |
26 – 35 | 30 | 46.9 | |
36 – 45 | 4 | 6.3 | |
Total | 64 | 100.0 | |
Marital Status | Married | 64 | 100.0 |
Absolute | 0 | 0.0 | |
Widowed | 0 | 0.0 | |
Separated | 0 | 0.0 | |
Total | 64 | 100.0 | |
Academic Achievement | Don't Read and Don't Write | 13 | 20.3 |
Elementary | 29 | 45.3 | |
Medium | 10 | 15.6 | |
Preparatory | 2 | 3.1 | |
Graduate | 10 | 15.6 | |
Total | 64 | 100.0 | |
Economic Status | low | 14 | 21.9 |
moderate | 38 | 59.4 | |
Good | 12 | 18.8 | |
Total | 64 | 100.0 | |
Number of Family Members | One Child | 11 | 17.2 |
Two Children | 22 | 34.4 | |
More than Three Children | 31 | 48.4 | |
Total | 64 | 100.0 | |
Type of Accommodation | Rural | 24 | 37.5 |
Urban | 40 | 62.5 | |
Total | 64 | 100.0 | |
Maternal Health Medical Information | |||
Number of Births | Less than Three | 23 | 35.9 |
More than Three | 41 | 64.1 | |
Total | 64 | 100.0 | |
Number of Abortions | One | 28 | 43.8 |
Two | 23 | 35.9 | |
More | 8 | 12.5 | |
No Abortions | 5 | 7.8 | |
Total | 64 | 100.0 | |
Mother's Weight | 50 – 65 | 20 | 31.3 |
66 – 75 | 25 | 39.1 | |
76 – 100 | 19 | 29.7 | |
Total | 64 | 100.0 | |
Mother's Length | 100 – 150 | 16 | 25.0 |
151 – 175 | 34 | 53.1 | |
176 – 200 | 14 | 21.9 | |
Total | 64 | 100.0 | |
Baby Weight | Less than 2000 Grams | 64 | 100.0 |
More than 2000 Grams | 0 | 0.0 | |
Total | 64 | 100.0 |
The table provides a comprehensive overview of the demographic data of the mothers consisting of 64 participants, with a notable distribution across age groups. In the 18–25 age category, the percentage was 46.9%, which is the same as for the 26–35 age group, indicating that the vast majority of participants (93.8%) are under the age of 35, while the percentage of participants in the 36–45 age group did not exceed 6.3%. Regarding marital status, the results showed that all participants (100%) are married, with no cases of childbirth outside of marriage or instances of separation or widowhood, which may reflect the nature of the targeted sample focusing on mothers within a marital framework.
Item | I Agree | I Don't Know | I Don't Agree | Mean | Std. Deviation | |||
F | % | F | % | F | % | |||
Question 1 | 4 | 6.3 | 40 | 62.5 | 20 | 31.3 | 2.25 | .563 |
Question 2 | 24 | 37.5 | 28 | 43.8 | 12 | 18.8 | 1.81 | .731 |
Question 3 | 26 | 40.6 | 22 | 34.4 | 16 | 25.0 | 1.84 | .801 |
Question 4 | 27 | 42.4 | 19 | 29.7 | 18 | 28.1 | 1.85 | .833 |
Question 5 | 16 | 25.0 | 18 | 28.1 | 30 | 46.9 | 2.21 | .825 |
Question 6 | 26 | 40.6 | 19 | 29.7 | 19 | 29.7 | 1.89 | .837 |
Question 7 | 24 | 37.5 | 24 | 37.5 | 16 | 25.0 | 1.87 | .786 |
Question 8 | 21 | 32.8 | 20 | 31.3 | 23 | 35.9 | 2.03 | .835 |
This table presents the opinions of 64 participants regarding eight questions that investigate the connection between low birth weight and eating practices. The responses were categorized into three categories: Agree, Don't Know, and Disagree, with the mean and standard deviation calculated for each question.
Assessment Knowledge | Frequency | Percent | Mean | Std. Deviation |
Low | 9 | 14.1 | 1.92 | .447 |
Fair | 51 | 79.7 | ||
Good | 4 | 6.3 | ||
Total | 64 | 100.0 |
Low (mean 1-1.66), Fair (1.67-2.33), Good (2.34-3)
This table categorizes participants' knowledge levels into three categories: Low, Medium, and Good, based on the average responses. The overall mean knowledge score was 1.92, which is close to the cutoff between the medium and good classifications (according to the mentioned scale). These findings indicate that the majority of participants are aware of the basic risks associated with LBW, but their depth of understanding remains limited. The small number of participants with good knowledge (6.3%) reflects a knowledge gap that needs to be addressed through more focused health education and awareness campaigns. The low overall average (1.92), close to the lower end of the fair category, suggests that while participants may have heard of certain risk factors (like smoking or poor nutrition), their understanding is not strong or comprehensive. This emphasizes the need for widespread, accessible education across all social and academic groups not just targeting specific demographics since even educated or economically stable individuals may lack full understanding of the issue.
