Effect of Free Healthcare Policy for Children under Five Years Old on the Incidence of Reported Malaria Cases in Burkina Faso by Bayesian Modelling: "Not only the Ears but also the Head of the Hippopotamus"

Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions

INTRODUCTION

Africa has a higher burden of malaria-related cases and deaths globally. Children under five accounted for over two-thirds of all malaria deaths in sub-Saharan Africa (SSA). This scoping review aims to map evidence of the prevalence, contextual factors and health education interventions of malaria amongst children under 5 years (UN5) in SSA.

METHOD

Four main databases (PubMed, Central, Dimensions and JSTOR) produced 27,841 records of literature. Additional searches in Google, Google Scholar and institutional repositories produced 37 records. Finally, 255 full-text records were further screened, and 100 records were used for this review.

RESULTS

Low or no formal education, poverty or low income and rural areas are risk factors for malaria amongst UN5. Evidence on age and malnutrition as risk factors for malaria in UN5 is inconsistent and inconclusive. Furthermore, the poor housing system in SSA and the unavailability of electricity in rural areas and unclean water make UN5 more susceptible to malaria. Health education and promotion interventions have significantly reduced the malaria burden on UN5 in SSA.

CONCLUSION

Well-planned and resourced health education and promotion interventions that focus on prevention, testing and treatment of malaria could reduce malaria burden amongst UN5 in SSA.

To What Extent Do Free Healthcare Policies and Performance-Based Financing Reduce Out-of-Pocket Expenditures for Outpatient services? Evidence From a Quasi-experimental Study in Burkina Faso

BACKGROUND

Burkina Faso has been implementing financing reforms towards universal health coverage (UHC) since 2006. Recently, the country introduced a performance-based financing (PBF) program as well as user fee removal (gratuité) policy for health services aimed at pregnant and lactating women and children under 5. We aim to assess the effect of gratuité and PBF policies on facility-based out-of-pocket expenditures (OOPEs) for outpatient services.

METHODS

Our study is a controlled pre- and post-test design using healthcare facility data from the PBF program's impact evaluation collected in 2014 and 2017. We compared OOPE related to primary healthcare use incurred by children under 5 and individuals above 5 to assess the effect of the gratuité policy on OOPE. We further compared OOPE incurred by individuals residing in PBF districts and non-PBF districts to estimate the effect of the PBF on OOPE. Effects were estimated using difference-in-differences models, distinguishing the estimation of the probability of incurring OOPE from the estimation of the magnitude of OOPE using a generalized linear model (GLM).

RESULTS

The proportion of children under 5 incurring OOPE declined significantly from 90% in 2014 to 3% in 2017. Concurrently, mean OOPE also decreased. Differences in both the probability of incurring OOPE and mean OOPE between PBF and non-PBF facilities were small. Our difference in differences estimates indicated that gratuité produced an 84% (CI -86%, -81%) reduction in the probability of incurring OOPE and reduced total OOPE by 54% (CI 63%, 42%). We detected no significant effects of PBF, either in reducing the probability of incurring OOPE or in its magnitude.

CONCLUSION

User fee removal is an effective demand-side intervention for enhancing financial accessibility. As a supply-side intervention, PBF appears to have limited effects on reducing financial burden.

The effect of malaria on haemoglobin concentrations: a nationally representative household fixed-effects study of 17,599 children under 5 years of age in Burkina Faso

Background

Although the association between malaria and anaemia is widely studied in patient cohorts, the population-representative causal effects of malaria on anaemia remain unknown. This study estimated the malaria-induced decrease in haemoglobin levels among young children in malaria-endemic Burkina Faso.

Methods

The study was based on pooled individual-level nationally representative health survey data (2010–2011, 2014, 2017–2018) from 17 599 children under 5 years of age. This data was used to estimate the effects of malaria on haemoglobin concentration, controlling for household fixed-effects, age, and sex in a series of regression analyses. The fixed-effects controlled for observed and unobserved confounding on the household level and allowed to determine the impact of malaria infection status on haemoglobin levels and anaemia prevalence. Furthermore, the diagnostic results from microscopy and rapid diagnostic tests were leveraged to provide a quasi-longitudinal perspective of acute and prolonged effects after malaria infection.

