Risk Factors Associated with Cerebral Malaria among Children Aged Ten and Below in Selected Hospitals within Zaria Metropolis, Kaduna State, Nigeria

INRODUCTION

Malaria continues to pose a significant public health challenge, with sub-Saharan Africa responsible for about 94% of all malaria-related fatalities [1]. Plasmodium falciparum, the most lethal malaria parasite, is chiefly accountable for cerebral malaria (CM), a neurological condition marked by coma, convulsions, and elevated fatality rates in children under five [2]. Severe malaria is a critical infectious emergency mostly associated with Plasmodium falciparum, while cerebral malaria is prevalent and significantly linked to mortality and morbidity in children. Plasmodium falciparum is the sole species recognized for its direct impact on the central nervous system, resulting in neurological impairments and cognitive sequelae, so rendering cerebral malaria the most lethal form of severe malaria [3]. An estimated 20-50% of the millions of affected youngsters acquire cerebral malaria (CM), with the majority of fatalities occurring within the first 24 hours of hospitalization [4]. CM encompasses the clinical signs of P. falciparum malaria that result in alterations in mental status, culminating in coma and profound unconsciousness [5]. The pathophysiology of cerebral malaria (CM) is intricate, encompassing the sequestration of Plasmodium falciparum in cerebral microvasculature, inflammation, endothelial activation, and the breakdown of the blood-brain barrier [6]. The dynamic coexistence and interactions among these three systems elucidate the intricacy of this potentially lethal illness. Nonetheless, none of these pathways independently elucidates the pathophysiology of human cerebral malaria [7]; [8]. Notwithstanding progress in malaria management, cerebral malaria (CM) continues to be a predominant cause of paediatric encephalopathy, exhibiting fatality rates as high as 20% and resulting in long-term neurocognitive deficits among survivors [9]. Risk factors that may elevate a child’s likelihood of developing cerebral malaria (CM) include age, geographical location, prior exposure to malaria, malnutrition, delayed diagnosis and treatment, inadequate access to healthcare, non-utilization of bed nets, and travel history, among others [10]. For the purposes of this study, these factors are categorized into: Socio-Demographic, Household-related, and Patient-related factors. Socio-demographic aspects refer to the social and demographic attributes of a population or group, encompassing age and gender. These variables include many socioeconomic and demographic characteristics that affect susceptibility and severity of the disease, hence influencing health outcomes, including the likelihood of acquiring CM. Socio-demographic factors significantly influence the risk profile for childhood CM [11]. Household-related factors are a significant component of the risk factors for childhood CM. These variables encompass factors in the local living environment that can directly or indirectly affect the likelihood of contracting the disease. These domestic factors collectively influence the risk profile for CM. Implementing targeted interventions, such as the removal of stagnant water sources, the promotion of insecticide-treated net (ITN) usage, and the consideration of habitation type in malaria control programs, can substantially reduce the risk for children in endemic regions [12]. Factors connected to the patient significantly influence the susceptibility and severity of CM in paediatric populations. These personal traits and actions can profoundly affect the progression of the disease and the probability of negative outcomes. Addressing these patient-related factors necessitates a comprehensive strategy, encompassing community education on malaria symptoms, enhancing accessibility to healthcare facilities, and ensuring that healthcare providers are proficient in making accurate diagnoses and administering timely antimalarial treatments [13; [14]. Comprehending these risk variables is essential for formulating effective preventive interventions. This study seeks to assess the prevalence and risk variables linked to CM in children under 10 years of age in designated hospitals within Zaria city.

MATERIALS AND METHODS

Study Area

The research was carried out in four designated hospitals located in Zaria town, Kaduna State, Nigeria.  The hospitals comprise Hospital A, Hospital B, Hospital C, and Hospital D, which are healthcare facilities located within the metropolis.  Zaria is endemic for malaria, with Plasmodium falciparum as the primary species. The region exhibits a tropical climate that facilitates mosquito proliferation, hence elevating malaria transmission rates.

Study Population

The study focused on children aged 10 years with severe malaria symptoms.  A selective sampling strategy was employed to pick 240 participants from four hospitals, based on malaria burden data, specifically targeting children with parasite densities over 5000 parasites per microlitre of blood, who were defined as having severe malaria, a precursor to cerebral malaria [15].

Criteria for Inclusion 

1. Children aged 10 years and below diagnosed with P. falciparum infection.
2. Patients without a history of neurological problems not associated with malaria.
3. Parental or legal guardian consent. 

Criteria for Exclusion 

1. Children with alternative verified etiologies of coma (e.g., meningitis, encephalitis).
2. Patients receiving antecedent antimalarial therapy before hospital admission

Sampling Methods 

A purposive sample method was employed to identify children who fulfilled the study criteria.  The sample size was calculated utilizing Cochran’s technique, predicated on a prior prevalence estimate of 19.8% [16].  The determined sample size was 240; however, to accommodate incomplete responses, 260 questionnaires were sent.

