Abstract
Background
The surge in malaria-related mortality among children under-five in African countries, mainly owing to late treatment, poses a major concern. The failure to effectually treat malaria early has mired efforts to reduce the disease burden in the region, as early treatment failure is linked to the endemic nature of the disease. The study examined the prevalence as well as determinants of treatment delays for childhood malaria in Mali, Niger, Nigeria, and Senegal.
Methods
The cross-sectional study analysed secondary data (Malaria Indicator Survey data) from Mali. Niger, Nigeria, and Senegal using Stata package for the analyses. The descriptive statistics, chi-square test and binary logistic regression were employed to identify factors influencing treatment responsiveness.
Results
The prevalence of the treatment delay was highest in Mali. Children’s sex (AOR = 0.41 L; CI (0.25–0.66), mothers’ wealth index (AOR = 0.037; CI (0.3–0.96)) and place of residence (AOR = 1.82; CI(1.04–3.16)) were the factors influencing treatment delay in Senegal. Mothers’ wealth index (AOR = 0.54; CI (0.3–0.96 and education attained (AOR = 0.49; CI (0.29–0.83)) were the factors that influenced late treatment of malaria in Nigeria. Mothers’ place of residence (AOR = 1.24; CI (0.54–2.87)) was the only factor that influenced late treatment in Niger, while the age of mothers (AOR = 0.6; CI (0.38–0.95) was the only factor that caused delayed treatment in Mali.
Conclusion
Intervention targeting these populations would play a significant role in curbing the menace of malaria in these countries.
Keywords: Delay, Childhood, Poverty, Infectious, Africa
Introduction
The global burden of malaria is high and endemic in sub-Saharan African countries. Despite combined efforts to combat malaria in Africa, the disease remains a significant threat [1, 2].This is one of the causes of rise in child mortality in some countries of the region [3, 4]. According to the World Health Organization, there were an estimated 263 million cases and 597,000 deaths in 2023 against 219 million cases of malaria globally in 2017 [5] with a high incidence in West Africa [6].
Previous research findings on communicable and non-communicable diseases have led to a series of discoveries in understanding the prevalence and effects of malaria in children in global countries [7, 8].
Between 2021 and 2022, there was significant change in malaria incidence, with Africa accounting for 94% of reported cases in both years. Various organizations have launched initiatives to eradicate malaria, such as UNICEFs vaccine distribution in Sierra Leone and Cameroon [9]. In Nigeria, the Global Fund for malaria provided grants from 2021 to 2023 [10] to eradicate the threat. However, West Africa still struggles with high malaria rates, largely due to delayed treatment. It is worthy of note that understanding the disease’s burden is crucial to reducing under – five mortality and early intervention is key to improving health outcomes as delayed treatment is a major contributor to the spread of the disease [11, 12].
The World Health Organization defines Malaria treatment delay as failure to diagnose malaria within 24 h of symptom onset [13]. One of the major causes of delay in malaria treatment is unawareness of the severity of malaria parasites and its associated problems among nursing mothers in Africa [14]. Lately, some parents fail to recognize the severity of the disease and a large number of mothers in both rural and urban dwellings are still ignorant of how harmful is malaria parasite. A varied attitude among mothers also contributes to the late treatment. For instance, the Treated Mosquito Nets that were distributed among nursing mothers in both rural and urban areas of Nigeria were being used for other purposes. The reduced distribution of malaria treatment facilities in some communities aided the upsurge. In South Africa, the success made in averting the disease burden is attributed to successful malaria prevention through the use of a Vaccine [15]. Another major impediment to treating malaria quickly is the acceptance of its control measures [16]. Beyond these, attending to the disease immediately when the symptoms are noticed can have an immeasurable impact in reducing the disease burden. Having linked child mortality to delay in malaria treatment across the globe, this study is guided by the research objectives. The study objectives are to:
examine the prevalence of women who delay treatment of malaria among their children under-five in the selected countries;
determine the levels of delay in the treatment of malaria among children under-five in the selected countries; and.
investigate the determinants of delay in the treatment of malaria among children under- five in the selected countries.
Literature review
In the low and middle-income countries, children under-five are mostly affected by the malaria parasite [17–19]. It is expected that a quick response to malaria immediately it is detected will reduce the disease burden. A considerable body of literature has investigated the effects of malaria upsurge on mortality in countries, especially low and middle-income countries. However, the failure in reduction has been attributed to the delay in treatment of the disease immediately the symptoms are noticed [20] Delay in malaria treatment can be attributed to various factors. While there is limited specific research in West Africa, studies in Ethiopia shared some issues on malaria transmission patterns, but not on determinants of late treatment, nor prevalence of late treatment and its patterns. In the country, marriage type and poor access to health facilities were correlated with the early or late treatment of malaria; mothers who were in a monogamous family setting were likely to delay the treatment [21] Other studies from the country have also confirmed that waiting time at the facilities and the decision of the household head were responsible for the delay in the treatment, which is responsible for under-five mortality in the country [22] In Uganda, seeking drugs at a drug shop and distance from health facilities were the stated factors responsible for the delay in the treatment [23]. A study in Kaduna, Nigeria, showed that place of residence has a positive correlation with treatment delay. Also, women in rural settlements delay treatment compared to their urban counterparts [24]. These have made the achievement of targeted goals difficult. For instance, the achievement of Sustainable Development Goal (Goal 3) in Nigeria has become a herculean task.
