Malaria parasitaemia, anaemia and malnutrition in children less than 15 years residing in different altitudes along the slope of Mount Cameroon: prevalence, intensity and risk factors

The study was carried out in Batoke (Limbe) and Tole (Buea), which are two different altitudinal ranges along the slope of the Mount Cameroon area. The sites were classified as lowlands [< 200 m above sea level (asl)] and highlands (> 600 m asl). The coordinates of Batoke ranged from altitude 8 m, latitude 04°01.364′N, longitude 009°05.971′E to 47 m, 04°02.039′N and009°05.808′E. Tole is located between 627 m, latitude 04°07.057′N, longitude 009°15.178′E and 630 m, latitude 04°6.906′N, longitude 09°14.434′E. The Mount Cameroon area stretches from the Atlantic Ocean at the Gulf of Guinea, gradually increasing from Limbe to 800–1200 meters in Buea on the eastern flank of the Mountain. The temperatures in lowlands (Limbe) are high and fairly constant ranging between 25 and 30 °C on average [25] with abundant rainfall ranging from 5500 to 6500 mm per annum. In the highlands (Buea), weather records from the Cameroon Development Corporation indicate a mean relative humidity of 80%, an average rainfall of 4000 mm and a temperature range of 18–27 °C [26]. The Tole study area has been described in detail by Ndamukong et al. [27]. The Mount Cameroon study area is subjected to a Cameroonian-type equatorial climate characterized by fairly constant temperatures and two seasons: a short dry season (November–February) and a long rainy season (March–October) with abundant precipitation (2000–10,000 mm) [28]. Malaria transmission is perennial with two peaks in season, the first between April and May and the second between October and November. These correspond with the beginning and end of the rainy season, respectively. Plasmodium falciparum is also the main species and Anopheles gambiae is the main vector species [29].

The study was carried out among pre and school age children of both sexes aged 6 months to 14 years old, weighed > 5 kg, free from other clinical conditions not related to malaria and sickle test negative. Children with severe malaria (unable to drink or breastfeed, vomiting more than twice in the preceding 24 h before presentation, recent history of convulsions, unconscious state or unable to sit or stand and other diseases requiring hospital admission) were excluded from the study.

This cross sectional community based study was carried out between the months of July and November 2017, reported as the peak malaria transmission period in the Mount Cameroon area [26]. Ensuing administrative clearances and ethical approval for the study, informed consent/assent forms explaining the purpose, risks, and benefits of the study were given to parent/caregivers. Participants were invited to the data collection location in each community by their local chiefs and coordination was organized by the head/leader of a block within a neighbourhood (quarter head) of the various communities. Upon obtaining consent/assent from the parents/care givers, the study team proceeded for sample collection. Following administration of a semi-structured questionnaire, body temperature, anthropometric measurements and blood sample were collected from each child for malaria parasite identification and a full blood count assessment. The sample size for each study altitude was calculated using the 66.2% prevalence of malaria in children in the study area [27]. Sample size was determined using the formula n = Z²pq/d² [30] where n = the sample size required, z = 1.96: which is the standard normal deviate (for a 95% confidence interval, CI), p = 66.2%: proportion of malaria prevalence, q = 1 − p: proportion of malaria negative children and d = acceptable error willing to be committed. The minimum sample size was estimated as n = 344 for each site. Considering a possible participation of more than one child per family, loss of samples due to blood clotting and incomplete data entry, the sample size was adjusted by 10% to a minimum of 379. Following education of the community for each family to ensure the participation of at most a child less than 15 years in the study a date for collection of sample was set. Potential participants were reminded of the collection dates per block by the head/leader of the block. A convenience sampling method was used in all the blocks in each altitude until the required sample was attained. At the start of the study in each site, the parents, guardians and children were educated on the study protocol and the benefits of participation highlighted at their various neighbourhoods.

The axillary temperature was measured using a digital thermometer and fever was defined as temperature ≥ 37.5 °C [12]. Anthropometric measurements such as height and weight were measured using a measuring tape and a Terraillon weighing scale (Terraillon, Paris), respectively. To ensure the accuracy of using a tape to measure the height of ambulant children, the tape was attached to a locally constructed wood work that served as a stadiometer. Under-nutrition indices such height-for age (HA), weight-for-age (WA), and weight-for-height (WH) standard deviation (SD) scores (Z scores) were computed based on the WHO growth reference curves using the WHO AnthroPlus for personal computers manual [31]. A child was identified as being malnourished if he or she scored < − 2 in one of the anthropometric indices of HA (stunting), WA (underweight) and WH (wasting) indices, while corresponding Z scores of < − 3 SD were considered indicative of severe under-nutrition [9].

