From admissions to licensure: education data associations from a multi-centre undergraduate medical education collaboration

This paper reports the findings of a Canada based multi-institutional study designed to investigate the relationships between admissions criteria, in-program assessments, and performance on licensing exams. The study's objective is to provide valuable insights for improving educational practices across different institutions. Data were gathered from six medical schools: McMaster University, the Northern Ontario School of Medicine University, Queen's University, University of Ottawa, University of Toronto, and Western University. The dataset includes graduates who undertook the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1) between 2015 and 2017. The data were categorized into five distinct sections: demographic information as well as four matrices: admissions, course performance, objective structured clinical examination (OSCE), and clerkship performance. Common and unique variables were identified through an extensive consensus-building process. Hierarchical linear regression and a manual stepwise variable selection approach were used for analysis. Analyses were performed on data set encompassing graduates of all six medical schools as well as on individual data sets from each school. For the combined data set the final model estimated 32% of the variance in performance on licensing exams, highlighting variables such as Age at Admission, Sex, Biomedical Knowledge, the first post-clerkship OSCE, and a clerkship theta score. Individual school analysis explained 41-60% of the variance in MCCQE1 outcomes, with comparable variables to the analysis from of the combined data set identified as significant independent variables. Therefore, strongly emphasising the need for variety of high-quality assessment on the educational continuum. This study underscores the importance of sharing data to enable educational insights. This study also had its challenges when it came to the access and aggregation of data. As such we advocate for the establishment of a common framework for multi-institutional educational research, facilitating studies and evaluations across diverse institutions. This study demonstrates the scientific potential of collaborative data analysis in enhancing educational outcomes. It offers a deeper understanding of the factors influencing performance on licensure exams and emphasizes the need for addressing data gaps to advance multi-institutional research for educational improvements.

A situational assessment of treatments received for childhood diarrhea in the Federal Republic of Nigeria

We assess progress towards improved case management of childhood diarrhea in Nigeria over a period of targeted health systems reform from 2013 to 2018. Individual and community data from three Demographic and Health Survey rounds are leveraged in a geospatial model designed for stratified estimation by venue of treatment seeking and State. Our analysis reveals a highly regionalised health system undergoing rapid change. Nationally, there have been substantial increases in the proportion of children under 5 years old with diarrhea receiving the recommended oral rehydration therapy after seeking treatment at either a health clinic (0.57 [0.44-0.69; 95% CI] in 2008; 0.70 [0.54-0.83] in 2018) or chemist/pharmacy (0.28 [0.17-0.42] in 2008; 0.48 [0.31-0.64] in 2018). Yet State-level variations in venue attendance and performance by venue have conspired to hold the overall proportion receiving this potentially life-saving therapy (0.45 [0.35-0.55] in 2018) to well-below ideal coverage levels. High performing states that have demonstrated significant improvements include Kano, Jigawa and Borno, while under-performing states that have suffered declines in coverage include Kaduna and Taraba. The use of antibiotics is not recommended for mild cases of childhood diarrhea yet remains concerningly high nationally (0.27 [0.19-0.36] in 2018) with negligible variation between venues. Antibiotic use rates are particularly high in Enugu, Kaduna, Taraba, Kano, Niger and Kebbi, yet welcome reductions were identified in Jigawa, Adamawa and Osun. These results support the conclusions of previous studies and build the strength of evidence that urgent action is needed throughout the multi-tiered health system to improve the quality and equity of care for common childhood illnesses in Nigeria.

Childhood-onset type 1 diabetes in Western Australia: An update on incidence and temporal trends from 2001 to 2022

OBJECTIVE

To determine the incidence and incidence trends over 2001-2022 of childhood-onset type 1 diabetes (T1D) in Western Australia and assess the impact of the COVID-19 pandemic.

METHODS

Children newly diagnosed with T1D aged 0-14 years in Western Australia from 1 January 2001 to 31 December 2022 were identified from the population-based Western Australian Children's Diabetes Database. Annual age- and sex-specific incidence was calculated, and Poisson regression was used to analyse trends by calendar year, month, sex and age group at diagnosis. Pandemic era impacts were also examined using the regression model adjusted for sex and age group.

RESULTS

Between 2001 and 2022, 2311 children (1214 boys, 1097 girls) were newly diagnosed with T1D aged 0-14 years, giving an overall mean annual incidence of 22.9 per 100,000 person-years (95% CI: 22.0, 23.9), with no significant difference observed between boys and girls. A significant linear increasing trend was only observed in 10-14 year olds with boys and girls combined (1.2% per year [IRR 1.012 (95% CI: 1.002, 1.022)]). No significant difference in the incidence was observed between the pre- and post-pandemic period.

CONCLUSIONS

The incidence of type 1 diabetes in 0-14 year old Western Australian children continues to increase in the oldest age group. Longer term monitoring of the incidence during the COVID-19 pandemic is needed to determine its impact on this globally unique population which experienced a delayed start to the pandemic with severe containment measures remaining in place until January 2022.