There is a large body of literature on malaria across Africa however, the majority of the works concentrate on the effects and causes of the disease [25]. There were special focus on the particular population that the diseases affect most, while some works were limited to a particular population of interest [26, 27] Most authors on the subject matter in low and middle-income countries have written on causes, effects and solutions of malaria burden. The majority highlighted that lack of health facilities were the major causes. It was revealed that economic factors, which include a poor wealth quintile, contribute significantly to the delay in treatment [28]. Another study indicated unemployment, weak health policy, which affects health services in the country, as a cause of the delay [29, 30]. In a study on the factors that influence prompt or delay in malaria treatment in Guinea in the 2022–2023 period, it was revealed that self-medication practice and low level of education had a significant influence on the delay of the treatment [31].
Across West Africa, different factors have been responsible for the late treatment of the disease. Some of the key determinants were categorised under: environmental factors, behavioural factors, and socio-demographic factors; the presence or absence of health care facilities around households, which increases the accessibility of the mothers, which may eventually encourage or discourage the mothers from prompt response to the treatment. On behavioural factors, irregular use of Long-Lasting Insecticide-Treated Nets (LLlNs) increases the courage in mothers to assume that the children who regularly sleep under LLlNs cannot develop malaria. The socio-demographic factors include age, sex, and residence of the child, which may influence malaria risk, although these factors may not always be significant predictors in some women. A study in Nigeria revealed that failure to attend to malaria diagnosis on time in some way contributed to problems of addressing malaria burden. It further described the problem of increased mortality of children under five in the country [32].
Theoretical framework
This study was based on Health Belief Model. The Health Belief Model (HBM) is a psychological framework developed to observe and predict health-related behaviour’s by concentrating on individuals’ beliefs and attitudes [33] The Health Belief Model was formulated by social psychologists Irwin Rosenstock, who was credited with formalizing its core concepts in the 1950s. Rosenstock, along with colleagues Godfrey Hochbaum and Stephen Kegeles, initially developed the model to understand why people did or did not participate in health screening programs, such as a particular ailment testing [34]. Over time, the HBM has been extensively applied to various health behaviour’s, including vaccination, nutrition, and disease prevention, due to its concentration on individual decision-making processes [35].
The theory suggests that the mother’s decision to engage in health actions, such as seeking treatment or adopting preventive measures, is influenced by their perceived susceptibility to a health problem (distance from or closeness to heath facilities), the severity of the condition(informed or not informed that the children had malaria), the benefits of taking action, and the barriers that might prevent them from acting [36] Additionally, cues to action (such as health campaigns or symptoms) and self-efficacy of the mothers (confidence in one’s ability to perform the behaviour) also play crucial roles. The model emphasizes that health behaviour are not solely driven by awareness or knowledge but by personal perceptions and assessments of risk and benefit. Relating the HBM to this study the model provides a valuable lens to understand mothers/caregivers’ behaviour’s. For example, a mother’s/caregiver’s perception of their child’s susceptibility to malaria and the severity of the disease may influence whether they seek treatment promptly or use preventive measures like bed nets. Understanding these beliefs can help in designing targeted interventions that address perceived barriers and enhance self-efficacy, ultimately promoting healthier behaviour’s and reducing malaria-related morbidity and mortality among children.
Methods
Study design
The study utilized secondary data from the Malaria Indicator Surveys (MIS) conducted in Mali (2021), Niger (2021), Nigeria (2021), and Senegal (2020-21), which are nationally representative cross-sectional surveys. These surveys employed a multi-stage stratified sampling design to capture data at various administrative levels, such as states, regions, and urban versus rural areas. Specifically, the Nigeria 2021 MIS adopted a two-stage sampling approach, where enumeration areas (EAs) served as the primary sampling units (PSUs). In the first stage, EAs were selected with probability proportional to size (PPS), ensuring that larger clusters had a higher chance of inclusion, thereby maintaining representativeness across diverse geographic and population densities. In the second stage, households within these clusters were systematically sampled, with households chosen in each cluster, allowing for comprehensive data collection on malaria-related indicators among children under five across different settings. This design facilitates the collection of detailed data while optimizing resource use and ensuring national representativeness.
The cross-sectional design of the MIS datasets is particularly suited for this study that allows for the assessment of relationships between individual, household, and community-level variables, such as socio-economic status, healthcare access, and geographic location, which are critical in understanding delays in treatment. Additionally, the stratified sampling method enhances the representativeness of the data, ensuring that findings can be generalized to the broader population within each country. The use of GPS coordinates and detailed geographic stratification further supports the multi-level analysis, enabling researchers to examine contextual influences at the local and regional levels that affect treatment-seeking behaviours among under-five children affected by malaria.
Data source
The data source for this study was derived from the Kid Recode dataset of the Malaria Indicator Survey (MIS), which is a nationally representative dataset designed to collect information on malaria prevention, treatment, and related health behaviour’s among children. The Kid Recode dataset specifically focuses on children aged five years and below, providing detailed data on various child health indicators, demographic characteristics, and household factors. This dataset was obtained through the Demographic and Health Survey (DHS) program, which employs standardized questionnaires and sampling techniques to ensure the data’s representativeness and reliability. The dataset includes variables related to child health, household environment, parental characteristics, and parents’ health-seeking behavior making it a comprehensive source investigate the occurrence and malaria treatment.
Study population
The study focused on children under five years of age, with data obtained directly from their mothers or primary caregivers. Since malaria is a common health concern among young children in this population, the study centered on children who were reported to have had malaria, rather than just those who experienced fever in the past two weeks, to better capture the true burden of the disease. The data reflected maternal reports, which included whether the child was told by a healthcare provider that they had malaria, rather than solely relying on self-reported fever episodes.
