Is there change over time in the season-of-birth effect for schizophrenia? Data from the southern hemisphere

The aim of this paper is to examine distributions of schizophrenia and general population births over time in order to determine whether (a) the pattern has changed over time, (b) any pattern was similar for both males and females, and (c) whether there is any indication that there is any relationship between the changes in pattern between schizophrenia and general population births. Birth month and year for 7807 individuals with ICD8/9 schizophrenia were gained from the Queensland Mental Health Statistical System for 1914-1975. Monthly births for the general population in Queensland for the same period were obtained from the Australian Bureau of Statistics. For each decade we obtained two comparisons, (1) between two 'seasons' (summer-autumn/winter-spring), and (2) between the third (coldest) quarter and the remaining quarters. Based on expected contrasts from general population proportions, odds ratios and their confidence intervals were used to analyse these comparisons for all subjects, and for males and females separately. The seasonality found in our previous studies was again evident (OR 1.09; 95% CI= 1.01-1.17). However there was no significant change in its pattern over time either for the total group or for males and females separately. When the general population births alone were examined using the same contrasts, seasonality was also observed, but here there were fluctuations over time. These results suggest that exposures linked to changes in general population births over time should be examined in disorders such as schizophrenia which demonstrate seasonality in births. The Stanley Foundation supported this project.

On utilising change over time in data mining

Magdeburg, Univ., Fak. für Informatik, Diss., 2013

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change over Time

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate.With the help of simulated longitudinal data of body mass index in children,we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.

Family interactions in IVF families: change over the transition to parenthood

Objective: This article presents a study of the change over time in the family interactions of couples who conceived through in-vitro fertilisation (IVF). Background: Observational methods are rarely used to study family interactions in families who used assisted reproductive techniques, but these methods are crucial for taking account of the communication that occurs in interactions with infants. Methods: Thirty-one couples expecting their first child were seen during the fifth month of pregnancy and when the child was nine months old. Family interactions were recorded in pre- and postnatal versions of the Lausanne Trilogue Play situation. Measures of marital satisfaction and parent-to-foetus/baby attachment or 'bonding' were also used to assess family relational dynamics. Results: Results showed that family alliance, marital satisfaction and parental attachment scores in the IVF sample were all similar to or higher than those in the reference sample during pregnancy. However, at nine months postnatally, the family alliance scores were lower. While marital satisfaction decreased over the period and parent-baby attachment increased, the family alliance scores were unstable, as no association was observed between the pre- and postnatal scores. In addition, neither prenatal marital satisfaction nor parent-foetus attachment predicted the postnatal family alliance. Conclusion: The change in the family alliance over the transition to parenthood appears to be specific to our IVF sample. Given that postnatal family functioning could not be predicted by prenatal family functioning, our observational data underline the importance of offering postnatal support to these families.

Comparison of sample size formulae for 2x2 cross-over designs applied to bioequivalence studies

We consider the comparison of two formulations in terms of average bioequivalence using the 2 × 2 cross-over design. In a bioequivalence study, the primary outcome is a pharmacokinetic measure, such as the area under the plasma concentration by time curve, which is usually assumed to have a lognormal distribution. The criterion typically used for claiming bioequivalence is that the 90% confidence interval for the ratio of the means should lie within the interval (0.80, 1.25), or equivalently the 90% confidence interval for the differences in the means on the natural log scale should be within the interval (-0.2231, 0.2231). We compare the gold standard method for calculation of the sample size based on the non-central t distribution with those based on the central t and normal distributions. In practice, the differences between the various approaches are likely to be small. Further approximations to the power function are sometimes used to simplify the calculations. These approximations should be used with caution, because the sample size required for a desirable level of power might be under- or overestimated compared to the gold standard method. However, in some situations the approximate methods produce very similar sample sizes to the gold standard method. Copyright © 2005 John Wiley & Sons, Ltd.

The ancient Britons: groundwater fauna survived extreme climate change over tens of millions of years across NW Europe

Global climate changes during the Cenozoic (65.5–0 Ma) caused major biological range shifts and extinctions. In northern Europe, for example, a pattern of few endemics and the dominance of wide-ranging species is thought to have been determined by the Pleistocene (2.59–0.01 Ma) glaciations. This study, in contrast, reveals an ancient subsurface fauna endemic to Britain and Ireland. Using a Bayesian phylogenetic approach, we found that two species of stygobitic invertebrates (genus Niphargus) have not only survived the entire Pleistocene in refugia but have persisted for at least 19.5 million years. Other Niphargus species form distinct cryptic taxa that diverged from their nearest continental relative between 5.6 and 1.0 Ma. The study also reveals an unusual biogeographical pattern in the Niphargus genus. It originated in north-west Europe approximately 87 Ma and underwent a gradual range expansion. Phylogenetic diversity and species age are highest in north-west Europe, suggesting resilience to extreme climate change and strongly contrasting the patterns seen in surface fauna. However, species diversity is highest in south-east Europe, indicating that once the genus spread to these areas (approximately 25 Ma), geomorphological and climatic conditions enabled much higher diversification. Our study highlights that groundwater ecosystems provide an important contribution to biodiversity and offers insight into the interactions between biological and climatic processes.

How do neighbouring populations affect local population change over time?

This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse how local population changes are affected by neighbouring populations. To do so we use the last 200 years of local population change that redistributed population in Sweden. We use literature to identify several different processes and spatial dependencies in the redistribution between a parish and its surrounding parishes. The analysis is based on a unique unchanged historical parish division, and we use an index of local spatial correlation to describe different kinds of spatial dependencies that have influenced the redistribution of the population. To control inherent time dependencies, we introduce a non-separable spatial temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies can be observed simultaneously over time. The main conclusions are that while local population changes have been highly dependent on the neighbouring populations in the 19th century, this spatial dependence have become insignificant already when two parishes is separated by 5 kilometres in the late 20th century. Another conclusion is that the time dependency in the population change is higher when the population redistribution is weak, as it currently is and as it was during the 19th century until the start of industrial revolution.

Predictors of health status do not change over three-year periods and exacerbation makes difference in chronic obstructive pulmonary disease

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Change over time in posttraumatic stress caused by myocardial infarction and predicting variables

The traumatic experience of a heart attack may evolve into symptoms of posttraumatic stress disorder, which can be diagnosed at the earliest 1 month after myocardial infarction (MI). While several predictors of posttraumatic stress in the first year after MI have been described, we particularly sought to identify longer-term predictors and predictors of change in posttraumatic stress over time.