Developing Bayesian spatio-temporal models of assess the relation between malaria transmission and mortality in Burkina Faso

Malaria control remains a major public health challenge especially in sub-Saharan African countries. In spite of the rapid decline observed in malaria mortality in Africa over the last decade due to scaling up of control interventions and social/economic development, malaria mortality figures remain unacceptably high. An estimated 198 million cases of malaria worldwide led to nearly 584,000 deaths in 2013. The majority of the illnesses (85%) and the case fatalities (90%) occur in Africa taking its greatest toll among young children under five years of age. Beside the deaths toll, repeated clinical malaria episodes cast an enormous economic burden on households. Predicting the effectiveness of malaria interventions at a given place requires appropriate information on both mortality and transmission levels in order to ascertain the level of efforts required to achieve a significant reduction in morbidity as well as the number of deaths that could be prevented. This quantitation is needed for estimating the burden of the disease based on different transmission levels and for building models, which incorporate this relationship in order to predict the likely effects of malaria interventions on mortality. Yet, for many of the sub-Saharan countries, most severely burdened by malaria, these crucial estimates are lacking making it difficult to accurately predict the likely impact of malaria interventions on mortality. The Malaria Transmission Intensity and Mortality Burden Across Africa, INDEPTH-MTIMBA project was initiated in the 2002 in a number of Health and Demographic Surveillance Systems (HDSS) sites. HDSS are sites that are routinely monitor all life events in a certain area and are used for estimating mortality in the absence of complete registration of deaths and births in many developing countries. The MTIMBA project aimed at assessing the levels of malaria transmission intensity, establishing the relationship between all-cause, malaria-specific mortality and malaria transmission intensity taking into account the effect of disease control interventions. MTIMBA collected georeferenced entomological data, biweekly during a period of 3-4 years. One of the HDSS sites of the MTIMBA project was Nouna in Burkina Faso, however data have not yet been analysed. Previous studies have tried to assess the relation between malaria endemicity and mortality using mortality data from the Demographic Health Surveys (DHS) and malaria data from Malaria Indicator Survey (MIS). In Burkina Faso, the DHS-Multiples Indicator Cluster Survey (BFDHS-MICS) of 2010 was the first survey that collected georeferenced data of both child mortality and malaria endemicity across the country. The overall goal of the thesis is to assess the association between malaria transmission and mortality at different geographical scales in Burkina Faso. The specific objectives of the research are to (i) obtain time- dependent and spatially explicit estimates of entomological inoculation rate (EIR) within the Nouna HDSS site; (ii) obtain spatially explicit estimates of malaria parasite risk, number of infected children and assess the effects of malaria interventions in Burkina Faso; (iii) assess the relation between infant and under-five mortality and malaria endemicity in Burkina; (iv) assess the relationship between malaria transmission and mortality (all-cause and malaria-specific) across different age groups in Nouna HDSS and (v) assess the ability of verbal autopsies to diagnose malaria as a cause of death using the malaria-transmission relation as a gold standard. We addressed the above objectives by employing Bayesian spatio-temporal models and analysing the Burkina Faso DHS (BFDHS-MICS) 2010, the MTIMBA and the mortality databases from the Nouna HDSS site. In chapter 2, the MTIMBA data were analysed to obtain surfaces of malaria transmission across the Nouna HDSS. In particular, Bayesian geostatistical zero-inflated binomial and negative binomial models including harmonic seasonal terms, temporal trends and climatic remotely sensed proxies were applied to assess spatio-temporal variation of sporozoite rate and mosquito density in the study area. Bayesian variable selection was applied to determine the most important climatic predictors and elapsing (lag) time between climatic suitability and malaria transmission. Bayesian kriging was used to predict mosquito density and sporozoite rate at unsampled locations. These estimates were converted to covariate and season-adjusted maps of entomological inoculation rates. The results showed that Anopheles gambiae is the most predominant vector (79.3%) and is more rain-dependant than its sibling Anopheles funestus (20.7%). Variable selection suggested that the two vector species react differently to different climatic conditions. Prediction maps of EIR depicted a strong spatial and temporal heterogeneity in malaria transmission risk despite the