True versus False Parasite Interactions: A Robust Method to Take Risk Factors into Account and Its Application to Feline Viruses

International audienceBACKGROUND: Multiple infections are common in natural host populations and interspecific parasite interactions are therefore likely within a host individual. As they may seriously impact the circulation of certain parasites and the emergence and management of infectious diseases, their study is essential. In the field, detecting parasite interactions is rendered difficult by the fact that a large number of co-infected individuals may also be observed when two parasites share common risk factors. To correct for these "false interactions", methods accounting for parasite risk factors must be used. METHODOLOGY/PRINCIPAL FINDINGS: In the present paper we propose such a method for presence-absence data (i.e., serology). Our method enables the calculation of the expected frequencies of single and double infected individuals under the independence hypothesis, before comparing them to the observed ones using the chi-square statistic. The method is termed "the corrected chi-square." Its robustness was compared to a pre-existing method based on logistic regression and the corrected chi-square proved to be much more robust for small sample sizes. Since the logistic regression approach is easier to implement, we propose as a rule of thumb to use the latter when the ratio between the sample size and the number of parameters is above ten. Applied to serological data for four viruses infecting cats, the approach revealed pairwise interactions between the Feline Herpesvirus, Parvovirus and Calicivirus, whereas the infection by FIV, the feline equivalent of HIV, did not modify the risk of infection by any of these viruses. CONCLUSIONS/SIGNIFICANCE: This work therefore points out possible interactions that can be further investigated in experimental conditions and, by providing a user-friendly R program and a tutorial example, offers new opportunities for animal and human epidemiologists to detect interactions of interest in the field, a crucial step in the challenge of multiple infections

Factors affecting the delivery, access, and use of interventions to prevent malaria in pregnancy in sub-Saharan Africa: a systematic review and meta-analysis.

BACKGROUND: Malaria in pregnancy has important consequences for mother and baby. Coverage with the World Health Organization-recommended prevention strategy for pregnant women in sub-Saharan Africa of intermittent preventive treatment in pregnancy (IPTp) and insecticide-treated nets (ITNs) is low. We conducted a systematic review to explore factors affecting delivery, access, and use of IPTp and ITNs among healthcare providers and women. METHODS AND RESULTS: We searched the Malaria in Pregnancy Library and Global Health Database from 1 January 1990 to 23 April 2013, without language restriction. Data extraction was performed by two investigators independently, and data was appraised for quality and content. Data on barriers and facilitators, and the effect of interventions, were explored using content analysis and narrative synthesis. We conducted a meta-analysis of determinants of IPTp and ITN uptake using random effects models, and performed subgroup analysis to evaluate consistency across interventions and study populations, countries, and enrolment sites. We did not perform a meta-ethnography of qualitative data. Ninety-eight articles were included, of which 20 were intervention studies. Key barriers to the provision of IPTp and ITNs were unclear policy and guidance on IPTp; general healthcare system issues, such as stockouts and user fees; health facility issues stemming from poor organisation, leading to poor quality of care; poor healthcare provider performance, including confusion over the timing of each IPTp dose; and women's poor antenatal attendance, affecting IPTp uptake. Key determinants of IPTp coverage were education, knowledge about malaria/IPTp, socio-economic status, parity, and number and timing of antenatal clinic visits. Key determinants of ITN coverage were employment status, education, knowledge about malaria/ITNs, age, and marital status. Predictors showed regional variations. CONCLUSIONS: Delivery of ITNs through antenatal clinics presents fewer problems than delivery of IPTp. Many obstacles to IPTp delivery are relatively simple barriers that could be resolved in the short term. Other barriers are more entrenched within the overall healthcare system or socio-economic/cultural contexts, and will require medium- to long-term strategies. Please see later in the article for the Editors' Summary

Appropriate statistical methods for analysing partially nested randomised controlled trials with continuous outcomes: a simulation study

BACKGROUND: In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. METHODS: We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. RESULTS: All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. CONCLUSIONS: In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3-6), small cluster sizes (5-10), and small ICC (≤0.05) this model underestimates Type I error rates and there is no optimal model