Demographic Data | P-Value | Sig. |
Age | .011 | S |
Academic Achievement | .224 | NS |
Economic Status | .529 | NS |
Number of Family Members | .078 | NS |
Type of Accommodation | .350 | NS |
This table shows the results of statistical analysis (P-Value) examining the relationship between demographic variables and health knowledge level. Growing older and more awareness of eating behaviors are statistically correlated, according to the age variable's P-Value of 0.011. In contrast, other variables such as economic status (P=0.529) or family size (P=0.078) did not show any statistical significance, reflecting similar knowledge challenges among different categories of these variables. Notably, the marital status variable was not assigned any statistical value because all participants were married, making it a fixed variable that is not analyzable. Academic achievement (p = 0.224), economic status (p = 0.529), number of family members (p = 0.078), and type of accommodation (p = 0.350) were not statistically significant (N.S), meaning these factors did not significantly affect knowledge levels. Marital status was marked as a constant, so no statistical test was performed. These results suggest that age is the only factor significantly related to awareness of LBW risks, which may be due to cumulative life experiences or previous pregnancies. However, it is somewhat surprising that education level and economic status did not show significant influence, as these are often linked to health literacy.
Assessment Knowledge | Frequency | Percent | Mean | Std. Deviation |
Low | 6 | 9.4 | 2.31 | .639 |
Fair | 32 | 50.0 | ||
Good | 26 | 40.6 | ||
Total | 64 | 100.0 |
Low (mean 1-1.66), Fair (1.67-2.33), Good (2.34-3)
Participants' understanding of the 13 risk variables that have been identified as potentially contributing to low birth weight in neonates is summarized in this table, including factors like poor nutrition, physical stress, exposure to chemicals, and hormonal influences. The overall mean knowledge score was 2.31 with a standard deviation of 0.639. This mean score falls within the Fair category (1.67–2.33), but is very close to the good range, indicating an upper-moderate level of awareness. Out of 64 participants: 6 participants (9.4%) were categorized as having Low knowledge. 32 participants (50%) had Fair knowledge. 26 participants (40.6%) had good knowledge. These results indicate that while half of the participants demonstrate a moderate understanding, a significant portion (over 40%) show a strong awareness of the various risk factors linked to LBW. The variability in knowledge is captured by the relatively high standard deviation (0.639), indicating that participant responses were spread out and not clustered closely around the mean. This variation suggests inconsistent awareness levels across different risk factors. The discussion paragraph accompanying the table adds that Question 1 (related to poor nutrition) had the highest agreement rate (56.3%), while Question 10 (related to secondhand smoke) had the lowest (18.8%). This pattern highlights a greater awareness of direct and visible risks, like nutrition, but a lack of awareness regarding less obvious risks, such as environmental and hormonal factors.
Assessment Knowledge | Frequency | Percent | Mean | Std. Deviation |
Low | 23 | 35.9 | 1.98 | .845 |
Fair | 19 | 29.7 | ||
Good | 22 | 34.4 | ||
Total | 64 | 100.0 |
Low (mean 1-1.66), Fair (1.67-2.33), Good (2.34-3)
This table categorizes participants into three groups based on their average responses: Low Knowledge (mean 1-1.66), Fair Knowledge (1.67-2.33), and Good Knowledge (2.34-3). Results show that 35.9% of participants (23 individuals) fall into the low-knowledge category, 29.7% (19 individuals) into fair knowledge, and 34.4% (22 individuals) into good knowledge. The classification relies on mean scores, though the standard deviation is not explicitly mentioned here but is included in the previous table to measure response dispersion. These percentages reflect a balanced distribution across categories, indicating varied levels of awareness among participants. The data shows a relatively balanced distribution across the three knowledge categories. The largest proportion of participants falls under the low knowledge category (35.9%), indicating a significant gap in understanding or awareness among more than a third of the participants. Meanwhile, 34.4% exhibit good knowledge, showing that a comparable segment of the population is well-informed. The fair knowledge group (29.7%) forms a bridge between the two extremes. The mean score of 1.98 places the overall assessment in the "Fair" category. However, the high standard deviation (0.845) suggests considerable variability in individual responses, implying that participants' knowledge levels are quite dispersed rather than consistent.