Study variables
The dependent variable, late malaria treatment, was derived from the number of days after the onset of fever that care was sought (h46b). Responses indicating treatment within 24 h (coded as 0 or recoded to “<= 24 hours”) were classified as timely treatment, while those seeking care after more than 24 h (coded as 2–60 and recoded to “> 24 hours”) were categorized as delayed treatment. This binary recoding facilitates the analysis of factors influencing promptness in malaria treatment among children under-five. The independent variables encompass several characteristics. Child characteristics include sex (b4), which was used as recorded without recoding, and the number of children aged five and under in the household (v137); this was recoded into two categories: fewer than four children and four or more children. The child’s age (b8) was generated from the current age used has it was reported, to capture developmental differences affecting treatment-seeking behaviour.
Parental factors included the mother’s age (v013) with no recode, level of education (v106) with no recode, wealth index (v190) with no recode, and place of residence (v025) either urban or rural area. Preventive behaviours were also considered, such as whether the child slept under a mosquito bed net, the type of mosquito net used, and whether the caregiver reported that the child had malaria. These variables help understand behavioural influences on treatment delays. Environmental factors comprised the type of toilet facility (v116), which was recoded into “Improved” (e.g., flush systems, ventilated improved pit latrines) and “Unimproved” (e.g., pit latrines without slabs, open pits, bush/field), and the time taken to access water sources (v115), recoded into categories such as less than 30 min, 30–60 min, 60–120 min, and over 120 min. The source of drinking water (v113) was classified as improved (piped sources, protected wells and springs) or unimproved (unprotected wells, rivers, rainwater, etc.), reflecting environmental conditions that may influence health-seeking behaviour and treatment delays.
Data analysis
The data analysis was conducted using STATA software (version 17), with appropriate application of survey weights to account for the complex sampling design. The univariate analysis involved generating weights by dividing the “women’s individual sample weight” by 1,000,000 to standardize the weights across the dataset. These weights were then adjusted based on the primary sampling unit (PSU) and sample strata, which are essential for accurately estimating sampling errors and ensuring representativeness. Additionally, the single-unit data was centred to improve model stability and interpretability. This approach allowed for the correction of potential biases arising from oversampling or under sampling of specific regions or demographic groups, enabling the results to be generalizable to the broader population.
The analysis was structured into three levels: univariate, bivariate, and multivariate. At the univariate level, the frequency distribution of key variables was calculated to describe the sample characteristics. Bivariate analysis involved applying chi-square tests to assess the association between independent variables and the dependent variable of late malaria treatment. Variables that showed no statistically significant association at this level were excluded from further multivariate analysis to streamline the model. The multivariate analysis employed binary logistic regression to identify predictors of late malaria treatment, with results presented as odds ratios (ORs), along with 95% confidence intervals (CIs) and P-values. The logistic regression model was specified as:
 |
where p represents the probability of experiencing delayed treatment, and the β coefficients quantify the effect of each explanatory variable. This approach, combined with the weighted data, ensures that the findings accurately reflect the population-level determinants of treatment delay, accounting for the study’s complex sampling design.
Ethical consideration
The study utilized secondary data with ethical considerations addressed during data collection and compilation. As a result, interviewee confidentiality was maintained and the study adhered to the ethical guidelines set by ICF macro for the data sets.
Results
Figure 1 shows the distribution among the selected countries presented in percentage format. About three–fifths of the participants delayed malaria treatment in Mali (59.6%), followed by those that delayed treatment in Nigeria and Senegal (49.8% and 45.3%) respectively. From these findings, it can be observed that almost half of the women delay in treatment in all the selected countries.
Fig. 1.
Distribution by delay malaria treatment among under five children
Figure 2 shows the average number days before treatment after the symptom was experienced. Participants sought malaria treatment in all the countries sought treatment close to two days after the symptoms was noticed.
Fig. 2.
Average number of days after began sought advice or trearment
Determinants of late malaria treatment among under-five children in West Africa
Table 1 revealed the mothers’ background characteristics and factors associated with delay in malaria treatment at 95% confidence level. The background characteristics revealed that more male children (50.7%) were reported than female children (49.3%) in Senegal and child sex was the significant factor associated with treatment delay (ꭓ2 =21.14, P < 0.05). In Mali, about a quarter (26.9%) of mothers were aged 25–29 years followed by age 30–34 years and 35–39 (21.5% and 14.2%) respectively. Mothers age was the only significant factor associated with the treatment delay in the country (ꭓ2 =16.16, p < 0.05 ) In Mali, more than half of the participants had no formal education (69.4%), followed by those reported secondary and primary education (14.09% and 15.0%) respectively. Mothers’ level of education is positively associated with delay in malaria treatment (ꭓ2 =23.15). In Nigeria, close to half of the participants had no formal education (46.03%), followed by those reported secondary and primary education (28.36% and 15.6%) respectively. In the country, mothers’ level of education is positively associated with delay in malaria treatment (ꭓ2 =25.84, p < 0.05). Wealth status of the women were positively associated with treatment delay in Mali (ꭓ2 =38.55, p < 0.05), Nigeria (ꭓ2 =81.86, p < 0.05) and Senegal (ꭓ2 = 28.75, p < 0.05). About one - fifth of the women were poorest in Mali, Nigeria and Senegal (20.47%, 21.57% and 22.97% respectively). More than two-thirds of the participants reported being from rural areas in Mali, Niger and Senegal (80.38%, 84.0% and 79.01% respectively) There is a significant association between mothers’ place of residence and delayed malaria treatment among children at a p-value < 0.05 in the three countries; Mali ; ꭓ2 = 28.23, Niger; ꭓ2 = 5.93 and Senegal ; ꭓ2 = 26.46 .
Table 1.
