Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias

The measurement of lifetime prevalence of depression in cross-sectional surveys is biased by recall problems. We estimated it indirectly for two countries using modelling, and quantified the underestimation in the empirical estimate for one. A microsimulation model was used to generate population-based epidemiological measures of depression. We fitted the model to 1-and 12-month prevalence data from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) and the Australian Adult Mental Health and Wellbeing Survey. The lowest proportion of cases ever having an episode in their life is 30% of men and 40% of women, for both countries. This corresponds to a lifetime prevalence of 20 and 30%, respectively, in a cross-sectional setting (aged 15-65). The NEMESIS data were 38% lower than these estimates. We conclude that modelling enabled us to estimate lifetime prevalence of depression indirectly. This method is useful in the absence of direct measurement, but also showed that direct estimates are underestimated by recall bias and by the cross-sectional setting.

Attentional bias to pictures of fear-relevant animals in a dot probe task

Attentional bias to fear-relevant animals was assessed in 69 participants not preselected on self-reported anxiety with the use of a dot probe task showing pictures of snakes, spiders, mushrooms, and flowers. Probes that replaced the fear-relevant stimuli (snakes and spiders) were found faster than probes that replaced the non-fear-relevant stimuli, indicating an attentional bias in the entire sample. The bias was not correlated with self-reported state or trait anxiety or with general fearfulness. Participants reporting higher levels of spider fear showed an enhanced bias to spiders, but the bias remained significant in low scorers. The bias to snake pictures was not related to snake fear and was significant in high and low scorers. These results indicate preferential processing of fear-relevant stimuli in an unselected sample.

Correcting for numerator/denominator bias when assessing changing inequalites in occupational class mortality, Australia 1981-2002

Objective Comparisons of the changing patterns of inequalities in occupational mortality provide one way to monitor the achievement of equity goals. However, previous comparisons have not corrected for numerator/denominator bias, which is a consequence of the different ways in which occupational details are recorded on death certificates and on census forms. The objective of this study was to measure the impact of this bias on mortality rates and ratios over time. Methods Using data provided by the Australian Bureau of Statistics, we examined the evidence for bias over the period 1981-2002, and used imputation methods to adjust for this bias. We compared unadjusted with imputed rates of mortality for manual/non-manual workers. Findings Unadjusted data indicate increasing inequality in the age-adjusted rates of mortality for manual/non-manual workers during 1981-2002, Imputed data suggest that there have been modest fluctuations in the ratios of mortality for manual/non-manual workers during this time, but with evidence that inequalities have increased only in recent years and are now at historic highs. Conclusion We found that imputation for missing data leads to changes in estimates of inequalities related to social class in mortality for some years but not for others. Occupational class comparisons should be imputed or otherwise adjusted for missing data on census or death certificates.

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation > 0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.

Benchmarking of performance of Mexican states with effective coverage

Benchmarking of the performance of states, provinces, or districts in a decentralised health system is important for fostering of accountability, monitoring of progress, identification of determinants of success and failure, and creation of a culture of evidence. The Mexican Ministry of Health has, since 2001, used a benchmarking approach based on the World Health Organization (WHO) concept of effective coverage of an intervention, which is defined as the proportion of potential health gain that could be delivered by the health system to that which is actually delivered. Using data collection systems, including state representative examination surveys, vital registration, and hospital discharge registries, we have monitored the delivery of 14 interventions for 2005-06. Overall effective coverage ranges from 54.0% in Chiapas, a poor state, to 65.1% in the Federal District. Effective coverage for maternal and child health interventions is substantially higher than that for interventions that target other health problems. Effective coverage for the lowest wealth quintile is 52% compared with 61% for the highest quintile. Effective coverage is closely related to public-health spending per head across states; this relation is stronger for interventions that are not related to maternal and child health than those for maternal and child health. Considerable variation also exists in effective coverage at similar amounts of spending. We discuss the implications of these issues for the further development of the Mexican health-information system. Benchmarking of performance by measuring effective coverage encourages decision-makers to focus on quality service provision, not only service availability. The effective coverage calculation is an important device for health-system stewardship. In adopting this approach, other countries should select interventions to be measured on the basis of the criteria of affordability, effect on population health, effect on health inequalities, and capacity to measure the effects of the intervention. The national institutions undertaking this benchmarking must have the mandate, skills, resources, and independence to succeed.

Verifying metamodel coverage of model transformations

Since the object management group (OMG) commenced its model driven architecture (MDA) initiative, there has been considerable activity proposing and building automatic model transformation systems to help implement the MDA concept. Much less attention has been given to the need to ensure that model transformations generate the intended results. This paper explores one aspect of validation and verification for MDA: coverage of the source and/or target metamodels by a set of model transformations. The paper defines the property of metamodel coverage and some corresponding algorithms. This property helps the user assess which parts of a source (or target) metamodel are referenced by a given model transformation set. Some results are presented from a prototype implementation that is built on the eclipse modeling framework (EMF).