Assessment Knowledge | Frequency | Percent | Mean | Std. Deviation |
Low | 19 | 29.7 | 2.17 | .865 |
Fair | 15 | 23.4 | ||
Good | 30 | 46.9 | ||
Total | 64 | 100.0 |
Low (mean 1-1.66), Fair (1.67-2.33), Good (2.34-3)
This table categorizes participants into three groups based on their average responses: Low Knowledge (mean 1-1.66), Fair Knowledge (1.67-2.33), and Good Knowledge (2.34-3). Results indicate that 29.7% of participants (19 individuals) fall into the low-knowledge category, 23.4% (15 individuals) into fair knowledge, and 46.9% (30 individuals) into good knowledge. The overall mean knowledge score is 2.17 with a standard deviation of 0.865, reflecting varied responses among participants and a clear inclination toward good knowledge. This suggests a relative awareness of the need for healthy habits during pregnancy, though gaps persist in specific areas. This table reflects participants’ awareness of the importance of healthy daily habits. Almost half of the participants (46.9%) fall into the "Good Knowledge" category, showing a strong understanding of the effects of daily activities on health outcomes. Meanwhile, 29.7% are in the "Low Knowledge" group, indicating a notable portion of participants who may lack essential information. The "Fair Knowledge" group makes up 23.4%. The average score of 2.17 falls within the "Fair Knowledge" range but is closer to "Good Knowledge", suggesting a general trend toward higher awareness. The standard deviation of 0.865 indicates a moderate level of variability in responses, highlighting that while many are knowledgeable, others still lack essential understanding. This distribution suggests progress in public awareness but also points to the need for targeted educational efforts to address specific knowledge gaps.
Result and Discussion
The results show that 93.8% of participants are under the age of 35, with equal percentages in the 18–25 and 26–35 age groups (46.9% each). This focus on young mothers may reflect a connection between younger age and higher birth rates in the studied community, or it may be due to a bias in sample selection aimed at a specific age group. However, it is advisable to compare these results with national demographic data to assess the representativeness of the sample. From a health perspective, younger ages may be associated with increased risks of complications during pregnancy if not accompanied by adequate awareness, especially with lower educational levels.
This result agreed with several studies [11-15] which states that the majority of the study's participants were under 30.
All participants (100%) are married, with no cases of childbirth outside of marriage or instances of widowhood or separation. This indicates that the sample is limited to mothers within a marital framework, possibly due to prevailing social or religious norms in the community where the study was conducted. This restriction may be attributed to difficulties in accessing other groups (such as unmarried mothers) or methodological reservations in the research. However, the exclusion of these groups limits the generalizability of the results to all mothers, especially in societies with different social compositions.
These findings concurred with some research [16-20] which mentions that the majority of the study's participants were married.
Most of the participants are either illiterate or have only primary education, while the percentage of those with secondary or higher education does not exceed 15.6%. This decline in educational level may weaken the mothers' ability to understand health guidelines related to pregnancy and childbirth, such as the importance of proper nutrition or avoiding strenuous activities. It may also hinder effective communication with medical teams. This finding highlights the need to design simple and clear awareness programs that use visual or auditory media suitable for this group, rather than relying on written texts.
These findings concurred with many research [21-25] which mentions that the majority of the study's participants have primary education.
Most of the participants classified their economic situation as moderate, while 21.9% indicated a low status. A low economic situation may be associated with difficulties in accessing adequate healthcare or nutritious food, making low birth weight more likely. However, the presence of 18.8% in a good economic situation shows that the problem is not confined to the poor, but may also be linked to other factors such as culture or lack of awareness. Studies are recommended to analyze the interaction between economic, educational, and health factors.
These findings concurred with a large body of research [26-30] which mentions the majority of the study's participants have a moderate income.
48.4% of participants have more than three children, reflecting a high birth culture in the community. Repeated births may lead to physical and psychological strain on mothers and increase the likelihood of having low-birth-weight infants due to the depletion of the body's resources. It may also indicate a lack of family planning services or social resistance to their use. This finding calls for the promotion of reproductive health programs and raising mothers' awareness of the importance of spacing pregnancies.
Most of the participants live in urban areas, compared to 37.5% in rural areas. This distribution may reflect better healthcare services available in cities; however, it does not necessarily mean that rural residents are less aware, as challenges in rural areas may be greater (such as distance from medical centers). It is important to compare health outcomes between the two areas to assess the impact of healthcare services on birth weight.
