Introduction
Preterm birth (PTB), as defined by the World Health Organization (WHO), refers to “a live birth taking place prior to the completion of 37 weeks of gestation” [1]. Annually, approximately 15 million infants are delivered prematurely, and this global pattern is experiencing an upward trajectory [1]. Notably, PTB emerged as the primary cause of neonatal mortality within the initial four weeks of life, ranking as the second leading factor behind under-five deaths on a global scale; prematurity is responsible for 40% of under-five fatalities [2]. Geographically, preterm birth prevails across 184 nations, displaying incidence rates ranging from 5 to 18% among all newborns. Specifically, Africa and South Asia jointly contribute to 60 to 85% of all instances of preterm births [3]. Comparing demographics, approximately 12% of newborns in less affluent nations are born prematurely, in contrast to 9% in more economically prosperous countries [1].
Preterm birth rates in Sub-Saharan Africa have been investigated, revealing estimates of 10.9% in Gambia and 12% in Tanzania [4]. Within global rankings, Uganda holds the 28th position for preterm births, standing at 13.6 per 1000 live births. Tragically, 25% of the 27 deaths per 1000 live births of newborns can be directly attributed to preterm births. In light of the new global infant mortality goals aiming for fewer than 10 deaths per 1000 live births by 2035, Uganda must intensify its efforts to reduce preterm birth rates [4]. Achieving this objective would demand a targeted intervention on addressing preventable causes of preterm birth in low-income nations [4]. While factors such as demographics, social circumstances, obstetric care, and pregnancy-related conditions have been implicated in preterm births worldwide, it’s important to note that these risks differ according to geographical regions, leading to marked disparities between affluent and low-income countries [1, 4, 5].
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To address the issue of premature births, the Ministry of Health, operating under the Government’s initiative, has established a nationwide framework for pregnancy care and assistance. This includes bolstering emergency medical services and referral systems, enhancing maternal, neonatal, and child health provisions across all tiers of healthcare, expanding access to sexual and reproductive health amenities with a particular emphasis on family planning and age-appropriate information, and elevating the quality of national laboratory and diagnostic services [6]. Additionally, the United States Agency for International Development (USAID) through its Regional Health Integration to Enhance Services-North, Lango Activity (RHITES-N, Lango), provides support to hospitals like the LRRH in terms of maternal healthcare services. Despite the implementation of these measures, data shows that over the past three years, 46% of admissions to the Neonatal Intensive Care Unit (NICU) at LRRH were attributed to preterm deliveries [7]. While, prior researches focusing on various contexts have produced inconclusive findings regarding the predictors of preterm birth, there is a notable absence of documented evidence concerning predictors of premature birth specifically within Lira district. Given this research gap, the current study aims to evaluate the risk factors linked to preterm birth at LRRH, with the ultimate goal of pinpointing potential interventions capable of mitigating the occurrence of preterm births.
Methodology
Data source and study design
The collection of secondary data involved the retrieval of information from the medical documents (specifically, prenatal cards and registers) of mothers who gave birth at LRRH during the period from April 2020 to July 2021. A cross-sectional sample of 590 comprehensive records of mothers who gave birth at LRRH was examined.
Study variables and measurement
The dependent variable in this study was preterm birth, dichotomized as birth occurring before 37 weeks (preterm birth) and at 37 weeks or later (term birth). Accordingly, a mother who gave birth before 37 weeks was classified as having experienced preterm birth and assigned a code of 1, while a mother who delivered at 37 weeks or beyond was categorized as having undergone term birth and assigned a code of 0.