Trends in treatment-seeking for fever in children under five years old in 151 countries from 1990 to 2020

Access to medical treatment for fever is essential to prevent morbidity and mortality in individuals and to prevent transmission of communicable febrile illness in communities. Quantification of the rates at which treatment is accessed is critical for health system planning and a prerequisite for disease burden estimates. In this study, national data on the proportion of children under five years old with fever who were taken for medical treatment were collected from all available countries in Africa, Latin America, and Asia (n = 91). We used generalised additive mixed models to estimate 30-year trends in the treatment-seeking rates across the majority of countries in these regions (n = 151). Our results show that the proportions of febrile children brought for medical treatment increased steadily over the last 30 years, with the greatest increases occurring in areas where rates had originally been lowest, which includes Latin America and Caribbean, North Africa and the Middle East (51 and 50% increase, respectively), and Sub-Saharan Africa (23% increase). Overall, the aggregated and population-weighted estimate of children with fever taken for treatment at any type of facility rose from 61% (59-64 95% CI) in 1990 to 71% (69-72 95% CI) in 2020. The overall population-weighted average for fraction of treatment in the public sector was largely unchanged during the study period: 49% (42-58 95% CI) sought care at public facilities in 1990 and 47% (44-52 95% CI) in 2020. Overall, the findings indicate that improvements in access to care have been made where they were most needed, but that despite rapid initial gains, progress can plateau without substantial investment. In 2020 there remained significant gaps in care utilisation that must be factored in when developing control strategies and deriving disease burden estimates.

Impacts on Human Movement in Australian Cities Related to the COVID-19 Pandemic

No studies have yet examined high-resolution shifts in the spatial patterns of human movement in Australia throughout 2020 and 2021, a period coincident with the repeated enactment and removal of varied governmental restrictions aimed at reducing community transmission of SARS-CoV-2. We compared overlapping timeseries of COVID-19 pandemic-related restrictions, epidemiological data on cases and vaccination rates, and high-resolution human movement data to characterize population-level responses to the pandemic in Australian cities. We found that restrictions on human movement and/or mandatory business closures reduced the average population-level weekly movement volumes in cities, as measured by aggregated travel time, by almost half. Of the movements that continued to occur, long movements reduced more dramatically than short movements, likely indicating that people stayed closer to home. We also found that the repeated lockdowns did not reduce their impact on human movement, but the effect of the restrictions on human movement waned as the duration of restrictions increased. Lastly, we found that after restrictions ceased, the subsequent surge in SARS-CoV-2 transmission coincided with a substantial, non-mandated drop in human movement volume. These findings have implications for public health policy makers when faced with anticipating responses to restrictions during future emergency situations.

Acceptability, Feasibility, Drug Safety, and Effectiveness of a Pilot Mass Drug Administration with a Single Round of Sulfadoxine-Pyrimethamine Plus Primaquine and Indoor Residual Spraying in Communities with Malaria Transmission in Haiti, 2018

For a malaria elimination strategy, Haiti's National Malaria Control Program piloted a mass drug administration (MDA) with indoor residual spraying (IRS) in 12 high-transmission areas across five communes after implementing community case management and strengthened surveillance. The MDA distributed sulfadoxine-pyrimethamine and single low-dose primaquine to eligible residents during house visits. The IRS campaign applied pirimiphos-methyl insecticide on walls of eligible houses. Pre- and post-campaign cross-sectional surveys were conducted to assess acceptability, feasibility, drug safety, and effectiveness of the combined interventions. Stated acceptability for MDA before the campaign was 99.2%; MDA coverage estimated at 10 weeks post-campaign was 89.6%. Similarly, stated acceptability of IRS at baseline was 99.9%; however, household IRS coverage was 48.9% because of the high number of ineligible houses. Effectiveness measured by Plasmodium falciparum prevalence at baseline and 10 weeks post-campaign were similar: 1.31% versus 1.43%, respectively. Prevalence of serological markers were similar at 10 weeks post-campaign compared with baseline, and increased at 6 months. No severe adverse events associated with the MDA were identified in the pilot; there were severe adverse events in a separate, subsequent campaign. Both MDA and IRS are acceptable and feasible interventions in Haiti. Although a significant impact of a single round of MDA/IRS on malaria transmission was not found using a standard pre- and post-intervention comparison, it is possible there was blunting of the peak transmission. Seasonal malaria transmission patterns, suboptimal IRS coverage, and low baseline parasitemia may have limited the effectiveness or the ability to measure effectiveness.

Evaluating COVID-19-Related Disruptions to Effective Malaria Case Management in 2020-2021 and Its Potential Effects on Malaria Burden in Sub-Saharan Africa

The COVID-19 pandemic has led to far-reaching disruptions to health systems, including preventative and curative services for malaria. The aim of this study was to estimate the magnitude of disruptions in malaria case management in sub-Saharan Africa and their impact on malaria burden during the COVID-19 pandemic. We used survey data collected by the World Health Organization, in which individual country stakeholders reported on the extent of disruptions to malaria diagnosis and treatment. The relative disruption values were then applied to estimates of antimalarial treatment rates and used as inputs to an established spatiotemporal Bayesian geostatistical framework to generate annual malaria burden estimates with case management disruptions. This enabled an estimation of the additional malaria burden attributable to pandemic-related impacts on treatment rates in 2020 and 2021. Our analysis found that disruptions in access to antimalarial treatment in sub-Saharan Africa likely resulted in approximately 5.9 (4.4-7.2 95% CI) million more malaria cases and 76 (20-132) thousand additional deaths in the 2020-2021 period within the study region, equivalent to approximately 1.2% (0.3-2.1 95% CI) greater clinical incidence of malaria and 8.1% (2.1-14.1 95% CI) greater malaria mortality than expected in the absence of the disruptions to malaria case management. The available evidence suggests that access to antimalarials was disrupted to a significant degree and should be considered an area of focus to avoid further escalations in malaria morbidity and mortality. The results from this analysis were used to estimate cases and deaths in the World Malaria Report 2022 during the pandemic years.