Socio – Demographic Characteristics of Mothers in the selected West African Countries
| Variables | Mali (n = 9033) | Niger (n = 4,688) | Nigeria (n = 10,659) | Senegal (n = 5,471) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| zN | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
|
| Sex of Child | ||||||||||||
| Male | 4,685 | 39.2 | 60.8 | 2,385 | 58.5 | 41.5 | 5,450 | 50.9 | 49.1 | 2,771 | 47.3 | 52.7 |
| Female | 4,348 | 41.9 | 58.1 | 2,303 | 55.9 | 44.1 | 5,209 | 49.4 | 50.6 | 2,699 | 62.4 | 37.7 |
| Statistics | ꭓ2 =1.11 | P = 0.2846 | ꭓ2 =0.68 | P = 0.4921 | ꭓ2 =0.54 | P = 0.5342 | ꭓ2 =21.14 | P = 0.0008 | ||||
| Number of children in Household | ||||||||||||
| < 4 children | 4,994 | 42.0 | 58.0 | 3,944 | 57.3 | 42.7 | 9,064 | 50.6 | 49.4 | 3,539 | 55.9 | 44.1 |
| 4 children above | 4,039 | 38.7 | 61.3 | 744 | 56.7 | 43.3 | 1,594 | 48.1 | 51.9 | 1,932 | 52.1 | 47.9 |
| Statistics | ꭓ2 =1.75 | P = 0.2946 | ꭓ2 =0.024 | P = 0.9154 | ꭓ2 =0.69 | P = 0.6000 | ꭓ2 =1.18 | P = 0.4005 | ||||
| Age of child | ||||||||||||
| 0 | 1,876 | 44.4 | 55.6 | 899 | 57.2 | 42.8 | 2,037 | 52.4 | 47.6 | 1,067 | 60.2 | 39.8 |
| 1 | 1,696 | 39.6 | 60.5 | 1,015 | 55.8 | 44.2 | 1,988 | 50.4 | 49.6 | 1,254 | 61.6 | 38.4 |
| 2 | 1,820 | 42.2 | 57.8 | 956 | 57.2 | 42.8 | 2,089 | 47.6 | 52.4 | 1,014 | 45.9 | 54.1 |
| 3 | 1,822 | 34.2 | 65.8 | 883 | 58.3 | 41.8 | 2,147 | 48.8 | 51.2 | 1,005 | 50.6 | 49.4 |
| 4 | 1,819 | 41.1 | 58.9 | 934 | 58.8 | 41.3 | 2,399 | 52.6 | 47.4 | 1,129 | 45.1 | 54.9 |
| Statistics | ꭓ2 =7.07 | P = 0.2273 | ꭓ2 =0.47 | P = 0.9836 | ꭓ2 =3.55 | P = 0.5749 | ꭓ2 =19.36 | P = 0.1323 | ||||
| Age of Mothers | ||||||||||||
| 15–19 years | 667 | 29.9 | 70.1 | 299 | 54.2 | 45.8 | 508 | 46.1 | 53.9 | 311 | 42.7 | 57.3 |
| 20–24 years | 1,915 | 42.6 | 57.4 | 1,113 | 56.6 | 43.4 | 2,003 | 55.9 | 44.1 | 1,139 | 57.2 | 42.8 |
| 25–29 years | 2,429 | 39.8 | 60.2 | 1,192 | 54.3 | 45.7 | 2,861 | 53.1 | 46.9 | 1,320 | 52.1 | 47.9 |
| 30–34 years | 1,943 | 40.7 | 59.3 | 980 | 59.5 | 40.5 | 2,605 | 44.4 | 55.6 | 1,264 | 59.8 | 40.2 |
| 35–39 years | 1,287 | 48.9 | 51.1 | 667 | 61.9 | 38.1 | 1,668 | 51.3 | 48.7 | 810 | 55.2 | 44.8 |
| 40–44 years | 613 | 33.7 | 66.3 | 322 | 58.0 | 42.0 | 776 | 43.9 | 56.1 | 501 | 56.3 | 43.7 |
| 45–49 years | 181 | 27.7 | 72.3 | 115 | 54.3 | 45.7 | 238 | 46.2 | 53.8 | 126 | 22.0 | 78.0 |
| Statistics | ꭓ2 =16.16 | P = 0.0476 | ꭓ2 =3.03 | P = 0.8924 | ꭓ2 =19.38 | P = 0.0527 | ꭓ2 =15.59 | P = 0.4787 | ||||
| Mothers’ level of Education | ||||||||||||
| No formal education | 6,270 | 37.9 | 62.1 | 3,519 | 57.3 | 42.7 | 4,870 | 38.2 | 61.8 | 3,440 | 48.4 | 51.6 |
| Primary | 1,273 | 36.6 | 63.4 | 702 | 54.3 | 45.8 | 1,650 | 57.8 | 42.2 | 1,023 | 60.2 | 39.8 |
| Secondary | 1,358 | 51.1 | 48.9 | 437 | 61.8 | 38.2 | 3,080 | 64.1 | 35.9 | 857 | 69.4 | 30.6 |
| Higher | 132 | 61.7 | 38.3 | 31 | 77.5 | 22.5 | 1,059 | 57.1 | 42.9 | 149 | 55.8 | 44.3 |
| Statistics | ꭓ2 =23.15 | P = 0.0020 | ꭓ2 =1.58 | P = 0.6642 | ꭓ2 =125.13 | P = 0.0000 | ꭓ2 =25.84 | P = 0.0718 | ||||
| Wealth index of mothers | ||||||||||||
| Poorest | 1,849 | 30.7 | 69.3 | 969 | 52.5 | 47.5 | 2,299 | 35.5 | 64.5 | 1,256 | 55.2 | 44.8 |
| Poorer | 1,842 | 38.4 | 61.7 | 936 | 52.4 | 47.6 | 2,360 | 46.3 | 53.7 | 1,222 | 47.8 | 52.2 |
| Middle | 1,932 | 36.9 | 63.1 | 999 | 58.2 | 41.8 | 2,122 | 55.8 | 44.3 | 1,125 | 44.9 | 55.1 |
| Richer | 1,882 | 40.5 | 59.5 | 943 | 58.9 | 41.1 | 1,925 | 60.8 | 39.2 | 1,006 | 68.9 | 31.1 |
| Richest | 1,528 | 54.2 | 45.8 | 841 | 66.6 | 33.4 | 1,954 | 58.6 | 41.4 | 860 | 58.6 | 41.4 |
| Statistics | ꭓ2 =38.55 | P = 0.0002 | ꭓ2 =9.02 | P = 0.2714 | ꭓ2 =81.86 | P = 0.0000 | ꭓ2 =28.75 | P = 0.0065 | ||||