The independent variables included various aspects of family socio-demographics. Age was measured in completed years and subsequently grouped into three categories during the analysis: <18 years, 18–30 years, and 31 years or older. Marital status was stratified into four categories: married, single, divorced, and widow/widower. Education level was divided into four categories: no education, primary education, secondary education, and tertiary education. Employment status was dichotomized as employed or not employed. Location was classified as rural or urban. Other independent variables included Body Mass Index (BMI) measured in kg/m², which was segmented into four categories during analysis: underweight, normal weight, overweight, and obese. Substance use was categorized as active drug user or not an active drug user. Maternal factors were also considered, with mode of delivery categorized as either normal delivery or caesarean section. Antenatal Care (ANC) attendance was categorized as either less than 3 times or 3 times or more. Anemia was divided into two categories: <10 g/dl and ≥ 10 g/dl. History of abortion was classified as either having experienced abortion or never having experienced abortion. The birth weight of the mother’s baby was grouped into “yes” (< 2,500 g) or “no” (≥ 2,500 g). Comorbidities were classified as, whether present or absent. HIV status was categorized as sero-positive or sero-negative. Parity was stratified as having fewer than 4 children or having 4 or more children. Inter-pregnancy interval was segmented as less than 24 months or 24 months or more. Previous preterm birth was dichotomized as “yes” or “no,“ and preeclampsia was similarly classified as present or absent. Furthermore, fetal factors were considered, including the sex of the child, categorized as male, female, or both (in the case of twins of different sexes). Pregnancy outcome was classified as singleton or twins. Congenital abnormalities were recorded as “yes” or “no.“
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Data analysis
The data analysis was conducted using STATA version 17.0 software. In the initial univariate analysis, frequencies and percentages were computed to describe the variables considered in the study. At bivariate analysis, associations were examined using the Pearson Chi-square test with a significance level set at p < 0.05. Factors that demonstrated significance in this analysis were selected for inclusion in the subsequent multivariate analysis. The purpose of the multivariate analysis was to estimate the individual net effects of each independent factor on the dependent variable. Notably, due to the low prevalence of preterm births (ranging from 5 to 18%) as indicated in the literature, which categorizes it as a rare event, the binary complementary log-log model was a potential choice for isolating net effects, as opposed to the logistic and probit models. However, an evaluation of three Link functions (Logistic, Complementary log-log, and Probit) was performed to determine their suitability for fitting the data. Based on the Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC), the logistic model exhibited the lowest AIC and BIC values, rendering it the most plausible model for identifying predictors of preterm birth. Subsequently, two diagnostic tests were executed: a multicollinearity test and a model fitness test employing the Variance Inflation Factor (VIF) and link test, respectively.
Ethics and consent to participate
This study employed the Research Ethics Committee of Makerere University’s School of Statistics and Planning to obtain approval and oversee the research process, ensuring adherence to both the regulations set forth by the Uganda National Council for Science and Technology (UNCST) and other international guidelines concerning the involvement of human subjects. The study made use of aggregated data sourced from a hospital, for which institutional authorization had been obtained to carry out the research. Informed consent was not pursued, as the data extracted did not contain any elements that could potentially reveal the identities of the patients. Physical copies of the data were securely stored and accessible solely to the research team. Similarly, electronic databases were safeguarded with passwords, with access restricted exclusively to the research team. Lastly, a request for a waiver of consent was submitted to Lira Regional Referral Hospital, an entity established to ensure the protection of study participants.
Limitations
The research employed pre-existing data gathered at the establishment. The occurrence of the Covid-19 pandemic influenced the quantity of mothers who gave birth at the facility during the specified time frame (April 2020 to May 2021). To tackle this issue, the investigation period was prolonged until July 2021. Certain instances of insufficient or absent information, notably within the records, were faced. Nevertheless, this challenge was managed by incorporating information from antenatal cards to supplement the absent data.
Results
Background characteristics and differentials in preterm births by Socio-demographic, maternal and fetal characteristics of the mothers
Table 1 shows the distribution of participants based on socio-demographic, maternal, and fetal attributes. Some entries exhibited a lack of approximately 10% information in specific variables, causing a deviation from the total count of 590 respondents. These incompletely recorded instances were omitted from the analysis. The tabulated data indicates that out of the total 590 female participants, roughly 36% experienced preterm deliveries. The largest segment (82%) of the respondents fell within the age range of 18 to 30, approximately 56% were not engaged in employment, and nearly 48% resided in rural areas. In relation to marital status, a significant majority of the mothers (87%) were married, while an estimated 6% possessed limited or no literacy. Concerning maternal characteristics, nearly 9% of the mothers attended less than three antenatal care sessions, and around 40% underwent Caesarean section deliveries. Moreover, about 14% of the mothers tested positive for HIV, 6% gave birth to low weight infants, and almost 23% experienced preeclampsia. In regard to fetal attributes, a considerable proportion of the mothers (97%) had single babies, 54% of whom were female, and roughly 3% exhibited congenital abnormalities.