Identifying individual, household and environmental risk factors for malaria infection on Bioko Island to inform interventions

BACKGROUND

Since 2004, malaria transmission on Bioko Island has declined significantly as a result of the scaling-up of control interventions. The aim of eliminating malaria from the Island remains elusive, however, underscoring the need to adapt control to the local context. Understanding the factors driving the risk of malaria infection is critical to inform optimal suits of interventions in this adaptive approach.

METHODS

This study used individual and household-level data from the 2015 and 2018 annual malaria indicator surveys on Bioko Island, as well as remotely-sensed environmental data in multilevel logistic regression models to quantify the odds of malaria infection. The analyses were stratified by urban and rural settings and by survey year.

RESULTS

Malaria prevalence was higher in 10-14-year-old children and similar between female and male individuals. After adjusting for demographic factors and other covariates, many of the variables investigated showed no significant association with malaria infection. The factor most strongly associated was history of travel to mainland Equatorial Guinea (mEG), which increased the odds significantly both in urban and rural settings (people who travelled had 4 times the odds of infection). Sleeping under a long-lasting insecticidal net decreased significantly the odds of malaria across urban and rural settings and survey years (net users had around 30% less odds of infection), highlighting their contribution to malaria control on the Island. Improved housing conditions indicated some protection, though this was not consistent across settings and survey year.

CONCLUSIONS

Malaria risk on Bioko Island is heterogeneous and determined by a combination of factors interacting with local mosquito ecology. These interactions grant further investigation in order to better adapt control according to need. The single most important risk factor identified was travel to mEG, in line with previous investigations, and represents a great challenge for the success of malaria control on the Island.

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

Background

Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence.

Methods

A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals.

Results

We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements.

Conclusions

Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00981-1.

Improving access to care and community health in Haiti with optimized community health worker placement

The national deployment of polyvalent community health workers (CHWs) is a constitutive part of the strategy initiated by the Ministry of Health to accelerate efforts towards universal health coverage in Haiti. Its implementation requires the planning of future recruitment and deployment activities for which mathematical modelling tools can provide useful support by exploring optimised placement scenarios based on access to care and population distribution. We combined existing gridded estimates of population and travel times with optimisation methods to derive theoretical CHW geographical placement scenarios including constraints on walking time and the number of people served per CHW. Four national-scale scenarios that align with total numbers of existing CHWs and that ensure that the walking time for each CHW does not exceed a predefined threshold are compared. The first scenario accounts for population distribution in rural and urban areas only, while the other three also incorporate in different ways the proximity of existing health centres. Comparing these scenarios to the current distribution, insufficient number of CHWs is systematically identified in several departments and gaps in access to health care are identified within all departments. These results highlight current suboptimal distribution of CHWs and emphasize the need to consider an optimal (re-)allocation.

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

A simulation study of disaggregation regression for spatial disease mapping

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While these simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

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.

DETECT Schools Study Protocol: A Prospective Observational Cohort Surveillance Study Investigating the Impact of COVID-19 in Western Australian Schools

Introduction: Amidst the evolving COVID-19 pandemic, understanding the transmission dynamics of the SARS-CoV-2 virus is key to providing peace of mind for the community and informing policy-making decisions. While available data suggest that school-aged children are not significant spreaders of SARS-CoV-2, the possibility of transmission in schools remains an ongoing concern, especially among an aging teaching workforce. Even in low-prevalence settings, communities must balance the potential risk of transmission with the need for students' ongoing education. Through the roll out of high-throughput school-based SARS-CoV-2 testing, enhanced follow-up for individuals exposed to COVID-19 and wellbeing surveys, this study investigates the dynamics of SARS-CoV-2 transmission and the current psychosocial wellbeing impacts of the pandemic in school communities. Methods: The DETECT Schools Study is a prospective observational cohort surveillance study in 79 schools across Western Australia (WA), Australia. To investigate the incidence, transmission and impact of SARS-CoV-2 in schools, the study comprises three "modules": Module 1) Spot-testing in schools to screen for asymptomatic SARS-CoV-2; Module 2) Enhanced surveillance of close contacts following the identification of any COVID-19 case to determine the secondary attack rate of SARS-CoV-2 in a school setting; and Module 3) Survey monitoring of school staff, students and their parents to assess psycho-social wellbeing following the first wave of the COVID-19 pandemic in WA. Clinical Trial Registration: Trial registration number: ACTRN12620000922976.

Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: a geospatial modelling analysis

BACKGROUND

Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control.

METHODS

Using an established set of spatiotemporal Bayesian geostatistical models, we generated geospatial estimates across malaria-endemic African countries of the clinical case incidence and mortality of malaria, incorporating an updated database of parasite rate surveys, insecticide-treated net (ITN) coverage, and effective treatment rates. We established a baseline estimate for the anticipated malaria burden in Africa in the absence of COVID-19-related disruptions, and repeated the analysis for nine hypothetical scenarios in which effective treatment with an antimalarial drug and distribution of ITNs (both through routine channels and mass campaigns) were reduced to varying extents.

