Routine data for malaria morbidity estimation in Africa: challenges and prospects

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets-ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

Socioeconomic, Demographic, and Environmental Factors May Inform Malaria Intervention Prioritization in Urban Nigeria

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.

Diagnosis and management of malaria in the intensive care unit

Malaria is responsible for approximately three-quarters of a million deaths in humans globally each year. Most of the morbidity and mortality reported are from Sub-Saharan Africa and Asia, where the disease is endemic. In non-endemic areas, malaria is the most common cause of imported infection and is associated with significant mortality despite recent advancements and investments in elimination programs. Severe malaria often requires intensive care unit admission and can be complicated by cerebral malaria, respiratory distress, acute kidney injury, bleeding complications, and co-infection. Intensive care management includes prompt diagnosis and early initiation of effective antimalarial therapy, recognition of complications, and appropriate supportive care. However, the lack of diagnostic capacities due to limited advances in equipment, personnel, and infrastructure presents a challenge to the effective diagnosis and management of malaria. This article reviews the clinical classification, diagnosis, and management of malaria as relevant to critical care clinicians, highlighting the role of diagnostic capacity, treatment options, and supportive care.

Malaria rapid diagnostic tests in community pharmacies in Rwanda: availability, knowledge of community pharmacists, advantages, and disadvantages of licensing their use

BACKGROUND

Presumptive treatment of malaria is often practiced in community pharmacies across sub-Saharan Africa (SSA).To address this issue, the World Health Organization (WHO) recommends that malaria Rapid Diagnostic Tests (m-RDTs) be used in these settings, as they are used in the public sector. However, their use remains unlicensed in the community pharmacies in Rwanda. This can lessen their availability and foster presumptive treatment. Therefore, this study investigated the availability of m-RDTs, knowledge of community pharmacists on the use of m-RDTs, and explored Pharmacists' perceptions of the advantages and disadvantages of licensing the use of m-RDTs in community pharmacies.

METHODS

This was a cross-sectional study among 200 licensed community pharmacists who were purposefully sampled nationwide from 11th February to 12th April 2022. Data was collected using an online data collection instrument composed of open-ended and closed-ended questions. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25.0. The chi-square test was used to evaluate the association between the availability of m-RDTs and independent variables of interest. Content analysis was used for qualitative data.

RESULTS

Although 59% were consulted by clients requesting to purchase m-RDTs, only 27% of the participants had m-RDTs in stock, 66.5% had no training on the use of m-RDTs, and 18.5% were not at all familiar with using the m-RDTs. Most of the participants (91.5%) agreed that licensing the use of m-RDTs in community pharmacies could promote the rational use of antimalarials. The chi-square test indicated that being requested to sell m-RDTs (x2 = 6.95, p = 0.008), being requested to perform m-RDTs (x2 = 5.39, p  = 0.02),familiarity using m-RDTs (x2 = 17.24, p = 0.002), availability of a nurse in the Pharmacy (x2 = 11.68, p < 0.001), and location of the pharmacy (x2 = 9.13, p = 0.048) were all significantly associated with the availability of m-RDTs in the pharmacy.

CONCLUSIONS

The availability of m-RDTs remains low in community pharmacies in Rwanda, and less training is provided to community pharmacists regarding the use of m-RDTs. Nevertheless, community pharmacists had positive perceptions of the advantages of licensing the use of m-RDTs. Thus, licensing the use of m-RDTs is believed to be the first step toward promoting the rational use of antimalarial medicines in Rwanda.

Description of the design of a mixed-methods study to assess the burden and determinants of malaria transmission for tailoring of interventions (microstratification) in Ibadan and Kano metropolis

BACKGROUND

Rapid urbanization in Nigerian cities may lead to localized variations in malaria transmission, particularly with a higher burden in informal settlements and slums. However, there is a lack of available data to quantify the variations in transmission risk at the city level and inform the selection of appropriate interventions. To bridge this gap, field studies will be undertaken in Ibadan and Kano, two major Nigerian cities. These studies will involve a blend of cross-sectional and longitudinal epidemiological research, coupled with longitudinal entomological studies. The primary objective is to gain insights into the variation of malaria risk at the smallest administrative units, known as wards, within these cities.

METHODS/RESULTS

The findings will contribute to the tailoring of interventions as part of Nigeria's National Malaria Strategic Plan. The study design incorporates a combination of model-based clustering and on-site visits for ground-truthing, enabling the identification of environmental archetypes at the ward-level to establish the study's framework. Furthermore, community participatory approaches will be utilized to refine study instruments and sampling strategies. The data gathered through cross-sectional and longitudinal studies will contribute to an enhanced understanding of malaria risk in the metropolises of Kano and Ibadan.

CONCLUSIONS

This paper outlines pioneering field study methods aimed at collecting data to inform the tailoring of malaria interventions in urban settings. The integration of multiple study types will provide valuable data for mapping malaria risk and comprehending the underlying determinants. Given the importance of location-specific data for microstratification, this study presents a systematic process and provides adaptable tools that can be employed in cities with limited data availability.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Mapping malaria incidence using routine health facility surveillance data in Uganda

INTRODUCTION

Maps of malaria risk are important tools for allocating resources and tracking progress. Most maps rely on cross-sectional surveys of parasite prevalence, but health facilities represent an underused and powerful data source. We aimed to model and map malaria incidence using health facility data in Uganda.

METHODS

Using 24 months (2019-2020) of individual-level outpatient data collected from 74 surveillance health facilities located in 41 districts across Uganda (n=445 648 laboratory-confirmed cases), we estimated monthly malaria incidence for parishes within facility catchment areas (n=310) by estimating care-seeking population denominators. We fit spatio-temporal models to the incidence estimates to predict incidence rates for the rest of Uganda, informed by environmental, sociodemographic and intervention variables. We mapped estimated malaria incidence and its uncertainty at the parish level and compared estimates to other metrics of malaria. To quantify the impact that indoor residual spraying (IRS) may have had, we modelled counterfactual scenarios of malaria incidence in the absence of IRS.

