Background: Sub-Saharan Africa (SSA) is severely affected by HIV/AIDS and conflict. Sexual violence as a weapon of war has been associated with concerns about heightened HIV incidence among women. Widespread rape by combatants has been documented in Burundi, Sierra Leone, Rwanda, Democratic Republic of Congo, Liberia, Sudan and Uganda. To examine the assertion that widespread rape may not directly increase HIV prevalence at the population level, we built a model to determine the potential impact of varying scenarios of widespread rape on HIV prevalence in the above seven African countries. DiscussionOur findings show that even in the most extreme situations, where 15% of the female population was raped, where HIV prevalence among assailants was 8 times the country population prevalence, and where the HIV transmission rate was highest at 4 times the average high rate, widespread rape increased the absolute HIV prevalence of these countries by only 0.023%. These projections support the finding that widespread rape in conflict-affected countries in SSA has not incurred a major direct population-level change in HIV prevalence. However, this must not be interpreted to say that widespread rape does not pose serious problems to women's acquisition of HIV on an individual basis or in specific settings. Furthermore, direct and indirect consequences of sexual violence, such as physical and psychosocial trauma, unwanted pregnancies, and stigma and discrimination cannot be understated. SummaryThe conclusions of this article do not significantly change current practices in the field from an operational perspective. Proper care and treatment must be provided to every survivor of rape regardless of the epidemiological effects of HIV transmission at the population level. Sexual violence must be treated as a protection issue and not solely a reproductive health and psychosocial issue. It is worth publishing data and conclusions that could be misconstrued and may not make much of a programmatic difference in the field. Data, if collected, analysed and interpreted carefully, help to improve our understanding of complicated and nuanced situations. Ultimately, our understanding of what the outcomes of such interventions can achieve will be more realistic. It also helps decision-makers prioritise their funding and interventions.

There is a multiplicity of journals originating in Spain and the Spanish-speaking countries of Latin America and the Caribbean (SSLAC) in the health sciences of relevance to the fields of epidemiology and public health. While the subject matter of epidemiology in Spain shares many features with its neighbours in Western Europe, many aspects of epidemiology in Latin America are particular to that region. There are also distinctive theoretical and philosophical approaches to the study of epidemiology and public health arising from traditions such as the Latin American social medicine movement, of which there may be limited awareness. A number of online bibliographic databases are available which focus primarily on health sciences literature arising in Spain and Latin America, the most prominent being Literatura Latinoamericana en Ciencias de la Salud (LILACS) and LATINDEX. Some such as LILACS also extensively index grey literature. As well as in Spanish, interfaces are provided in English and Portuguese. Abstracts of articles may also be provided in English with an increasing number of journals beginning to publish entire articles written in English. Free full text articles are becoming accessible, one of the most comprehensive sources being the Scientific Electronic Library Online (SciELO). There is thus an extensive range of literature originating in Spain and SSLAC freely identifiable and often accessible online, and with the potential to provide useful inputs to the study of epidemiology and public health provided that any reluctance to explore these resources can be overcome. In this article we provide an introduction to such resources.