| Place of residence of mothers | ||||||||||||
| Urban | 1,772 | 52.9 | 47.1 | 750 | 67.8 | 32.2 | 3,012 | 53.2 | 46.8 | 1,970 | 65.9 | 34.1 |
| Rural | 7,261 | 36.9 | 63.1 | 3,938 | 55.8 | 44.2 | 7,646 | 49.2 | 50.8 | 3,501 | 48.4 | 51.6 |
| Statistics | ꭓ2 =28.23 | P = 0.0001 | ꭓ2 =5.93 | P = 0.0377 | ꭓ2 =2.78 | P = 0.2066 | ꭓ2 =26.46 | P = 0.0054 | ||||
Significant at P < 0.05
Table 2 revealed the distribution by environmental characteristics such as toilet facility improvement, drinking water availability and time to get water sources. More than four-fifths (93.01%) of the participants had unimproved toilet facilities in Mali while about four – fifths (65.80%) had unimproved toilet facilities in Nigeria. There were significant associations between toilet facilities available and delay malaria treatment in the two countries, Mali and Nigeria (ꭓ2 = 20.59; p < 0.05, ꭓ2 = 18.74; P < 0.05 respectively). Four-fifths (79.93%) of the participants had improved water source in Mali while half of the women (52.18%) had improved water source in Niger. There were significant associations between water source available and delay malaria treatment in the two countries Mali and Niger (ꭓ2 = 9.83’p < 0.05, ꭓ2 = 22.887; P < 0.05, respectively. Also, there was a significant association between the time to reach a water source and delayed malaria treatment in Mali (ꭓ2 = 18.21’p < 0/05), Niger (ꭓ2 = 18.37’p < 0/05) and Senegal (ꭓ2 = 35.43,’P < 0/05).
Table 2.
Distribution of Malaria treatment delay by Environmental Characteristics
| Countries | Mali | Niger | Nigeria | Senegal | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24hurs (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
| Environmental Factor | ||||||||||||
| Toilet Facility available | ||||||||||||
| Improved | 626 | 61.3 | 38.7 | 338 | 64.4 | 35.6 | 3,645 | 57.0 | 43.0 | 3,207 | 55.0 | 45.0 |
| Unimproved | 8407 | 38.9 | 61.1 | 4350 | 56.8 | 43.3 | 7,013 | 47.3 | 52.8 | 2,263 | 53.9 | 46.1 |
| Statistics | 9033 | ꭓ2 =20.59 | P = 0.0004 | 4688 | ꭓ2 =1.3793 | P = 0.2379 | 10,659 | ꭓ2 =18.74 | P = 0.0019 | 5,471 | ꭓ2 =0.09 | P = 0.8590 |
| Availability of Drinking water | ||||||||||||
| Non-Improved | 1813 | 32.5 | 67.5 | 2242 | 48.8 | 51.2 | 3,798 | 50.1 | 49.9 | 821 | 54.5 | 45.5 |
| Improved Source | 7220 | 42.4 | 57.6 | 2446 | 64.0 | 36.0 | 6,861 | 50.3 | 49.8 | 4,649 | 54.7 | 45.3 |
| Statistics | 9033 | ꭓ2 =9.83 | P = 0.0183 | 4688 | ꭓ2 =22.8702 | P = 0.0025 | 10,659 | ꭓ2 =0.002 | P = 0.9745 | 5,471 | ꭓ2 =0.004 | P = 0.9830 |
| Time to get water source(s) | ||||||||||||
| On Premises | 3086 | 47.5 | 52.5 | 604 | 66.2 | 33.8 | 4,324 | 49.2 | 50.9 | 4,269 | 53.2 | 46.9 |
| Less than 30 min | 5436 | 36.8 | 63.2 | 2122 | 61.8 | 38.2 | 5,219 | 51.0 | 49.0 | 842 | 62.1 | 37.9 |
| 30–60 min | 460 | 35.0 | 65.0 | 934 | 49.0 | 51.0 | 714 | 48.9 | 51.1 | 167 | 16.0 | 84.0 |
| 60–120 min | 47 | 24.2 | 75.8 | 680 | 52.7 | 47.3 | 187 | 62.6 | 37.5 | 89 | 95.9 | 4.1 |
| > 120 min | 5 | 40.0 | 60.0 | 348 | 42.9 | 57.1 | 214 | 47.5 | 52.5 | 104 | 49.0 | 51.0 |
| Statistics | 9033 | ꭓ2 =18.21 | P = 0.0196 | 4688 | ꭓ2 =19.3731 | P = 0.0379 | 10,659 | ꭓ2 =3.13 | P = 0.8279 | 5,471 | ꭓ2 =35.43 | P = 0.0008 |
Significant at P<0.05
Table 3 revealed the distribution by preventive behaviour, had information that the child had malaria. About a quarter of the women from Mali (38.91%) and a quarter (41.02%) from Niger were informed that their children had malaria. There were significant associations between had information that child had malaria and delayed malaria treatment in the two countries, Mali and Niger (ꭓ2 = 10.36’p < 0/05, ꭓ2 = 22.99’p < 0/05, respectively). Significant at P < 0.05.
