Estimating the prevalence of breast cancer using a disease model: data problems and trends

BACKGROUND: Health policy and planning depend on quantitative data of disease epidemiology. However, empirical data are often incomplete or are of questionable validity. Disease models describing the relationship between incidence, prevalence and mortality are used to detect data problems or supplement missing data. Because time trends in the data affect their outcome, we compared the extent to which trends and known data problems affected model outcome for breast cancer. METHODS: We calculated breast cancer prevalence from Dutch incidence and mortality data (the Netherlands Cancer Registry and Statistics Netherlands) and compared this to regionally available prevalence data (Eindhoven Cancer Registry, IKZ). Subsequently, we recalculated the model adjusting for 1) limitations of the prevalence data, 2) a trend in incidence, 3) secondary primaries, and 4) excess mortality due to non-breast cancer deaths. RESULTS: There was a large discrepancy between calculated and IKZ prevalence, which could be explained for 60% by the limitations of the prevalence data plus the trend in incidence. Secondary primaries and excess mortality had relatively small effects only (explaining 17% and 6%, respectively), leaving a smaller part of the difference unexplained. CONCLUSION: IPM models can be useful both for checking data inconsistencies and for supplementing incomplete data, but their results should be interpreted with caution. Unknown data problems and trends may affect the outcome and in the absence of additional data, expert opinion is the only available judge

Early hospital discharge of infants born to group B streptococci‐positive mothers: a decision analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90120/1/j.1471-0528.2011.03249.x.pd

Parasitic weed incidence and related economic losses in rice in Africa

Parasitic weeds pose increasing threats to rain-fed rice production in Africa. Most important species are Striga asiatica, S. aspera and S. hermonthica in rain-fed uplands, and Rhamphicarpa fistulosa in rain-fed lowlands. Information on the regional spread and economic importance of parasitic weeds in cereal production systems is scant. This article presents the first multi-species, multi-country, single-crop impact assessment of parasitic weeds in Africa. A systematic search of public international and national herbaria and the scientific literature was conducted to collect all available data on the regional distribution, incidences and related yield losses of the most important parasitic weeds in rice. Herbaria specimens were geo-referenced and these coordinates were overlapped with rain-fed rice areas. Probabilistic diffusion waves of parasitic weeds were generated to derive most likely incidence values. Estimates from this spatial analysis were then combined with secondary data from the literature into a stochastic impact assessment model to generate a confidence interval of the likely economic impact per country and for sub-Saharan Africa as a whole. Rhamphicarpa fistulosa occurs in at least 36 African countries, 28 of which produce rice in rain-fed lowlands where this species thrives. Striga hermonthica is found in at least 32 countries, Striga asiatica in at least 44 and S. aspera in at least 17. A total of 50 countries have at least one of these three species of Striga, 31 of which produce rice in the rain-fed uplands where these species can be encountered. An estimated 1.34 million ha of rain-fed rice is infested with at least one species of a parasitic weed in Africa. Our stochastic model estimates that annual economic losses inflicted by all parasitic weeds exceeds, with 95% certainty, a minimum value of US $111 million and most likely reaches roughly US$200 million and increases by US $30 million annually. To reverse this trend and support small-holder rice farmers in Africa with effective, sustainable and affordable solutions for control, targeted investments in research, development and capacity building are required. The top-10 priority countries where such investments would probably have the highest return are Nigeria, Guinea, Mali, Côte d’Ivoire, Cameroon, Tanzania, Madagascar, Uganda, Sierra Leone and Burkina Faso

The societal benefits of reducing six behavioural risk factors: an economic modelling study from Australia