 Study Sample Size 

The study’s sample size was determined based on a prevalence of 19.8% as reported by [16], utilizing the formula established by [17].

Where;

n          =         Z² P (1 − P)

      E²

n          =        desired sample size

E          =          margin of error (0.05 for 5%)

p          =          estimated prevalence 19.8% (0.194) as prevalence for CM according to [16]

Z          =          is the Z-score for 95% confidence level (approximately 1.96)

n          =          Desired Sample size

n          =          1.962 .0.194 (1 − 0.194)

                                   0.052

n          =          240.275

n          ≈          240

Ethical Considerations

Ethical approval was secured by the Kaduna State Ministry of Health (NHREC/17/03/2018) before to the initiation of the study.  Parental or guardian consent was also secured for all study participants. 

Study Design

This was a cross-sectional, hospital-based study conducted from March to December 2024. This entails the acquisition of clinical data, blood specimens, and coma scale evaluations.  The Blantyre Coma Scale assessed awareness levels, while laboratory testing evaluated parasite density and confirmed the presence of P. falciparum [18]. 

Data Collection 

Data were gathered by a standardized questionnaire, clinical evaluations, and laboratory analyses. 

Administration of the Questionnaire.

A validated questionnaire was employed to gather demographic, household, and patient-related data. The questionnaire was conducted in English and translated into local dialects as needed.

 Clinical Assessment Using Blantyre Coma Scale (BCS) 

The Blantyre Coma Scale was employed to evaluate consciousness levels, serving as a precursor to the severity of cerebral malaria, with a score below 3 deemed indicative of the condition [19]. The temperature was measured with a digital thermometer.

Laboratory Analyses 

1. Malaria Diagnosis: Thick and thin blood films were produced and stained with Giemsa dye for the microscopic identification of P. falciparum.
2. Parasite Density Estimation: The quantity of parasites per microlitre (µL) of blood was determined. 
3. Complete Blood Count (CBC): Blood samples were examined to evaluate haemoglobin concentrations, leukocyte counts, and platelet counts to detect haematological anomalies linked to CM. 

Data Analysis

Data were input into IBM’s Statistical Package for Social Sciences (IBM SPSS Version 23) and examined through descriptive and inferential statistics.  Chi-square (χ²) testing was employed to identify relationships among categorical variables.  A logistic regression analysis was conducted to ascertain risk factors for CM.  Odds ratios (ORs) with 95% confidence intervals (CIs) were computed to assess the strength of connections.

RESULT

RISK FACTORS FOR CEREBRAL MALARIA 

The development and advancement of CM is influenced by a combination of socio-demographic, household, and patient-related variables. 

SOCIO-DEMOGRAPHIC VARIABLES 

Age and Gender as Socio-Demographic Variables. Cerebral malaria primarily impacts younger children, especially those between the ages of 2 and 5 years, owing to their insufficient immunity against P. falciparum  [20].

 This study found that children aged 2–5 years had the highest prevalence of CM at 11%, followed by those aged 6–10 years at 9.4%, and children under 2 years at 5%.  Despite lacking statistical significance, male children exhibited a greater prevalence (10.6%) compared to females (7.9%), aligning with other research indicating potential sex-related disparities in immunological response to malaria [21].

Socioeconomic Status and Parental Education as Socio-Demographic Factors

 Low socioeconomic status correlates with increased malaria morbidity owing to limited access to healthcare and insufficient malaria prevention strategies [22].  This study demonstrated no significant association between parental education and CM prevalence (p>0.05), corroborating the findings of [23], which indicate that environmental factors exert a more substantial influence than education level alone.

HOUSEHOLD-RELATED FACTORS

Proximity of Stagnant Water and Bushes as a Household-Related Factor  

Children residing in families adjacent to stagnant water and vegetation exhibited a markedly elevated risk of CM (OR: 4.49, 95% CI: 1.60–12.60, p=0.003).  Stagnant water acts as a breeding ground for Anopheles mosquitoes, heightening the risk of malaria transmission [24]. 

Inadequate Home Ventilation as a Household-Related Factor

 Children residing in inadequately ventilated homes exhibited a 14.2% prevalence of CM.  Poor house ventilation and infrequent sanitation were significant predictors of CM (OR: 3.33, 95% CI: 1.25–8.82). 

Utilization of Insecticide-Treated Nets (ITNs) as a Household-Related Factor 

Despite ITNs being a crucial malaria reduction technique, their utilization did not demonstrate a significant correlation with CM prevalence in this study.  This aligns with the findings of [25], which indicated that although ITNs diminish malaria incidence, they may not entirely avert severe sequelae such as CM.

