Samuel O. M. Manda

Evaluation Of Risk Scores For Risk Stratification Of Acute Coronary Syndromes In The Myocardial Infarction National Audit Project (MINAP) Database

To compare the discriminative performance of the PURSUIT, GUSTO-1, GRACE, SRI and EMMACE risk models, assess their performance among risk supergroups and evaluate the EMMACE risk model over the wider spectrum of acute coronary syndrome (ACS). Design: Observational study of a national registry. Setting: All acute hospitals in England and Wales. Patients: 100 686 cases of ACS between 2003 and 2005. Main outcome measures: Model performance (C-index) in predicting the likelihood of death over the time period for which they were designed. The C-index, or area under the receiver-operating curve, range 0–1, is a measure of the discriminative performance of a model. Results: The C-indexes were: PURSUIT C-index 0.79 (95% confidence interval 0.78 to 0.80); GUSTO-1 0.80 (0.79 to 0.81); GRACE in-hospital 0.80 (0.80 to 0.81); GRACE 6-month 0.80 (0.79 to 0.80); SRI 0.79 (0.78 to 0.80); and EMMACE 0.78 (0.77 to 0.78). EMMACE maintained its ability to discriminate 30-day mortality throughout different ACS diagnoses. Recalibration of the model offered no notable improvement in performance over the original risk equation. For all models the discriminative performance was reduced in patients with diabetes, chronic renal failure or angina. Conclusion: The five ACS risk models maintained their discriminative performance in a large unselected English and Welsh ACS population, but performed less well in higher-risk supergroups. Simpler risk models had comparable performance to more complex risk models. The EMMACE risk score performed well across the wider spectrum of ACS diagnoses.

Joint disease mapping using six cancers in the Yorkshire region of England

ObjectivesThe aims of this study were to model jointly the incidence rates of six smoking related cancers in the Yorkshire region of England, to explore the patterns of spatial correlation amongst them, and to estimate the relative weight of smoking and other shared risk factors for the relevant disease sites, both before and after adjustment for socioeconomic background (SEB).MethodsData on the incidence of oesophagus, stomach, pancreas, lung, kidney, and bladder cancers between 1983 and 2003 were extracted from the Northern & Yorkshire Cancer Registry database for the 532 electoral wards in the Yorkshire region. Using postcode of residence, each case was assigned an area-based measure of SEB using the Townsend index. Standardised incidence ratios (SIRs) were calculated for each cancer site and their correlations investigated. The joint analysis of the spatial variation in incidence used a Bayesian shared-component model. Three components were included to represent differences in smoking (for all six sites), bodyweight/obesity (for oesophagus, pancreas and kidney cancers) and diet/alcohol consumption (for oesophagus and stomach cancers).ResultsThe incidence of cancers of the oesophagus, pancreas, kidney, and bladder was relatively evenly distributed across the region. The incidence of stomach and lung cancers was more clustered around the urban areas in the south of the region, and these two cancers were significantly associated with higher levels of area deprivation. The incidence of lung cancer was most impacted by adjustment for SEB, with the rural/urban split becoming less apparent. The component representing smoking had a larger effect on cancer incidence in the eastern part of the region. The effects of the other two components were small and disappeared after adjustment for SEB.ConclusionThis study demonstrates the feasibility of joint disease modelling using data from six cancer sites. Incidence estimates are more precise than those obtained without smoothing. This methodology may be an important tool to help authorities evaluate healthcare system performance and the impact of policies.

Predictors of in-hospital mortality for patients admitted with ST-elevation myocardial infarction: a real-world study using the Myocardial Infarction National Audit Project (MINAP) database