relatively small geographical extend of the study area. In chapter 3, Bayesian geostatistical models and BFDHS-MICS 2010 survey data were used to assess the effects of health interventions related to insecticide-treated nets (ITNs), indoor residual spray (IRS), artemisinin-based combinations therapy (ACT) coverage associated with childhood malaria parasite risk at national and sub-national level after taking into account geographical disparities of climatic/environmental and socioeconomic factors. Several ITN coverage measures were calculated and Bayesian variable selection was used to identify the most important ones. Parasitaemia risk surfaces depicting spatial patterns of infections were estimated. The results showed that the population adjusted predicted parasitaemia risk ranges from 4.0 % in Kadiogo province to 82% in Kompienga province. The effect of ITN coverage was not important at national level; however, ITNs had an important protective effect in Ouagadougou as well as in three districts in the western part of the country with high parasitaemia prevalence and low-to- moderate coverage. There was a large variation in ACT coverage between the districts. Although at national level the ACT effects on parasitaemia risk was not important, at sub-national level, 18 districts around Ouagadougou delivered effective treatment. In chapter 4, we used data form the Burkina Faso first nationally representative household survey focusing on malaria-related indicators, BFDHS-MICS 2010 and apply Bayesian geostatistical Weibull survival models to explore the relationship between malaria and infant/child mortality in Burkina Faso after adjusting for, both individual child and household or family characteristics as well as mother’s birth history. There is a significant relationship between malaria endemicty and child survival in urban settings. Children living in the urban settings with endemicity level above 75% are at higher mortality hazards. Other predictors of infants and child survival are those related to biological (birth size, mother age at birth), demographic socioeconomic and antenatal care factors. In chapter 5, we used entomological data, which, were collected biweekly from 2001-2004, and mortality data extracted from the Nouna HDSS database. We address spatial misalignment between the two data sources by obtaining EIR estimates at the mortality locations using Bayesian spatio-temporal models. Analyses were adjusted for socioeconomic status (SES) and ITN coverage. Time to death was treated at monthly interval and Bayesian geostatistical logistic regression approximating Cox proportional hazard model and incorporating the predicted EIR as covariate with measurement error were fitted. The mortality rates were highest in 2001, 17 (95% CI: 15.1, 19.1) and 2003, 13.8 (95% CI: 12.95, 14.8). The overall mortality rate over the study period was 11.3 (95% CI: 10.8, 11.7). The highest mortality rates were observed in children and old age groups with the respective rates of 23.9 (95% CI: 22.4, 25.4) and 81.9 (95% CI: 75.8, 88.5). A positive log-natural relationship between mortality and EIR was found among children (1-4 years), while a protective effect was found among adolescents/adults (15-59 years). The highest mortality risk associated with EIR was observed among children (5%). In chapter 6, we used the same approach as in the previous chapter however focusing the interest on malaria specific mortality in order to assess the relationship between malaria specific mortality and EIR within the Nouna MTIMBA-HDSS site. The sensitivity and specificity of the physician-certified verbal autopsy (PCVA) were also assessed. Results showed that malaria mortality rates were highest in years 2001, 5.4 (95% CI: 4.4, 6.6) and 2003, 4.1 (95% CI: 3.6, 4.7). A significant positive natural logarithmic relationship was found between malaria exposure and mortality among children, with hazard ratio (HR) of 1.06(95%CI:1.03,1.08). Thepercentageofdeathsassigned-malariaascauseinVAwashighestinchildren and adults with respectively 45% and 35.3%. The percentage of deaths attributable to malaria exposure was in old-age group (93.9%). The overall specificity of the PCVA is 0.70. Results of this work contribute to a better understanding of the interplay between environmental/climatic conditions and malaria transmission, which is important not only for delivering interventions at the right time but also for developing predictive models to support early warning systems (EWS). The estimated risk and intervention effect maps are valuable tools for identifying high-risk areas and areas with less effective interventions in order to improve malaria control in Burkina Faso. These outputs can serve as benchmarks to evaluate the effectiveness of future control interventions and progress of the efforts towards disease control. Results from the mortality-malaria transmission analyses improve our understanding of the relationship between malaria transmission, all-cause and malaria specific mortality in Nouna region