FINDINGS

We estimated 215·2 (95% uncertainty interval 143·7-311·6) million cases and 386·4 (307·8-497·8) thousand deaths across malaria-endemic African countries in 2020 in our baseline scenario of undisrupted intervention coverage. With greater reductions in access to effective antimalarial drug treatment, our model predicted increasing numbers of cases and deaths: 224·1 (148·7-326·8) million cases and 487·9 (385·3-634·6) thousand deaths with a 25% reduction in antimalarial drug coverage; 233·1 (153·7-342·5) million cases and 597·4 (468·0-784·4) thousand deaths with a 50% reduction; and 242·3 (158·7-358·8) million cases and 715·2 (556·4-947·9) thousand deaths with a 75% reduction. Halting planned 2020 ITN mass distribution campaigns and reducing routine ITN distributions by 25%-75% also increased malaria burden to a total of 230·5 (151·6-343·3) million cases and 411·7 (322·8-545·5) thousand deaths with a 25% reduction; 232·8 (152·3-345·9) million cases and 415·5 (324·3-549·4) thousand deaths with a 50% reduction; and 234·0 (152·9-348·4) million cases and 417·6 (325·5-553·1) thousand deaths with a 75% reduction. When ITN coverage and antimalarial drug coverage were synchronously reduced, malaria burden increased to 240·5 (156·5-358·2) million cases and 520·9 (404·1-691·9) thousand deaths with a 25% reduction; 251·0 (162·2-377·0) million cases and 640·2 (492·0-856·7) thousand deaths with a 50% reduction; and 261·6 (167·7-396·8) million cases and 768·6 (586·1-1038·7) thousand deaths with a 75% reduction.

INTERPRETATION

Under pessimistic scenarios, COVID-19-related disruption to malaria control in Africa could almost double malaria mortality in 2020, and potentially lead to even greater increases in subsequent years. To avoid a reversal of two decades of progress against malaria, averting this public health disaster must remain an integrated priority alongside the response to COVID-19.

FUNDING

Bill and Melinda Gates Foundation; Channel 7 Telethon Trust, Western Australia.

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.

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

BACKGROUND

Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time.

METHODS

This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries.

RESULTS

The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials.

CONCLUSIONS

This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Does the design of the NHS Diabetes Prevention Programme intervention have fidelity to the programme specification? A document analysis

AIMS

To assess fidelity of the Healthier You: NHS Diabetes Prevention Programme (NHS-DPP), a behavioural intervention for people in England at high risk of developing type 2 diabetes, to the specified programme features.

METHODS

Document analysis of the NHS-DPP programme specification, including National Institute for Health and Care Excellence (NICE) PH38 diabetes prevention guidance. This was compared with the intervention design (framework response documents and programme manuals) from all four independent providers delivering the NHS-DPP. Documents were coded using the Template for Intervention Description and Replication framework (describing service parameters) and the Behaviour Change Technique Taxonomy v1.

RESULTS

Providers demonstrated good fidelity to service parameters of the NHS-DPP. The NHS-DPP specification indicated 19 unique behaviour change techniques. Framework responses for the four providers contained between 24 and 32 distinct behaviour change techniques, and programme manuals contained between 23 and 45 distinct behaviour change techniques, indicating variation in behaviour change content between providers' intervention documents. Thus, each provider planned to deliver 74% of the unique behaviour change techniques specified, and a large amount of behaviour change content not mandated.

CONCLUSIONS

There is good fidelity to the specified service parameters of the NHS-DPP; however, the four providers planned to deliver approximately three-quarters of behaviour change techniques specified by the NHS-DPP. Given that behaviour change techniques are the 'active ingredients' of interventions, and some of these techniques in the programme manuals may be missed in practice, this highlights possible limitations with fidelity to the NHS-DPP programme specification at the intervention design stage.

Association between the proportion of Plasmodium falciparum and Plasmodium vivax infections detected by passive surveillance and the magnitude of the asymptomatic reservoir in the community: a pooled analysis of paired health facility and community data

BACKGROUND

Passively collected malaria case data are the foundation for public health decision making. However, because of population-level immunity, infections might not always be sufficiently symptomatic to prompt individuals to seek care. Understanding the proportion of all Plasmodium spp infections expected to be detected by the health system becomes particularly paramount in elimination settings. The aim of this study was to determine the association between the proportion of infections detected and transmission intensity for Plasmodium falciparum and Plasmodium vivax in several global endemic settings.

METHODS

The proportion of infections detected in routine malaria data, P(Detect), was derived from paired household cross-sectional survey and routinely collected malaria data within health facilities. P(Detect) was estimated using a Bayesian model in 431 clusters spanning the Americas, Africa, and Asia. The association between P(Detect) and malaria prevalence was assessed using log-linear regression models. Changes in P(Detect) over time were evaluated using data from 13 timepoints over 2 years from The Gambia.

FINDINGS

The median estimated P(Detect) across all clusters was 12·5% (IQR 5·3-25·0) for P falciparum and 10·1% (5·0-18·3) for P vivax and decreased as the estimated log-PCR community prevalence increased (adjusted odds ratio [OR] for P falciparum 0·63, 95% CI 0·57-0·69; adjusted OR for P vivax 0·52, 0·47-0·57). Factors associated with increasing P(Detect) included smaller catchment population size, high transmission season, improved care-seeking behaviour by infected individuals, and recent increases (within the previous year) in transmission intensity.

INTERPRETATION

The proportion of all infections detected within health systems increases once transmission intensity is sufficiently low. The likely explanation for P falciparum is that reduced exposure to infection leads to lower levels of protective immunity in the population, increasing the likelihood that infected individuals will become symptomatic and seek care. These factors might also be true for P vivax but a better understanding of the transmission biology is needed to attribute likely reasons for the observed trend. In low transmission and pre-elimination settings, enhancing access to care and improvements in care-seeking behaviour of infected individuals will lead to an increased proportion of infections detected in the community and might contribute to accelerating the interruption of transmission.

FUNDING

Wellcome Trust.