PATIENT-RELATED FACTORS

Delayed Diagnosis and Healthcare-Seeking Behavior as a Patient-Related Factor

Children whose caregivers pursued treatment within 48 hours of symptom start exhibited a 13.0% prevalence of CM, whereas those whose caregivers delayed seeking medical attention beyond 48 hours had an elevated risk of CM (OR: 3.45, 95% CI: 1.30–9.16, p=0.008).  Postponed treatment permits malaria to advance to severe phases, heightening the probability of neurological sequelae [26].

Duration of Symptoms before Medical Attention as a Patient-Related Factor

The length of fever and other malaria symptoms before to hospital presentation was a crucial factor in the development of cerebral malaria (CM).  Children exhibiting symptoms for over three days before therapy faced a heightened risk of CM, underscoring the necessity for prompt care [27]. 

Travel History and Previous Malaria Episodes as a Patient-Related Factor

Children with a recent history of travel to malaria-endemic regions exhibited a greater, albeit statistically insignificant, prevalence of CM (p>0.05).  This indicates that although travel heightens the risk of malaria exposure, other factors such as immunity and timely treatment, affect the course of cerebral malaria [28].

LOGISTIC REGRESSION ANALYSIS OF RISK FACTORS ASSOCIATED WITH CEREBRAL MALARIA IN CHILDREN UNDER TEN IN ZARIA METROPOLIS. 

Logistic regression was conducted to find significant predictors of CM in children under ten at selected hospitals in Zaria city.  This logistic regression evaluates the impact of parental or guardian education, awareness of preventive measures, consistent utilization of insecticide-treated nets, presence of stagnant water and vegetation around the residence, home ventilation, frequency of sanitation practices, initiation of antimalarial treatment during the initial hospital visit, duration of symptomatic presentation before seeking medical attention, and children’s travel history on the probability of a diagnosis of cerebral malaria (CM).  The comprehensive model demonstrated statistical significance relative to the null model (χ2 (10) = 25.388, p = 0.005), accounted for 21.9% of the variance in cerebral malaria positivity (Nagelkerke R2), and accurately predicted 90.8% of the cases.  Stagnant water and shrubs surrounding the residence (p = 0.014), the frequency of sanitation observations (p = 0.009), and residing in inadequately ventilated buildings (p = 0.042) were significant predictors of children’s diagnosis with CM.  The logistic regression model created for this study finds significant risk factors linked to CM.  Although the model offers significant insights, it is crucial to recognize specific constraints, such as the relatively small sample size (n=240) and the necessity for external validation across varied populations.  Notwithstanding this restriction, our model provides a fundamental framework for comprehending CM risk factors and acts as a platform for subsequent research and clinical applications.

 The anticipated logistic regression model is expressed as:

 Log (CM) = βo + β1 X1 + β2 X2 + … + βn Xn,

Specifically Log (CM) = βo + β1 X1 + β2 X2 + β3 X3,

Where βo = intercept (-4.524) β1 = coefficient for residing in a dwelling with stagnant water or vegetation (1.336) β2 = coefficient for the absence of regular sanitation (1.334)  β3 = coefficient for residing in inadequately ventilated dwellings (1.055) 

DISCUSSIONS

For the analysis of risk factors for CM in children across the four hospitals, the prevalence of CM was similar across the hospitals, but variations in odds were observed. Children at Hospital D had a 1.59 times higher likelihood of a CM diagnosis compared to Hospital A (OR: 1.588; 95% CI: 1.528–4.779), while children at Hospital B and Hospital C had lower odds of CM diagnosis, at 0.4 and 0.64 times, respectively. These findings are similar with the study of [29], who states that these variations could be due to differences in hospital resources, diagnostic capabilities, and patient demographics, and healthcare access influencing diagnosis rates.

Socio-Demographic Risk Factors

Age was identified as a critical risk factor, with children aged 2–5 years exhibiting the highest prevalence of CM at 11%, followed by those aged 6–10 years at 9.4%, and those under 2 years at 5%.  This aligns with prior research suggesting that young infants had immature immune responses to Plasmodium falciparum, rendering them more susceptible to severe outcomes such as cerebral malaria [20].  A study conducted by [30] in Lagos revealed a comparable age distribution, with children under 5 years representing the majority, indicative of their heightened susceptibility to infectious diseases. [31] revealed that children aged 2 to 5 years were the most often hospitalized for malaria in southwestern Nigeria.  Male children exhibited a marginally higher prevalence of CM (10.6%) compared to females (7.9%); however, this disparity lacked statistical significance.  Some research indicates that sex-based immunological variations may influence susceptibility to malaria [21].