Results

The prevalence of both malaria (survey prevalence ranging from 17.4% to 65.2%) and anaemia (survey prevalence ranging from 74% to 88.2%) was very high in the included surveys. Malaria was estimated to significantly reduce haemoglobin levels, with an overall effect of − 7.5 g/dL (95% CI − 8.5, − 6.5). Acute malaria resulted in a − 7.7 g/dL (95% CI − 8.8, − 6.6) decrease in haemoglobin levels. Recent malaria without current parasitaemia decreased haemoglobin concentration by − 7.1 g/dL (95% CI − 8.3, − 5.9). The in-sample predicted prevalence of severe anaemia was 9.4% among malaria positives, but only 2.2% among children without malaria.

Conclusion

Malaria infection has a strong detrimental effect on haemoglobin levels among young children in Burkina Faso. This effect seems to carry over even after acute infection, indicating prolonged haemoglobin reductions even after successful parasite-elimination. The quasi-experimental fixed-effect approach adds a population level perspective to existing clinical evidence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03948-z.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Addressing challenges in routine health data reporting in Burkina Faso through Bayesian spatiotemporal prediction of weekly clinical malaria incidence

Sub-Saharan African (SSA) countries’ health systems are often vulnerable to unplanned situations that can hinder their effectiveness in terms of data completeness and disease control. For instance, in Burkina Faso following a workers' strike, comprehensive data on several diseases were unavailable for a long period in 2019. Weather, seasonal-malaria-chemoprevention (SMC), free healthcare, and other contextual data, which are purported to influence malarial disease, provide opportunities to fit models to describe the clinical malaria data and predict the disease spread. Bayesian spatiotemporal modeling was applied to weekly malaria surveillance data from Burkina Faso (2011–2018) while considering the effects of weather, health programs and contextual factors. Then, a prediction was used to deal with weekly missing data for the entire year of 2019, and SMC and free healthcare effects were quantified. Our proposed model accurately predicted weekly clinical malaria incidence (correlation coefficient, r = 0.90). The distribution of clinical malaria incidence was heterogeneous across the country. Overall, national predicted clinical malaria incidence in 2019 (605 per 1000 [95% CrI: 360–990]) increased by 24.7% compared with the year 2015. SMC and the interaction between free healthcare and health facility attendance were associated with a reduction in clinical malaria incidence. Our modeling approach could be a useful tool for strengthening health systems’ resilience by addressing data completeness and could support SSA countries in developing appropriate targets and indicators to facilitate the subnational control effort.

Determinants of Plasmodium falciparum multiplicity of infection and genetic diversity in Burkina Faso

BACKGROUND

Investigating malaria transmission dynamics is essential to inform policy decision making. Whether multiplicity of infection (MOI) dynamic from individual infections could be a reliable malaria metric in high transmission settings with marked variation in seasons of malaria transmission has been poorly assessed. This study aimed at investigating factors driving Plasmodium falciparum MOI and genetic diversity in a hyperendemic area of Burkina Faso.

METHODS

Blood samples collected from a pharmacovigilance trial were used for polymerase chain reaction genotyping of the merozoite surface proteins 1 and 2. MOI was defined as the number of distinct parasite genotypes co-existing within a particular infection. Monthly rainfall data were obtained from satellite data of the Global Precipitation Measurement Database while monthly malaria incidence aggregated data were extracted from District Health Information Software 2 medical data of the Center-West health regional direction.

RESULTS

In the study area, infected people harboured an average of 2.732 (± 0.056) different parasite genotypes. A significant correlation between the monthly MOI and the monthly malaria incidence was observed, suggesting that MOI could be a good predictor of transmission intensity. A strong effect of season on MOI was observed, with infected patients harbouring higher number of parasite genotypes during the rainy season as compared to the dry season. There was a negative relationship between MOI and host age. In addition, MOI decreased with increasing parasite densities, suggesting that there was a within-host competition among co-infecting genetically distinct P. falciparum variants. Each allelic family of the msp1 and msp2 genes was present all year round with no significant monthly fluctuation.

CONCLUSIONS

In high malaria endemic settings with marked variation in seasons of malaria transmission, MOI represents an appropriate malaria metric which provides useful information about the longitudinal changes in malaria transmission in a given area. Besides transmission season, patient age and parasite density are important factors to consider for better understanding of variations in MOI. All allelic families of msp1 and msp2 genes were found in both dry and rainy season. The approach offers the opportunity of translating genotyping data into relevant epidemiological information for malaria control.

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

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.