Investigating ethnic inequalities in the incidence of sexually transmitted infections: mathematical modelling study.

OBJECTIVES: To investigate ethnic differences in rates of gonorrhoea using empirical sexual behaviour data in a simple mathematical model. To explore the impact of different intervention strategies in this simulated population. METHODS: The findings from cross sectional studies of gonorrhoea rates and sexual behaviour in three ethnic groups in south east London were used to determine the parameters for a deterministic, mathematical model of gonorrhoea transmission dynamics, in a population stratified by sex, sexual activity (rate of partner change), and ethnic group (white, black African, and black Caribbean). We compared predicted and observed rates of infection and simulated the effects of targeted and population-wide intervention strategies. RESULTS: In model simulations the reported sexual behaviours and mixing patterns generated major differences in the rates of gonorrhoea experienced by each subpopulation. The fit of the model to observed data was sensitive to assumptions about the degree of mixing by level of sexual activity, the numbers of sexual partnerships reported by men and women, and the degree to which observed data underestimate female infection rates. Interventions to reduce duration of infection were most effective when targeted at black Caribbeans. CONCLUSIONS: Average measures of sexual behaviour in large populations are inadequate descriptors for the epidemiology of gonorrhoea. The consistency between the model results and empirical data shows that profound differences in gonorrhoea rates between ethnic groups can be explained by modest differences in a limited number of sexual behaviours and mixing patterns. Targeting effective services to particular ethnic groups can have a disproportionate influence on disease reduction in the whole community

Investigating and dealing with publication bias and other reporting biases in meta-analyses:a review

A P value, or the magnitude or direction of results can influence decisions about whether, when, and how research findings are disseminated. Regardless of whether an entire study or a particular study result is unavailable because investigators considered the results to be unfavourable, bias in a meta-analysis may occur when available results differ systematically from missing results. In this paper, we summarize the empirical evidence for various reporting biases that lead to study results being unavailable for inclusion in systematic reviews, with a focus on health research. These biases include publication bias and selective nonreporting bias. We describe processes that systematic reviewers can use to minimize the risk of bias due to missing results in meta-analyses of health research, such as comprehensive searches and prospective approaches to meta-analysis. We also outline methods that have been designed for assessing risk of bias due to missing results in meta-analyses of health research, including using tools to assess selective nonreporting of results, ascertaining qualitative signals that suggest not all studies were identified, and generating funnel plots to identify small-study effects, one cause of which is reporting bias. This article is protected by copyright. All rights reserved

Adjusting trial results for biases in meta-analysis: combining data-based evidence on bias with detailed trial assessment.

Flaws in the conduct of randomized trials can lead to biased estimation of the intervention effect. Methods for adjustment of within-trial biases in meta-analysis include the use of empirical evidence from an external collection of meta-analyses, and the use of expert opinion informed by the assessment of detailed trial information. Our aim is to present methods to combine these two approaches to gain the advantages of both. We make use of the risk of bias information that is routinely available in Cochrane reviews, by obtaining empirical distributions for the bias associated with particular bias profiles (combinations of risk of bias judgements). We propose three methods: a formal combination of empirical evidence and opinion in a Bayesian analysis; asking experts to give an opinion on bias informed by both summary trial information and a bias distribution from the empirical evidence, either numerically or by selecting areas of the empirical distribution. The methods are demonstrated through application to two example binary outcome meta-analyses. Bias distributions based on opinion informed by trial information alone were most dispersed on average, and those based on opinions obtained by selecting areas of the empirical distribution were narrowest. Although the three methods for combining empirical evidence with opinion vary in ease and speed of implementation, they yielded similar results in the two examples

Differences in access and patient outcomes across antiretroviral treatment clinics in the Free State province: A prospective cohort study

Objective. To assess differences in access to antiretroviral treatment (ART) and patient outcomes across public sector treatment facilities in the Free State province, South Africa. Design. Prospective cohort study with retrospective database linkage. We analysed data on patients enrolled in the treatment programme across 36 facilities between May 2004 and December 2007, and assessed percentage initiating ART and percentage dead at 1 year after enrolment. Multivariable logistic regression was used to estimate associations of facility-level and patient-level characteristics with both mortality and treatment status. Results. Of 44 866 patients enrolled, 15 219 initiated treatment within 1 year; 8 778 died within 1 year, 7 286 before accessing ART. Outcomes at 1 year varied greatly across facilities and more variability was explained by facility-level factors than by patient-level factors. The odds of starting treatment within 1 year improved over calendar time. Patients enrolled in facilities with treatment initiation available on site had higher odds of starting treatment and lower odds of death at 1 year compared with those enrolled in facilities that did not offer treatment initiation. Patients were less likely to start treatment if they were male, severely immunosuppressed (CD4 count ≤50 cells/μl), or underweight