Table 1
Differentials in preterm births by Socio-demographic, maternal and fetal characteristics of the mothers (N = 590)
Variable | Frequency | % | No. of preterm birth | % | \({\varvec{\chi }}^{2}\) (p-value) | |
|---|---|---|---|---|---|---|
Preterm birth status | Term birth (\(\ge\)37 weeks ) | 379 | 64.2 | |||
Preterm birth (< 37weeks) | 211 | 35.8 | ||||
Socio-demographic characteristics | ||||||
Age group | < 18 | 17 | 2.9 | 8 | 47.1 | 2.62 (0.269) |
18–30 | 479 | 82.2 | 164 | 34.2 | ||
31 and above | 87 | 14.9 | 36 | 41.4 | ||
BMI | Underweight | 6 | 6 | 3 | 50 | 4.84 (0.184) |
Normal | 248 | 248 | 81 | 32.7 | ||
Overweight | 254 | 254 | 99 | 39 | ||
Obese | 19 | 19 | 10 | 52.6 | ||
Marital status | Married | 513 | 87.2 | 180 | 35.1 | 0.83 (0.842) |
Single | 14 | 2.4 | 5 | 35.7 | ||
Divorced | 54 | 9.2 | 22 | 40.7 | ||
Widow/Widower | 7 | 1.2 | 2 | 28.6 | ||
Education Level | No Education | 32 | 5.5 | 10 | 31.3 | 4.0 (0.262) |
Primary | 203 | 34.6 | 66 | 32.5 | ||
Secondary | 302 | 51.6 | 120 | 39.7 | ||
Tertiary | 50 | 8.5 | 15 | 30 | ||
Location | Rural | 267 | 47.7 | 86 | 32.2 | 1.75 (0.186) |
Urban | 293 | 52.3 | 110 | 37.5 | ||
Employment1 | Employed | 257 | 43.7 | 105 | 40.9 | 4.9 (0.027*) |
Not employed | 331 | 56.3 | 106 | 32 | ||
Drug use (Smoking/Alcohol) | Not active | 379 | 64.3 | 137 | 36.2 | 0.05 (0.825) |
Active | 210 | 35.7 | 74 | 35.2 | ||
Maternal Characteristics | ||||||
ANC Attendance | < 3 times | 55 | 9.3 | 21 | 38.2 | 0.15 (0.702) |
\(\ge\)3 times | 534 | 90.7 | 190 | 35.6 | ||
Mode of delivery | Normal delivery | 346 | 60.1 | 114 | 33 | 3.76 (0.053) |
Caesarian section | 230 | 39.9 | 94 | 40.9 | ||
Parity | < 4 children | 539 | 92.4 | 185 | 34.3 | 4.37 (0.037*) |
\(\ge\)4 children | 44 | 7.6 | 22 | 50 | ||
History of abortion | Aborted | 60 | 10.2 | 27 | 45 | 2.45 (0.118) |
Never aborted | 529 | 89.8 | 184 | 34.7 | ||
Inter-pregnancy interval (in months) | < 24 months | 66 | 11.2 | 27 | 40.9 | 0.86 (0.355) |
\(\ge\)24 months | 524 | 88.8 | 184 | 35.1 | ||
Low birth weight baby | Yes (< 2500g) | 37 | 6.3 | 26 | 70.3 | 20.46 (< 0.001*) |
No (\(\ge\)2500g) | 553 | 93.7 | 185 | 33.5 | ||
HIV status | Sero-positive | 80 | 13.9 | 32 | 40 | 0.69 (0.407) |
Sero-negative | 497 | 86.1 | 175 | 35.2 | ||
Comorbidity | Present | 41 | 7.1 | 10 | 24.4 | 2.62 (0.105) |
Absent | 538 | 92.9 | 199 | 37 | ||
Anemia/Level of hemoglobin | < 10g/dl | 13 | 2.3 | 4 | 30.8 | 0.17 (0.678) |
\(\ge\)10g/dl | 561 | 97.7 | 204 | 36.4 | ||
Preeclampsia | Yes | 134 | 22.8 | 92 | 68.7 | 81.34 (< 0.001*) |
No | 455 | 77.2 | 119 | 26.2 | ||
Previous preterm | Yes | 63 | 10.8 | 33 | 52.4 | 8.66 (0.003*) |
No | 521 | 89.2 | 175 | 33.6 | ||
Fetal Characteristics | ||||||
Sex of child | Male | 259 | 43.9 | 94 | 36.3 | 6.98 (0.031*) |
Female | 320 | 54.2 | 109 | 34.1 | ||
Both Male and Female2 | 11 | 1.9 | 8 | 72.7 | ||
Pregnancy outcome | Singleton | 570 | 96.6 | 197 | 34.6 | 10.56 (< 0.001*) |
Twins | 20 | 3.4 | 14 | 70 | ||
Congenital abnormalities | Yes | 19 | 3.4 | 8 | 42.1 | 0.35 (0.557) |
No | 546 | 96.6 | 194 | 35.5 | ||
Table 1 also provides an overview of variations in preterm birth across different socio-demographic, maternal, and fetal characteristics. The table demonstrates that several factors are significantly linked to preterm birth, including maternal employment, parity, low birth weight babies, preeclampsia, previous preterm births, the sex of the child, and pregnancy outcome. Differences in preterm birth based on socio-demographic characteristics reveal that maternal employment (chi-square = 4.90, p = 0.027) is notably associated with preterm birth.
