Incidence and Risk Factors for Low Birthweight and Preterm Birth in Post-Conflict Northern Uganda: A Community-Based
Beatrice Odongkara1,2,3, Victoria Nankabirwa4, Grace Ndeezi3
1Department of Paediatrics and Child Health, Faculty of Medicine, Gulu University, Gulu P.O. Box 166, Uganda.
International Journal of Environmental Research and Public Health
|October 14, 2022
Summary
In Uganda, 7.3% of infants had low birthweight (LBW) and 5.0% were born preterm. Maternal malaria, HIV, and older age increased risks, while education and malaria treatment reduced them.
Area of Science:
- Public Health
- Epidemiology
- Maternal and Child Health
Background:
- Globally, millions of low birthweight (LBW) and preterm births (PB) occur annually, with significant data gaps from low-income countries.
- LBW and PB are leading causes of neonatal and under-five mortality, necessitating localized data.
- This study addresses the need for data on LBW and PB incidence and risk factors in Northern Uganda.
Purpose of the Study:
- To determine the incidence of LBW and preterm birth in Lira district, Northern Uganda.
- To identify key risk factors associated with LBW and preterm birth in this population.
- To inform targeted interventions for improving infant health outcomes in the region.
Main Methods:
- A community-based cohort study was conducted within a cluster-randomized trial.
- 1877 pregnant women were recruited and followed from 28 weeks gestation.
- Birthweight and preterm status (using New Ballard Score) were assessed for 1556 and 1279 infants, respectively. Risk factors were analyzed using multivariable generalized estimation equations.
Main Results:
- The incidence of LBW was 7.3% and preterm birth was 5.0%.
- Risk factors for LBW included maternal age ≥35 years, history of a small newborn, and maternal malaria. Intermittent preventive treatment for malaria reduced LBW risk.
- Maternal HIV infection increased preterm birth risk, while maternal education of ≥7 years reduced it.
Conclusions:
- Significant proportions of LBW (7.3%) and preterm births (5.0%) were observed in post-conflict Northern Uganda.
- Maternal malaria, older maternal age, and prior small newborns are risk factors for LBW, with malaria prevention showing protective effects.
- Maternal HIV infection is a risk factor for preterm birth, whereas higher maternal education is protective. Interventions should focus on education, malaria, and HIV management.
Related Concept Videos
Factors Affecting the Risk of Infection
12.2K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.2K
Prevalence and Incidence
728
In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
728
Factors Affecting Illness
4.3K
When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness,...
For instance, risk factors are connected to illness,...
4.3K
Pathophysiology of Diabetes
1.1K
Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
1.1K
Confounding in Epidemiological Studies
236
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
236
Bias in Epidemiological Studies
525
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
525