In 2004, Garcia-Berthou and Alcaraz published "Incongruence between test statistics and P values in medical papers," a critique of statistical errors that received a tremendous amount of attention. One of their observations was that the final reported digit of p-values in articles published in the journal Nature departed substantially from the uniform distribution that they suggested should be expected. In 2006, Jeng critiqued that critique, observing that the statistical analysis of those terminal digits had been based on comparing the actual distribution to a uniform continuous distribution, when digits obviously are discretely distributed. Jeng corrected the calculation and reported statistics that did not so clearly support the claim of a digit preference. However delightful it may be to read a critique of statistical errors in a critique of statistical errors, we nevertheless found several aspects of the whole exchange to be quite troubling, prompting our own meta-critique of the analysis. The previous discussion emphasized statistical significance testing. But there are various reasons to expect departure from the uniform distribution in terminal digits of p-values, so that simply rejecting the null hypothesis is not terribly informative. Much more importantly, Jeng found that the original p-value of 0.043 should have been 0.086, and suggested this represented an important difference because it was on the other side of 0.05. Among the most widely reiterated (though often ignored) tenets of modern quantitative research methods is that we should not treat statistical significance as a bright line test of whether we have observed a phenomenon. Moreover, it sends the wrong message about the role of statistics to suggest that a result should be dismissed because of limited statistical precision when it is so easy to gather more data. In response to these limitations, we gathered more data to improve the statistical precision, and analyzed the actual pattern of the departure from uniformity, not just its test statistics. We found variation in digit frequencies in the additional data and describe the distinctive pattern of these results. Furthermore, we found that the combined data diverge unambiguously from a uniform distribution. The explanation for this divergence seems unlikely to be that suggested by the previous authors: errors in calculations and transcription.

Background: Only four out of 31 completed randomized controlled trials (RCTs) of HIV prevention strategies against sexual transmission have shown significant efficacy. Poor adherence may have contributed to the lack of effect in some of these trials. In this paper we explore the impact of various levels of adherence on measured efficacy within an RCT. Analysis We used simple quantitative methods to illustrate the impact of various levels of adherence on measured efficacy by assuming a uniform population in terms of sexual behavior and the binomial model for the transmission probability per partnership. At 100% adherence the measured efficacy within an RCT is a reasonable approximation of the true biological efficacy. However, as adherence levels fall, the efficacy measured within a trial substantially under-estimates the true biological efficacy. For example, at 60% adherence, the measured efficacy can be less than half of the true biological efficacy. Conclusions: Poor adherence during a trial can substantially reduce the power to detect an effect, and improved methods of achieving and maintaining high adherence within trials are needed. There are currently 12 ongoing HIV prevention trials, all but one of which require ongoing user-adherence. Attention must be given to methods of maximizing adherence when piloting and designing RCTs and HIV prevention programmes.

IntroductionCluster surveys are frequently used to measure key nutrition and health indicators in humanitarian emergencies. The survey design of 30 clusters of 7 children (30x7) was initially proposed by the World Health Organization for measuring vaccination coverage, and later a design of 30 clusters of 30 children (30x30) was introduced to measure acute malnutrition in emergency settings. Recently, designs of 33 clusters of 6 children (33x6) and 67 clusters of 3 children (67x3) have been proposed as alternatives that enable measurement of several key indicators with sufficient precision, while offering substantial savings in time. This paper explores expected effects of using 67x3, 33x6, or 30x7 designs instead of a "standard" 30x30 design on precision and accuracy of estimates, and on time required to complete the survey. Analysis The 67x3, 33x6, and 30x7 designs are expected to be more statistically efficient for measuring outcomes having high design effects (e.g., vaccination coverage, vitamin A distribution coverage, or access to safe water sources), and less efficient for measuring outcomes with more within-cluster variability, such as global acute malnutrition or anemia. Because of small sample sizes, these designs may not provide sufficient levels of precision to measure crude mortality rates. Given the small number (3 to 7) of survey subjects per cluster, it may be hard to select representative samples of subjects within clusters. The smaller sample size in these designs will likely result in substantial time savings. The magnitude of the savings will depend on several factors, including the average travel time between clusters. The 67x3 design will provide the least time savings. The 33x6 and 30x7 designs perform similarly to each other, both in terms of statistical efficiency and in terms of time required to complete the survey. Conclusion: Cluster designs discussed in this paper may offer substantial time and cost savings compared to the traditional 30x30 design, and may provide acceptable levels of precision when measuring outcomes that have high intracluster homogeneity. Further investigation is required to determine whether these designs can consistently provide accurate point estimates for key outcomes of interest. Organizations conducting cluster surveys in emergency settings need to build their technical capacity in survey design to be able to calculate context-specific sample sizes individually for each planned survey.