Table 3.
Malaria preventive behaviour of mothers of under five Children in the selected West African countries
| Mali | Niger | Nigeria | Senegal | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N | ≤ 24hurs (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 h (%) | > 24 h (%) |
N | ≤ 24 hurs (%) | > 24 h (%) |
|
| Preventive Behaviour | ||||||||||||
| Was told that child had Malaria | ||||||||||||
| No | 1,496 | 44.5 | 55.5 | 966 | 66.2 | 33.8 | NA | 1,386 | 56.5 | 43.5 | ||
| Yes | 953 | 37.8 | 62.2 | 672 | 51.6 | 48.4 | NA | 208 | 46.9 | 53.1 | ||
| Don’t know | 12 | 11.5 | 88.5 | 28 | 39.6 | 60.4 | NA | 45 | 29.8 | 70.2 | ||
| Statistics | ꭓ2 =10.36 | P = 0.0199 | ꭓ2 =22.99 | P = 0.0004 | 1,639 | ꭓ2 =6.99 | P = 0.1965 | |||||
Significant at P < 0.05
Table 4 revealed the distribution by adjusted odds ratio on binary logistics regression of background characteristics, preventive behaviour and environmental factors associated with delayed malaria treatment at a P-value less than 0.05. Mothers that were informed that their child had malaria in Niger were 87% more likely to delay malaria treatment till after 24 h than those reported not informed (RC). Mothers staying in households with improved water sources in Niger were 0.56 times less likely to delay malaria treatment till after 2 h compared to those reported unimproved water sources (RC).On the contrary, mothers having access to improved water sources in Nigeria were 33% more likely to delay child malaria treatment till after 24 h than those reported unimproved (RC). Mothers reported using 30–60 min to reach water sources in Senegal were 7.63 times more likely to delay malaria treatment till after 24 h than those having water on premises (RC). Furthermore, female children born to mothers in Senegal were 0.41 times less likely to delay malaria treatment compared to male children (RC). Mothers having three-year-old children in Mali were 65% more likely to delay malaria till after 24 h compared to children less than one year of age (RC). Mothers aged 20–24 years and 35–39 years in Mali were 60% and 42% respectively less likely to delay malaria treatment till after 24 h compared to women aged 15–19 years (RC). Children born to mothers with primary education in Nigeria were 50% less likely to delay malaria treatment till after 24 h compared to those with no formal education (RC). Children born to mothers with secondary education in Nigeria and Senegal were 0.42 times and 0.29 times respectively, less likely to delay malaria treatment till after 24 h compared to those with no formal education (RC). Children born to women with higher education were 0.49 times less likely to delay malaria treatment till after 24 h compared to those with no formal education (RC). Children born to poorer, middle wealth quintile, richer and richest women in Nigeria were 0.67 times, 0.51 times, 0.46 times and 0.54 times, respectively, less likely to delay malaria treatment till after 24 h compared to poorest women (RC). On the contrary, children born to poorer mothers in Senegal were 96% more likely to delay malaria treatment compared to the poorest women (RC). Children born to mothers who resided in urban areas in Senegal were 82% more likely to delay malaria treatment compared to the poorest women (RC).
Table 4.
Background Characteristics, Preventive Behaviour and Environmental Factors Associated with Delay Malaria Treatment (Adjusted Odds Ratio Based on Binary Logistic Regression Analysis)
| Mali | Niger | Nigeria | Senegal | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds ratio | P > t | [95%CI] | Odds ratio | P > t | [95%CI] | Odds ratio | P > t | [95%CI] | Odds ratio | P > t | [95%CI] | |
| Was told that child had Malaria | ||||||||||||
| No | RC | |||||||||||
| Yes | 1.13 | 0.376 | [0.87–1.46] | 1.87 | 0.000 | [1.32–2.64] | NA | NA | NA | 0.96 | 0.929 | [0.4–2.32] |
| don’t knows | 4.22 | 0.186 | [0.5-35.76] | 2.67 | 0.062 | [0.95–7.49] | NA | NA | NA | 1.45 | 0.667 | [0.27–7.83] |
| Toilet Facility available | ||||||||||||
| Improved | RC | |||||||||||
| Unimproved | 1.6 | 0.132 | [0.87–2.97] | 0.66 | 0.259 | [0.32–1.36] | 0.96 | 0.820 | [0.68–1.36] | 0.66 | 0.395 | [0.25–1.73] |
| Sources of Drinking Water | ||||||||||||
| Non-Improved | RC | |||||||||||
| Improved Source | 0.78 | 0.225 | [0.53–1.16] | 0.56 | 0.01 | [0.37–0.87] | 1.33 | 0.033 | [1.02–1.72] | 1.08 | 0.93 | [0.2–5.84] |
| Time to get water Source(s) | ||||||||||||
| On-Premises | RC | |||||||||||
| Less than 30 min | 1.17 | 0.373 | [0.82–1.67] | 0.75 | 0.434 | [0.37–1.54] | 0.84 | 0.217 | [0.63–1.11] | 0.61 | 0.085 | [0.35–1.07] |
| 30–60 min | 1.25 | 0.554 | [0.6–2.6] | 1.28 | 0.551 | [0.57–2.86] | 0.81 | 0.462 | [0.45–1.43] | 7.63 | 0.000 | [2.72–21.34] |
| 60–120 min | 2.2 | 0.404 | [0.34–14.14] | 1.1 | 0.843 | [0.44–2.73] | 0.58 | 0.215 | [0.25–1.37] | 0.02 | 0.072 | [0-0.12] |
| > 120 min | 1.02 | 0.99 | [0.05–19.86] | 1.29 | 0.61 | [0.48–3.5] | 1.15 | 0.727 | [0.52–2.54] | 1.5 | 0.608 | [0.31–7.24] |
| Sex of Child | ||||||||||||
| Male | RC | |||||||||||
| Female | 0.86 | 0.174 | [0.7–1.07] | 1.05 | 0.761 | [0.77–1.44] | 1.07 | 0.482 | [0.89–1.3] | 0.41 | 0.000 | [0.25–0.66] |
