Factors Influencing Malaria Morbidity and Mortality Among Under 15 Years Children in Parts of Ghana: The Role of Guardians’ Socio-Demographic Characteristics Demonstrated Through Modelling

Abstract

Background To understand the underlying factors that led to failure to achieve the millennium development goal 6c by Ghana, we modelled guardians’ socio-demographic characteristics and the factors that could influence child health to provide directions for improving anti-malaria interventions in Ghana and sub-Sahara Africa.

Methods Clinical data of 274 malaria patients, age up to 15 years, were collected from a cross-sectional study conducted in two districts in the central region of Ghana. Guardians of the children were interviewed to obtain socio-demographic characteristics and factors that could influence child health. The data obtained were analysed using multivariate GLM, type III ANOVA and multilayer perceptron network analysis.

Results Of the 274 children enrolled into the study, 69.34% were between the age of 0-5 years. The median parasite density, haemoglobin and platelet were 89,923 (64,056-149,541) parasites/mm3, 8.40 (4.70-11.60) g/dL and 126 (87-146) x10^9/L respectively. 12.41% of the enrolled children had severe clinical conditions. The outcome using multivariate GLM analysis indicate a significant association between the guardians’ socio-demographic characteristics and factors that influence child health, p<0.0001. CSOC, FPOCCH, DOOSCS, and CS were all influenced by the guardians’ occupational status, p<0.0001; CSOC and MCID were influenced by the guardians’ educational levels, p<0.0001 and FPOCCH and MCID were also influenced by guardians age category, p<0.0001. The multilayer perceptron network analysis showed that the association between guardians’ socio-demographic characteristics and factors that influence child health are affected by several hidden confounding factors.

Conclusion It was demonstrated an association between factors that influence child health care and guardians’ socio-demographic characteristics and this is confounded by hidden socio-economic factors and biases through modelling. I, therefore, propose a model called a network of socio-economic progenitors to provide a simple explanation for the underlying factors that influence child health in malaria-endemic areas.

Introduction

The instigation of multiple malaria interventions to reduce malaria incidence were widely accepted phenomenon to achieve the millennium development goal 6c (MDGs) between 2000 and 2015 [1]. However, Ghana like many sub-Sahara African countries did not make any significant improvement [2, 3]. The implementation of malaria control program has not seen any significant translation to the reduction in malaria prevalence and child mortality, especially in Sub-Saharan Africa [4, 5]. The World Health Organization (WHO) estimates that 90% of all global malaria mortality occurs in Africa of which 75% of the mortality cases are associated with children under five years [6, 7]. Ghana, like most of the sub-Sahara African countries, has a slow reduction in malaria after conscious effects of the implementation of malaria interventions [8]. In 2013 alone, Ghana recorded 11.3 million cases with an average of 30,300 malaria cases per day reported in the outpatient departments (OPD) of the health facilities [9].

The empirical variations of healthcare interventions among a set of the population have given rise to many health-associated behavioural studies across the world [10]. These studies are conducted to explicitly identify factors associated with health and health-seeking behaviours in a given community to provide patterns for the variations in healthcare interventions [10, 11]. The results of such studies provide the baseline data for evaluation of interventions such as the use of insecticide-treated bed nets, and indoor residual spraying programs [12, 13]. However, no comprehensive direction to consolidate the running intervention programs are suggested by these studies to mitigate the health risks within a given community. Although there are interesting associations between social, cultural, and economic factors as well as disease patterns and issues related to health services and community health, yet these results have not shown to have increased the efficiency of integrated malaria control programs in reducing malaria morbidity and mortality especially among children in Ghana [14, 15].