RESULTS

Over 4567 parish-months, malaria incidence averaged 705 cases per 1000 person-years. Maps indicated high burden in the north and northeast of Uganda, with lower incidence in the districts receiving IRS. District-level estimates of cases correlated with cases reported by the Ministry of Health (Spearman's r=0.68, p<0.0001), but were considerably higher (40 166 418 cases estimated compared with 27 707 794 cases reported), indicating the potential for underreporting by the routine surveillance system. Modelling of counterfactual scenarios suggest that approximately 6.2 million cases were averted due to IRS across the study period in the 14 districts receiving IRS (estimated population 8 381 223).

CONCLUSION

Outpatient information routinely collected by health systems can be a valuable source of data for mapping malaria burden. National Malaria Control Programmes may consider investing in robust surveillance systems within public health facilities as a low-cost, high benefit tool to identify vulnerable regions and track the impact of interventions.

Application of mathematical modelling to inform national malaria intervention planning in Nigeria

BACKGROUND

For their 2021-2025 National Malaria Strategic Plan (NMSP), Nigeria's National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical models of malaria transmission were used to predict the impact of proposed intervention strategies on malaria burden.

METHODS

An agent-based model of Plasmodium falciparum transmission was used to simulate malaria morbidity and mortality in Nigeria's 774 LGAs under four possible intervention strategies from 2020 to 2030. The scenarios represented the previously implemented plan (business-as-usual), the NMSP at an 80% or higher coverage level and two prioritized plans according to the resources available to Nigeria. LGAs were clustered into 22 epidemiological archetypes using monthly rainfall, temperature suitability index, vector abundance, pre-2010 parasite prevalence, and pre-2010 vector control coverage. Routine incidence data were used to parameterize seasonality in each archetype. Each LGA's baseline malaria transmission intensity was calibrated to parasite prevalence in children under the age of five years measured in the 2010 Malaria Indicator Survey (MIS). Intervention coverage in the 2010-2019 period was obtained from the Demographic and Health Survey, MIS, the NMEP, and post-campaign surveys.

RESULTS

Pursuing a business-as-usual strategy was projected to result in a 5% and 9% increase in malaria incidence in 2025 and 2030 compared with 2020, while deaths were projected to remain unchanged by 2030. The greatest intervention impact was associated with the NMSP scenario with 80% or greater coverage of standard interventions coupled with intermittent preventive treatment in infants and extension of seasonal malaria chemoprevention (SMC) to 404 LGAs, compared to 80 LGAs in 2019. The budget-prioritized scenario with SMC expansion to 310 LGAs, high bed net coverage with new formulations, and increase in effective case management rate at the same pace as historical levels was adopted as an adequate alternative for the resources available.

CONCLUSIONS

Dynamical models can be applied for relative assessment of the impact of intervention scenarios but improved subnational data collection systems are required to allow increased confidence in predictions at sub-national level.

Investigating rapid diagnostic testing in Kenya's health system, 2018-2020: validating non-reporting in routine data using a health facility service assessment survey

BACKGROUND

Understanding the availability of rapid diagnostic tests (RDTs) is essential for attaining universal health care and reducing health inequalities. Although routine data helps measure RDT coverage and health access gaps, many healthcare facilities fail to report their monthly diagnostic test data to routine health systems, impacting routine data quality. This study sought to understand whether non-reporting by facilities is due to a lack of diagnostic and/or service provision capacity by triangulating routine and health service assessment survey data in Kenya.

METHODS

Routine facility-level data on RDT administration were sourced from the Kenya health information system for the years 2018-2020. Data on diagnostic capacity (RDT availability) and service provision (screening, diagnosis, and treatment) were obtained from a national health facility assessment conducted in 2018. The two sources were linked and compared obtaining information on 10 RDTs from both sources. The study then assessed reporting in the routine system among facilities with (i) diagnostic capacity only, (ii) both confirmed diagnostic capacity and service provision and (iii) without diagnostic capacity. Analyses were conducted nationally, disaggregated by RDT, facility level and ownership.

RESULTS

Twenty-one per cent (2821) of all facilities expected to report routine diagnostic data in Kenya were included in the triangulation. Most (86%) were primary-level facilities under public ownership (70%). Overall, survey response rates on diagnostic capacity were high (> 70%). Malaria and HIV had the highest response rate (> 96%) and the broadest coverage in diagnostic capacity across facilities (> 76%). Reporting among facilities with diagnostic capacity varied by test, with HIV and malaria having the lowest reporting rates, 58% and 52%, respectively, while the rest ranged between 69% and 85%. Among facilities with both service provision and diagnostic capacity, reporting ranged between 52% and 83% across tests. Public and secondary facilities had the highest reporting rates across all tests. A small proportion of health facilities without diagnostic capacity submitted testing reports in 2018, most of which were primary facilities.

CONCLUSION

Non-reporting in routine health systems is not always due to a lack of capacity. Further analyses are required to inform other drivers of non-reporting to ensure reliable routine health data.

Effectiveness of community case management of malaria on severe malaria and inpatient malaria deaths in Zambia: a dose-response study using routine health information system data

BACKGROUND

Community case management of malaria (CCM) has been expanded in many settings, but there are limited data describing the impact of these services in routine implementation settings or at large scale. Zambia has intensively expanded CCM since 2013, whereby trained volunteer community health workers (CHW) use rapid diagnostic tests and artemether-lumefantrine to diagnose and treat uncomplicated malaria.

METHODS

This retrospective, observational study explored associations between changing malaria service point (health facility or CHW) density per 1000 people and severe malaria admissions or malaria inpatient deaths by district and month in a dose-response approach, using existing routine and programmatic data. Negative binomial generalized linear mixed-effect models were used to assess the impact of increasing one additional malaria service point per 1000 population, and of achieving Zambia's interim target of 1 service point per 750 population. Access to insecticide-treated nets, indoor-residual spraying, and rainfall anomaly were included in models to reduce potential confounding.