Joint modelling rationale for chained equations

BACKGROUND: Chained equations imputation is widely used in medical research. It uses a set of conditional models, so is more flexible than joint modelling imputation for the imputation of different types of variables (e.g. binary, ordinal or unordered categorical). However, chained equations imputation does not correspond to drawing from a joint distribution when the conditional models are incompatible. Concurrently with our work, other authors have shown the equivalence of the two imputation methods in finite samples. METHODS: Taking a different approach, we prove, in finite samples, sufficient conditions for chained equations and joint modelling to yield imputations from the same predictive distribution. Further, we apply this proof in four specific cases and conduct a simulation study which explores the consequences when the conditional models are compatible but the conditions otherwise are not satisfied. RESULTS: We provide an additional “non-informative margins” condition which, together with compatibility, is sufficient. We show that the non-informative margins condition is not satisfied, despite compatible conditional models, in a situation as simple as two continuous variables and one binary variable. Our simulation study demonstrates that as a consequence of this violation order effects can occur; that is, systematic differences depending upon the ordering of the variables in the chained equations algorithm. However, the order effects appear to be small, especially when associations between variables are weak. CONCLUSIONS: Since chained equations is typically used in medical research for datasets with different types of variables, researchers must be aware that order effects are likely to be ubiquitous, but our results suggest they may be small enough to be negligibl

The median and the mode as robust meta‐analysis estimators in the presence of small‐study effects and outliers

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.  Meta‐analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta‐analysis is constrained by that of its constituent studies. One major limitation is the possibility of small study effects, when estimates from smaller and larger studies differ systematically. Small study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta‐analysis by mitigating the influence of small study effects. By re‐examining data from published meta‐analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta‐analyses are suspected to be at risk of bias because of small study effects, we recommend reporting the mean, median and modal pooled estimates.Medical Research Council (MRC)Brazilian National Council for Scientific and Technological Development (CNPq

Systematic Review and Meta-Analysis of Preterm Birth and Later Systolic Blood Pressure

Lower birth weight because of fetal growth restriction is associated with higher blood pressure later in life, but the extent to which preterm birth ( <37 completed weeks' gestation) or very low birth weight ( <1500 g) predicts higher blood pressure is less clear. We performed a systematic review of 27 observational studies that compared the resting or ambulatory systolic blood pressure or diagnosis of hypertension among children, adolescents, and adults born preterm or very low birth weight with those born at term. We performed a meta-analysis with the subset of 10 studies that reported the resting systolic blood pressure difference in millimeters of mercury with 95% CIs or SEs. We assessed methodologic quality with a modified Newcastle-Ottawa Scale. The 10 studies were composed of 1342 preterm or very low birth weight and 1738 term participants from 8 countries. The mean gestational age at birth of the preterm participants was 30.2 weeks (range: 28.8-34.1 weeks), birth weight was 1280 g (range: 1098-1958 g), and age at systolic blood pressure measurement was 17.8 years (range: 6.3-22.4 years). Former preterm or very low birth weight infants had higher systolic blood pressure than term infants (pooled estimate: 2.5 mm Hg [95% CI: 1.7-3.3 mm Hg]). For the 5 highest quality studies, the systolic blood pressure difference was slightly greater, at 3.8 mm Hg (95% CI: 2.6-5.0 mm Hg). We conclude that infants who are born preterm or very low birth weight have modestly higher systolic blood pressure later in life and may be at increased risk for developing hypertension and its sequela

The duration of protection of school-aged BCG vaccination in England: a population-based case–control study

BACKGROUND: Evidence of protection from childhood Bacillus Calmette-Guerin (BCG) against tuberculosis (TB) in adulthood, when most transmission occurs, is important for TB control and resource allocation. METHODS: We conducted a population-based case–control study of protection by BCG given to children aged 12–13 years against tuberculosis occurring 10–29 years later. We recruited UK-born White subjects with tuberculosis and randomly sampled White community controls. Hazard ratios and 95% confidence intervals (CIs) were estimated using case–cohort Cox regression, adjusting for potential confounding factors, including socio-economic status, smoking, drug use, prison and homelessness. Vaccine effectiveness (VE = 1 – hazard ratio) was assessed at successive intervals more than 10 years following vaccination. RESULTS: We obtained 677 cases and 1170 controls after a 65% response rate in both groups. Confounding by deprivation, education and lifestyle factors was slight 10–20 years after vaccination, and more evident after 20 years. VE 10–15 years after vaccination was 51% (95% CI 21, 69%) and 57% (CI 33, 72%) at 15–20 years. Subsequently, BCG protection appeared to wane; 20–25 years VE = 25% (CI –14%, 51%) and 25–29 years VE = 1% (CI –84%, 47%). Based on multiple imputation of missing data (in 17% subjects), VE estimated in the same intervals after vaccination were similar [56% (CI 33, 72%), 57% (CI 36, 71%), 25% (–10, 48%), 21% (–39, 55%)]. CONCLUSIONS: School-aged BCG vaccination offered moderate protection against tuberculosis for at least 20 years, which is longer than previously thought. This has implications for assessing the cost-effectiveness of BCG vaccination and when evaluating new TB vaccines