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

Short-term and medium-term survival of critically ill patients with solid tumours admitted to the intensive care unit:A retrospective analysis

OBJECTIVES: Patients with cancer frequently require unplanned admission to the intensive care unit (ICU). Our objectives were to assess hospital and 180-day mortality in patients with a non-haematological malignancy and unplanned ICU admission and to identify which factors present on admission were the best predictors of mortality. DESIGN: Retrospective review of all patients with a diagnosis of solid tumours following unplanned admission to the ICU between 1 August 2008 and 31 July 2012. SETTING: Single centre tertiary care hospital in London (UK). PARTICIPANTS: 300 adult patients with non-haematological solid tumours requiring unplanned admission to the ICU. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOMES: Hospital and 180-day survival. RESULTS: 300 patients were admitted to the ICU (median age 66.5 years; 61.7% men). Survival to hospital discharge and 180 days were 69% and 47.8%, respectively. Greater number of failed organ systems on admission was associated with significantly worse hospital survival (p<0.001) but not with 180-day survival (p=0.24). In multivariate analysis, predictors of hospital mortality were the presence of metastases (OR 1.97, 95% CI 1.08 to 3.59), Acute Physiology and Chronic Health Evaluation II (APACHE II) Score (OR 1.07, 95% CI 1.01 to 1.13) and a Glasgow Coma Scale Score <7 on admission to ICU (OR 5.21, 95% CI 1.65 to 16.43). Predictors of worse 180-day survival were the presence of metastases (OR 2.82, 95% CI 1.57 to 5.06), APACHE II Score (OR 1.07, 95% CI 1.01 to 1.13) and sepsis (OR 1.92, 95% CI 1.09 to 3.38). CONCLUSIONS: Short-term and medium-term survival in patients with solid tumours admitted to ICU is better than previously reported, suggesting that the presence of cancer alone should not be a barrier to ICU admission

Impacts of 1.5°C Global Warming on Natural and Human Systems

An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate povert

Effect of a Perioperative, Cardiac Output-Guided Hemodynamic Therapy Algorithm on Outcomes Following Major Gastrointestinal Surgery A Randomized Clinical Trial and Systematic Review

Importance: small trials suggest that postoperative outcomes may be improved by the use of cardiac output monitoring to guide administration of intravenous fluid and inotropic drugs as part of a hemodynamic therapy algorithm.Objective: to evaluate the clinical effectiveness of a perioperative, cardiac output–guided hemodynamic therapy algorithm.Design, setting, and participants: OPTIMISE was a pragmatic, multicenter, randomized, observer-blinded trial of 734 high-risk patients aged 50 years or older undergoing major gastrointestinal surgery at 17 acute care hospitals in the United Kingdom. An updated systematic review and meta-analysis were also conducted including randomized trials published from 1966 to February 2014.Interventions: patients were randomly assigned to a cardiac output–guided hemodynamic therapy algorithm for intravenous fluid and inotrope (dopexamine) infusion during and 6 hours following surgery (n=368) or to usual care (n=366).Main outcomes and measures: the primary outcome was a composite of predefined 30-day moderate or major complications and mortality. Secondary outcomes were morbidity on day 7; infection, critical care–free days, and all-cause mortality at 30 days; all-cause mortality at 180 days; and length of hospital stay.Results: baseline patient characteristics, clinical care, and volumes of intravenous fluid were similar between groups. Care was nonadherent to the allocated treatment for less than 10% of patients in each group. The primary outcome occurred in 36.6% of intervention and 43.4% of usual care participants (relative risk [RR], 0.84 [95% CI, 0.71-1.01]; absolute risk reduction, 6.8% [95% CI, ?0.3% to 13.9%]; P?=?.07). There was no significant difference between groups for any secondary outcomes. Five intervention patients (1.4%) experienced cardiovascular serious adverse events within 24 hours compared with none in the usual care group. Findings of the meta-analysis of 38 trials, including data from this study, suggest that the intervention is associated with fewer complications (intervention, 488/1548 [31.5%] vs control, 614/1476 [41.6%]; RR, 0.77 [95% CI, 0.71-0.83]) and a nonsignificant reduction in hospital, 28-day, or 30-day mortality (intervention, 159/3215 deaths [4.9%] vs control, 206/3160 deaths [6.5%]; RR, 0.82 [95% CI, 0.67-1.01]) and mortality at longest follow-up (intervention, 267/3215 deaths [8.3%] vs control, 327/3160 deaths [10.3%]; RR, 0.86 [95% CI, 0.74-1.00]).Conclusions and relevance: in a randomized trial of high-risk patients undergoing major gastrointestinal surgery, use of a cardiac output–guided hemodynamic therapy algorithm compared with usual care did not reduce a composite outcome of complications and 30-day mortality. However, inclusion of these data in an updated meta-analysis indicates that the intervention was associated with a reduction in complication rate

The diversity and evolution of pollination systems in large plant clades: Apocynaceae as a case study

Background and Aims Large clades of angiosperms are often characterized by diverse interactions with pollinators, but how these pollination systems are structured phylogenetically and biogeographically is still uncertain for most families. Apocynaceae is a clade of >5300 species with a worldwide distribution. A database representing >10 % of species in the family was used to explore the diversity of pollinators and evolutionary shifts in pollination systems across major clades and regions. Methods The database was compiled from published and unpublished reports. Plants were categorized into broad pollination systems and then subdivided to include bimodal systems. These were mapped against the five major divisions of the family, and against the smaller clades. Finally, pollination systems were mapped onto a phylogenetic reconstruction that included those species for which sequence data are available, and transition rates between pollination systems were calculated. Key Results Most Apocynaceae are insect pollinated with few records of bird pollination. Almost three-quarters of species are pollinated by a single higher taxon (e.g. flies or moths); 7 % have bimodal pollination systems, whilst the remaining approx. 20 % are insect generalists. The less phenotypically specialized flowers of the Rauvolfioids are pollinated by a more restricted set of pollinators than are more complex flowers within the Apocynoids + Periplocoideae + Secamonoideae + Asclepiadoideae (APSA) clade. Certain combinations of bimodal pollination systems are more common than others. Some pollination systems are missing from particular regions, whilst others are over-represented. Conclusions Within Apocynaceae, interactions with pollinators are highly structured both phylogenetically and biogeographically. Variation in transition rates between pollination systems suggest constraints on their evolution, whereas regional differences point to environmental effects such as filtering of certain pollinators from habitats. This is the most extensive analysis of its type so far attempted and gives important insights into the diversity and evolution of pollination systems in large clades

Procalcitonin Is Not a Reliable Biomarker of Bacterial Coinfection in People With Coronavirus Disease 2019 Undergoing Microbiological Investigation at the Time of Hospital Admission

Abstract Admission procalcitonin measurements and microbiology results were available for 1040 hospitalized adults with coronavirus disease 2019 (from 48 902 included in the International Severe Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical Characterisation Protocol UK study). Although procalcitonin was higher in bacterial coinfection, this was neither clinically significant (median [IQR], 0.33 [0.11–1.70] ng/mL vs 0.24 [0.10–0.90] ng/mL) nor diagnostically useful (area under the receiver operating characteristic curve, 0.56 [95% confidence interval, .51–.60]).</jats:p

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

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