RESULTS

The study captured 310,855 malaria admissions and 7158 inpatient malaria deaths over 83 districts (seven provinces) from January 2015 to May 2020. Total CHWs increased from 43 to 4503 during the study period, while health facilities increased from 1263 to 1765. After accounting for covariates, an increase of one malaria service point per 1000 was associated with a 19% reduction in severe malaria admissions among children under five (incidence rate ratio [IRR] 0.81, 95% confidence interval [CI] 0.75-0.87, p < 0.001) and 23% reduction in malaria deaths among under-fives (IRR 0.77, 95% CI 0.66-0.91). After categorizing the exposure of population per malaria service point, there was evidence for an effect on malaria admissions and inpatient malaria deaths among children under five only when reaching the target of one malaria service point per 750 population.

CONCLUSIONS

CCM is an effective strategy for preventing severe malaria and deaths in areas such as Zambia where malaria diagnosis and treatment access remains challenging. These results support the continued investment in CCM scale-up in similar settings, to improve access to malaria diagnosis and treatment.

Spatial variation and inequities in antenatal care coverage in Kenya, Uganda and mainland Tanzania using model-based geostatistics: a socioeconomic and geographical accessibility lens

BACKGROUND

Pregnant women in sub-Saharan Africa (SSA) experience the highest levels of maternal mortality and stillbirths due to predominantly avoidable causes. Antenatal care (ANC) can prevent, detect, alleviate, or manage these causes. While eight ANC contacts are now recommended, coverage of the previous minimum of four visits (ANC4+) remains low and inequitable in SSA.

METHODS

We modelled ANC4+ coverage and likelihood of attaining district-level target coverage of 70% across three equity stratifiers (household wealth, maternal education, and travel time to the nearest health facility) based on data from malaria indicator surveys in Kenya (2020), Uganda (2018/19) and Tanzania (2017). Geostatistical models were fitted to predict ANC4+ coverage and compute exceedance probability for target coverage. The number of pregnant women without ANC4+ were computed. Prediction was at 3 km spatial resolution and aggregated at national and district -level for sub-national planning.

RESULTS

About six in ten women reported ANC4+ visits, meaning that approximately 3 million women in the three countries had <ANC4+ visits. The majority of the 366 districts in the three countries had ANC4+ coverage of 50-70%. In Kenya, 13% of districts had < 70% coverage, compared to 10% and 27% of the districts in Uganda and mainland Tanzania, respectively. Only one district in Kenya and ten districts in mainland Tanzania were likely met the target coverage. Six percent, 38%, and 50% of the districts had at most 5000 women with <ANC4+ visits in Kenya, Uganda, and mainland Tanzania, respectively, while districts with > 20,000 women having <ANC4+ visits were 38%, 1% and 1%, respectively. In many districts, ANC4+ coverage and likelihood of attaining the target coverage was lower among the poor, uneducated and those geographically marginalized from healthcare.

CONCLUSIONS

These findings will be invaluable to policymakers for annual appropriations of resources as part of efforts to reduce maternal deaths and stillbirths.

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Background

Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts.

Methods

Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum.

Results

Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding.

Conclusion

The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions.

Spatial analysis of climatic factors and plasmodium falciparum malaria prevalence among children in Ghana

Malaria is a major public health problem especially in Africa where 94% of global malaria cases occur. Malaria prevalence and mortalities are disproportionately higher among children. In 2019, children accounted for 67% of malaria deaths globally. Recently, climatic factors have been acknowledged to influence the number and severity of malaria cases. Plasmodium falciparum-the most deadly malaria parasite, accounts for more than 95% of malaria infections among children in Ghana. Using the 2017 Ghana Demographic Health Survey data, we examined the local variation in the prevalence and climatic determinants of child malaria. The findings showed that climatic factors such as temperature, rainfall aridity and Enhanced Vegetation Index are significantly and positively associated with Plasmodium falciparum malaria prevalence among children in Ghana. However, there are local variations in how these climatic factors affect child malaria prevalence. Plasmodium falciparum malaria prevalence was highest among children in the south western, north western and northern Ghana.

Automated measurement of malaria parasitaemia among asymptomatic blood donors in Malawi using the Sysmex XN-31 analyser: could such data be used to complement national malaria surveillance in real time?

BACKGROUND

The recent worldwide increase in malaria cases highlights the need for renewed efforts to eliminate malaria. The World Health Organization advocates that malaria surveillance becomes a core intervention. Current methods to estimate the malaria burden rely on clinical malaria case reports and surveys of asymptomatic parasite infection mainly from children  < 5 years. In this study the hypothesis was that screening blood donors for malaria parasites would provide real-time information on the asymptomatic reservoir of parasites in the adult population and mirror other surveillance data.

METHODS

This study was conducted in Malawi, a high malaria burden country, at the Malawi Blood Transfusion Service, which collects blood units at donation sites countrywide. A secondary analysis was conducted on data obtained from a prior Sysmex XN-31 analyser malaria diagnostic evaluation study utilizing residual donor blood samples. XN-31 malaria results, donor age, sex, geographical location, and collection date, were analysed using standard statistical methods.

RESULTS

The malaria parasite prevalence in blood donors was 11.6% (614/5281 samples) increasing seasonally from December (8.6%) to April (18.3%). The median age was 21 years and 45.9% of donors were from urban areas, which showed a lower prevalence compared to non-urban regions. The Central administrative region had the highest and the Northern region the lowest malaria parasite prevalence. The donors were predominantly male (80.2%), 13.1% of whom had malaria parasites, which was significantly higher (p < 0.0001) than for female donors (7.4%). Multivariable logistic regression analysis showed that age, location, and collection month were significant predictors of malaria positivity in males, whereas in females only location was significant. There was no gender difference in parasite density nor gametocyte carriage.