Malaria is a treatable disease with highly efficacious artemisinin-based combination therapies (ACTs) which has an absolute cure rate [16]. Therefore, malaria infection is not expected to cause a death toll on children especially among those under five years. Delayed diagnosis and treatment have negative impacts on malaria morbidity and mortality [17]. The desire to halt and reverse malaria prevalence has fallen short due to the unrealized complexity associated with malaria morbidity and mortality. The ability of individuals to identify the classical symptoms of malaria is an important stimulus for voluntary and conscious effort for selecting and pursuing a prompt treatment of malaria. Unambiguously, malaria mortality is associated with a problem for recognising the symptoms and making a prompt decision to select the appropriate treatment for it [18]. The decision to seek treatment for a sick child as shown in literature is influenced by socio-demographic characteristics, socio-cultural and economic factors [19, 20]. To understand these inherent factors that influence guardians’ health-seeking behaviours that impact on the child health, we modelled the guardians’ socio-demographic characteristics and the factors that influence child health to provide direction for improvement of malaria interventions and to reduce malaria child mortality in Ghana.

Methods

Study sites

A cross-sectional study was conducted in two districts in the Central region of Ghana. The Central region occupies an area of 9826 square kilometres with an estimated population of 2,107,209. The region which is bordered by Ashanti, Eastern, Greater Accra and the Atlantic Ocean is the second-most densely populated after Greater Accra region in Ghana. Samples were collected from two municipal and two district hospitals in the Region: Assin North and Twifo Hemang Lower Denkyira districts (figure 1). Both districts lie at semi-equatorial climate zone with annual rainfall ranging between 1750 and 2000mm which peaks in June and October. These coincide with the highest malaria prevalence in the year, although the districts are holoendemic. 

The rationale of the study

The purpose of the study was to use real-time field data to model guardians’ socio-demographic characteristics and the factors that influence child’s clinical condition (child health) in malaria-endemic communities that have adopted all the national malaria interventions yet without significant improvement in malaria incidence especially among children under-five years.

Sampling and data collection

Children between the age 0-15 years who visited the Twifo Praso and St. Francis Xavier health facilities at Twifo Hemang Lower Denkyira and Assin North districts respectively, who were diagnosed with malaria were enrolled into the study. Clinical data of the children were extracted from the hospital records. The guardians were interviewed using a standardised questionnaire to obtain guardians’ socio-demographic characteristics which include Occupational status of Guardian (OCSG), Educational level of Guardian (EDULG), Sex of Guardian (SOG) and Age category of Guardian (AGECOG). other factors which could have influenced the clinical condition of the children such as Clinical severity of the child (CSOC), Days of on-set of symptoms before hospital visit (DOOSCS), the first point of call for child health (FPOCCH), Clinical symptoms (CS) and Malaria control intervention adopted by Guardian (MCID) were obtained. 

A total of 274 children who were enrolled in the study and their blood samples taken. Haemoglobin concentration (HB), Mean Cell Haemoglobin (MCH), and platelet count were obtained using Sysmex KX-21 haematology analyser. The mean values of the results were determined for each participant.

Thick blood films were also prepared, stained with Giemsa and parasitaemia estimated for each sample under a light microscope. The parasitaemia levels, haemoglobin levels and general classification of the child condition by the clinicians were used to determine clinical severity.

Data analysis

The data collected were entered using Microsoft access (MS access) 2010 and imported to SPSS statistical software version 16 (SPSS Inc.) for analysis. All data were categorised, analysed descriptively and presented as proportions. The clinical characteristics data was measured for its central tendencies and presented as medians and ranges. The effects of the guardians’ socio-demographic characteristics on the factors that influence child health were analysed using Multivariate Generalized module test (Multivariate GLM test) and Type III ANOVA statistics. The statistic significant was assumed at p<0.05. The patterns of association between guardians’ socio-demographic characteristics and the factors that influence child health were analysed using multilayer perceptron network analysis.

Results

Socio-demographic data of guardians 

A total of 274 subjects were interviewed to identify factors that influence child health in malaria-endemic districts in the Central region of Ghana. Majority of the guardians (73.4%) were above 25 years of age with 216 (78.8%) and 58 (21.2%) representing Females and males respectively. The educational level of the participants ranges from informal education to tertiary education. Participants with informal education were 52 (19.0%), middle school/JSS 106 (38.7%), secondary school/vocational education 97 (35.4%), and tertiary education 12 (4.4%). The main occupations of the study participants were trading/business 92 (33.6%), pension 9 (3.3%), farming 55 (20.1%), hairdressing/seamstress 42 (15.3%) whilst the remaining 20 (7.3%) were unemployed (Table 1).

