Maarten Postma

Sodium Intake, ACE Inhibition, and Progression to ESRD

High sodium intake limits the antihypertensive and antiproteinuric effects of angiotensin-converting enzyme (ACE) inhibitors in patients with CKD; however, whether dietary sodium also associates with progression to ESRD is unknown. We conducted a post hoc analysis of the first and second Ramipril Efficacy in Nephropathy trials to evaluate the association of sodium intake with proteinuria and progression to ESRD among 500 CKD patients without diabetes who were treated with ramipril (5 mg/d) and monitored with serial 24-hour urinary sodium and creatinine measurements. Urinary sodium/creatinine excretion defined low (<100 mEq/g), medium (100 to <200 mEq/g), and high (≥200 mEq/g) sodium intake. During a follow-up of >4.25 years, 92 individuals (18.4%) developed ESRD. Among those with low, medium, and high sodium intakes, the incidence of ESRD was 6.1 (95% confidence interval [95% CI], 3.8-9.7), 7.9 (95% CI, 6.1-10.2), and 18.2 (95% CI, 11.3-29.3) per 100 patient-years, respectively (P<0.001). Patients with high dietary sodium exhibited a blunted antiproteinuric effect of ACE inhibition despite similar BP among groups. Each 100-mEq/g increase in urinary sodium/creatinine excretion associated with a 1.61-fold (95% CI, 1.15-2.24) higher risk for ESRD; adjusting for baseline proteinuria attenuated this association to 1.38-fold (95% CI, 0.95-2.00). This association was independent from BP but was lost after adjusting for changes in proteinuria. In summary, among patients with CKD but without diabetes, high dietary salt (>14 g daily) seems to blunt the antiproteinuric effect of ACE inhibitor therapy and increase the risk for ESRD, independent of BP control.

Sodium intake affects urinary albumin excretion especially in overweight subjects

Objectives.  To examine the relationship between sodium intake and urinary albumin excretion, being an established risk marker for later cardiovascular morbidity and mortality.

Need for differential discounting of costs and health effects in cost effectiveness analyses

The decision of the National Institute for Health and Clinical Excellence to abandon differential discounting of future health is a step backwards and could change funding decisions

Epidemiology of Chlamydia trachomatis infection in women and the cost-effectiveness of screening

BACKGROUND The majority of Chlamydia trachomatis infections in women are asymptomatic, but may give rise to pelvic inflammatory disease (PID) and tubal infertility. Screening programmes aim at reducing morbidity in individuals by early detection and treatment, and at decreasing the overall prevalence of infection in the population. A number of modelling studies have tried to calculate the threshold prevalence of chlamydia lower genital tract infection above which screening becomes cost-effective. There is considerable debate over the exact complication rates after chlamydia infections, and more precise estimates of PID and tubal infertility are needed, for instance to be inserted in economic models. METHODS With reference to key studies and systematic reviews, an overview is provided focusing on the epidemiology of chlamydia infection and the risk-estimates of its late complications. RESULTS In the literature, the generally assumed risk of developing PID after lower genital tract chlamydia infection varies considerably, and is up to 30%. For developing tubal infertility after PID the risks are 10-20%. This implies that the risk of test-positive women of developing tubal infertility would range between 0.1 and 6%. We included chlamydia IgG antibody testing in a model and estimated a risk of tubal infertility up to 4.6%. CONCLUSION The risk of developing late complications after chlamydia lower genital tract infection appears low. High quality RCTs dealing with the transition from cervicitis to infertility are needed to broaden the evidence. In screening programmes, chlamydia antibody testing, as an intermediate marker for potential adverse sequelae, might enable more precise estimates.

Economic evaluation of influenza pandemic mitigation strategies in the United States using a stochastic microsimulation transmission model.

OBJECTIVES To project the potential economic impact of pandemic influenza mitigation strategies from a societal perspective in the United States. METHODS We use a stochastic agent-based model to simulate pandemic influenza in the community. We compare 17 strategies: targeted antiviral prophylaxis (TAP) alone and in combination with school closure as well as prevaccination. RESULTS In the absence of intervention, we predict a 50% attack rate with an economic impact of

Dynamic transmission modeling: A report of the ISPOR-SMDM modeling good research practices task force-5

187 per capita as loss to society. Full TAP (FTAP) is the most effective single strategy, reducing number of cases by 54% at the lowest cost to society (

Cost-effectiveness of screening programs for Chlamydia trachomatis: a population-based dynamic approach.

127 per capita). Prevaccination reduces number of cases by 48% and is the second least costly alternative (

Meta-analysis: comparing the efficacy of proton pump inhibitors in short-term use

140 per capita). Adding school closure to FTAP or prevaccination further improves health outcomes but increases total cost to society by approximately

Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes : A systematic review on content and adherence to guidelines

2700 per capita. CONCLUSION FTAP is an effective and cost-saving measure for mitigating pandemic influenza.

ISPOR Task Force reportDynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-5

Abstract The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this article.