Household and Environmental Risk Factors

Environmental conditions significantly influenced CM risk. Children living in households surrounded by stagnant water and bushes had a 4.14 times higher likelihood of developing CM compared to those in cleaner environments (p=0.007). Stagnant water serves as a breeding site for Anopheles mosquitoes, increasing malaria transmission rates [1]. Additionally, poor home ventilation and lack of frequent sanitation were associated with 3.33 times (p=0.017) and 3.45 times (p=0.011) increased CM risk, respectively. These findings highlight the need for vector control strategies, improved household sanitation, and community education on malaria prevention. However, insecticide-treated net (ITN) usage did not show a significant association with CM prevalence. This is consistent with [25], who reported that while ITNs reduce malaria incidence, they may not fully prevent severe complications like CM. It is possible that improper ITN use, low durability, or mosquito resistance to insecticides in the study area contributed to this finding.

Patient-Related Factors and Healthcare-Seeking Behavior

Delayed healthcare-seeking behavior was a critical predictor of CM. Children who received medical attention more than 48 hours after symptom onset had a 3.38 times higher risk of developing CM (p=0.014). This delay often results in disease progression from uncomplicated malaria to severe forms, emphasizing the need for early diagnosis and treatment to prevent neurological complications [26]. Furthermore, fever duration before medical consultation was a significant factor. Children who experienced fever for more than three days before hospital presentation had a 3.87 times higher CM risk (p=0.009). Prolonged parasitemia increases the likelihood of endothelial dysfunction, microvascular obstruction, and blood-brain barrier disruption, key mechanisms in CM pathogenesis [32]. Although a history of travel to malaria-endemic areas was associated with a higher CM prevalence, the relationship was not statistically significant (p>0.05). This may be due to protective factors such as prophylactic antimalarial use among travelers, warranting further investigation.

For gender, age and education level of parents/guardians as a Socio-Demographic Factor: Male children had a higher risk of CM (OR: 1.390; 95% CI: 0.576–3.353) with a prevalence of 10.6%, compared to 7.9% in females. However, the difference was not statistically significant and the highest prevalence for age category was in children aged 2–5 years (11.0%), followed by those aged 6–10 years (9.4%), and the lowest prevalence in children under 2 years (5.0%). Children aged 2–5 years had the highest odds of CM (OR: 2.354; 95% CI: 1.650–8.528). There was no significant association between the education level of parents/guardians and CM prevalence, though the prevalence was slightly higher in children whose parents had average education (9.5%) compared to other education levels. Children who did not always use ITNs had a higher prevalence of CM (11.3%) compared to those who consistently used them (5.0%), but the association was not statistically significant (OR: 2.408; 95% CI: 0.787–7.372). There was a statistically significant association between the presence of stagnant water and bushes around residences and CM prevalence (p < 0.05). Children in such environments had a prevalence of 15.3% (OR: 4.485; 95% CI: 1.597–12.595), compared to 3.9% in those with cleaner surroundings. Poor home ventilation (OR: 3.326; 95% CI: 1.254–8.824) and lack of regular sanitation (OR: 3.453; 95% CI: 1.301–9.160) also significantly increased CM prevalence for household-related factors. A significant association was found between delayed medical attention and CM prevalence (p < 0.05). Children seeking care after two days of symptoms had a higher prevalence (13.0%) than those who sought care earlier (5.1%). For antimalarial treatment during the initial visit to the health facility, children who did not receive antimalarial treatment during their initial visit had higher CM prevalence (10.9%) compared to those treated during their initial visit (7.2%). Although not statistically significant. Also, There was no significant association between travel history and CM prevalence, with similar rates in children with (9.3%) and without (9.1%) travel history. These findings are comparable with some existing literature and also highlight the importance of addressing environmental conditions, promoting regular sanitation, ensuring consistent ITN use, and encouraging prompt medical care to reduce CM risk in children.

Implications for Public Health and Future Research

The pressing necessity for focused malaria control interventions in Zaria city.  Priority should be given to community-based measures, including early malaria diagnosis, timely treatment, household vector control, and enhanced sanitation.  Furthermore, awareness initiatives highlighting the significance of prompt medical consultation may mitigate CM-related mortality and long-term neurological deficits.  Future study necessitates bigger sample sizes and multi-center validation to improve the predictive accuracy and generalizability of the identified risk factors.  Moreover, a longitudinal study into genetic susceptibility, host immune responses, and parasite virulence factors could provide deeper insights into CM pathophysiology.

CONCLUSIONS

This study highlights the complex interplay of socio-demographic, household, and patient-related factors in CM development among children under ten years in Zaria metropolis. While male gender and young age were associated with higher CM risk, significant predictors included proximity to stagnant water, poor home sanitation, and delayed healthcare-seeking behavior.

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