Clinical data of children

 274 children whose Guardians were enrolled in the study were diagnosed with malaria and presented in mild to severe clinical conditions. About 190 (69.34%) of the children were within the age category 0-5 years whilst 84 (30.66%) were within 6-15 years. The median clinical presentations were; parasite density 89923 (64056-149541) parasites/mm3, haemoglobin concentration 8.40 (4.70-11.60) g/dl, and platelet count 126 (87-146) x10^9/L of blood (Table 2).

Factors influencing child health

The factors that influence child health were identified using both clinical data and interviews. The clinical condition of the children whose guardians were enrolled in the study ranges from mild clinical condition 154 (56.20%) to severe clinical condition 34 (12.41%). The main clinical symptoms of the children that were identified by the guardians included high body temperature 233 (85.04%), less activeness 153 (55.84%), loss of appetite 189 (68.98%), and excessive crying 154 (56.20%). Most of the guardians sought treatment for their children at the hospital 89 (32.5%) whilst the rest resorted to drug/chemical shops 60 (21.9%), clinic/health centres 58 (21.2%) and pharmacy shops 51 (18.6%). Majority of the guardians waited 2-3 days after clinical symptoms of their wards before seeking medical treatment 181 (66.1%). Guardians also adopted some form of malaria prevention intervention such as the usage of mosquito net, mosquito spraying, and mosquito coils (Table 3).

Effect of guardians’ socio-demographic characteristics on factors of child health

Guardians’ socio-demographic characteristics were modelled to test for its canonic effects on the factors that influence child health using multivariate GLM statistics. The multivariate GLM test showed significant association between guardians’ socio-demographic characteristics and the factors that influence child health [CSOC F (6,516) =41.059a, Wilks’ L=0.45, p<0.0001; FPOCCH F (12, 682.895) =12.541, Wilks’ L=0.59, p<0.0001; DOOSCS F (6, 516) =2.506b, Wilks’ L=0.944, p=0.021; CS F (3, 258) =36.523, Wilks’ L=0.702, p<0.0001; and MCID F (9, 628.055) =57.908, Wilks’ L=0.23, p<0.0001] (Table 4). To identify the specific guardians’ socio-demographic characteristics that have an overall effect on the factors that influence child health, the associations were further tested using type-III ANOVA statistics. The analysis indicated that CSOC (F=98.997, SS=33.224, df=2), FPOCCH (F=29.838, SS=20.028, df=4), DOOSCS (F=7.655, SS=2.569, df=2), CS (F=109.977, SS=18.455, df=1) and MCID (F=11.827, SS=5.954, df=3) were all influenced by the occupational status of the guardian, p<0.0001. On the other hand, CSOC, FPOCCH, and MCID were also influenced by educational level of guardians with p values of <0.0001, 0.076, and <0.0001 respectively. It was also revealed that FPOCCH and MCID were the only factors that were influenced by the guardian age category, p<0.0001. However, the gender of the guardians did not show any statistical significance with the factors influencing child health (Table 5).

Patterns between guardians’ socio-demographic characteristics and factors influencing child health