CONCLUSIONS

This study demonstrates the powerful utility of screening blood donors for malaria parasites using the XN-31, which not only improves the safety of blood transfusion, but provides valuable complementary surveillance data for malaria control, especially targeting males, who are generally excluded from periodic household surveys. Blood donations are sourced countrywide, year-round, and thus provide dynamic, real-time information on the malaria burden. Furthermore, the XN-31 identifies the asymptomatic human reservoir of infectious gametocytes, which must be targeted to eliminate malaria.

Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

Inferring the epidemiological benefit of indoor vector control interventions against malaria from mosquito data

The cause of malaria transmission has been known for over a century but it is still unclear whether entomological measures are sufficiently reliable to inform policy decisions in human health. Decision-making on the effectiveness of new insecticide-treated nets (ITNs) and the indoor residual spraying of insecticide (IRS) have been based on epidemiological data, typically collected in cluster-randomised control trials. The number of these trials that can be conducted is limited. Here we use a systematic review to highlight that efficacy estimates of the same intervention may vary substantially between trials. Analyses indicate that mosquito data collected in experimental hut trials can be used to parameterize mechanistic models for Plasmodium falciparum malaria and reliably predict the epidemiological efficacy of quick-acting, neuro-acting ITNs and IRS. Results suggest that for certain types of ITNs and IRS using this framework instead of clinical endpoints could support policy and expedite the widespread use of novel technologies.

Estimating the effectiveness of malaria vector control interventions has typically relied on resource-intensive cluster randomised trials. Here, the authors estimate changes in malaria prevalence using entomological data from experimental hut trials, which may provide an alternative route to approval of interventions in some situations.

Malaria Threatens to Bounce Back in Abergele District, Northeast Ethiopia: Five-Year Retrospective Trend Analysis from 2016-2020 in Nirak Health Center

Background

In Sub-Saharan African countries, malaria is a leading cause of morbidity and mortality. In Ethiopia, malaria is found in three-fourths of its land mass with more than 63 million people living in malaria endemic areas. Nowadays, Ethiopia is implementing a malaria elimination program with the goal of eliminating the disease by 2030. To assist this goal, the trends of malaria cases should be evaluated with a function of time in different areas of the country to develop area-specific evidence-based interventions. Therefore, this study was aimed at analysing a five year trend of malaria in Nirak Health Center, Abergele District, Northeast Ethiopia, from 2016 to 2020.

Methods

A retrospective study was conducted at Nirak Health Center, Abergele District, Northeast Ethiopia from February to April 2021. Five-year (2016 to 2020) retrospective data were reviewed from the malaria registration laboratory logbook. The sociodemographic and malaria data were collected using a predesigned data collection sheet. Data were entered, cleaned, and analysed using SPSS version 26.

Results

In the five-year period, a total of 19,433 malaria suspected patients were diagnosed by microscopic examination. Of these, 6,473 (33.3%) were positive for malaria parasites. Of the total confirmed cases, 5,900 (91.2%) were P. falciparum and 474 (7.2%) were P. vivax. Majority of the cases were males (62.2%) and in the age group of 15-45 years old (52.8%). The findings of this study showed an increasing trend in malaria cases in the past five years (2016-2020). The maximum number of confirmed malaria cases reported was in the year 2020, while the minimum number of confirmed malaria cases registered was in 2016. Regarding the seasonal distribution of malaria, the highest number of malaria cases (55.2%) was observed in Dry season (September to January) and also the least (15.9%) was observed in Autumn (March to May) replaced by the least (21.6%) was observed in Rainy season (June to August), that is, the major malaria transmission season in Ethiopia and the least (15.9%) was observed in autumn (March to May).

Conclusion

The trends of malaria in Nirak Health Center showed steadily increasing from the year 2016–2020, and the predominant species isolated was P. falciparum. This showed that the malaria control and elimination strategy in the area were not properly implemented or failed to achieve its designed goal. Therefore, this finding alarms the local governments and other stack holders urgently to revise their intervention strategies and take action in the locality.

Impact of seasonal malaria chemoprevention on prevalence of malaria infection in malaria indicator surveys in Burkina Faso and Nigeria

BACKGROUND

In 2012, the WHO issued a policy recommendation for the use of seasonal malaria chemoprevention (SMC) to children 3-59 months in areas of highly seasonal malaria transmission. Clinical trials have found SMC to prevent around 75% of clinical malaria. Impact under routine programmatic conditions has been assessed during research studies but there is a need to identify sustainable methods to monitor impact using routinely collected data.

METHODS

Data from Demographic Health Surveys were merged with rainfall, geographical and programme data in Burkina Faso (2010, 2014, 2017) and Nigeria (2010, 2015, 2018) to assess impact of SMC. We conducted mixed-effects logistic regression to predict presence of malaria infection in children aged 6-59 months (rapid diagnostic test (RDT) and microscopy, separately).

RESULTS

We found strong evidence that SMC administration decreases odds of malaria measured by RDT during SMC programmes, after controlling for seasonal factors, age, sex, net use and other variables (Burkina Faso OR 0.28, 95% CI 0.21 to 0.37, p<0.001; Nigeria OR 0.40, 95% CI 0.30 to 0.55, p<0.001). The odds of malaria were lower up to 2 months post-SMC in Burkina Faso (1-month post-SMC: OR 0.29, 95% CI 0.12 to 0.72, p=0.01; 2 months post-SMC: OR: 0.33, 95% CI 0.17 to 0.64, p<0.001). The odds of malaria were lower up to 1 month post-SMC in Nigeria but was not statistically significant (1-month post-SMC 0.49, 95% CI 0.23 to 1.05, p=0.07). A similar but weaker effect was seen for microscopy (Burkina Faso OR 0.38, 95% CI 0.29 to 0.52, p<0.001; Nigeria OR 0.53, 95% CI 0.38 to 0.76, p<0.001).

