A Qualitative Study of the Experience of COVID-19 Patients in Burkina Faso.

In Burkina Faso, the health system is characterized by systemic insufficient and antiquated health-care infrastructures. Consequently, few health-care establishments have the required resources to diagnose and manage patients with COVID-19, and fewer still have intensive care facilities for severely ill patients with COVID. Furthermore, there is a widespread scarcity of qualified health-care staff. The aim of this study was to explore the experiences of patients with COVID-19 who recovered after being cared for in Bobo Dioulasso and Ouagadougou. Using individual semistructured interviews, we performed a cross-sectional qualitative, descriptive study from June 12 to 30, 2020 with the aid of 13 well-educated patients who had survived COVID-19. The results reveal that prior to hospital admission, the main reason that prompted patients to seek care was onset of symptoms of COVID-19, regardless of whether they had been in contact with suspected or confirmed cases. Transmission was mainly believed to have occurred in the community, in the hospital, and during travel. Patient management was punctuated by frequent self-medication with medicinal plants or pharmaceutical drugs. The participants reported a negative perception of hospitalization or home-based management, with several forms of stigmatization, but a positive perception influenced by the satisfactory quality of management in health-care centers. This report of patient experiences could be helpful in improving the management of COVID-19 in Burkina Faso, both in the health-care setting and in home-based care.

The burden of the coronavirus disease 2019 virus infection in Burkina Faso: Results from a World Health Organization UNITY population-based, age-stratified sero-epidemiological investigation.

BACKGROUND: This study aimed to estimate the anti-SARS-CoV-2 antibody seroprevalence in the general population of Bobo-Dioulasso and Ouagadougou (Burkina Faso). METHODS: We collected from March to April 2021 blood samples from randomly selected residents in both main cities based on the World Health Organization (WHO) sero-epidemiological investigations protocols and tested them with WANTAI SARS-CoV-2 total antibodies enzyme-linked immunosorbent assay (ELISA) kits intended for qualitative assessment. We also recorded participants' socio-demographic and clinical characteristics and information on exposure to SARS-CoV-2. Data were analysed with descriptive and comparative statistics. RESULTS: We tested 5240 blood samples collected between 03 March and 16 April 2021. The overall test-adjusted seroprevalence for SARS-CoV-2 antibodies was (67.8% [95% CI 65.9-70.2]) (N = 3553/3982). Seroprevalence was highest among participants aged 15-18 years old (74.2% [95% CI 70.5-77.5]) (N = 465/627), compared with those aged 10-14 years old (62.6% [95% CI 58.7-66.4]) (N = 395/631), or those over 18 (67.6% [95% CI 66.2-69.1]) (N = 2693/3982). Approximately 71.0% (601/860) of participants aged 10-18 years old who tested positive for SARS-CoV-2 antibodies experienced no clinical COVID-19 symptoms in the weeks before the survey, compared with 39.3% (1059/2693) among those aged over 18 years old. CONCLUSION: This study reports the results of the first known large serological survey in the general population of Burkina Faso. It shows high circulation of SARS-CoV-2 in the two cities and a high proportion of asymptomatic adolescents. Further studies are needed to identify the SARS-CoV-2 variants and to elucidate the factors protecting some infected individuals from developing clinical COVID-19.

Spatio-Temporal Determinants of Dengue Epidemics in the Central Region of Burkina Faso.

The aim of this study was to analyze the spatio-temporal distribution and determinants of the 2017 dengue epidemic in Burkina Faso. A principal component analysis of meteorological and environmental factors was performed to reduce dimensions and avoid collinearities. An initial generalized additive model assessed the impact of the components derived from this analysis on dengue incidence. Dengue incidence increased mainly with relative humidity, precipitation, normalized difference vegetation index and minimum temperature with an 8-week lag. A Kulldoff Satscan scan was used to identify high-risk dengue clusters, and a second generalized additive model assessed the risk of a health area being at high risk according to land-use factors. The spatio-temporal distribution of dengue fever was heterogeneous and strongly correlated with meteorological factors. The rural communes of Sabaa and Koubri were the areas most at risk. This study provides useful information for planning targeted dengue control strategies in Burkina Faso.

Incidence rate and predictors of COVID-19 in the two largest cities of Burkina Faso - prospective cohort study in 2021 (ANRS-COV13).