Objective: Although early thrombolysis reduces the risk of death in STEMI patients, mortality remains high. We evaluated factors predicting inpatient mortality for patients with STEMI in a “real-world” population. Design: Analysis of the Myocardial Infarction National Audit Project (MINAP) database using multivariate logistic regression and area under the receiver operating curve analysis. Setting: All acute hospitals in England and Wales. Patients: 34 722 patients with STEMI from 1 January 2003 to 31 March 2005. Results: Inpatient mortality was 10.6%. The highest odds ratios for inpatient survival were aspirin therapy given acutely and out-of-hospital thrombolysis, independently associated with a mortality risk reduction of over half. A 10-year increase in age doubled inpatient mortality risk, whereas cerebrovascular disease increased it by 1.7. The risk model comprised 14 predictors of mortality, C index  =  0.82 (95% CI 0.82 to 0.83, p<0.001). A simple model comprising age, systolic blood pressure (SBP) and heart rate (HR) offered a C index of 0.80 (0.79 to 0.80, p<0.001). Conclusion: The strongest predictors of in-hospital survival for STEMI were aspirin therapy given acutely and out-of-hospital thrombolysis, Previous STEMI models have focused on age, SBP and HR We have confirmed the importance of these predictors in the discrimination of death after STEMI, but also demonstrated that other potentially modifiable variables impact upon the prediction of short-term mortality.

The role of the PCA3 assay in predicting prostate biopsy outcome in a South African setting

Study Type – Diagnostic (exploratory cohort)

Revisiting the interaction between birth weight and current body size in the foetal origins of adult disease

The four models proposed for exploring the foetal origins of adult disease (FOAD) hypothesis use the product term between size at birth and current size to determine the relative importance of pre- and post-natal growth on disease in later life. This is a common approach for testing the interaction between an exposure (in this instance size at birth) and an effect modifier (in this instance current size)—incorporating the product term obtained by multiplying the exposure and effect modifier variables within a statistical regression model. This study examines the mathematical basis for this approach and uses computer simulations to demonstrate two potential statistical flaws that might generate misleading findings. The first of these is that the expected value of the partial regression coefficient for the product term (between exposure and effect modifier) will be zero when the outcome, exposure and effect modifier are all continuously distributed and follow a multivariate normal distribution. This is because testing the product interaction term amounts to testing for multivariate normality among the three variables, irrespective of the pair-wise correlations amongst them. The second flaw is that it is possible to generate a statistically significant interaction between exposure and effect modifier, even when none exists, simply by categorising either or both of these variables. These flaws pose a serious challenge to the four models approach proposed for exploring the FOAD hypothesis. The interaction between exposure and effect modifier variables should be interpreted with caution both here and elsewhere in epidemiological analyses.

A full Bayesian hierarchical mixture model for the variance of gene differential expression

BackgroundIn many laboratory-based high throughput microarray experiments, there are very few replicates of gene expression levels. Thus, estimates of gene variances are inaccurate. Visual inspection of graphical summaries of these data usually reveals that heteroscedasticity is present, and the standard approach to address this is to take a log2 transformation. In such circumstances, it is then common to assume that gene variability is constant when an analysis of these data is undertaken. However, this is perhaps too stringent an assumption. More careful inspection reveals that the simple log2 transformation does not remove the problem of heteroscedasticity. An alternative strategy is to assume independent gene-specific variances; although again this is problematic as variance estimates based on few replications are highly unstable. More meaningful and reliable comparisons of gene expression might be achieved, for different conditions or different tissue samples, where the test statistics are based on accurate estimates of gene variability; a crucial step in the identification of differentially expressed genes.ResultsWe propose a Bayesian mixture model, which classifies genes according to similarity in their variance. The result is that genes in the same latent class share the similar variance, estimated from a larger number of replicates than purely those per gene, i.e. the total of all replicates of all genes in the same latent class. An example dataset, consisting of 9216 genes with four replicates per condition, resulted in four latent classes based on their similarity of the variance.ConclusionThe mixture variance model provides a realistic and flexible estimate for the variance of gene expression data under limited replicates. We believe that in using the latent class variances, estimated from a larger number of genes in each derived latent group, the p-values obtained are more robust than either using a constant gene or gene-specific variance estimate.

Geographic distribution of cardiovascular comorbidities in South Africa: a national cross-sectional analysis