CONCLUSIONS

Impact of SMC can be detected in reduced prevalence of malaria from data collected through household surveys if conducted during SMC administration or within 2 months afterwards. Such evidence could contribute to broader evaluation of impact of SMC programmes.

Unmet need for COVID-19 vaccination coverage in Kenya

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum

Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum. Some major challenges include the dearth of suitable animal models for anti-P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium. Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC₅₀ values of bioactive compounds against P. falciparum. The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

Resurgence of malaria in Uganda despite sustained indoor residual spraying and repeated long lasting insecticidal net distributions

Five years of sustained indoor residual spraying (IRS) of insecticide from 2014 to 2019, first using a carbamate followed by an organophosphate, was associated with a marked reduction in the incidence of malaria in five districts of Uganda. We assessed changes in malaria incidence over an additional 21 months, corresponding to a change in IRS formulations using clothianidin with and without deltamethrin. Using enhanced health facility surveillance data, our objectives were to 1) estimate the impact of IRS on monthly malaria case counts at five surveillance sites over a 6.75 year period, and 2) compare monthly case counts at five facilities receiving IRS to ten facilities in neighboring districts not receiving IRS. For both objectives, we specified mixed effects negative binomial regression models with random intercepts for surveillance site adjusting for rainfall, season, care-seeking, and malaria diagnostic. Following the implementation of IRS, cases were 84% lower in years 4-5 (adjusted incidence rate ratio [aIRR] = 0.16, 95% CI 0.12-0.22), 43% lower in year 6 (aIRR = 0.57, 95% CI 0.44-0.74), and 39% higher in the first 9 months of year 7 (aIRR = 1.39, 95% CI 0.97-1.97) compared to pre-IRS levels. Cases were 67% lower in IRS sites than non-IRS sites in year 6 (aIRR = 0.33, 95% CI 0.17-0.63) but 38% higher in the first 9 months of year 7 (aIRR = 1.38, 95% CI 0.90-2.11). We observed a resurgence in malaria to pre-IRS levels despite sustained IRS. The timing of this resurgence corresponded to a change of active ingredient. Further research is needed to determine causality.

Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6-36.9) in Kenya, 10.6% (3.4-39.2) in mainland Tanzania, and 9.5% (4.0-48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data

Towards the goal of malaria elimination on Hispaniola, the National Malaria Control Program of Haiti and its international partner organisations are conducting a campaign of interventions targeted to high-risk communities prioritised through evidence-based planning. Here we present a key piece of this planning: an up-to-date, fine-scale endemicity map and seasonality profile for Haiti informed by monthly case counts from 771 health facilities reporting from across the country throughout the 6-year period from January 2014 to December 2019. To this end, a novel hierarchical Bayesian modelling framework was developed in which a latent, pixel-level incidence surface with spatio-temporal innovations is linked to the observed case data via a flexible catchment sub-model designed to account for the absence of data on case household locations. These maps have focussed the delivery of indoor residual spraying and focal mass drug administration in the Grand’Anse Department in South-Western Haiti.

Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review

BACKGROUND

Routine health information system(s) (RHIS) facilitate the collection of health data at all levels of the health system allowing estimates of disease prevalence, treatment and preventive intervention coverage, and risk factors to guide disease control strategies. This core health system pillar remains underdeveloped in many low-income and middle-income countries. Efforts to improve RHIS data coverage, quality and timeliness were launched over 10 years ago.

METHODS

A systematic review was performed across 12 databases and literature search engines for both peer-reviewed articles and grey literature reports on RHIS interventions. Studies were analysed in three stages: (1) categorisation of RHIS intervention components and processes; (2) comparison of intervention component effectiveness and (3) whether the post-intervention outcome improved above the WHO integrated disease surveillance response framework data quality standard of 80% or above.

RESULTS

5294 references were screened, resulting in 56 studies. Three key performance determinants-technical, organisational and behavioural-were proposed as critical to RHIS strengthening. Seventy-seven per cent [77%] of studies identified addressed all three determinants. The most frequently implemented intervention components were 'providing training' and 'using an electronic health management information systems'. Ninety-three per cent [93%] of pre-post or controlled trial studies showed improvements in one or more data quality outputs, but after applying a standard threshold of >80% post-intervention, this number reduced to 68%. There was an observed benefit of multi-component interventions that either conducted data quality training or that addressed improvement across multiple processes and determinants of RHIS.

CONCLUSION

Holistic data quality interventions that address multiple determinants should be continuously practised for strengthening RHIS. Studies with clearly defined and pragmatic outcomes are required for future RHIS improvement interventions. These should be accompanied by qualitative studies and cost analyses to understand which investments are needed to sustain high-quality RHIS in low-income and middle-income countries.

Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

BACKGROUND

The over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast.

METHODS

Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test.

RESULTS

Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected.

CONCLUSION

Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

The impact of child health interventions and risk factors on child survival in Kenya, 1993-2014: a Bayesian spatio-temporal analysis with counterfactual scenarios

BACKGROUND

During the millennium development goals period, reduction in under-five mortality (U5M) and increases in child health intervention coverage were characterised by sub-national disparities and inequities across Kenya. The contribution of changing risk factors and intervention coverage on the sub-national changes in U5M remains poorly defined.

METHODS

Sub-national county-level data on U5M and 43 factors known to be associated with U5M spanning 1993 and 2014 were assembled. Using a Bayesian ecological mixed-effects regression model, the relationships between U5M and significant intervention and infection risk ecological factors were quantified across 47 sub-national counties. The coefficients generated were used within a counterfactual framework to estimate U5M and under-five deaths averted (U5-DA) for every county and year (1993-2014) associated with changes in the coverage of interventions and disease infection prevalence relative to 1993.

