Malaria Case Fatality Rate among Children under Five in Burkina Faso: An Assessment of the Spatiotemporal Trends Following the Implementation of Control Programs

Abstract

Reducing the 2015 level of malaria mortality by 90% by 2030 is a goal set by the World Health Organization (WHO). In Burkina Faso, several malaria control programs proven to be effective were implemented over the last decade. In parallel, the progressive strengthening of the health surveillance system is generating valuable data, which represents a great opportunity for analyzing the trends in malaria burden and assessing the effect of these control programs. Complementary programs were rolled out at different time points and paces, and the present work aims at investigating both the spatial and temporal pattern of malaria case fatality rate (mCFR) by considering the effect of combining specific and unspecific malaria control programs. To this end, data on severe malaria cases and malaria deaths, aggregated at health district level between January 2013 and December 2018, were extracted from the national health data repository (ENDOS-BF). A Bayesian spatiotemporal zero-inflated Poisson model was fitted to quantify the strength of the association of malaria control programs with monthly mCFR trends at health district level. The model was adjusted for contextual variables. We found that monthly mCFR decreased from 2.0 (95% IC 1.9-2.1%) to 0.9 (95% IC 0.8-1.0%) deaths for 100 severe malaria cases in 2013 and 2018, respectively. Health districts with high mCFR were identified in the northern, northwestern and southwestern parts of the country. The availability of malaria rapid diagnosis tests (IRR: 0.54; CrI: 0.47, 0.62) and treatment (IRR: 0.50; CrI: 0.41, 0.61) were significantly associated with a reduction in the mCFR. The risk of dying from malaria was lower in the period after the free healthcare policy compared with the period before (IRR: 0.47; CrI: 0.38, 0.58). Our findings highlighted locations that are most in need of targeted interventions and the necessity to sustain and strengthen the launched health programs to further reduce the malaria deaths in Burkina Faso.

Keywords: Bayesian; fatality; health programs; malaria; spatiotemporal.

Figure A1

Spatiotemporal trends in the crude mCFR in children under five (January 2013–December 2018. T1, T2, T3 and T4 represents the first, second, third and fourth trimester of each year respectively.

Figure A1

Spatiotemporal trends in the crude mCFR in children under five (January 2013–December 2018. T1, T2, T3 and T4 represents the first, second, third and fourth trimester of each year respectively.

Figure A1

Spatiotemporal trends in the crude mCFR in children under five (January 2013–December 2018. T1, T2, T3 and T4 represents the first, second, third and fourth trimester of each year respectively.

Figure A1

Spatiotemporal trends in the crude mCFR in children under five (January 2013–December 2018. T1, T2, T3 and T4 represents the first, second, third and fourth trimester of each year respectively.

Figure 1

Evolution of malaria case fatality rate in children under five from 2013 to 2018.

Figure 2

Spatiotemporal trends in the predicted malaria case fatality rate per 100 severe malaria cases in children under five, estimated from the Bayesian spatiotemporal model (January 2013–December 2018). T1, T2, T3 and T4 represent the first, second, third and fourth trimester (quarter) of each year respectively.

Figure 2

Spatiotemporal trends in the predicted malaria case fatality rate per 100 severe malaria cases in children under five, estimated from the Bayesian spatiotemporal model (January 2013–December 2018). T1, T2, T3 and T4 represent the first, second, third and fourth trimester (quarter) of each year respectively.

Figure 2

Spatiotemporal trends in the predicted malaria case fatality rate per 100 severe malaria cases in children under five, estimated from the Bayesian spatiotemporal model (January 2013–December 2018). T1, T2, T3 and T4 represent the first, second, third and fourth trimester (quarter) of each year respectively.

Figure 3

Burkina Faso health districts severe malaria deaths among children under five: exceedance probability ($\widehat{P_r}(e^{u_i+{\zeta }_i}>1.01/y))$ of posterior relative risk based on Richardson’s classification. The number in bracket indicates the number of health districts within each category.