These results agreed with numerous studies [31-34] which mention that most of the study samples were from urban areas.
Number of Births: The high percentage of mothers who have given birth to more than three children (64.1%) may be linked to increased pressure on mothers' health, partially explaining the high incidence of low-birth-weight infants.
Abortions: 79.7% of participants experienced at least one miscarriage, a concerning rate that requires investigation into its causes (medical, social, or economic).
Mother's Weight and Height: The normal distribution of mothers' weight (with a concentration in the 66–75 kg range) and height (151–175 cm) suggests that these factors may not be the primary reasons for low birth weight; rather, other factors such as nutrition or stress may play a role.
Birth Weight: The study's focus on cases of low birth weight (100%) aims to understand the contributing factors to this issue, but it excludes comparison with infants of normal weight, which may limit the identification of preventive factors.
In table (2)The results showed that 62.5% of participants were unaware of the relationship between dietary habits and low birth weight, while only 6.3% agreed that such a link exists. This indicates severe gaps in nutritional awareness, particularly among mothers with limited education, who may not recognize the importance of essential nutrients (e.g., protein and iron) in fetal development. The high percentage of "I don’t know" responses reflects insufficient health education during pregnancy, whether in clinics or through media. This knowledge gap could lead to poor dietary practices, such as relying on unbalanced meals or consuming nutritionally deficient foods. To address this, weekly educational sessions in healthcare centers focusing on balanced nutrition are recommended, along with distributing illustrated pamphlets showcasing examples of healthy meals.
79.7% of participants were classified as having "Fair" knowledge, while only 6.3% were rated as having "Good" knowledge. This suggests that most mothers possess a superficial understanding of factors affecting newborn weight, lacking awareness of critical details (e.g., the impact of vitamin deficiencies or excessive caffeine intake). This stems from a lack of specialized educational programs addressing these specifics, especially in rural areas with limited healthcare access. The high percentage of "Fair" knowledge might misleadingly imply adequate awareness, but it highlights the need for deeper understanding through interactive training using real-life models or case studies. For example, workshops demonstrating the effects of malnutrition on fetal models could be organized.
These findings concurred with a large body of research [35-38] that mention most mothers have moderate knowledge.
The classification of 50% of participants in the "Fair" knowledge category, despite the overall mean being "Good" (2.31), reveals a critical insight: while many mothers grasp basic concepts, their understanding lacks depth. The 40.6% in the "Good" category likely represent individuals with access to targeted health resources or prior education, whereas the 9.4% with "Low" knowledge may face barriers such as limited healthcare access or language challenges. The dominance of "Fair" scores suggests superficial familiarity with risk factors, where participants might recognize terms like "poor nutrition" but struggle to link them to specific outcomes (e.g., micronutrient deficiencies). This superficiality could hinder effective preventive behaviors, indicating a need for interactive training modules that move beyond theoretical knowledge to practical applications, such as meal planning or identifying environmental hazards.
Age showed a statistically significant correlation (P=0.011) with knowledge levels, as older mothers (aged 36–45) demonstrated greater awareness of dietary habits compared to younger ones. This might result from accumulated experience from previous pregnancies or longer exposure to educational programs. Conversely, variables like economic status or number of family members showed no significant impact, indicating that knowledge challenges are similar across social classes. This calls for targeted educational efforts toward younger age groups (under 35), where needs are greater, alongside flexible campaigns reaching both urban and rural mothers.
The results highlight a notable disparity in participants’ awareness of risk factors. While 54.7% recognized poor nutrition as a critical factor, only 18.8% acknowledged the role of secondhand smoke, reflecting a gap in understanding environmental and behavioral risks. This discrepancy may stem from cultural or educational biases, where direct factors like diet are more emphasized in health campaigns, while indirect factors (e.g., environmental toxins) receive less attention. The mean knowledge score of 2.31 ("Good") and the standard deviation of 0.639 suggests that while a significant portion of participants (40.6%) demonstrated advanced awareness, variability in responses underscores inconsistencies in health literacy. For instance, even among those with "Good" knowledge, nuanced risks like hormonal imbalances or genetic predispositions might remain poorly understood. These findings emphasize the need for holistic educational programs that address both visible and hidden risk factors to bridge knowledge gaps.
Conclusion
The vast majority of mothers have a basic understanding of the influencing factors but lack comprehensive awareness of specific details, such as the effects of certain nutrients or medications.
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