BACKGROUND: Early data on COVID-19 (based primarily on PCR testing) indicated a low burden in Sub-Saharan Africa. To better understand this, this study aimed to estimate the incidence rate and identify predictors of SARS-CoV-2 seroconversion in the two largest cities of Burkina Faso. This study is part of the EmulCOVID-19 project (ANRS-COV13). METHODS: Our study utilized the WHO Unity protocol for cohort sero-epidemiological studies of COVID-19 in general population. We conducted random sampling stratified by age group and sex. Individuals aged 10 years and older in the cities of Ouagadougou and Bobo-Dioulasso, Burkina Faso were included and surveyed at 4 time points, each 21 days apart, from March 3 to May 15, 2021. WANTAI SARS-CoV-2 Ab ELISA serological tests were used to detect total antibodies (IgM, IgG) in serum. Predictors were investigated using Cox proportional hazards regression. RESULTS: We analyzed the data from 1399 participants (1051 in Ouagadougou, 348 in Bobo-Dioulasso) who were SARS-CoV-2 seronegative at baseline and had at least one follow-up visit. The incidence rate of SARS-CoV-2 seroconversion was 14.3 cases [95%CI 13.3-15.4] per 100 person-weeks. The incidence rate was almost three times higher in Ouagadougou than in Bobo-Dioulasso (Incidence rate ratio: IRR = 2.7 [2.2-3.2], p < 0.001). The highest incidence rate was reported among women aged 19-59 years in Ouagadougou (22.8 cases [19.6-26.4] per 100 person-weeks) and the lowest among participants aged 60 years and over in Bobo-Dioulasso, 6.3 cases [4.6-8.6] per 100 person-weeks. Multivariable analysis showed that participants aged 19 years and older were almost twice as likely to seroconvert during the study period compared with those aged 10 to 18 years (Hazard ratio: HR = 1.7 [1.3-2.3], p < 0.001). Those aged 10-18 years exhibited more asymptomatic forms than those aged 19 years and older, among those who achieved seroconversion (72.9% vs. 40.4%, p < 0.001). CONCLUSION: The spread of COVID-19 is more rapid in adults and in large cities. Strategies to control this pandemic in Burkina Faso, must take this into account. Adults living in large cities should be the priority targets for vaccination efforts against COVID-19.

Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country.

The COVID-19 outbreak has spread all around the world in less than four months. However, the pattern of the epidemic was different according to the countries. We propose this paper to describe the transmission network and to estimate the serial interval and the reproductive number of the novel coronavirus disease (COVID-19) in Burkina Faso, a Sub-Saharan African country. Data from the COVID-19 response team was analyzed. Information on the 804 first detected cases were pulled together. From contact tracing information, 126 infector-infectee pairs were built. The principal infection clusters with their index cases were observed, principally the two major identified indexes in Burkina. However, the generations of infections were usually short (less than four). The serial interval was estimated to follow a gamma distribution with a shape parameter 1.04 (95% credibility interval: 0.69-1.57) and a scale parameter of 5.69 (95% credibility interval: 3.76-9.11). The basic reproductive number was estimated at 2.36 (95% confidence interval: 1.46-3.26). However, the effective reproductive number decreases very quickly, reaching a minimum value of 0.20 (95% confidence interval: 0.06-0.34). Estimated parameters are made available to monitor the outbreak in Sub-Saharan African countries. These show serial intervals like in the other continents but less infectiousness.

Incidence and Predictors of Imported Cases of COVID-19 in Burkina Faso.

BACKGROUND: To limit the spread of COVID-19 due to imported cases, Burkina Faso has set up quarantine measures for arriving passengers. We aimed to determine the incidence and predictors of imported cases of COVID-19 in Burkina Faso. METHODS: A prospective cohort study was performed using data from passengers arriving at the airport from April 9 to August 31, 2020. The data was extracted from the District Health Information Software 2 (DHIS2) platform. Cox regression was used to identify predictors of imported cases of COVID-19. RESULTS: Among 6,332 travelers who arrived in the study period, 173 imported cases (2.7%) were recorded. The incidence rate was 1.9 cases per 1,000 traveler-days (95%CI: 1.6-2.2 per 1,000). Passengers arriving in April (Adjusted hazard ratio [aHR] = 3.56; 95%CI: 1.62-7.81) and May (aHR = 1.92; 95% CI: 1.18-3.12) were more at risk of being tested positive compared to those arriving in August, as well as, passengers presenting with one symptom (aHR = 3.71; 95% CI: 1.63-8.43) and at least two symptoms (aHR = 10.82; 95% CI: 5.24-22,30) compared to asymptomatic travelers. CONCLUSIONS: The incidence of imported cases was relatively low in Burkina Faso between April and August 2020. The period of travel and the presence of symptoms at arrival predicted the risk of being tested positive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This is essential in the context of the high circulation of virus variants worldwide and the low local capacity to perform genotyping tests to strengthen the surveillance and screening capacities at the points of entry into the country.

Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016-2017.

Malaria control and prevention programs are more efficient and cost-effective when they target hotspots or select the best periods of year to implement interventions. This study aimed to identify the spatial distribution of malaria hotspots at the village level in Diébougou health district, Burkina Faso, and to model the temporal dynamics of malaria cases as a function of meteorological conditions and of the distance between villages and health centres (HCs). Case data for 27 villages were collected in 13 HCs. Meteorological data were obtained through remote sensing. Two synthetic meteorological indicators (SMIs) were created to summarize meteorological variables. Spatial hotspots were detected using the Kulldorf scanning method. A General Additive Model was used to determine the time lag between cases and SMIs and to evaluate the effect of SMIs and distance to HC on the temporal evolution of malaria cases. The multivariate model was fitted with data from the epidemic year to predict the number of cases in the following outbreak. Overall, the incidence rate in the area was 429.13 cases per 1000 person-year with important spatial and temporal heterogeneities. Four spatial hotspots, involving 7 of the 27 villages, were detected, for an incidence rate of 854.02 cases per 1000 person-year. The hotspot with the highest risk (relative risk = 4.06) consisted of a single village, with an incidence rate of 1750.75 cases per 1000 person-years. The multivariate analysis found greater variability in incidence between HCs than between villages linked to the same HC. The time lag that generated the better predictions of cases was 9 weeks for SMI1 (positively correlated with precipitation variables) and 16 weeks for SMI2 (positively correlated with temperature variables. The prediction followed the overall pattern of the time series of reported cases and predicted the onset of the following outbreak with a precision of less than 3 weeks. This analysis of malaria cases in Diébougou health district, Burkina Faso, provides a powerful prospective method for identifying and predicting high-risk areas and high-transmission periods that could be targeted in future malaria control and prevention campaigns.