Childhood mortality and its association with household wealth in rural and semi-urban Burkina Faso

Background This study aimed to investigate the relationship between household wealth and under-5 year mortality in rural and semi-urban Burkina Faso. Methods The study included 15 543 children born between 2005 and 2010 in the Nouna Health and Demographic Surveillance System. Information on household wealth was collected in 2009. Two separate wealth indicators were calculated by principal components analysis for the rural and the semi-urban households, which were then divided into quintiles accordingly. Multivariable Cox proportional hazards regression was used to study the effect of the respective wealth measure on under-5 mortality. Results We observed 1201 childhood deaths, corresponding to 5-year survival probability of 93.6% and 88% in the semi-urban and rural area, respectively. In the semi-urban area, household wealth was significantly related to under-5 mortality after adjustment for confounding. There was a similar but non-significant effect of household wealth on infant mortality, too. There was no effect of household wealth on under-5 mortality in rural children. Conclusions Results from this study indicate that the more privileged children from the semi-urban area with access to piped water and electricity have an advantage in under-5 survival, while under-5 mortality in the rural area is rather homogeneous and still relatively hig

Episodes of confirmed malaria and associated factors in patients attending selected health facilities, Liberia, 2015-2016: a cross-sectional study.

Introduction: Malaria remains a public health concern and one of the top three causes of morbidities at outpatient department in Liberia. To implement preventive, diagnostic, and malaria treatment measures, the annual malaria episodes is required for planning of interventions and procurement. We determined the median annual malaria episodes and factors associated with more than one episode. Methods: A retrospective cross-sectional study; January 2015 to December 2016-confirmed malaria in 15 randomly selected health facilities was conducted. Facilities stratified into three groups based on cases reported per annum; those with > 1,500 malaria cases per annum as high burden facility, 1,000 – 1,500 cases as moderate, and < 1,000 cases as low. One health facility per strata randomly selected from each of the five health divisions. Data extracted from health records were patient's ID(identifier), age, sex, address, visit date and diagnosis. Frequency, proportion, mean, median and interquartile range of episodes' data calculated. Results: Of the 35,249 malaria cases reported, 82% (29,236) met the study criteria. Children age ≤ 5 had the highest individual annual episodes. The overall annual median (Interquartile range, IQR) malaria episodes was 1 (IQR, 12). Trends of confirmed malaria increased from 454 cases in January 2015 to 909 in December 2016. Attendance in North Central region, and age less than 5 were significant factors associated with more than one episode of malaria. Conclusion: Children under five reported high annual individual episodes of malaria. The overall annual median episode is one. No difference between the WHO estimated annual episode used for malaria programmatic planning

Bayesian variable selection in modelling geographical heterogeneity in malaria transmission from sparse data : an application to Nouna Health and Demographic Surveillance System (HDSS) data, Burkina Faso

Quantification of malaria heterogeneity is very challenging, partly because of the underlying characteristics of mosquitoes and also because malaria is an environmentally driven disease. Furthermore, in order to assess the spatial and seasonal variability in malaria transmission, vector data need to be collected repeatedly over time (at fixed geographical locations). Measurements collected at locations close to each other and over time tend to be correlated because of common exposures such as environmental or climatic conditions. Non- spatial statistical methods, when applied to analyze such data, may lead to biased estimates. We developed rigorous methods for analyzing sparse and spatially correlated data. We applied Bayesian variable selection to identify the most important predictors as well as the elapsing time between climate suitability and changes in entomological indices.; Bayesian geostatistical zero-inflated binomial and negative binomial models including harmonic seasonal terms, temporal trends and climatic remotely sensed proxies were applied to assess spatio-temporal variation of sporozoite rate and mosquito density in the study area. Bayesian variable selection was employed to determine the most important climatic predictors and elapsing (lag) time between climatic suitability and malaria transmission. Bayesian kriging was used to predict mosquito density and sporozoite rate at unsampled locations. These estimates were converted to covariate and season-adjusted maps of entomological inoculation rates. Models were fitted using Markov chain Monte Carlo simulation. The results show that Anophele. gambiae is the most predominant vector (79.29%) and is more rain-dependant than its sibling Anophele. funestus (20.71%). Variable selection suggests that the two species react differently to different climatic conditions. Prediction maps of entomological inoculation rate (EIR) depict a strong spatial and temporal heterogeneity in malaria transmission risk despite the relatively small geographical extend of the study area. CONCLUSION: Malaria transmission is very heterogeneous over the study area. The EIR maps clearly depict a strong spatial and temporal heterogeneity despite the relatively small geographical extend of the study area. Model based estimates of transmission can be used to identify high transmission areas in order to prioritise interventions and support research in malaria epidemiology