An analysis of maternal characteristics demonstrates significant associations with preterm birth. These include parity (chi-square = 4.37, p = 0.037), low birth weight baby (chi-square = 20.46, p < 0.001), preeclampsia (chi-square = 81.34, p < 0.001), and previous preterm births (chi-square = 8.66, p = 0.003).
Furthermore, an analysis of fetal characteristics indicates that the sex of the child (chi-square = 6.98, p = 0.031) and pregnancy outcome (chi-square = 10.56, p < 0.001) are also significantly associated with preterm birth.
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Multivariate analysis
The factors that were found to be statistically and significantly associated with preterm birth during the bivariate analysis were further examined in multivariate logistic regression. These factors were; employment, parity, low birth weight baby, preeclampsia, previous preterm births, pregnancy outcomes, and the sex of the child. In Table 2, it is evident that the factors indicating a significant association with preterm birth were: maternal employment, the presence of a low birth weight baby, and the occurrence of preeclampsia. The results from the logistic regression analysis indicated the predictive potential of certain socio-demographic factors in relation to preterm birth. Specifically, the analysis indicated that maternal employment played a role in predicting preterm birth. Accordingly, mothers who were not employed exhibited a roughly 34% reduced odds of giving birth to a preterm baby when compared to those who were employed (AOR = 0.657, p = 0.037, 95% CI: 0.443–0.975).
Table 2
Multivariate Analysis of factors associated with preterm birth
Variable | Logistic regression Model | |||
|---|---|---|---|---|
AOR | p–value | [95% CI] | ||
Socio–demographic Characteristics | ||||
Employment | Employed | 1.000 | . | . |
Not employed | 0.657 | 0.037** | 0.443–0.975 | |
Maternal Characteristics | ||||
Mode of delivery | Normal delivery | 1.000 | . | . |
Caesarian section | 1.026 | 0.902 | 0.681–1.545 | |
Parity | < 4 children | 1.000 | . | . |
\(\ge\)4 children | 1.708 | 0.150 | 0.823–3.542 | |
Low birth weight baby | Yes (< 2500g) | 1.000 | . | . |
No (\(\ge\)2500g) | 0.228 | < 0.001*** | 0.099–0.527 | |
Preeclampsia | Yes | 1.000 | . | . |
No | 0.142 | < 0.001*** | 0.088–0.229 | |
Previous preterm | Yes | 1.000 | . | . |
No | 0.588 | 0.110 | 0.307–1.128 | |
Fetal Characteristics | ||||
Sex of child | Male | 1.000 | . | . |
Female | 0.704 | 0.089* | 0.470–1.055 | |
Both male and female | 1.640 | 0.639 | 0.207–13.007 | |
Pregnancy outcome | Single tone | 1.000 | . | . |
Twins | 3.825 | 0.090* | 0.810–18.060 | |
Constant | 21.488 | < 0.001*** | 7.278–64.449 | |
Examining maternal factors, the results revealed significant associations between preterm birth and having a baby with low birth weight and experiencing preeclampsia. In this regard, mothers who gave birth to babies weighing 2500 g or more displayed a nearly 77% reduced odds of having a preterm birth, in comparison to those who gave birth to babies weighing less than 2500 g (AOR = 0.228, p < 0.001, 95% CI: 0.099–0.527). Furthermore, mothers without preeclampsia exhibited an approximately 86% reduced odds of experiencing preterm birth when contrasted with mothers who had preeclampsia (AOR = 0.142, p < 0.001, 95% CI: 0.088–0.229).