RESULTS

Nationally, the stagnation and increase in U5M in the 1990s were associated with rising human immunodeficiency virus (HIV) prevalence and reduced maternal autonomy while improvements after 2006 were associated with a decline in the prevalence of HIV and malaria, increase in access to better sanitation, fever treatment-seeking rates and maternal autonomy. Reduced stunting and increased coverage of early breastfeeding and institutional deliveries were associated with a smaller number of U5-DA compared to other factors while a reduction in high parity and fully immunised children were associated with under-five lives lost. Most of the U5-DA occurred after 2006 and varied spatially across counties. The highest number of U5-DA was recorded in western and coastal Kenya while northern Kenya recorded a lower number of U5-DA than western. Central Kenya had the lowest U5-DA. The deaths averted across the different regions were associated with a unique set of factors.

CONCLUSION

Contributions of interventions and risk factors to changing U5M vary sub-nationally. This has important implications for targeting future interventions within decentralised health systems such as those operated in Kenya. Targeting specific factors where U5M has been high and intervention coverage poor would lead to the highest likelihood of sub-national attainment of sustainable development goal (SDG) 3.2 on U5M in Kenya.

Subnational estimates of factors associated with under-five mortality in Kenya: a spatio-temporal analysis, 1993-2014

BACKGROUND

To improve child survival, it is necessary to describe and understand the spatial and temporal variation of factors associated with child survival beyond national aggregates, anchored at decentralised health planning units. Therefore, we aimed to provide subnational estimates of factors associated with child survival while elucidating areas of progress, stagnation and decline in Kenya.

METHODS

Twenty household surveys and three population censuses conducted since 1989 were assembled and spatially aligned to 47 subnational Kenyan county boundaries. Bayesian spatio-temporal Gaussian process regression models accounting for inadequate sample size and spatio-temporal relatedness were fitted for 43 factors at county level between 1993 and 2014.

RESULTS

Nationally, the coverage and prevalence were highly variable with 38 factors recording an improvement. The absolute percentage change (1993-2014) was heterogeneous ranging between 1% and 898%. At the county level, the estimates varied across space and over time with a majority showing improvements after 2008 which was preceded by a period of deterioration (late-1990 to early-2000). Counties in Northern Kenya were consistently observed to have lower coverage of interventions and remained disadvantaged in 2014 while areas around Central Kenya had and historically have had higher coverage across all intervention domains. Most factors in Western and South-East Kenya recorded moderate intervention coverage although having a high infection prevalence of both HIV and malaria.

CONCLUSION

The heterogeneous estimates necessitates prioritisation of the marginalised counties to achieve health equity and improve child survival uniformly across the country. Efforts are required to narrow the gap between counties across all the drivers of child survival. The generated estimates will facilitate improved benchmarking and establish a baseline for monitoring child development goals at subnational level.

Beyond national indicators: adapting the Demographic and Health Surveys' sampling strategies and questions to better inform subnational malaria intervention policy

In malaria-endemic countries, prioritizing intervention deployment to areas that need the most attention is crucial to ensure continued progress. Global and national policy makers increasingly rely on epidemiological data and mathematical modelling to help optimize health decisions at the sub-national level. The Demographic and Health Surveys (DHS) Program is a critical data source for understanding subnational malaria prevalence and intervention coverage, which are used for parameterizing country-specific models of malaria transmission. However, data to estimate indicators at finer resolutions are limited, and surveys questions have a narrow scope. Examples from the Nigeria DHS are used to highlight gaps in the current survey design. Proposals are then made for additional questions and expansions to the DHS and Malaria Indicator Survey sampling strategy that would advance the data analyses and modelled estimates that inform national policy recommendations. Collaboration between the DHS Program, national malaria control programmes, the malaria modelling community, and funders is needed to address the highlighted data challenges.

Malaria micro-stratification using routine surveillance data in Western Kenya

BACKGROUND

There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya.

METHODS

Routine data from health facilities (n = 1804) representing all ages over 24 months (2018-2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility.

RESULTS

The overall monthly reporting rate was 78.7% (IQR 75.0-100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3-7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability < 30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017.

CONCLUSION

The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels.

Estimating malaria incidence from routine health facility-based surveillance data in Uganda

Background

Accurate measures of malaria incidence are essential to track progress and target high-risk populations. While health management information system (HMIS) data provide counts of malaria cases, quantifying the denominator for incidence using these data is challenging because catchment areas and care-seeking behaviours are not well defined. This study’s aim was to estimate malaria incidence using HMIS data by adjusting the population denominator accounting for travel time to the health facility.

Methods

Outpatient data from two public health facilities in Uganda (Kihihi and Nagongera) over a 3-year period (2011–2014) were used to model the relationship between travel time from patient village of residence (available for each individual) to the facility and the relative probability of attendance using Poisson generalized additive models. Outputs from the model were used to generate a weighted population denominator for each health facility and estimate malaria incidence. Among children aged 6 months to 11 years, monthly HMIS-derived incidence estimates, with and without population denominators weighted by probability of attendance, were compared with gold standard measures of malaria incidence measured in prospective cohorts.

Results

A total of 48,898 outpatient visits were recorded across the two sites over the study period. HMIS incidence correlated with cohort incidence over time at both study sites (correlation in Kihihi = 0.64, p < 0.001; correlation in Nagongera = 0.34, p = 0.045). HMIS incidence measures with denominators unweighted by probability of attendance underestimated cohort incidence aggregated over the 3 years in Kihihi (0.5 cases per person-year (PPY) vs 1.7 cases PPY) and Nagongera (0.3 cases PPY vs 3.0 cases PPY). HMIS incidence measures with denominators weighted by probability of attendance were closer to cohort incidence, but remained underestimates (1.1 cases PPY in Kihihi and 1.4 cases PPY in Nagongera).

Conclusions

Although malaria incidence measured using HMIS underestimated incidence measured in cohorts, even when adjusting for probability of attendance, HMIS surveillance data are a promising and scalable source for tracking relative changes in malaria incidence over time, particularly when the population denominator can be estimated by incorporating information on village of residence.