Abstract 339: Runx1 Knock-down by Adeno Associated Viral Mediated-delivery of Short Hairpin RNA Preserves Cardiac Contractility Following Myocardial Infarction

Myocardial infarction (MI) is a leading cause of heart failure (HF) and mortality worldwide. Preservation of contractile function and prevention of adverse cardiac remodelling following MI are essential to limit progression to HF. Expression of the Runx1 transcription factor in border zone cardiomyocytes is increased within 24 hours after MI. Genetically modified mice with Runx1 deficiency demonstrate an absence of cardiac dilation and preservation of left ventricular (LV) contractility following MI without a change of infarct size. Here, we investigate if adeno-associated viral (AAV)-mediated gene transfer to knock-down Runx1 expression post-MI attenuates adverse cardiac remodelling in mice. Male C57BL/6J mice underwent LAD ligation followed by intravenous injection of AAV-Runx1-shRNA or control AAV-scramble-shRNA (1x10 11 viral genomes). LV contractility (% fractional shortening, FS) was assessed by M-mode echocardiography. RNAscope assessed Runx1 expression in border zone cardiomyocytes and infarct size was histologically quantified using Picro sirius red stain. Runx1 expression within border zone cardiomyocytes was reduced by 30% in AAV-Runx1-shRNA treated mice (37.8±4.5 vs. 53.9±6.4% of cardiomyocytes; AAV-Runx1-shRNA vs. control). LV contractile function was significantly preserved as early as 1 day post-MI (%FS, day 1; 37.7±1.4 vs . 27.6±1.4, day 7; 37.2 ±1.2 vs .28.2±1.4; LV internal dimension during systole day 1; 2.9±0.2 vs . 2.4±0.1, day 7; 3.1±0.2 vs . 2.5±0.1mm; AAV-Runx1-shRNA vs. control, P <0.05). Interestingly, infarct size was reduced with AAV-Runx1-shRNA (31.7±2.0 vs . 40.4±1.4% of LV; AAV-Runx1-shRNA vs . control, P <0.05). In conclusion, a therapeutic approach utilising AAV-mediated knock-down of Runx1 preserved LV contractility and reduced infarct size following MI. These data further highlight Runx1 as a key driver of adverse cardiac remodelling and a therapeutic target with translational potential to prevent adverse cardiac remodelling following MI.

RUNX1: an emerging therapeutic target for cardiovascular disease

Runt-related transcription factor-1 (RUNX1), also known as acute myeloid leukaemia 1 protein (AML1), is a member of the core-binding factor family of transcription factors which modulate cell proliferation, differentiation, and survival in multiple systems. It is a master-regulator transcription factor, which has been implicated in diverse signalling pathways and cellular mechanisms during normal development and disease. RUNX1 is best characterized for its indispensable role for definitive haematopoiesis and its involvement in haematological malignancies. However, more recently RUNX1 has been identified as a key regulator of adverse cardiac remodelling following myocardial infarction. This review discusses the role RUNX1 plays in the heart and highlights its therapeutic potential as a target to limit the progression of adverse cardiac remodelling and heart failure.

Mapping trends in insecticide resistance phenotypes in African malaria vectors

Mitigating the threat of insecticide resistance in African malaria vector populations requires comprehensive information about where resistance occurs, to what degree, and how this has changed over time. Estimating these trends is complicated by the sparse, heterogeneous distribution of observations of resistance phenotypes in field populations. We use 6,423 observations of the prevalence of resistance to the most important vector control insecticides to inform a Bayesian geostatistical ensemble modelling approach, generating fine-scale predictive maps of resistance phenotypes in mosquitoes from the Anopheles gambiae complex across Africa. Our models are informed by a suite of 111 predictor variables describing potential drivers of selection for resistance. Our maps show alarming increases in the prevalence of resistance to pyrethroids and DDT across sub-Saharan Africa from 2005 to 2017, with mean mortality following insecticide exposure declining from almost 100% to less than 30% in some areas, as well as substantial spatial variation in resistance trends.

Mapping malaria seasonality in Madagascar using health facility data

BACKGROUND

Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data.

METHODS

With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings.

RESULTS

Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty.

CONCLUSIONS

Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Disrupting MLV integrase:BET protein interaction biases integration into quiescent chromatin and delays but does not eliminate tumor activation in a MYC/Runx2 mouse model

Murine leukemia virus (MLV) integrase (IN) lacking the C-terminal tail peptide (TP) loses its interaction with the host bromodomain and extraterminal (BET) proteins and displays decreased integration at promoter/enhancers and transcriptional start sites/CpG islands. MLV lacking the IN TP via an altered open reading frame was used to infect tumorigenesis mouse model (MYC/Runx2) animals to observe integration patterns and phenotypic effects, but viral passage resulted in the restoration of the IN TP through small deletions. Mice subsequently infected with an MLV IN lacking the TP coding sequence (TP^-) showed an improved median survival by 15 days compared to wild type (WT) MLV infection. Recombination with polytropic endogenous retrovirus (ERV), Pmv20, was identified in seven mice displaying both fast and slow tumorigenesis, highlighting the strong selection within the mouse to maintain the full-length IN protein. Mapping the genomic locations of MLV in tumors from an infected mouse with no observed recombination with ERVs, TP^-16, showed fewer integrations at TSS and CpG islands, compared to integrations observed in WT tumors. However, this mouse succumbed to the tumor in relatively rapid fashion (34 days). Analysis of the top copy number integrants in the TP^-16 tumor revealed their proximity to known MLV common insertion site genes while maintaining the MLV IN TP^- genotype. Furthermore, integration mapping in K562 cells revealed an insertion preference of MLV IN TP^- within chromatin profile states associated with weakly transcribed heterochromatin with fewer integrations at histone marks associated with BET proteins (H3K4me1/2/3, and H3K27Ac). While MLV IN TP^- showed a decreased overall rate of tumorigenesis compared to WT virus in the MYC/Runx2 model, MLV integration still occurred at regions associated with oncogenic driver genes independently from the influence of BET proteins, either stochastically or through trans-complementation by functional endogenous Gag-Pol protein.