However, fetal factors such as the sex of the child, pregnancy outcome, and the presence of a congenitally abnormal baby were not found to have a statistically significant association with preterm birth.
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Discussions
Prevalence of preterm birth
The prevalence of preterm birth among mothers who delivered in LRRH was 35.8%. This indicated high prevalence compared to 13.6% in Mulago National Referral Hospital, Uganda [4], 18.3% in Nairobi, Kenya [8], 17.5% in a rural district hospital, Rwanda [9], 24.4% in Kilimanjaro Christian Medical Centre, Northern Tanzania [10], 25.9% in Jimma University Specialized Teaching & Referral Hospital, South–West Ethiopia [11], 24.3% in Mansoura University Hospital, Egypt [12], and 12.3% in Fafen Zone, Somalia area, Eastern Ethiopia [13]. The high prevalence of preterm birth in LRRH could be due to poor quality of antenatal care. This is a result of inadequate equipment such as antenatal ultrasound machines to identify fetal antenatal conditions and skills to manage these diagnoses, inadequate skills for the early identification of mothers at risk of preterm birth for timely management, inadequate multiple micronutrient supplementation, poor referral systems, inadequate and poor health infrastructures, inadequate health supplies, and health system structural factors. These technical, interpersonal, resource, and infrastructural factors impede the provision and experience of good quality maternity care at health facilities [6].
Socio–demographic factors
The study anticipated that socio-demographic characteristics, such as age, BMI, marital status, education level, location, employment, and drug use (smoking/alcohol), would not exhibit a significant association with preterm birth among mothers delivered at LRRH. However, the findings revealed that employment emerged as a noteworthy predictor of preterm birth. According to the results, mothers who were unemployed demonstrated a significant difference (AOR = 0.657, p = 0.037, 95% CI: 0.443–0.975), being 0.657 times less likely to deliver preterm babies compared to those who were employed. This outcome aligns with studies conducted in Indonesia, which identified that working mothers faced a 16.2 times higher risk of delivering late preterm infants (LPI) in comparison to housewives (OR = 16.2; 95% CI: 2.315-123.444) [14]; in Mulago Hospital, Uganda, being unemployed (AOR = 0.36, 95%CI: 0.15–0.86, p = 0.021) was associated with a 64% reduction in the likelihood of experiencing preterm birth [4]. Similarly, in Cyprus, long working hours (OR: 3.77, 95% CI: 2.08–6.84) were about 4 times linked to preterm birth [15]. At Mansoura University Hospital, Egypt, increased risks were also observed between long working hours and temporary contracts, and the risk of preterm delivery (AOR = 2.36, CI: 1.18–7.78) and (AOR = 1.98, CI: 1.72–8.74) respectively [12]. However, the findings of this study are not corroborated by research conducted in Nigeria, which revealed that maternal occupation did not significantly affect gestational age (χ²=10.143, p = 0.428) and birth weight (χ²=16.807, p = 0.079) at delivery. Nevertheless, it did significantly affect stillbirth rates (χ²=28.134, p = 0.002) [16]. Furthermore, the nature of employment might impact a pregnant woman differently, depending on whether it involves manual or labor-intensive work. Similarly, a heavy workload could subject an expectant mother to stress, potentially leading to pregnancy complications and resulting in preterm delivery.