Changing malaria fever test positivity among paediatric admissions to Tororo district hospital, Uganda 2012-2019

BACKGROUND

The World Health Organization (WHO) promotes long-lasting insecticidal nets (LLIN) and indoor residual house-spraying (IRS) for malaria control in endemic countries. However, long-term impact data of vector control interventions is rarely measured empirically.

METHODS

Surveillance data was collected from paediatric admissions at Tororo district hospital for the period January 2012 to December 2019, during which LLIN and IRS campaigns were implemented in the district. Malaria test positivity rate (TPR) among febrile admissions aged 1 month to 14 years was aggregated at baseline and three intervention periods (first LLIN campaign; Bendiocarb IRS; and Actellic IRS + second LLIN campaign) and compared using before-and-after analysis. Interrupted time-series analysis (ITSA) was used to determine the effect of IRS (Bendiocarb + Actellic) with the second LLIN campaign on monthly TPR compared to the combined baseline and first LLIN campaign periods controlling for age, rainfall, type of malaria test performed. The mean and median ages were examined between intervention intervals and as trend since January 2012.

RESULTS

Among 28,049 febrile admissions between January 2012 and December 2019, TPR decreased from 60% at baseline (January 2012-October 2013) to 31% during the final period of Actellic IRS and LLIN (June 2016-December 2019). Comparing intervention intervals to the baseline TPR (60.3%), TPR was higher during the first LLIN period (67.3%, difference 7.0%; 95% CI 5.2%, 8.8%, p < 0.001), and lower during the Bendiocarb IRS (43.5%, difference - 16.8%; 95% CI - 18.7%, - 14.9%) and Actellic IRS (31.3%, difference - 29.0%; 95% CI - 30.3%, - 27.6%, p < 0.001) periods. ITSA confirmed a significant decrease in the level and trend of TPR during the IRS (Bendicarb + Actellic) with the second LLIN period compared to the pre-IRS (baseline + first LLIN) period. The age of children with positive test results significantly increased with time from a mean of 24 months at baseline to 39 months during the final IRS and LLIN period.

CONCLUSION

IRS can have a dramatic impact on hospital paediatric admissions harbouring malaria infection. The sustained expansion of effective vector control leads to an increase in the age of malaria positive febrile paediatric admissions. However, despite large reductions, malaria test-positive admissions continued to be concentrated in children aged under five years. Despite high coverage of IRS and LLIN, these vector control measures failed to interrupt transmission in Tororo district. Using simple, cost-effective hospital surveillance, it is possible to monitor the public health impacts of IRS in combination with LLIN.

Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data

Malaria transmission in Madagascar is highly heterogeneous, exhibiting spatial, seasonal and long-term trends. Previous efforts to map malaria risk in Madagascar used prevalence data from Malaria Indicator Surveys. These cross-sectional surveys, conducted during the high transmission season most recently in 2013 and 2016, provide nationally representative prevalence data but cover relatively short time frames. Conversely, monthly case data are collected at health facilities but suffer from biases, including incomplete reporting and low rates of treatment seeking. We combined survey and case data to make monthly maps of prevalence between 2013 and 2016. Health facility catchment populations were estimated to produce incidence rates from the case data. Smoothed incidence surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence-to-prevalence relationship was learned. Modelled spatial trends were consistent over time, with highest prevalence in the coastal regions and low prevalence in the highlands and desert south. Prevalence was lowest in 2014 and peaked in 2015 and seasonality was widely observed, including in some lower transmission regions. These trends highlight the utility of monthly prevalence estimates over the four year period. By combining survey and case data using this two-step modelling approach, we were able to take advantage of the relative strengths of each metric while accounting for potential bias in the case data. Similar modelling approaches combining large datasets of different malaria metrics may be applicable across sub-Saharan Africa.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

Background

Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.

Methods

Geographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model.

Results

Results showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year.

Conclusions

Geographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

Evaluation of the malaria reporting system supported by the District Health Information System 2 in Solomon Islands

Background

District Health Information Systems 2 (DHIS2) is used for supporting health information management in 67 countries, including Solomon Islands. However, there have been few published evaluations of the performance of DHIS2-enhanced disease reporting systems, in particular for monitoring infectious diseases such as malaria. The aim of this study was to evaluate DHIS2 supported malaria reporting in Solomon Islands and to develop recommendations for improving the system.

Methods

The evaluation was conducted in three administrative areas of Solomon Islands: Honoria City Council, and Malaita and Guadalcanal Provinces. Records of nine malaria indicators including report submission date, total malaria cases, Plasmodium falciparum case record, Plasmodium vivax case record, clinical malaria, malaria diagnosed with microscopy, malaria diagnosed with (rapid diagnostic test) (RDT), record of drug stocks and records of RDT stocks from 1st January to 31st December 2016 were extracted from the DHIS2 database. The indicators permitted assessment in four core areas: availability, completeness, timeliness and reliability. To explore perceptions and point of view of the stakeholders on the performance of the malaria case reporting system, focus group discussions were conducted with health centre nurses, whilst in-depth interviews were conducted with stakeholder representatives from government (province and national) staff and World Health Organization officials who were users of DHIS2.

Results

Data were extracted from nine health centres in Honoria City Council and 64 health centres in Malaita Province. The completeness and timeliness from the two provinces of all nine indicators were 28.2% and 5.1%, respectively. The most reliable indicator in DHIS2 was ‘clinical malaria’ (i.e. numbers of clinically diagnosed malaria cases) with 62.4% reliability. Challenges to completeness were a lack of supervision, limited feedback, high workload, and a lack of training and refresher courses. Health centres located in geographically remote areas, a lack of regular transport, high workload and too many variables in the reporting forms led to delays in timely reporting. Reliability of reports was impacted by a lack of technical professionals such as statisticians and unavailability of tally sheets and reporting forms.