The Wilk’s Lambda statistics showed that there is some significant proportion of variance in the factors that influence child health which could not be explained by the guardians’ socio-demographic characteristics (Table 4). To further model the association to have some insight into the underlying patterns within the data, we employed multilayer perceptron network analysis. The model predicted strong confidence indicating a suitable pattern between guardians’ socio-demographic characteristics and the factors influencing child health (SSE=13.899, 186 (67.9%) in training, and 88 (32.1%) in testing from the total sample of 274). The relative error for scale dependents in training sets were OCSG=0.043, EDULG=0.058, SOG=0.0, and AGECOG=0.050 with only 3.8% (SSE=13.899, error=0.038). The overall misclassification of training sets was 4.3% (SSE=9.193, error=0.043) compared to testing sets with scale dependents determined OCSG=0.053, EDULG=0.047, SOG=0.0, and AGECOG=0.08. This predicts 96.2% and 95.7% accuracy for the association between guardians’ socio-demographic characteristics and factors that influencing child health respectively (Table 6). The graphical presentation of the association between guardians’ socio-demographic characteristics and the factors influencing child health is shown in figure 2. The figure 2 gives a complete insight into the underlying patterns such as biases and hidden factors (H [1:1-9]). The nature of the underlying factors on the associations either reduces the effects of the hidden factors (as shown by blue lines; synaptic weight <0) or increases the effects of the hidden factors (as shown by grey lines; synaptic weight >0). This analysis provides a potential tool for data mining or knowledge discoveries that can impact interventions aimed at improving child health in malaria-endemic areas.

Impact of Socio-economic progenitors on child health

The multilayer perceptron analysis showed that the effects of guardians’ socio-demographic characteristics on child health are influenced by some hidden factors (Figure 2). For further elucidation of the interactions, we propose a network of socio-economic progenitors that impact on child health. Understanding the elements that affect child health can serve as the foundation for the promotion of health interventions. Therefore, the awareness of socio-economic factors has become an indispensable tool to model effective interventional measures. To significantly reduced malaria prevalence among children in the malaria-endemic areas, our ability to predict and evaluate accurately the determinants that can either affect the introduction of health-related interventions negatively or positively is required. Our socio-economic progenitor network provides easy visualisation, identification, prediction and evaluation of factors that affect health-related interventions. The model predicts health-seeking behaviours, issues related to health services, social, cultural, and economic factors as the major hidden factors that affect the association between guardians’ socio-demographic characteristics and the factors influencing child health in malaria-endemic communities (figure 3).

Discussion

Identification of specific guardian’s socio-demographic characteristics that impact malaria morbidity and mortality among children is essential for the formulation, prioritization and implementation of malaria interventions in disease-endemic regions [21, 22]. The current anti-malarial interventions have the potential to eliminate malaria, however, Ghana like many other sub-Sahara African countries could not achieve a significant reduction in malaria incidence especially among children after adaptation and implementation of malaria interventions [23-26]. A syndicate of social, cultural, behavioural, religious and economic factors have been implicated to influence malaria morbidity and mortality within malaria-endemic communities [27-29]. Although these factors are well studied, the findings are yet to be translated into effective antimalarial interventions. This requires a conscientious understanding of the shrouded dynamism of malaria morbidity and mortality, especially among children. In this study, we have modelled guardian’s socio-demographic factors and factors influencing child health in malaria-endemic regions in real-time to provide a roadmap for the design of effective antimalarial interventions in Ghana.

This study showed that a sizable number of the enrolled children had poor prognosis which could have been prevented with early detection and selection of appropriate treatment regimen. Delay in pre-hospital consultation for malaria treatment in malaria-endemic countries could account for the severity of the observed morbidity and mortality [30-32]. Anti-malaria controls require early diagnosis, absolute treatment and the use of appropriate interventions. Nearly half of the participating guardian's practised self-medication (by purchasing drugs from pharmacy and drug stores or used traditional medicines) without any diagnosis. These behavioural practices by guardians have negative implications on malaria control strategies resulting in the induction of drug resistance, delay in malaria diagnosis and treatment culminating in complicated malaria conditions among the children. 