Maternal factors
The study hypothesized that antenatal care (ANC) visits, mode of delivery, parity, history of abortion, inter–pregnancy interval, mothers with low birth weight babies, HIV status, comorbidity, anemia, preeclampsia, and previous preterm births are not significantly associated with preterm birth. However, this study found that mothers with low birth weight babies and preeclampsia were statistically significant predictors of preterm birth. The statistical analysis indicated that a mother with a low birth weight baby was a statistically significant predictor of preterm birth (AOR = 0.228, p < 0.001, 95% CI: 0.099–0.527). Accordingly, mothers who gave birth to babies with a weight of ≥2500 g were 0.228 times less likely to experience preterm birth than those who gave birth to babies weighing < 2500 g. This finding aligns with studies conducted at Shire Suhul General Hospital in Northern Ethiopia. Those studies discovered that mothers with a history of bearing neonates weighing less than 2500 g, including the most recent birth (AOR: 2.78, 95% CI: 1.39–5.55), were 2.8 times more likely to have preterm deliveries compared to their counterparts [17]. Similarly, a study at Jimma University Specialized Teaching and Referral Hospital in South West Ethiopia demonstrated that a history of low birth weight (OR = 0.085, CI: 0.04–0.18, p < 0.001) increased the likelihood of preterm delivery by a factor of 0.085 compared to those without a history of poor birth outcomes [11]. Further studies conducted in Abu Dhabi, United Arab Emirates, revealed that low birth weight babies (AOR = 17.62, CI: 11.05–28.10, p < 0.001) were nearly 18 times more likely to experience preterm births than their counterparts [5]. Additionally, in Public Hospitals of Fafen Zone, Somali Region, Eastern Ethiopia, newborns with birth weights less than 2500 g (AOR = 3.78, 95% CI: 1.55–9.84, p < 0.001) had a 3.78 times higher likelihood of being delivered preterm than babies with birth weights of ≥2500 g [13]. These findings could be attributed to various factors, including intrauterine growth restriction resulting from genetic factors and uterine infections, inadequate prenatal nutrition, chronic health conditions like diabetes, heart problems, and high blood pressure, placental issues, or maternal infections preventing proper oxygen and nutrient delivery to the fetus. Such conditions may contribute to the occurrence of preterm births and low birth weight babies [13, 17].
Additionally, preeclampsia was a statistically significant predictor of preterm birth (AOR = 0.254, p < 0.001, 95% CI: 0.185–0.348). This implies that mothers without preeclampsia were 0.254 times less likely to experience preterm birth than those with preeclampsia. This result is consistent with studies conducted at Mukalla Maternity and Childhood (MCH) Hospital in Yemen, which revealed that mothers with pre-eclampsia (AOR = 4.120; CI: 1.818–9.340, p < 0.001) were 4.12 times more likely to deliver preterm babies than those without [18]. Similarly, at Mulago Hospital in Uganda, mothers with preeclampsia (AOR = 16.24, 95% CI: 3.11–84.70, p < 0.001) were 16 times more likely to have a preterm birth than mothers without preeclampsia [4]. In Nanjing Maternity and Child Health Care Hospital, China, the odds of preterm birth among mothers with preeclampsia were 2.46 times higher (AOR = 2.46, 95% CI: 1.78–3.40, p < 0.001) than for mothers without the condition [19]. Likewise, in a regional hospital in Accra, Ghana, mothers with pre-eclampsia/eclampsia (AOR = 3.4, 95% CI: 1.0-11.9, p < 0.001) were 3.4 times more likely to experience preterm delivery than those without preeclampsia [20]. Preeclampsia may result from pregnancy complications characterized by high blood pressure, poor nutrition, high body fat, insufficient blood flow to the uterus, genetic factors, and signs of damage to other organ systems, most frequently affecting the liver and kidneys.
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Fetal factors
The study also hypothesized that fetal factors (such as the sex of the child, pregnancy outcome, and congenital abnormalities status) are not associated with preterm birth. However, this study revealed that no fetal factors were associated with preterm birth, which contradicts studies conducted in Public Hospitals of the central zone, Tigray, Ethiopia. These previous studies showed that neonates with congenital/birth defects (AOR = 3.19, 95% CI: 1.22, 8.34, p < 0.05) were three times more likely than those without any birth defects to experience preterm birth [21]. Similarly, research conducted at Shire Suhul General Hospital in Northern Ethiopia found that visible physical neonatal congenital anomalies in the most recent baby (AOR = 10.4; 95% CI: 1.66–65.2, p < 0.05) increased the odds of preterm birth occurrence compared to normal babies [17]. This discrepancy could be attributed to the interaction of genetic and environmental risk factors contributing to preterm delivery in Ethiopia, which may not be a prevalent issue in Uganda, especially in Lira District [21].
