Trends in malaria admissions at the Mbakong Health Centre of the North West Region of Cameroon: a retrospective study

Abstract

Background: Malaria is the leading cause of death worldwide. It is urgent to assess the impact of interventions and scaled-up control efforts. Despite reported reduction in malaria prevalence in Africa, the trends in Cameroon are not yet fully understood. The aim of this study was to investigate the trends in malaria admissions among febrile patients seeking treatment over a seven-year period (2006-2012) in an endemic area in Cameroon, hypothesizing a declining trend. This period followed changes in malaria treatment policy. The objectives were to identify possible trends in malaria admissions and to evaluate the impact of changes to treatment guidelines on the prevalence.

Methods: Data was collected through consultation and perusal of laboratory and prescription registers of the Mbakong Health Centre. Data analysis was conducted using SPSS and SAS Statistics.

Results: Analysis revealed that 4,230 febrile patients were received from 2006-2012. Of these febrile cases, 29.30% were confirmed positive. Between 2006 and 2012 confirmed malaria positive cases of those tested fluctuated, dropping from 53.21% in 2006 to 17.20% in 2008; then rising to 35.00% in 2011 and, finally, dropping to 18.2% of those tested in 2012. The prevalence in females and males across all age groups were similar: a slightly higher risk of males to have malaria (OR = 1.08, 95% CI 0.94-1.25) were not practically significant. Of those tested, the 5 to < 15 years and the 1 to < 5 years age groups were the hardest hit by malaria in the area. A practically visible and significant association was observed between the age and gender with regards to the number of malaria positive results (Pearson ×2 = 153.675, p < 0.00001, Cramer's V = 0.352). Malaria prevalence exhibited a fluctuating yet declining trend, as observed over the 28 quarters between January, 2006 and December, 2012.

Conclusions: The changes to the treatment guidelines appear to result in a declining trend as was observed between 2006 and 2008. However, malaria admissions fluctuated between 2008 and 2012. There is, therefore, a need to step up control efforts of especially the vulnerable groups, such as the very young.

Figure 1

The map of Cameroon showing the location of the study site.

Figure 2

Distribution of malaria positive cases over seven years at the Mbakong Health Centre 2006–2012. a) Numbered dots indicate when different interventions were introduced: 1) implemented change in malaria treatment policy from monotherapy to ACTs, 2) New treatment guidelines made available to healthcare workers, 3) Nationwide distribution of 8 million LLIN and re-introduction of community management of malaria, 4) Roll out of RDTs for testing before treatment both at the community and health facility level. b) Piecewise Regression of admissions over months illustrating the change in prevalence from January 2006 to December 2012: Line 1 represents a sharp decline in malaria prevalence from 2006–2008 and Line 2 represents a gradual rise in prevalence from 2008–2012.

Figure 3

Three models were used to investigate seasonal trend in malaria occurrence. The graph shows monthly proportions fitted into the curve estimation model. Both confirm the absence of a seasonal trend as none of the models were statistically or practically significant.

Figure 4

Graph illustrating the distribution of the proportion of positive cases according to the months of the year over time using the curve estimation model ( Linear Quadratic, and Cubic). The cubic model was found to best fit the data with R² = 0.638. Time is divided into 84 monthly observations and includes Jan. 2006 to Dec. 2012.

Figure 5

Graph illustrating the distribution of positive cases according to the quarters of the year over time using the curve estimation model: Linear Quadratic, and Cubic? The cubic model was found to best fit the data with R² = 0.638.

Figure 6

Graphs depicting malaria prevalence as a proportion of those tested over the study period across different age groups from 2006–2012. The age group 1 to under 5 years and 5 to under 15 years old children are revealed to be the most affected across all the years and were the hardest hit during the upsurge in 2011.