BackgroundA large proportion of disease burden is attributed to behavioural risk factors. However, funding for public health programs in Australia remains limited. Government and non-government organisations are interested in the productivity effects on society from reducing chronic diseases. We aimed to estimate the potential health status and economic benefits to society following a feasible reduction in the prevalence of six behavioural risk factors: tobacco smoking; inadequate fruit and vegetable consumption; high risk alcohol consumption; high body mass index; physical inactivity; and intimate partner violence.MethodsSimulation models were developed for the 2008 Australian population. A realistic reduction in current risk factor prevalence using best available evidence with expert consensus was determined. Avoidable disease, deaths, Disability Adjusted Life Years (DALYs) and health sector costs were estimated. Productivity gains included workforce (friction cost method), household production and leisure time. Multivariable uncertainty analyses and correction for the joint effects of risk factors on health status were undertaken. Consistent methods and data sources were used.ResultsOver the lifetime of the 2008 Australian adult population, total opportunity cost savings of AUD2,334 million (95% Uncertainty Interval AUD1,395 to AUD3,347; 64% in the health sector) were found if feasible reductions in the risk factors were achieved. There would be 95,000 fewer DALYs (a reduction of about 3.6% in total DALYs for Australia); 161,000 less new cases of disease; 6,000 fewer deaths; a reduction of 5 million days in workforce absenteeism; and 529,000 increased days of leisure time.ConclusionsReductions in common behavioural risk factors may provide substantial benefits to society. For example, the total potential annual cost savings in the health sector represent approximately 2% of total annual health expenditure in Australia. Our findings contribute important new knowledge about productivity effects, including the potential for increased household and leisure activities, associated with chronic disease prevention. The selection of targets for risk factor prevalence reduction is an important policy decision and a useful approach for future analyses. Similar approaches could be applied in other countries if the data are available.<br /

Cost-effectiveness of psychological and pharmacological interventions for generalized anxiety disorder and panic disorder

Objective: To assess from a health sector perspective the incremental cost-effectiveness of interventions for generalized anxiety disorder (cognitive behavioural therapy [CBT] and serotonin and noradrenaline reuptake inhibitors [SNRIs]) and panic disorder (CBT, selective serotonin reuptake inhibitors [SSRIs] and tricyclic antidepressants [TCAs]).Method: The health benefit is measured as a reduction in disability-adjusted life years (DALYs), based on effect size calculations from meta-analyses of randomised controlled trials. An assessment on second stage filters (\u27equity\u27, \u27strength of evidence\u27, \u27feasibility\u27 and \u27acceptability to stakeholders\u27) is also undertaken to incorporate additional factors that impact on resource allocation decisions. Costs and benefits are calculated for a period of one year for the eligible population (prevalent cases of generalized anxiety disorder/panic disorder identified in the National Survey of Mental Health and Wellbeing, extrapolated to the Australian population in the year 2000 for those aged 18 years and older). Simulation modelling techniques are used to present 95% uncertainty intervals (UI) around the incremental cost-effectiveness ratios (ICERs).Results: Compared to current practice, CBT by a psychologist on a public salary is the most cost-effective intervention for both generalized anxiety disorder (A$6900/DALY saved; 95$4000 to A$12 000) and panic disorder (A$6800/DALY saved; 95% UI A$2900 to A$15 000). Cognitive behavioural therapy results in a greater total health benefit than the drug interventions for both anxiety disorders, although equity and feasibility concerns for CBT interventions are also greater.Conclusions: Cognitive behavioural therapy is the most effective and cost-effective intervention for generalized anxiety disorder and panic disorder. However, its implementation would require policy change to enable more widespread access to a sufficient number of trained therapists for the treatment of anxiety disorders.<br /

Model-based assessment of energy-efficiency, dependability, and cost-effectiveness of waste heat recovery systems onboard ship

YesTechnological systems are not merely designed with a narrow function in mind. Good designs typically aim at reducing operational costs, e.g. through achieving high energy efficiency and improved dependability (i.e. reliability, availability and maintainability). When there is a choice of alternative design options that perform the same function, it makes sense to compare alternatives so that the variant that minimises operational costs can be selected. In this paper, we examine this issue in the context of the design of Waste Heat Recovery Systems (WHRS) for main engines of large commercial freight vessels. We propose a method that can predict the operational cost of a WHRS via thermodynamic analysis which shows costs related to energy utilisation, and dependability analysis which shows costs related to system unavailability and repair. Our approach builds on recent advances in thermodynamic simulation and compositional dependability analysis techniques. It is a model-based approach, and allows reuse of component libraries, and a high degree of automation which simplify application of the method. Our case study shows that alternative designs can be explored in fast iterations of this method, and that this facilitates the evidence-based selection of a design that minimises operational costs