A semi-structured questionnaire was administered to the child’s parent/caregiver to collect data on (i) demographics (sex, age, literacy, occupation and marital status); (ii) Socioeconomic status related variables (number of house occupants, house type, toilet type, and water sources) [26] and knowledge of malaria including sign/symptoms, complications, transmission and prevention methods (iii) fever management practices; and (iv) malaria prevention practices bed net (long lasting insecticide net (LLIN) ownership, physical integrity, number and use) and indoor residual spraying (IRS) experience. LLIN use was defined as having slept under a LLIN the night prior to the survey and the physical integrity of LLIN was assessed by checking for holes in the nets and counting them using the WHO Pesticide Evaluation Scheme [32].

Four millilitres of venous blood samples were collected from the children using sterile disposable syringes. The collected blood was aliquot into labelled ethylenediaminetetraacetate (EDTA) tubes and the remaining blood dispensed on slides for the preparation of thick and thin blood films. Labelled blood samples in EDTA tubes were transported on ice in a cool box to the Malaria Research Laboratory, University of Buea for a full blood count analysis. Thin blood films were fixed with absolute methanol and later stained along with thick blood films using 10% Giemsa for 20 min. The films were examined following standard procedure for the detection and identification of malaria parasites [33]. As a quality control measure, slides were read by two independent parasitologists, and in the case of any disparity they were read by a third parasitologist. Slides were considered positive when asexual forms and/or gametocytes of any Plasmodium species were observed on the blood film. The number of parasite were counted per 200 leukocytes on thick blood film and asexual parasite densities per μL of blood was obtained by multiplying the parasite count with the participants’ white blood cell count obtained from the complete blood count analysis. If gametocytes were seen, the count was extended to 500 leukocytes. Parasitaemia was categorized as low (< 1000 parasites/μL blood), moderate (1000–4999 parasites/μL blood), high (5000–99,999 parasites/μL blood), and hyperparasitemia (≥ 100,000 μL) [3].

Haematological parameters were assessed using an auto-haematology analyser (MINRAY 2800 BC), following the manufacturer’s instructions. A complete blood count was obtained and anaemia was defined as Hb < 11.0 g/dL [27] and further categorized as severe (Hb < 7.0 g/dL), moderate (Hb between 7.0 and 10.0 g/dL), and mild (> 10 Hb < 11 g/dL) as reported by Cheesbrough [34]. Malarial anaemia (MA) was defined as children with a malaria-positive smear for P. falciparum parasitaemia (of any density) and Hb < 11 g/dL. Non-malarial anaemia was defined as children with anaemia and without a malaria-positive smear for P. falciparum.

Data collected was cleaned up and analysed using the IBM-Statistical Package for Social Sciences (IBM-SPSS) version 20 and Epi-info version 7. Continuous variables were summarized into means and standard deviations and categorical variables reported as frequencies and percentages were used to evaluate the descriptive statistics. The differences in proportions were evaluated using Pearson’s Chi Square (χ²). Group means were compared using analysis of variance (ANOVA), Student’s t test and non-parametric tests such Mann-Withney U test and Kruskal–Wallis test where appropriate. Parasite density was log transformed before analysis. Associations between predictor variables and primary outcomes were assessed using both bivariate and multivariate logistic regression analysis. Multi-collinearity test was performed for all potentially correlated variables and only variables with variance inflation factors less 2 were included in the models. Odd ratios (ORs) and 95% confidence intervals (CIs) were computed. Any covariate with a P value < 0.2 in the bivariate analysis was subsequently included in the final multivariable logistic model. Significant levels were measured at 95% CI with the level of significance set at P < 0.05.

The socio-demographic and clinical characteristics of the study participants are shown in Table 1. A total of 828 children with a mean (SD) age of 6.5 (3.6) years, residing at lowland (48.9%, 405) and highland (51.1%, 423) in the Mount Cameroon area were evaluated. There was a slight majority of females (52.4%) than males (47.6%) although not significant. Most of the study participants were in the age group 5–9 years (41.3%) and the least in the age group 10–14 years (23.8%). A greater proportion of the parents/guardians of the children had a primary level of education (52.2%) followed by those having secondary school education (35.6%).