A malaria risk map of Kinshasa, Democratic Republic of Congo

In Kinshasa, malaria remains a major public health problem but its spatial epidemiology has not been assessed for decades now. The city's growth and transformation, as well as recent control measures, call for an update. To identify highly exposed communities and areas where control measures are less critically needed, detailed risk maps are required to target control and optimize resource allocation.; In 2009 (end of the dry season) and 2011 (end of the rainy season), two cross-sectional surveys were conducted in Kinshasa to determine malaria prevalence, anaemia, history of fever, bed net ownership and use among children 6-59 months. Geo-referenced data for key parameters were mapped at the level of the health area (HA) by means of a geographic information system (GIS).; Among 7517 children aged 6-59 months from 33 health zones (HZs), 6661 (3319 in 2009 and 3342 in 2011) were tested for both malaria (by Rapid Diagnostic Tests) and anaemia, and 856 (845 in 2009 and 11 in 2011) were tested for anaemia only. Fifteen HZs were sampled in 2009, 25 in 2011, with seven HZs sampled in both surveys. Mean prevalence for malaria and anaemia was 6.4 % (5.6-7.4) and 65.1 % (63.7-66.6) in 2009, and 17.0 % (15.7-18.3) and 64.2 % (62.6-65.9) in 2011. In two HZs sampled in both surveys, malaria prevalence was 14.1 % and 26.8 % in Selembao (peri-urban), in the 2009 dry season and 2011 rainy season respectively, and it was 1.0 % and 0.8 % in Ngiri Ngiri (urban). History of fever during the preceding two weeks was 13.2 % (12.5-14.3) and 22.3 % (20.8-23.4) in 2009 and 2011. Household ownership of at least one insecticide-treated net (ITN) was 78.7 % (77.4-80.0) and 65.0 % (63.7-66.3) at both time points, while use was 57.7 % (56.0-59.9) and 45.0 % (43.6-46.8), respectively.; This study presents the first malaria risk map of Kinshasa, a mega city of roughly 10 million inhabitants and located in a highly endemic malaria zone. Prevalence of malaria, anaemia and reported fever was lower in urban areas, whereas low coverage of ITN and sub-optimal net use were frequent in peri-urban areas

Health worker preferences for performance-based payment schemes in a rural health district in Burkina Faso

Background: One promising way to improve the motivation of healthcare providers and the quality of healthcare services is performance-based incentives (PBIs) also referred as performance-based financing. Our study aims to explore healthcare providers’ preferences for an incentive scheme based on local resources, which aimed at improving the quality of maternal and child health care in the Nouna Health District. Design: A qualitative and quantitative survey was carried out in 2010 involving 94 healthcare providers within 34 health facilities. In addition, in-depth interviews involving a total of 33 key informants were conducted at health facility levels. Results: Overall, 85% of health workers were in favour of an incentive scheme based on the health district's own financial resources (95% CI: [71.91; 88.08]). Most health workers (95 and 96%) expressed a preference for financial incentives (95% CI: [66.64; 85.36]) and team-based incentives (95% CI: [67.78; 86.22]), respectively. The suggested performance indicators were those linked to antenatal care services, prevention of mother-to-child human immunodeficiency virus transmission, neonatal care, and immunization. Conclusions: The early involvement of health workers and other stakeholders in designing an incentive scheme proved to be valuable. It ensured their effective participation in the process and overall acceptance of the scheme at the end. This study is an important contribution towards the designing of effective PBI schemes

Childhood mortality and its association with household wealth in rural and semi-urban Burkina Faso

This study aimed to investigate the relationship between household wealth and under-5 year mortality in rural and semi-urban Burkina Faso.; The study included 15 543 children born between 2005 and 2010 in the Nouna Health and Demographic Surveillance System. Information on household wealth was collected in 2009. Two separate wealth indicators were calculated by principal components analysis for the rural and the semi-urban households, which were then divided into quintiles accordingly. Multivariable Cox proportional hazards regression was used to study the effect of the respective wealth measure on under-5 mortality.; We observed 1201 childhood deaths, corresponding to 5-year survival probability of 93.6% and 88% in the semi-urban and rural area, respectively. In the semi-urban area, household wealth was significantly related to under-5 mortality after adjustment for confounding. There was a similar but non-significant effect of household wealth on infant mortality, too. There was no effect of household wealth on under-5 mortality in rural children.; Results from this study indicate that the more privileged children from the semi-urban area with access to piped water and electricity have an advantage in under-5 survival, while under-5 mortality in the rural area is rather homogeneous and still relatively high

Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF) classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km2 in total, with a spatial resolution of 0.5 m.info:eu-repo/semantics/publishe