Conclusion

The availability, completeness, timeliness and reliability of nine malaria indicators collected in DHIS2 were variable within the study area, but generally low. Continued onsite support, supervision, feedback and additional enhancements, such as electronic reporting will be required to further improve the malaria reporting system.

The relationship between facility-based malaria test positivity rate and community-based parasite prevalence

INTRODUCTION

Malaria surveillance is a key pillar in the control of malaria in Africa. The value of using routinely collected data from health facilities to define malaria risk at community levels remains poorly defined.

METHODS

Four cross-sectional parasite prevalence surveys were undertaken among residents at 36 enumeration zones in Kilifi county on the Kenyan coast and temporally and spatially matched to fever surveillance at 6 health facilities serving the same communities over 12 months. The age-structured functional form of the relationship between test positivity rate (TPR) and community-based parasite prevalence (PR) was explored through the development of regression models fitted by alternating the linear, exponential and polynomial terms for PR. The predictive ranges of TPR were explored for PR endemicity risk groups of control programmatic value using cut-offs of low (PR <5%) and high (PR ≥ 30%) transmission intensity.

RESULTS

Among 28,134 febrile patients encountered for malaria diagnostic testing in the health facilities, 12,143 (43.2%: 95% CI: 42.6%, 43.7%) were positive. The overall community PR was 9.9% (95% CI: 9.2%, 10.7%) among 6,479 participants tested for malaria. The polynomial model was the best fitting model for the data that described the algebraic relationship between TPR and PR. In this setting, a TPR of ≥ 49% in all age groups corresponded to an age-standardized PR of ≥ 30%, while a TPR of < 40% corresponded to an age-standardized PR of < 5%.

CONCLUSION

A non-linear relationship was observed between the relative change in TPR and changes in the PR, which is likely to have important implications for malaria surveillance programs, especially at the extremes of transmission. However, larger, more spatially diverse data series using routinely collected TPR data matched to community-based infection prevalence data are required to explore the more practical implications of using TPR as a replacement for community PR.

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.

How useful are malaria risk maps at the country level? Perceptions of decision-makers in Kenya, Malawi and the Democratic Republic of Congo

Background

Declining malaria prevalence and pressure on external funding have increased the need for efficiency in malaria control in sub-Saharan Africa (SSA). Modelled Plasmodium falciparum parasite rate (PfPR) maps are increasingly becoming available and provide information on the epidemiological situation of countries. However, how these maps are understood or used for national malaria planning is rarely explored. In this study, the practices and perceptions of national decision-makers on the utility of malaria risk maps, showing prevalence of parasitaemia or incidence of illness, was investigated.

Methods

A document review of recent National Malaria Strategic Plans was combined with 64 in-depth interviews with stakeholders in Kenya, Malawi and the Democratic Republic of Congo (DRC). The document review focused on the type of epidemiological maps included and their use in prioritising and targeting interventions. Interviews (14 Kenya, 17 Malawi, 27 DRC, 6 global level) explored drivers of stakeholder perceptions of the utility, value and limitations of malaria risk maps.

Results

Three different types of maps were used to show malaria epidemiological strata: malaria prevalence using a PfPR modelled map (Kenya); malaria incidence using routine health system data (Malawi); and malaria prevalence using data from the most recent Demographic and Health Survey (DRC). In Kenya the map was used to target preventative interventions, including long-lasting insecticide-treated nets (LLINs) and intermittent preventive treatment in pregnancy (IPTp), whilst in Malawi and DRC the maps were used to target in-door residual spraying (IRS) and LLINs distributions in schools. Maps were also used for operational planning, supply quantification, financial justification and advocacy. Findings from the interviews suggested that decision-makers lacked trust in the modelled PfPR maps when based on only a few empirical data points (Malawi and DRC).

Conclusions

Maps were generally used to identify areas with high prevalence in order to implement specific interventions. Despite the availability of national level modelled PfPR maps in all three countries, they were only used in one country. Perceived utility of malaria risk maps was associated with the epidemiological structure of the country and use was driven by perceived need, understanding (quality and relevance), ownership and trust in the data used to develop the maps.

Determinants of subnational disparities in antenatal care utilisation: a spatial analysis of demographic and health survey data in Kenya

BACKGROUND

The spatial variation in antenatal care (ANC) utilisation is likely associated with disparities observed in maternal and neonatal deaths. Most maternal deaths are preventable through services offered during ANC; however, estimates of ANC coverage at lower decision-making units (sub-county) is mostly lacking. In this study, we aimed to estimate the coverage of at least four ANC (ANC4) visits at the sub-county level using the 2014 Kenya Demographic and Health Survey (KDHS 2014) and identify factors associated with ANC utilisation in Kenya.

METHODS

Data from the KDHS 2014 was used to compute sub-county estimates of ANC4 using small area estimation (SAE) techniques which relied on spatial relatedness to yield precise and reliable estimates at each of the 295 sub-counties. Hierarchical mixed-effect logistic regression was used to identify factors influencing ANC4 utilisation. Sub-county estimates of factors significantly associated with ANC utilisation were produced using SAE techniques and mapped to visualise disparities.

RESULTS

The coverage of ANC4 across sub-counties was heterogeneous, ranging from a low of 17% in Mandera West sub-county to over 77% in Nakuru Town West and Ruiru sub-counties. Thirty-one per cent of the 295 sub-counties had coverage of less than 50%. Maternal education, household wealth, place of delivery, marital status, age at first marriage, and birth order were all associated with ANC utilisation. The areas with low ANC4 utilisation rates corresponded to areas of low socioeconomic status, fewer educated women and a small number of health facility deliveries.

CONCLUSION

Suboptimal coverage of ANC4 and its heterogeneity at sub-county level calls for urgent, focused and localised approaches to improve access to antenatal care services. Policy formulation and resources allocation should rely on data-driven strategies to guide national and county governments achieve equity in access and utilisation of health interventions.