Awareness of the symptoms of a disease is an indispensable tool or knowledge for seeking appropriate treatment for a sick child. Such an action has a direct impact on the clinical presentation and the overall prognosis of a given disease. The clinical symptoms that stimulated the eagerness of the guardians to seek treatment for their children were very diverse. Interestingly, even after the detection of the disease symptoms, it took two to three days for most guardians to seek treatment for their children at different preferential sources. Although the outcome of a child’s health depends on these factors, guardians overlook the importance of these factors. However, the underlying factors which promote such behaviour and practices by guardians are very difficult to intuit. For instance, the reasons for waiting two to three days before seeking treatment for a sick child and what influences their choice of place for treatment are all unknown. Clinical data indicated that the participating children had severe to moderate malaria infections with high parasite densities, anaemia and mild thrombocytopenic conditions. Severe anaemia has been well characterised as an important cause of malaria mortality mostly among children [33, 34]. Although thrombocytopenia had not been associated with malaria mortality, a recent study has shown that severe thrombocytopenia has a 2.4-fold risk of severe morbidity leading to hospital admission and 4.7-fold mortality among falciparum malaria infection [35]. The concurrent thrombocytopenia and anaemia with high parasite densities are a risk for severe morbidity and mortality as well as later disabilities due to malaria. Malaria morbidity and mortality among children have been attributed to insufficient protective immunity to protect them from the severe forms of the disease [36]. Malaria prevention, prompt disease detection and appropriate treatment which are important to resolve and restore child health depend on guardians’ socio-demographic factors [37, 38].

Several studies have highlighted the importance of social and behavioural effects on the design and implementation of effective antimalarial interventions in malaria-endemic areas [28, 39]. However, how directly these factors impact on the interventions are still not understood. Maternal age, education and socioeconomics have been demonstrated to have a significant association with malaria [40, 41]. Education has an impact on health-seeking behaviours whilst increasing socioeconomic status has an association with the prevention of severe clinical conditions among children and effective implementation of health care interventions [42]. This study has shown that the guardians’ educational level significantly influences CSOC, FPOCCH and MCID. It has been shown that children whose mothers have less than primary school education have a higher odd of developing cerebral malaria [43]. Furthermore, mothers’ education has shown to be a predictive factor for adopting disease prevention strategies and treatment-seeking behaviour [44]. 

The study also revealed that the occupation of Guardians significantly affects the health of the child. Mothers’ occupation and location have been linked respectively with high malaria parasitaemia and clinical severity among children [45, 46]. Several other studies have also shown an association between income and child health, suggesting that low-income families are less likely to seek prompt treatment for their sick children [47]. Although the study did not directly measure the income of the participants most of them were traders, farmers and seamstresses who delay in seeking treatment for their ill children. This suggests that many of the participants work more hours to keep up with living expenses and to reduce their poor living conditions leading to the neglect of taking a keen interest in the health of their children. Hence many changes, clinical signs and symptoms of their children are sometimes unnoticed in time. For instance, many of the guardians wake up very early in the morning to undertake their ventures with little or no attention to the health of their children. Unfortunately, many of the children especially those under-five years become exposed to several health risks because they are carried along by their guardians especially mothers as they embark on their chosen occupations.

In agreement with Siri, there are suspicions of inherent biases in socioeconomic factors and malaria infection [48]. Our multivariate analysis showed that socio-demographic characteristics of guardians alone could not explain the significant variance in the factors that influence child health in malaria-endemic areas. To have insight into the underlying variance and provide an explanation for the association between socio-demographic characteristics and the factors that influence child health, we further analysed the association using multilayer perceptron network analysis. The results predicted that each socio-demographic characteristic measured in this study was influenced by several hidden factors which intend to affect the factors that influence child health in malaria-endemic areas. We, therefore, propose a network of socio-economic progenitors to explain the underlying variances by factoring into the model social, cultural, economic factors as well as issues related to health services and health-seeking behaviour by guardians for children under 15 years in malaria-endemic areas. Each of the progenitors used in this model is a determinant of child health [29, 40]. However, the interaction between the projectors that affect the overall health of a child is yet to be accessed. For instance, health-seeking behaviour by guardians for their children is affected by multiple of factors such as socio-demographic characteristics, issues related to health services, social, cultural and economic factors as confounding factors that influence the outcome of the health of the child [40, 49]. Similarly, guardians’ socio-demographic characteristics are also influenced by social, cultural and economic factors. These explain the hidden confounding factors that affect the health of children in malaria-endemic areas. That is why using a single progenitor to measure the outcome of children health often results in inconsistencies in the results [40, 42, 50]. Health seeking behaviours varies from person to person which could be attributed to factors such as a person's perception of sickness, belief in the efficacy of a drug or herbal medicine, poverty, cultural or social believes and access to health services [51-53]. We propose a simple unambiguous model that explains the underlying inherent problems associated with socioeconomic factors as determinants of child health in malaria-endemic areas. This model provides a clear direction that can be used to design effective anti-malaria interventions to reduce malaria morbidity and mortality in children especially in sub-Sahara Africa where malaria interventions have failed to yield expected results.