The proportion of children who slept under a mosquito net the previous night of the study was (49.9%). Fever, malaria parasite, anaemia and malnutrition were observed in 9.5% (79), 41.7% (345), 56.2% (465) and 34.8% of the children, respectively.

The prevalence of falciparum malaria among the 828 children varied with altitude. Children of the lowlands had a significantly higher (P = 0.004) prevalence of malaria parasite (46.7%) than those of the highlands (36.9%), as shown in Table 2. Similarly, the geometric mean parasite density (GMPD) was higher in children of the lowland than their highland counterparts, although the difference was not significant. Malaria parasite prevalence was comparable between males (40.1%) and females (43.1%). A significant difference (P = 0.018) was observed with age, with the 5–9 years age group having the highest prevalence (46.8%) followed by the < 5 years age group (40.5%) and least, the 10–14 age group.

As shown in Fig. 1, the prevalence of low and moderate malaria parasitaemia was marginally significant (P = 0.048) in children from low altitude when compared with their high-altitude counterparts and the low parasite density category was the most common in both settings.

A logistic regression model demonstrated altitude (P = 0.008), age groups (P = 0.006) and water source (P = 0.005) as significant predictors of malaria parasite prevalence as shown in Table 3. Children of the lowland were 1.48 times more likely to suffer from malaria than those of the highland. Children in the age group 5–9 years were significantly associated with a 1.69 fold higher odds of infection than their contemporaries. In addition, children from households where the domestic water was collected from an open source (streams/springs) were 1.81 fold more likely to have Plasmodium infection when compared with those whose domestic water was collected from a close source (tap and borehole).

The prevalence of anaemia was significantly higher (P < 0.001) in the youngest age group (66.1%) than those older and in children who were malaria parasite positive (66.4%) than those negative as shown in Table 4. Anaemia prevalence was however comparable in children of the different altitudes, sexes and nutritional status.

Relating to anaemia severity, children in highland had a higher prevalence of severe (2.5%) and moderate anaemia (14.8%) than children in lowland (0.9% and 6.3%, respectively) and the difference was statistically significant at P = 0.005.

As shown in Table 4, the prevalence of moderate and mild anaemia was comparable in males and females. Although the difference was not statistically significant, the youngest age group had the highest prevalence in severe (2.6%) and moderate anaemia (14.1%) when compared with the older age groups while, malnourished children suffered more with moderate anaemia (12.3%) when compared with healthy children.

As shown on Fig. 2, malnourished children had significantly lower mean haemoglobin level when compared with well-nourished children (P < 0.008). In addition, stunted children had a significantly lower mean haemoglobin level when compared with those without stunting (P < 0.001).

The logistic regression model with anaemia status as dependent variable and altitude, age, gender, level of education and marital status of guardian, nutritional status, malaria status as well as fever history as the independent variables revealed the age group (P = < 0.001) malaria status (P = < 0.001) and fever in the past 2 days (P < 0.04) as significant risk factors of anaemia as shown in Table 5. Children < 5 years of age, those between 5 and 9 years, malaria parasite positive and those who had fever in the past 2 days were 3, 2, 2 and 1.52 times, respectively more likely to be anaemic than their counterparts.

As shown in Fig. 3, the prevalence of malnutrition was higher in children of the lowland (37.3%) than highland (32.4%) although not statistically significant. However, more males were malnourished (38.6%) when compared with females (31.3%) and the difference was significant at P = 0.03. While stunting (P = 0.01) and underweight (P = 0.04) were significantly higher in males, the difference in prevalence of wasting among the sexes was not significant (P = 0.89).

The prevalence of malnutrition varied significantly (P < 0.001) with the age group. The highest prevalence was observed in children of the < 5 years age group (55.7%) and lowest in children 5–9 years old (21.1%). Similarly, the prevalence of stunting (35.6%), underweight (32.9%) and wasting (29.0%) was higher in the under-five age group compared to the older children (Fig. 3).

A bivariate analysis revealed being male (P = 0.004) and children of the < 5 years age group (P < 0.001) were significantly at odds of being malnourished. However, in the final multivariable model, age was the only significant predictor where children under 5 years of age were three times more likely to be malnourished than those older as shown in Table 6.