| Number of Children in Household | ||||||||||||
| < 4 children | RC | |||||||||||
| 4 children above | 1.02 | 0.876 | [0.79–1.33] | 0.91 | 0.738 | [0.52–1.58] | 0.94 | 0.765 | [0.61–1.43] | 0.81 | 0.405 | [0.5–1.33] |
| Age of Child | ||||||||||||
| 0 | RC | |||||||||||
| 1 | 1.31 | 0.2 | [0.86-2] | 0.99 | 0.952 | [0.63–1.55] | 1.05 | 0.774 | [0.77–1.43] | 1.45 | 0.342 | [0.67–3.14] |
| 2 | 1.11 | 0.565 | [0.78–1.57] | 0.91 | 0.659 | [0.59–1.4] | 1.12 | 0.470 | [0.83–1.51] | 2.65 | 0.015 | [1.21–5.82] |
| 3 | 1.65 | 0.009 | [1.13–2.41] | 0.92 | 0.726 | [0.56–1.49] | 1.01 | 0.926 | [0.75–1.38] | 2.81 | 0.091 | [0.85–9.28] |
| 4 | 1.25 | 0.311 | [0.81–1.95] | 0.87 | 0.601 | [0.53–1.45] | 0.84 | 0.330 | [0.6–1.19] | 2.26 | 0.061 | [0.96–5.31] |
| Age of mothers | ||||||||||||
| 15–19 years | RC | |||||||||||
| 20–24 years | 0.60 | 0.029 | [0.38–0.95] | 1.05 | 0.913 | [0.47–2.33] | 0.81 | 0.383 | [0.51–1.3] | 0.62 | 0.351 | [0.22–1.71] |
| 25–29 years | 0.63 | 0.067 | [0.38–1.03] | 1.08 | 0.827 | [0.54–2.16] | 0.94 | 0.804 | [0.58–1.52] | 0.97 | 0.953 | [0.33–2.88] |
| 30–34 years | 0.64 | 0.084 | [0.39–1.06] | 0.92 | 0.825 | [0.42–2.01] | 1.38 | 0.176 | [0.87–2.19] | 0.4 | 0.109 | [0.13–1.23] |
| 35–39 years | 0.42 | 0.002 | [0.24–0.72] | 0.83 | 0.642 | [0.38–1.81] | 1.07 | 0.789 | [0.64–1.79] | 1.08 | 0.874 | [0.43–2.72] |
| 40–44 years | 0.86 | 0.589 | [0.5–1.49] | 0.76 | 0.54 | [0.32–1.81] | 1.1 | 0.773 | [0.58–2.07] | 0.66 | 0.557 | [0.16–2.69] |
| 45–49 years | 0.96 | 0.951 | [0.25–3.68] | 0.99 | 0.989 | [0.31–3.19] | 1.06 | 0.893 | [0.44–2.55] | 3.3 | 0.232 | [0.46–23.6)1 |
| Level of Education | ||||||||||||
| No formal Education | RC | |||||||||||
| Primary | 1.1 | 0.625 | [0.75–1.6] | 1.1 | 0.728 | [0.64–1.88] | 0.50 | 0.000 | [0.36–0.7] | 0.67 | 0.206 | [0.35–1.25] |
| Secondary | 0.77 | 0.219 | [0.5–1.17] | 0.94 | 0.818 | [0.55–1.61] | 0.42 | 0.000 | [0.3–0.59] | 0.29 | 0.016 | [0.11–0.79] |
| Higher | 0.67 | 0.424 | [0.25–1.79] | 0.58 | 0.621 | [0.07–5.09] | 0.49 | 0.008 | [0.29–0.83] | 0.53 | 0.437 | [0.1–2.67] |
| Wealth Index | ||||||||||||
| Poorest | RC | |||||||||||
| Poorer | 0.73 | 0.138 | [0.48–1.11] | 1.11 | 0.727 | [0.63–1.95] | 0.67 | 0.037 | [0.45–0.98] | 1.96 | 0.039 | [1.03–3.7] |
| Middle | 0.79 | 0.196 | [0.54–1.13] | 1.02 | 0.951 | [0.6–1.73] | 0.51 | 0.002 | [0.33–0.79] | 2.04 | 0.064 | [0.96–4.35] |
| Richer | 0.72 | 0.186 | [0.44–1.17] | 1.05 | 0.891 | [0.55-2] | 0.46 | 0.003 | [0.27–0.77] | 0.62 | 0.218 | [0.29–1.33] |
| Richest | 0.6 | 0.183 | [0.28–1.28] | 0.85 | 0.727 | [0.35–2.1] | 0.54 | 0.037 | [0.3–0.96] | 1.41 | 0.517 | [0.49–4.06] |
| Place of Residence | ||||||||||||
| Urban | RC | |||||||||||
| Rural | 1.04 | 0.901 | [0.55–1.96] | 1.24 | 0.609 | [0.54–2.87] | 0.89 | 0.396 | [0.67–1.17] | 1.82 | 0.035 | [1.04–3.16] |
Significant at P < 0.05
Discussion
The studymined the determinants of late malaria treatment among the children under-five in West Africa despite several efforts that have been made to curb the menace of malaria in the region. The study used health belief model to explain the dependent variable and the socio demographic variables of the children mothers. The study objectives were to examine the prevalence of women who delay treatment of malaria among their children under five in four selected countries: Mali, Niger, Nigeria, and Senegal; determine the extent of delay in treatment of malaria among children under five in the selected countries; and investigate the determinants of delay in treatment of malaria among the children under-five in the selected countries. It was discovered that more than half of the women delayed malaria treatment in Mali and Nigeria. Also, close to half of the women delayed in Niger and Senegal. In all the countries, the treatment was delayed close to two days. By implication, the prevalence of treatment delay was high in the selected countries. The results of this study show that the sex of the child determines delay in malaria treatment in Senegal (AOR = 0.41; CI; (0.25–0.66); p < 0.05), which is in accordance with the work of Salim & Cavallaro [15] The women with female children were 0.41 less likely to delay malaria treatment than mothers with male children. While Shah, Emina, Eckert & Ye; Paraiso et al. [37], discovered a high prevalence of late treatment in Africa, our study also discovered a high prevalence of late treatment. In all the selected countries, it was observed that the majority of the women who had no formal education, within the age group 25–29 years, poorest, living in rural areas, had information that their children had malaria were the women that delayed the treatment. The implication is that they delayed due to some demographic and economic characteristics, not that they were not informed that their children had malaria. Similar to findings by Babalola, Ajumobi & Ajayi [38], ; and Degarege et al. [39], education, place of residence and poor financial status were the socio-demographic and economic factors influencing delay in malaria treatment. In Mali, women with higher education were 0.69 times less likely to delay malaria treatment than those with no formal education and women with primary education were 1.1 times more likely to delay malaria treatment than those with no formal education. In the same vein, in Nigeria, women with higher education were 0.49 times less likely to delay malaria treatment than those with no formal education, while those with primary education were 0.5 times less likely to delay malaria treatment than those with no formal education. In Mali, the women who reside in rural areas were 1.04 times more likely to delay malaria treatment than those living in urban areas and the women who resides in rural areas in Niger were 1.24 times more likely to delay malaria treatment than those living in urban areas. The women who reside in rural areas in Senegal were 1.82 times more likely to delay malaria treatment than those living in urban areas.
