The Temporal and Spatial Distribution of Malaria in Africa,\ud with Emphasis on Southern Africa

The three-way relationship between the Plasmodium parasite, the Anopheles mosquito vector and the human host determines the incidence of malaria disease. The three life cycles, the interactions respectively between human and parasite, human and mosquito, and mosquito and parasite, and the ultimate transmission cycle, vary in time and space. Environmental, genetic and behavioural factors influence the three life cycles and the interactions. These factors also vary in time and space. At every level the variation itself, whether random or cyclical, is not uniform but varies in frequency and magnitude. Explaining, and particularly predicting, malaria transmission rates in time and space thus becomes a difficult undertaking. Knowing and understanding some of this variation, and its causes, is important for well-timed and well-targeted malaria interventions. In the fringe areas of malaria in Africa, which are prone to epidemics, some forewarning of unusually high incidence periods would be valuable to malaria control and management services. This thesis investigated the temporal and spatial effects on malaria transmission of various environmental factors, particularly climate, and of non-climatic factors, particularly those relating to malaria control. Different data sets and methodological approaches were applied in seven separate studies, and malaria distribution in time and space was investigated at different scales. At the continental scale, the distribution of malaria in Africa was modelled as a factor of climate using raster GIS techniques. At the national scale, using prevalence data from Botswana, spatial variation in prevalence was modelled as a factor of environmental determinants, prior to comprehensive malaria control. The spatial and inter-annual variation in prevalence, in the presence of intense control, was also modelled as a factor of climate. At the sub-national level South Africa was used as an example. Inter-annual variation in malaria incidence in the highest-risk province was explored for possible links with climatic and non-climatic factors. Finally, inter-annual and spatial variation in sub-provincial level incidence data for South Africa, were analysed with respect to climatic and non-climatic determinants, for which data were available. The two study areas (Botswana and South Africa) both lie at the fringe of malaria distribution, experience strongly seasonal transmission and epidemics, and both benefit from intensive malaria control. The two study areas represent two slightly different scenarios: in Botswana the analysis period covered the steady introduction of comprehensive control, while in South Africa the study period covered a time when effective control was being threatened by the spread of insecticide- and drug resistance, and the general health of the population was increasingly affected by the HIV pandemic. The main findings were the following: • It was possible to estimate the distribution of malaria in Africa fairly successfully from long term mean climate data via simple GIS methods. The model compared well with contemporary malaria data and historical ‘expert opinion’ maps, excepting small-scale ecological anomalies. The model provided a numerical basis for further refinement and prediction of the impact of climate change on transmission. Together with population, morbidity and mortality data, it has provided a fundamental tool for strategic control of malaria. • In Botswana the spatial variation in childhood malaria prevalence, prior to intense comprehensive control, was significantly associated with underlying environmental factors. It could be predicted and mapped using only three environmental predictors, namely summer rainfall, mean annual temperature and altitude. After starting with a long list of candidate variables, this parsimonious model was achieved by applying a systematic and repeatable staged variable exclusion procedure that included a spatial analysis. All this was accomplished using general-purpose statistical software. • In the presence of intense control, the spatial and temporal variability in childhood malaria prevalence in Botswana could no longer be explained by variation in climate. The effects of malaria control and good access to treatment seem to have replaced climate as the main determinant of prevalence. This also suggests that prevalence, a less direct measure of transmission rate, is more prone to non-climatic effects than incidence rate. • Total population malaria incidence in KwaZulu-Natal, the highest risk province of South Africa, remained significantly influenced by climate over a 30 year period, even in the presence of intense control. The inter-annual variation in case numbers were significantly associated with several climate variables, mainly mean annual daily temperatures and summer rainfall. However, climate factors did not explain the longer term total incidence rates. • The longer term trends in total malaria incidence in KwaZulu-Natal province, over the same 30 years period, were significantly associated with the spread of anti-malarial drug resistance and HIV prevalence. Cross-border movements of people, agricultural activities and emergence of insecticide resistance also affected the level of malaria transmission at certain periods and to some degree, but this could not be formally quantified. • When considering malaria incidence in three malarious provinces of South Africa at a sub-provincial level, the observed temporal and spatial variation could largely be explained by available weather, HIV prevalence and drug-resistance data. However, much of the region-specific temporal trends remained unexplained. Temporal forecasts, based on 18 years of data, predicted for six years for six regions, were not very accurate and lacked precision. It seems that the interplay of climatic and non-climatic factors in the South African context is too complex to allow forecasts that are suitable for decision-making at the provincial level. • The findings of this thesis emphasize that in addition to shorter-term variation, which seems to be driven by climate in many cases, malaria transmission is largely determined by non-climatic factors in southern Africa. This appears to be particularly true where the natural malaria endemicity has been modified by control interventions. As the drive to control malaria in Africa continues and intensifies, the need for long-term surveillance of not merely malaria transmission, but also of the coverage and effectiveness of control interventions, will grow

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

<p>Abstract</p> <p>Background</p> <p>Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.</p> <p>Results</p> <p>Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.</p> <p>Conclusion</p> <p>We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Implementation of corticosteroids in treating COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK:prospective observational cohort study

BACKGROUND: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. METHODS: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. FINDINGS: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70–0·89], p=0·0001, for 70–79 years; 0·52 [0·46–0·58], p80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75–80% in January, 2021. INTERPRETATION: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant women. This could reflect appropriate clinical decision making, but the possibility of inequitable access to life-saving care should be considered. FUNDING: UK National Institute for Health Research and UK Medical Research Council

Joseph Franz Barwirsch levele Lukács Györgynek

_Background:_ Acute kidney injury (AKI) is a frequently encountered complication of imported Plasmodium falciparum infection. Markers of structural kidney damage have been found to detect AKI earlier than serum creatinine-based prediction models but have not yet been evaluated in imported malaria. This pilot study aims to explore the predictive performance of neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) for AKI in travellers with imported P. falciparum infection. _Methods:_ Thirty-nine patients with imported falciparum malaria from the Rotterdam Malaria Cohort with available serum and urine samples at presentation were included. Ten of these patients met the criteria for severe malaria. The predictive performance of NGAL and KIM-1 as markers for AKI was compared with that of serum creatinine. _Results:_ Six of the 39 patients (15 %) developed AKI. Serum and urine NGAL and urine KIM-1 were all found to have large areas under the receiver operating characteristics curves (AUROC) for predicting AKI. Urine NGAL was found to have an excellent performance with positive predictive value (PPV) of 1.00 (95 % CI 0.54-1.00), a negative predictive value (NPV) of 1.00 (95 % CI 0.89-1.00) and an AUROC of 1.00 (95 % CI 1.00-1.00). _Conclusion:_ A good diagnostic performance of NGAL and KIM-1 for AKI was found. Particularly, urine NGAL was found to have an excellent predictive performance. Larger studies are needed to demonstrate whether these biomarkers are superior to serum creatinine as predictors for AKI in P. falciparum malaria

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p&lt;0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p&lt;0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p&lt;0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP &gt;5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely