Costs and effects of prenatal screening methods for Down syndrome and neural tube defects.

OBJECTIVES: To evaluate prenatal screening methods for Down syndrome and neural tube defects (NTD) with regard to costs per detected case and the number of screening-related miscarriages. METHODS: The screening methods compared were risk assessment tests, i.e. serum tests and nuchal translucency measurement (NT), and invasive testing through chorionic villus sampling (CVS) or amniocentesis. Costs, the number of cases detected and screening-related miscarriages were calculated using a decision tree model. RESULTS: The costs per detected case of Down syndrome ranged from EUR 98,000 for the first-trimester (serum) double test to EUR 191,000 for invasive testing. If NTD detection was included, the (serum) triple test had the lowest costs, EUR 73,000, per detected case of Down syndrome or NTD. The number of screening-related miscarriages due to invasive diagnostic tests varied from 13 per 100,000 women for the (serum) first- and second-trimester combined test to 914 per 100,000 women for invasive testing. CONCLUSIONS: Considering screening for both Down syndrome and NTD favors the triple test in terms of costs per detected case. Compared to invasive testing, risk assessment tests in general substantially lower screening-related miscarriages, which raises the question of whether invasive testing should still be offered in a screening program for Down syndrome.

Modeling the future burden of stroke in The Netherlands: impact of aging, smoking, and hypertension.

BACKGROUND AND PURPOSE: In the near future, the number of stroke patients and their related healthcare costs are expected to rise. The purpose of this study was to estimate this expected increase in stroke patients in the Netherlands. We sought to determine what the future developments in the number of stroke patients due to demographic changes and trends in the prevalence of smoking and hypertension in terms of the prevalence, incidence, and potential years of life lost might be. METHODS: A dynamic, multistate life table was used, which combined demographic projections and existing stroke morbidity and mortality data. It projected future changes in the number of stroke patients in several scenarios for the Dutch population for the period 2000 to 2020. The model calculated the annual number of new patients by age and sex by using incidence rates, defined by age, sex, and major risk factors. The change in the annual number of stroke patients is the result of incident cases minus mortality numbers. RESULTS: Demographic changes in the population suggest an increase of 27% in number of stroke patients per 1000 in 2020 compared with 2000. Extrapolating past trends in the prevalence of smoking behavior, hypertension, and stroke incidence resulted in an increase of 4%. CONCLUSIONS: The number of stroke patients in the Netherlands will rise continuously until the year 2020. Our study demonstrates that a large part of this increase in the number of patients is an inevitable consequence of the aging of the population.

Future burden and costs of smoking-related disease in the Netherlands: a dynamic modeling approach.

OBJECTIVES: In this article, we explore the future health gain of different policy measures to reduce smoking prevalence: health education campaigns specifically aimed at keeping (young) people from starting to smoke, campaigns aimed at persuading smokers to quit, and tax measures. METHODS: We drew up different policy scenarios based on evaluations of several health promotion campaigns. Implementing these into the dynamic multistate models, we simulated smoking prevalence, loss of life-years, and costs for several decades into the next century. RESULTS: In the short run, campaigns aimed at potential "quitters" appear to be most effective in terms of health gain. However, their effect fades away after several decades, while campaigns aimed at young "starters" or tax measures in the end yield a larger and more lasting decrease in smoking attributable disease burden. CONCLUSION: Dynamic modeling is very useful tool in calculating costs and effects of preventive public health measures.