In Mali, the richest women were 0.6 times less likely to delay malaria treatment than the poorest women, Nigerian richest women were 0.54 times less likely to delay malaria treatment than the poorest women, while the richest women in Senegal were 1.41 times more likely to delay malaria treatment than the poorest women. Similar to findings by Anyasodor et al. and Mekomen et al. [40, 41], had information that the child had malaria is a factor determining delay in malaria treatment in Mali, Senegal and Niger. In Mali, the women who had information that the child has malaria were 1.13 times more likely to delay malaria treatment compared to those who had no information, and in Niger, the women who had information that the child has malaria were 1.87 times more likely to delay malaria treatment compared to those who had no information.
Limitations
This study is not without limitations. The study was conducted on women of reproductive age. Hence, the results cannot be generalised to the larger population of caregivers, which ought to include men. Caregivers may only be “told the child has malaria” after treatment is already delayed, which raise concerns about reverse causality The researchers used the most recent available Demographic and Health Survey dataset from the selected countries as at the time of study. It was believed that access to a more up-to-date dataset version would have provided current trends and improvements in the insights.
Suggestion for further studies
Future research should consider the use of primary data accompany the secondary data (the use of mixed method) to understand why mothers delay malaria treatment in their children under five and the determinants of their actions. Additionally, more qualitative research should be conducted to allow the nursing women to freely share their opinions concerning other factors influencing their delay in treatment behaviour. This will enable us to put in place effective interventions and adequate support services that address the real issues concerning the subject.
Conclusion and recommendation
Having found that there is high prevalence of delay in malaria treatment in all the selected countries, the study offers evidence of the influence of child sex, age of mothers, mothers’ wealth index, mothers’ level of education and mothers’ place of residence, availability of toilet facilities, closeness to drinking water source and number of hours spent to get drinking water source were the factors associated with delay in malaria treatment in the selected countries. It is therefore recommended that researchers, health planners and policy makers need to give due emphasis to educating women on early treatment of malaria and improving individual and household socio-economic status, with special attention given to the selected countries. Focus should be placed on creating awareness in rural areas regarding the issue of delay in malaria treatment. Intervention and educational programs on sex preference should be formulated to prevent delays in malaria treatment.
Authors’ contributions
Shittu S conceptualized the study, wrote the draft of the manuscript. Shittu R. analysed the data while Adedini S. reviewed and guided the writing of the manuscript. The three authors read and approved the final manuscript.
Funding
No funding was received for the study.
Data availability
The study used Malaria Indicator Survey Data (Nigeria (2021), Mali (2021), Senegal (2020-21) and Niger (2021). This is a DHS data set that is available at: https//dhsprogram.com/data/dataset/Nigeria\_Standard-DHS\_2021.cfm etc.
Declarations
Ethics approval and consent to participate
Both are not applicable to this study. Standard for ethical research, the DHS Ethics committee (NHREC), Nigeria and the ICF Institutional Review Board, USA, granted ethical permission to use the MISD of DHS data.
Consent for publication
The study utilized secondary data drawn from the Malaria Indicator Survey Data (MISD). DHS program authorizes the downloading of the survey data for statistical analysis and reporting for this research.
Competing interests
The authors declare no competing interests.
Clinical Trial Number
Not Applicable.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The study used Malaria Indicator Survey Data (Nigeria (2021), Mali (2021), Senegal (2020-21) and Niger (2021). This is a DHS data set that is available at: https//dhsprogram.com/data/dataset/Nigeria\_Standard-DHS\_2021.cfm etc.