Many different retroviruses, including murine leukemia virus (MLV), are used as vectors for human gene therapy and cancer immunotherapies (CAR-T) because of their stable and efficient delivery of genetic material into the host DNA. However, this process can result in activation and/or disruption of cellular genes, which has resulted in the outgrowth of tumors in previous clinical trials. Of critical importance is the preferred location within the host genome at which the retrovirus integrates. Our study presents a modified MLV virus that has lost the ability to bind to the host BET proteins, the drivers of insertion at promoter/enhancer regions of highly activated genes. This is the first direct study within an animal model to examine whether infection with this type of modified MLV virus affects tumor progression. We found that the modified virus improved the survival of the well-characterized MYC/Runx2 mouse compared to infections with the wild-type MLV. Most modified viral integrants mapped into less active regions of the genome, but some integration events still occurred near cancer-related genes. Thus, while the modified MLV virus can be a safer vector than the wildtype virus, it still maintains the potential to activate oncogenes. This study provides new insights on how to improve the safety of MLV retroviral vectors.

Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000-17: a spatial and temporal modelling study

BACKGROUND

Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with the proper diagnosis and treatment of P vivax, national malaria programmes-particularly those pursuing malaria elimination strategies-require up to date assessments of P vivax endemicity and disease impact. This study presents the first global maps of P vivax clinical burden from 2000 to 2017.

METHODS

In this spatial and temporal modelling study, we adjusted routine malariometric surveillance data for known biases and used socioeconomic indicators to generate time series of the clinical burden of P vivax. These data informed Bayesian geospatial models, which produced fine-scale predictions of P vivax clinical incidence and infection prevalence over time. Within sub-Saharan Africa, where routine surveillance for P vivax is not standard practice, we combined predicted surfaces of Plasmodium falciparum with country-specific ratios of P vivax to P falciparum. These results were combined with surveillance-based outputs outside of Africa to generate global maps.

FINDINGS

We present the first high-resolution maps of P vivax burden. These results are combined with those for P falciparum (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The burden of P vivax malaria decreased by 41·6%, from 24·5 million cases (95% uncertainty interval 22·5-27·0) in 2000 to 14·3 million cases (13·7-15·0) in 2017. The Americas had a reduction of 56·8% (47·6-67·0) in total cases since 2000, while South-East Asia recorded declines of 50·5% (50·3-50·6) and the Western Pacific regions recorded declines of 51·3% (48·0-55·4). Europe achieved zero P vivax cases during the study period. Nonetheless, rates of decline have stalled in the past five years for many countries, with particular increases noted in regions affected by political and economic instability.

INTERPRETATION

Our study highlights important spatial and temporal patterns in the clinical burden and prevalence of P vivax. Amid substantial progress worldwide, plateauing gains and areas of increased burden signal the potential for challenges that are greater than expected on the road to malaria elimination. These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact.

FUNDING

Bill & Melinda Gates Foundation and the Wellcome Trust.

Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study

BACKGROUND

Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden.

METHODS

We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics.

FINDINGS

We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000-17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8-277·7) to 193·9 million (156·6-240·2) and deaths declined from 925 800 (596 900-1 341 100) to 618 700 (368 600-952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017.

INTERPRETATION

High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period.

FUNDING

Bill & Melinda Gates Foundation.

Collaboration of MYC and RUNX2 in lymphoma simulates T-cell receptor signaling and attenuates p53 pathway activity

MYC and RUNX oncogenes each trigger p53‐mediated failsafe responses when overexpressed in vitro and collaborate with p53 deficiency in vivo. However, together they drive rapid onset lymphoma without mutational loss of p53. This phenomenon was investigated further by transcriptomic analysis of premalignant thymus from RUNX2/MYC transgenic mice. The distinctive contributions of MYC and RUNX to transcriptional control were illustrated by differential enrichment of canonical binding sites and gene ontology analyses. Pathway analysis revealed signatures of MYC, CD3, and CD28 regulation indicative of activation and proliferation, but also strong inhibition of cell death pathways. In silico analysis of discordantly expressed genes revealed Tnfsrf8/CD30, Cish, and Il13 among relevant targets for sustained proliferation and survival. Although TP53 mRNA and protein levels were upregulated, its downstream targets in growth suppression and apoptosis were largely unperturbed. Analysis of genes encoding p53 posttranslational modifiers showed significant upregulation of three genes, Smyd2, Set, and Prmt5. Overexpression of SMYD2 was validated in vivo but the functional analysis was constrained by in vitro loss of p53 in RUNX2/MYC lymphoma cell lines. However, an early role is suggested by the ability of SMYD2 to block senescence‐like growth arrest induced by RUNX overexpression in primary fibroblasts.

The contribution of non-malarial febrile illness co-infections to Plasmodium falciparum case counts in health facilities in sub-Saharan Africa

Background

The disease burden of Plasmodium falciparum malaria illness is generally estimated using one of two distinct approaches: either by transforming P. falciparum infection prevalence estimates into incidence estimates using conversion formulae; or through adjustment of counts of recorded P. falciparum-positive fever cases from clinics. Whilst both ostensibly seek to evaluate P. falciparum disease burden, there is an implicit and problematic difference in the metric being estimated. The first enumerates only symptomatic malaria cases, while the second enumerates all febrile episodes coincident with a P. falciparum infection, regardless of the fever’s underlying cause.

