The risk ratio quantifies the risk of disease in a study population relative to a reference population. Standard methods of estimation and testing assume a perfect diagnostic test having sensitivity and specificity of 100%. However, this assumption typically does not hold, and this may invalidate naive estimation and testing for the risk ratio. We propose procedures that control for sensitivity and specificity of the diagnostic test, given the risks are measured by proportions, as it is in cross-sectional studies or studies with fixed follow-up times. These procedures provide an exact unconditional test and confidence interval for the true risk ratio. The methods also cover the case when sensitivity and specificity differ in the two groups (differential misclassification). The resulting test and confidence interval may be useful in epidemiological studies as well as in clinical and vaccine trials. We illustrate the method with real-life examples which demonstrate that ignoring sensitivity and specificity of the diagnostic test may lead to considerable bias in the estimated risk ratio.

We present an age-structured mathematical model of malaria and pneumonia to study the effect of two capacity-building interventions: Integrated Management of Infectious Diseases (IMID) and On-site Support Services (OSS). IMID leads to a reduction in malaria prevalence by more than 2·4% across the [0,5), [5,14) and [14,50) age groups. IMID + OSS reduces it by more than 16·0% across all age groups. IMID decreases pneumonia prevalence by more than 3·0% across all age groups while IMID + OSS decreases it by more than 1·0% across all age groups. The number of malaria and pneumonia deaths is reduced by 7·8% by IMID across all age groups and IMID + OSS decreases this number by 30·5% across all age groups, which translates to saving a life of a child per month. Prevalence of malaria-pneumonia for the [0,5) age group is 0·52% at baseline, and IMID and IMID + OSS reduce it by 6·6% and 23·6%, respectively. There is no change in incidence of malaria or pneumonia disease episodes. The results also indicate that triaging of children contributes more than 50% to the effect of the interventions in reduction of deaths and a range of 14–91% in reduction of disease cases.

Pelvic inflammatory disease (PID) and more specifically salpingitis (visually confirmed inflammation) is the primary cause of tubal factor infertility and is an important risk factor for ectopic pregnancy. The risk of these outcomes increases following repeated episodes of PID. We developed a homogenous discrete-time Markov model for the distribution of PID history in the UK. We used a Bayesian framework to fully propagate parameter uncertainty into the model outputs. We estimated the model parameters from routine data, prospective studies, and other sources. We estimated that for women aged 35–44 years, 33·6% and 16·1% have experienced at least one episode of PID and salpingitis, respectively (diagnosed or not) and 10·7% have experienced one salpingitis and no further PID episodes, 3·7% one salpingitis and one further PID episode, and 1·7% one salpingitis and ⩾2 further PID episodes. Results are consistent with numerous external data sources, but not all. Studies of the proportion of PID that is diagnosed, and the proportion of PIDs that are salpingitis together with the severity distribution in different diagnostic settings and of overlap between routine data sources of PID would be valuable.