Conclusion

This study has revealed the association between guardians’ socio-demographic factors and the factors that influence child health. However, these associations are influenced by a network of socio-economic progenitors with hidden factors and biases. These hidden factors and biases explain why previously observed associations have not to help to improve anti-malaria interventions, especially in sub-Sahara Africa. The proposed network of socio-economic progenitors provides a simple explanation and the pattern of confounding factors that influence the outcome of child health in malaria-endemic areas. This model provides roadmaps for validation, verification and provides a potential tool for designing effective anti-malaria interventions to reduced malaria mortality among children in Ghana and other sub-Sahara African countries.

Definition Of Terms

CSOC [Clinical severity of the child]

FPOCCH [First point of call for child health]

DOOSCS [Days of on-set of child symptoms before the hospital visit]

CS [Clinical symptoms]

MCID [Malaria control intervention adopted by Guardian]

OCSG [Occupational status of Guardian]

EDULG [Educational level of Guardian]

SOG [Sex of Guardian]

AGECOG [Age category of Guardian]

Declarations

Ethical clearance and consent to participate

The study protocol was reviewed and approved by the Ghana Health Service Ethics Committee (GHS-ERC-17/01/12) and the University of Cape Coast Institutional Review Board (UCCIRB/28/09/3.1.1). The purpose of the study was explained to the guardian and written informed consent were obtained before interviewed. The study posed no risk to the subjects.

Consent for publication

Not applicable

Availability of data and materials

Not applicable

Funding 

No funding agent

Competing interests

None declared

Human and Animal Rights

Human participants, human material and human data were collected, used or performed in accordance with the 2008 Declaration of Helsinki.

Author contributions statement

A.K.K designed the study, collected the samples, performed the statistical analysis and wrote the paper.

Acknowledgements

I am grateful to the Department of Biomedical Sciences, University of Cape Coast laboratory and the medical staffs of St. Francis Xavier hospital, and Twifo Praso district hospital for their technical support.

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Tables

Table 1: Guardians’ socio-demographic data

Table 2: Clinical characteristics of children whose Guardians were enrolled into the study 

Table 3: Factors that influence child health

*Clinical severity was classify using haemoglobin levels, parasitaemia, and hospital admission. 

Table 4: Multivariate Tests^c showing the effect of guardians’ socio-demographic on the factors that influence child health

a. Exact statistic;

b. The statistic is an upper bound on F that yields a lower bound on the significance level;

c. Design: Intercept + CSOC + FPOCCH + DOOSCS + CS + MCID

NB: there is no interaction between Effects

Table 4: Continue

Table 5:  Association between guardians’ socio-demographic on the factors that influence child health

a. R Squared = .969 (Adjusted R Squared = .967); b. R Squared = .947 (Adjusted R Squared = .944);

b. R Squared = 1.000 (Adjusted R Squared = 1.000); d. R Squared = .940 (Adjusted R Squared = .937)

Table 5: Continue

Table 5: Continue

Table 5: Continue

Table 6: Multilayer Perceptron analysis showing patterns between guardians’ socio-demographic characteristics and the factors that influence child health

a. Excluding the bias unit

b. Error computations are based on the testing sample.

Table 6: Continue