Methods

Here, a novel approach was used to triangulate community-based data sources capturing P. falciparum infection, fever, and care-seeking to estimate the fraction of P. falciparum-positive fevers amongst children under 5 years of age presenting at health facilities that are attributable to P. falciparum infection versus other non-malarial causes. A Bayesian hierarchical model was used to assign probabilities of malaria-attributable fever (MAF) and non-malarial febrile illness (NMFI) to children under five from a dataset of 41 surveys from 21 countries in sub-Saharan Africa conducted between 2006 and 2016. Using subsequent treatment-seeking outcomes, the proportion of MAF and NMFI amongst P. falciparum-positive febrile children presenting at public clinics was estimated.

Results

Across all surveyed malaria-positive febrile children who sought care at public clinics across 41 country-years in sub-Saharan Africa, P. falciparum infection was estimated to be the underlying cause of only 37.7% (31.1–45.4, 95% CrI) of P. falciparum-positive fevers, with significant geographical and temporal heterogeneity between surveys.

Conclusions

These findings highlight the complex nature of the P. falciparum burden amongst children under 5 years of age and indicate that for many children presenting at health clinics, a positive P. falciparum diagnosis and a fever does not necessarily mean P. falciparum is the underlying cause of the child’s symptoms, and thus other causes of illness should always be investigated, in addition to prescribing an effective anti-malarial medication. In addition to providing new large-scale estimates of malaria-attributable fever prevalence, the results presented here improve comparability between different methods for calculating P. falciparum disease burden, with significant implications for national and global estimation of malaria burden.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2830-y) contains supplementary material, which is available to authorized users.

Mapping changes in housing in sub-Saharan Africa from 2000 to 2015

Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10-12%) to 23% (21-25%). However, 53 (50-57) million urban Africans (47% (44-50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.

malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project

Background

The Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs.

Methods and results

The current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis.

Conclusions

malariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2500-5) contains supplementary material, which is available to authorized users.

Mapping child growth failure in Africa between 2000 and 2015

Insufficient growth during childhood is associated with poor health outcomes and an increased risk of death. Between 2000 and 2015, nearly all African countries demonstrated improvements for children under 5 years old for stunting, wasting, and underweight, the core components of child growth failure. Here we show that striking subnational heterogeneity in levels and trends of child growth remains. If current rates of progress are sustained, many areas of Africa will meet the World Health Organization Global Targets 2025 to improve maternal, infant and young child nutrition, but high levels of growth failure will persist across the Sahel. At these rates, much, if not all of the continent will fail to meet the Sustainable Development Goal target—to end malnutrition by 2030. Geospatial estimates of child growth failure provide a baseline for measuring progress as well as a precision public health platform to target interventions to those populations with the greatest need, in order to reduce health disparities and accelerate progress.

Geospatial estimates of child growth failure in Africa provide a baseline for measuring progress and a precision public health platform to target interventions to those populations with the greatest need.

How long do rapid diagnostic tests remain positive after anti-malarial treatment?

BACKGROUND

Rapid diagnostic tests (RDTs) are increasingly becoming a paradigm for both clinical diagnosis of malaria infections and for estimating community parasite prevalence in household malaria indicator surveys in malaria-endemic countries. The antigens detected by RDTs are known to persist in the blood after treatment with anti-malarials, but reports on the duration of persistence (and the effect this has on RDT positivity) of these antigens post-treatment have been variable.

METHODS

In this review, published studies on the persistence of positivity of RDTs post-treatment are collated, and a bespoke Bayesian survival model is fit to estimate the number of days RDTs remain positive after treatment.

RESULTS

Half of RDTs that detect the antigen histidine-rich protein II (HRP2) are still positive 15 (5-32) days post-treatment, 13 days longer than RDTs that detect the antigen Plasmodium lactate dehydrogenase, and that 5% of HRP2 RDTs are still positive 36 (21-61) days after treatment. The duration of persistent positivity for combination RDTs that detect both antigens falls between that for HRP2- or pLDH-only RDTs, with half of RDTs remaining positive at 7 (2-20) days post-treatment. This study shows that children display persistent RDT positivity for longer after treatment than adults, and that persistent positivity is more common when an individual is treated with artemisinin combination therapy than when treated with other anti-malarials.

CONCLUSIONS

RDTs remain positive for a highly variable amount of time after treatment with anti-malarials, and the duration of positivity is highly dependent on the type of RDT used for diagnosis. Additionally, age and treatment both impact the duration of persistence of RDT positivity. The results presented here suggest that caution should be taken when using RDT-derived diagnostic outcomes from cross-sectional data where individuals have had a recent history of anti-malarial treatment.

Associated patterns of insecticide resistance in field populations of malaria vectors across Africa

The development of insecticide resistance in African malaria vectors threatens the continued efficacy of important vector control methods that rely on a limited set of insecticides. To understand the operational significance of resistance we require quantitative information about levels of resistance in field populations to the suite of vector control insecticides. Estimation of resistance is complicated by the sparsity of observations in field populations, variation in resistance over time and space at local and regional scales, and cross-resistance between different insecticide types. Using observations of the prevalence of resistance in mosquito species from the Anopheles gambiae complex sampled from 1,183 locations throughout Africa, we applied Bayesian geostatistical models to quantify patterns of covariation in resistance phenotypes across different insecticides. For resistance to the three pyrethroids tested, deltamethrin, permethrin, and λ-cyhalothrin, we found consistent forms of covariation across sub-Saharan Africa and covariation between resistance to these pyrethroids and resistance to DDT. We found no evidence of resistance interactions between carbamate and organophosphate insecticides or between these insecticides and those from other classes. For pyrethroids and DDT we found significant associations between predicted mean resistance and the observed frequency of kdr mutations in the Vgsc gene in field mosquito samples, with DDT showing the strongest association. These results improve our capacity to understand and predict resistance patterns throughout Africa and can guide the development of monitoring strategies.