Objectives: We sought to estimate the spatial coexistence of hypertension, coronary heart disease (CHD), stroke and hypercholesterolaemia in South Africa. Design: Cross-sectional. Setting: Sub-Saharan Africa and South Africa. Participants: Data were from 13,827 adults (mean±SD age 39±18 years, 58.4% women) interviewed in the 1998 South African Health and Demographic Survey. Interventions: N/A. Primary and secondary outcome measures: We used multivariate spatial disease models to estimate district-level shared and disease-specific spatial risk components, controlling for known individual risk factors. Results: In univariate analysis, observed prevalence of hypertension and CHD is was high in the south-western parts, and low in the north east. Stroke and high blood cholesterol prevalence appeared to be evenly distributed across the country. In multivariate analysis (adjusting for age, gender, ethnicity, education, urban-dwelling, smoking, alcohol consumption and obesity), hypertension and stroke prevalence were highly concentrated in the south-western parts, whilst CHD and hypercholesterolaemia were highly prevalent in central and top north-eastern corridor, respectively. The shared component, which we took to represent nutrition and other lifestyle factors not accounted for in the model, had a larger effect on cardiovascular disease prevalence in the south-western areas of the country. It appeared to have greater effect on hypertension and CHD. Conclusion: This study suggests a clear geographic distribution of cardiovascular disease in South Africa, driven possibly by shared lifestyle behaviours. These findings might be useful for public health resource allocation in low-income settings.

Pregnancy Incidence and Risk Factors among Women Participating in Vaginal Microbicide Trials for HIV Prevention: Systematic Review and Meta-Analysis

Introduction Pregnancy is contraindicated in vaginal microbicide trials for the prevention of HIV infection in women due to the unknown maternal and fetal safety of the microbicides. Women who become pregnant are taken off the microbicide during pregnancy period but this result in reduction of the power of the trials. Strategies to reduce the pregnancy rates require an understanding of the incidence and associated risk factors of pregnancy in microbicide trials. This systematic review estimates the overall incidence rate of pregnancy in microbicide trials and describes the associated risk factors. Methods A comprehensive literature search was carried out to identify eligible studies from electronic databases and other sources. Two review authors independently selected studies and extracted relevant data from included studies. Meta-analysis of incidence rates of pregnancy was carried out and risk factors of pregnancy were reported narratively. Results Fifteen studies reporting data from 10 microbicide trials (N=27,384 participants) were included. A total of 4,107 participants (15.0%) fell pregnant and a meta-analysis of incidence rates of pregnancy from 8 microbicide trials (N=25,551) yielded an overall incidence rate of 23.37 (95%CI: 17.78 to 28.96) pregnancies per 100 woman-years. However, significant heterogeneity was detected. Hormonal injectable, intra-uterine device (IUD) or implants or sterilization, older age, more years of education and condom use were associated with lower pregnancy. On the other hand, living with a man, history of pregnancy, self and partner desire for future baby, oral contraceptive use, increased number of unprotected sexual acts and inconsistent use of condoms were associated with higher pregnancy. Conclusions The incidence rate of pregnancy in microbicide trials is high and strategies for its reduction are urgently required in order to improve the sample size and power of these trials.

Meta-analysis of effect sizes reported at multiple time points using general linear mixed model

Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate meta-analyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results.

Assessment of Malawi’s success in child mortality reduction through the lens of the Catalytic Initiative Integrated Health Systems Strengthening programme: Retrospective evaluation

Background Malawi is estimated to have achieved its Millennium Development Goal (MDG) 4 target. This paper explores factors influencing progress in child survival in Malawi including coverage of interventions and the role of key national policies. Methods We performed a retrospective evaluation of the Catalytic Initiative (CI) programme of support (2007–2013). We developed estimates of child mortality using four population household surveys undertaken between 2000 and 2010. We recalculated coverage indicators for high impact child health interventions and documented child health programmes and policies. The Lives Saved Tool (LiST) was used to estimate child lives saved in 2013. Results The mortality rate in children under 5 years decreased rapidly in the 10 CI districts from 219 deaths per 1000 live births (95% confidence interval (CI) 189 to 249) in the period 1991–1995 to 119 deaths (95% CI 105 to 132) in the period 2006–2010. Coverage for all indicators except vitamin A supplementation increased in the 10 CI districts across the time period 2000 to 2013. The LiST analysis estimates that there were 10 800 child deaths averted in the 10 CI districts in 2013, primarily attributable to the introduction of the pneumococcal vaccine (24%) and increased household coverage of insecticide–treated bednets (19%). These improvements have taken place within a context of investment in child health policies and scale up of integrated community case management of childhood illnesses. Conclusions Malawi provides a strong example for countries in sub–Saharan Africa of how high impact child health interventions implemented within a decentralised health system with an established community–based delivery platform, can lead to significant reductions in child mortality.