Conclusions
This study examined the occurrence of preterm birth (PTB) and its related determinants within the context of deliveries at LRRH. The prevalence of preterm birth among mothers who gave birth at LRRH was documented at 35.8%. Specifically, the likelihood of preterm delivery was found to be lower among unemployed mothers in comparison to their employed counterparts. Similarly, mothers whose babies had a birth weight of less than 2500 g were observed to have a higher probability of delivering preterm compared to those whose babies had a normal birth weight. Conversely, the presence of preeclampsia was associated with a greater probability of preterm birth, whereas mothers without preeclampsia exhibited a reduced likelihood of delivering preterm babies.
Recommendations
Basing on the key findings from the study, the followings recommendations were made:
1.
To address the elevated occurrence of preterm birth within the LRRH and Lango sub-region, it is imperative for the Ministry of Health to evaluate the existing state of the healthcare system’s readiness to handle preterm births. This involves devising comprehensive plans and making necessary preparations, particularly in terms of procuring essential equipment. Furthermore, conducting training and mentorship programs aimed at imparting vital managerial and clinical proficiencies is essential. These programs are designed to empower frontline healthcare providers in promptly identifying and effectively managing expecting mothers who face a heightened risk of preterm birth. A crucial aspect of this initiative involves bolstering the referral system, ensuring a seamless pathway for timely and appropriate care. Concurrently, elevating the quality of antenatal care necessitates the implementation of routine antenatal ultrasounds. This step aims to enhance the identification of fetal antenatal conditions, contributing to improved care. Additionally, providing multiple micronutrient supplementation to pregnant mothers at risk of preterm birth emerges as a fundamental strategy to augment the overall quality of antenatal care.
2.
Expectant employed mothers within the area must receive safeguarding from the implementation of the Employment Act and Labor Laws by the Ministry of Gender, Labour, and Social Development. This can be achieved by deploying Labor Officers, a measure aimed at addressing the concern of premature childbirth among working mothers.
3.
To effectively reduce the occurrence of preterm birth and its associated consequences among mothers with preeclampsia and low birth weight infants in the LRRH and the broader Lango sub-region, it is crucial for healthcare professionals to recognize expectant mothers who are susceptible to delivering prematurely. This should be followed by the implementation of high-quality medical interventions, community-based health education, as well as informative awareness initiatives.
Suggestions for further research
The study emphasized the necessity for additional quantitative and qualitative investigation into the factors contributing to preterm birth. The comprehensive information gathered from maternal participants and other relevant sources will enable the identification of unexplored variables, thus influencing the development of treatments and policy strategies.
Acknowledgements
We acknowledge support from Lira Regional Referral Hospital to allow us extract the data set.
Declarations
Ethical approval and consent to participate
This study employed the Research Ethics Committee of the School of Statistics and Planning at Makerere University to obtain endorsement and oversee the research process in accordance with both the regulations of the Uganda National Council for Science and Technology (UNCST) and other global research standards related to the involvement of human subjects. The study utilized aggregated data from a hospital, for which official institutional authorization was secured to carry out the research. Additionally, due to the absence of data elements that could potentially reveal the identities of patients, informed consent was not actively pursued. All physical copies of the data were securely stored and accessible solely to the members of the research team. Likewise, electronic databases were shielded with passwords, and access to these passwords was limited to the research team exclusively. Finally, a request for a waiver of consent was submitted to Lira Regional Referral Hospital, an entity responsible for safeguarding the well-being of study participants. The approval for this waiver, signifying protection for participants, was granted by the Research Coordinator of the hospital and is documented through their signature on the approval correspondence.
Consent for publication
Not applicable. However, we sought a waiver of consent to extract the data.
Competing interests
The authors declare no competing interests.
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