Spatio-temporal mapping of Madagascar's Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

BACKGROUND

Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system.

METHODS

A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country.

RESULTS

Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country's population lived in areas of very low malaria risk (<1% parasite prevalence), but by 2016, this had dropped to only 26.7% of the population. Meanwhile, the population in high transmission areas (prevalence >20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season.

CONCLUSIONS

Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

Rapid improvements to rural Ugandan housing and their association with malaria from intense to reduced transmission: a cohort study

BACKGROUND

Rapid population growth in Africa requires an urgent expansion and improvement of housing options. Improving housing presents a promising opportunity for malaria control by reducing indoor exposure to mosquitoes. We measured recent changes in house design in rural Uganda and evaluated their association with malaria in relation to a mass scale-up of control efforts.

METHODS

This analysis was part of a cohort study designed to compare temporal changes in malaria incidence from a cohort of children and adults with temporal changes in malaria test positivity rate from health facility surveillance. All children aged 6 months to 10 years (n=384) living in 107 households in Nagongera sub-country, Tororo, Uganda, were given long-lasting insecticide-treated nets and followed between Aug 19, 2011, and June 30, 2017. Repeat rounds of indoor residual spraying of insecticide were initiated on Dec 5, 2014. Socioeconomic data were collected at two timepoints (Sept 25-Oct 9, 2013 and June 21-July 11, 2016) and houses were classified as modern (cement, wood, or metal walls, tiled or metal roof, and closed eaves) or traditional (all other homes). Associations between house design and three outcomes were evaluated before and after the introduction of indoor residual spraying: human biting rate estimated monthly in each household using US Centers for Disease Control and Prevention light traps; parasite prevalence measured routinely by microscopy every 3 months before indoor residual spraying and monthly after indoor residual spraying; and malaria incidence measured by passive surveillance.

FINDINGS

The implementation of indoor residual spraying was associated with significant declines in human biting rate (33·5 vs 2·7 Anopheles per house per night after indoor residual spraying, p<0·0001), parasite prevalence (32·0% vs 14·0%, p<0·0001), and malaria incidence (3·0 vs 0·5 episodes per person-year at risk, p<0·0001). The prevalence of modern housing increased from 23·4% in 2013 to 45·4% in 2016 (p=0·001). Compared with traditional houses, modern houses were associated with a 48% reduction in human biting rate before indoor residual spraying (adjusted incidence rate ratio [aIRR] 0·52, 95% CI 0·36-0·73, p=0·0002), and a 73% reduction after indoor residual spraying (aIRR 0·27, 0·17-0·42, p<0·0001). Before indoor residual spraying, there was no association between house type and parasite prevalence, but after indoor residual spraying there was a 57% reduction in the odds of parasitaemia in modern houses compared with traditional houses, controlling for age, sex, and socioeconomic position (adjusted odds ratio 0·43, 95% CI 0·24-0·77, p=0·004). House type was not associated with malaria incidence before or after indoor residual spraying.

INTERPRETATION

House design improved rapidly in rural Uganda and was associated with additional reductions in mosquito density and parasite prevalence following the introduction of indoor residual spraying. Changes to house design in endemic Africa, including closing eaves and the replacement of traditional building materials, might help further the gains achieved with more widely accepted malaria control interventions.

FUNDING

US National Institutes of Health, Bill & Melinda Gates Foundation, and Medical Research Council UK.

RUNX-mediated growth arrest and senescence are attenuated by diverse mechanisms in cells expressing RUNX1 fusion oncoproteins

RUNX gene over‐expression inhibits growth of primary cells but transforms cells with tumor suppressor defects, consistent with reported associations with tumor progression. In contrast, chromosomal translocations involving RUNX1 are detectable in utero, suggesting an initiating role in leukemias. How do cells expressing RUNX1 fusion oncoproteins evade RUNX‐mediated growth suppression? Previous studies showed that the TEL‐RUNX1 fusion from t(12;21) B‐ALLs is unable to induce senescence‐like growth arrest (SLGA) in primary fibroblasts while potent activity is displayed by the RUNX1‐ETO fusion found in t(8;21) AMLs. We now show that SLGA potential is suppressed in TEL‐RUNX1 but reactivated by deletion of the TEL HLH domain or mutation of a key residue (K99R). Attenuation of SLGA activity is also a feature of RUNX1‐ETO9a, a minor product of t(8;21) translocations with increased leukemogenicity. Finally, while RUNX1‐ETO induces SLGA it also drives a potent senescence‐associated secretory phenotype (SASP), and promotes the immortalization of rare cells that escape SLGA. Moreover, the RUNX1‐ETO SASP is not strictly linked to growth arrest as it is largely suppressed by RUNX1 and partially activated by RUNX1‐ETO9a. These findings underline the heterogeneous nature of premature senescence and the multiple mechanisms by which this failsafe process is subverted in cells expressing RUNX1 oncoproteins.

Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals

BACKGROUND

During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa.

METHODS

We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require.

FINDINGS

Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030.

INTERPRETATION

In the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030.

FUNDING

Bill & Melinda Gates Foundation.