COVID-19 epidemiological, sociological and anthropological investigation: study protocol for a multidisciplinary mixed methods research in Burkina Faso.

BACKGROUND: The world has high hopes of vaccination against COVID-19 to protect the population, boost economies and return to normal life. Vaccination programmes are being rolled out in high income countries, but the pandemic continues to progress in many low-and middle-income countries (LMICs) despite implementation of strict hygiene measures. We aim to present a comprehensive research protocol that will generate epidemiological, sociological and anthropological data about the COVID-19 epidemic in Burkina Faso, a landlocked country in West Africa with scarce resources. METHODS: We will perform a multidisciplinary research using mixed methods in the two main cities in Burkina Faso (Ouagadougou and Bobo-Dioulasso). Data will be collected in the general population and in COVID-19 patients, caregivers and health care professionals in reference care centers: (i) to determine cumulative incidence of SARS-CoV-2 infection in the Burkinabe population using blood samples collected from randomly selected households according to the WHO-recommended protocol; (ii) develop a score to predict severe complications of COVID-19 in persons infected with SARS-CoV-2 using retrospective and prospective data; (iii) perform semi-structured interviews and direct observation on site, to describe and analyze the healthcare pathways and experiences of patients with COVID-19 attending reference care centers, and to identify the perceptions, acceptability and application of preventive strategies among the population. DISCUSSION: This study will generate comprehensive data that will contribute to improving COVID-19 response strategies in Burkina Faso. The lessons learned from the management of this epidemic may serve as examples to the country authorities to better design preventive strategies in the case of future epidemics or pandemics. The protocol was approved by the Ministry for Health (N° 2020-00952/MS/CAB/INSP/CM) and the Health Research Ethics Committee in Burkina Faso (N° 2020-8-140).

Factors associated with the use of insecticide-treated nets: analysis of the 2018 Burkina Faso Malaria Indicator Survey.

BACKGROUND: Sleeping under an ITN reduces contact with mosquitoes through the combination of a physical barrier and an insecticidal effect, which reduces the incidence of malaria. The 2016-2020 Burkina Faso National Malaria Strategic Plan aims to have at least 90% of the population, 100% of children under age 5, and 100% of pregnant women sleep under an ITN. METHODS: The analysis examines individual, household, and community-level factors associated with ITN usage. According to the 2017-18 Burkina Faso MIS, 58% of individuals in households that own at least one ITN reported that they slept under an ITN on the night before the survey. RESULTS: The use of ITNs was significantly associated with individual, household, and community-level variables that included age, gender, age of household head, number of sleeping rooms, wealth, malaria prevalence, residence, and region. CONCLUSIONS: The results highlight areas of intervention at the individual, household, and community levels that can increase ITN use.

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012.

BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

"Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling".

BACKGROUND: In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity. METHODS: Using the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate. RESULTS: The level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates. CONCLUSION: Our results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method. TRIAL REGISTRATION: The dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry).

Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso.

BACKGROUND: With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. METHODS: Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff's elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. RESULTS: Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03-1.40). CONCLUSION: These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.

How does the cellular phone help in epidemiological surveillance? A review of the scientific literature.

BACKGROUND: In the field of epidemiological surveillance, no systematic literature review appears to exist of implemented projects using cellular phone technology. METHOD: We performed a systematic literature review using the Pubmed and Scopus databases to retrieve articles published up to December 2015. We analyzed information reported in these publications according to the mobile health (mHealth) evidence reporting and assessment (mERA) checklist, and complemented this work with specific items related to epidemiology, in order to clarify the types of results reported and summarized in this context. RESULTS: Thirty-three articles were selected and reviewed. Each article was related to a different project. Two mERA items were systematically and fully reported, while two others were never reported. Three projects were deployed in very specific zones. Most of the projects were implemented in Africa. Infectious diseases were the elements most monitored. Most projects were based on daily data collection and SMS transmission. Economic assessment was limited to SMS, mobile phone, and implementation costs. DISCUSSION-CONCLUSION: Although suitable for epidemiologic surveillance, the mERA checklist needs further interpretation. The technical and transmission modes of cellular phone use varied greatly from one study to another. No evaluation of the interoperability capabilities of cellular phones with other applications or sub-systems was possible.

Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015.

BACKGROUND: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. METHODS: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. RESULTS: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. CONCLUSION: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.