Research consultancy

Research consultancy

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter reviews existing literature on socio-economic and health facility-related factors influencing poor prognosis in pediatric coma cases. Sources include peer-reviewed journals and medical databases. Studies indicate that socio-economic and hospital-based factors significantly impact outcomes in non-traumatic pediatric coma, often more than previously acknowledged (Bondi et al., 2011). These factors encompass individual and family socio-economic conditions, as well as hospital resources and practices (Cullen et al., 2010).

2.2 Socio-Economic Factors Contributing to Poor Prognosis

Pediatric coma is a frequent critical care concern, particularly in children under 16, with non-traumatic cases being five times more common than in the general population (Bragatti et al., 2010). Infants under one year face the highest incidence (160 per 100,000 annually). In the U.S., infections are the leading cause of pediatric coma, with a mortality rate of 46% (Marcovitch, 2011). Traumatic brain injuries also contribute significantly, with males twice as likely as females to suffer concussions.

In Northern Nigeria, poverty correlates with higher pediatric coma prevalence (Kallela et al., 2014). Coma arises from structural brain damage, metabolic disturbances, or brainstem lesions, often due to infections, trauma, or medical conditions like hypoglycemia. Prognosis depends on etiology, coma duration, and patient age—infants under two have particularly poor outcomes (Lindsberg & Soinila, 2015).

Family support, education, and financial stability critically influence prognosis. Children with strong family support and access to medical funding fare better (Jorgensen et al., 2009). Conversely, poor nutrition, parental illiteracy, and delayed care-seeking worsen outcomes (Nathaniel et al., 2011). Cultural and religious beliefs further shape health-seeking behaviors, sometimes delaying treatment (Allison et al., 2017).

2.2.1 Age

Younger children, especially infants, face higher coma risks and poorer recovery (Dehmer et al., 2010). The SUPPORT study linked extreme ages (infancy and >70 years) to higher mortality in non-traumatic coma (Murphy et al., 2013).

2.2.2 Gender

While some studies report no gender-based differences (Kolawole et al., 2000), others suggest males are more vulnerable due to risk-prone behaviors or preferential care (Dada et al., 2009). In Uganda, male predominance (66%) did not significantly affect mortality (MoH, 2012).

2.2.3 Occupation

Parental occupation impacts care access. Self-employed parents (e.g., farmers, traders) had the highest mortality (81%), likely due to time constraints, while unemployed caregivers had better outcomes (Fenella et al., 2011).

2.2.4 Family Support

Strong family support reduces mortality (56.8% vs. 97.1% in unsupported cases) by facilitating timely care and funding (Castren et al., 2012).

2.2.5 Ignorance

Low parental education correlates with delayed treatment and higher mortality (84.1% vs. 64.8% in informed families) (Kallela et al., 2014).

2.2.6 Nutrition

Malnutrition weakens immunity and brain development, worsening outcomes (Nathaniel et al., 2011).

2.2.7 Cultural and Religious Beliefs

Though religion itself showed no significant impact, cultural beliefs (e.g., attributing illness to witchcraft) often delay medical care (Denslow et al., 2012).

2.3 Health Facility-Based Factors

2.3.1 Delayed Referrals

Late hospital admission (>6 hours post-onset) raised mortality to 76.7% (Venkatraman et al., 2011). In Uganda, 67% of cases faced treatment delays, worsening prognosis (Gillmore et al., 2011).

2.3.2 Equipment and Resources

Hospitals lacking EEG or ICU facilities struggle with diagnoses and care, increasing mortality (Sinclair et al., 2009). Rural facilities in sub-Saharan Africa often lack essential tools (Storvik-Sydänmaa et al., 2013).

2.3.3 Healthcare Worker Expertise

Early recognition and intervention improve survival (Malik et al., 2002). Pediatric-specific training is vital due to physiological differences (Salonen, 2009).

2.3.4 Diagnostic and Treatment Delays

Mortality rose to 85.4% when diagnoses took >24 hours (Lynn et al., 2012). Patients without diagnostic tests faced 100% mortality (Kallela et al., 2014).

2.3.5 ICU Limitations

Despite higher ICU mortality (83.3%), limited beds and costs restrict access in low-resource settings (Osuntokun et al., 2007).

Key Improvements:

1. Logical Flow: Organized by themes (socio-economic → facility factors) with clear subheadings.
2. Conciseness: Removed redundancies while retaining key data.
3. Clarity: Simplified complex sentences (e.g., “Family support had a strong influence…” → “Strong family support reduces mortality…”).
4. Active Voice: “Studies indicate” instead of “Literature was acquired.”
5. Consistency: Uniform formatting for studies (e.g., “In Northern Nigeria…” vs. original variations).