Sleep Difficulty and Disease in a Cohort of Very Old Women.

OBJECTIVE: The objective of this study was to investigate the association between chronic diseases and sleep difficulty in older women. METHOD: A total of 10,721 women from The Australian Longitudinal Study on Women's Health, aged 70 to 75 years at baseline (1996), who answered sleep questionnaire data over 15 years follow-up, were surveyed. Longitudinal sleep difficulty class was regressed on baseline diseases. RESULTS: Arthritis and heart disease were the strongest predictors of sleep difficulty; odds ratios for belonging to the greatest sleep difficulty class were 2.27 (95% confidence interval [CI] = [1.98, 2.61]) and 1.8 (95% CI [1.5, 2.16], respectively. Bronchitis/emphysema, osteoporosis, asthma, diabetes, and hypertension also predicted greater sleep difficulty. CONCLUSION: Older women diagnosed with the aforementioned significant diseases may also be at greater risk of sleep difficulty. These women may need counseling or treatment for their sleep difficulty, to prevent depression, cognitive function decline, falls, frailty, and increased mortality, as well as greater risk of nursing home placement, well known to be reinforced by sleep trouble, and the associated health care costs and societal impacts poor sleep quality has for older adults.

Sleeping difficulty, disease and mortality in older women: a latent class analysis and distal survival analysis.

The aim of this study is to identify patterns of sleep difficulty in older women, to investigate whether sleep difficulty is an indicator for poorer survival, and to determine whether sleep difficulty modifies the association between disease and death. Data were from the Australian Longitudinal Study on Women's Health, a 15-year longitudinal cohort study, with 10 721 women aged 70-75 years at baseline. Repeated-measures latent class analysis identified four classes of persistent sleep difficulty: troubled sleepers (N = 2429, 22.7%); early wakers (N = 3083, 28.8%); trouble falling asleep (N = 1767, 16.5%); and untroubled sleepers (N = 3442, 32.1%). Sleep difficulty was an indicator for mortality. Compared with untroubled sleepers, hazard ratios and 95% confidence intervals for troubled sleepers, early wakers, and troubled falling asleep were 1.12 (1.03, 1.23), 0.81 (0.75, 0.91) and 0.89 (0.79, 1.00), respectively. Sleep difficulty may modify the prognosis of women with chronic diseases. Hazard ratios (and 95% confidence intervals) for having three or more diseases (compared with 0 diseases) were enhanced for untroubled sleepers, early wakers and trouble falling asleep [hazard ratio = 1.86 (1.55, 2.22), 1.91 (1.56, 2.35) and 1.98 (1.47, 2.66), respectively], and reduced for troubled sleepers [hazard ratio = 1.57 (1.24, 1.98)]. Sleep difficulty in older women is more complex than the presence or absence of sleep difficulty, and should be considered when assessing the risk of death associated with disease.

Continental scale analysis of bird migration timing: influences of climate and life history traits-a generalized mixture model clustering and discriminant approach.

There is substantial evidence of climate-related shifts to the timing of avian migration. Although spring arrival has generally advanced, variable species responses and geographical biases in data collection make it difficult to generalise patterns. We advance previous studies by using novel multivariate statistical techniques to explore complex relationships between phenological trends, climate indices and species traits. Using 145 datasets for 52 bird species, we assess trends in first arrival date (FAD), last departure date (LDD) and timing of peak abundance at multiple Australian locations. Strong seasonal patterns were found, i.e. spring phenological events were more likely to significantly advance, while significant advances and delays occurred in other seasons. However, across all significant trends, the magnitude of delays exceeded that of advances, particularly for FAD (+22.3 and -9.6 days/decade, respectively). Geographic variations were found, with greater advances in FAD and LDD, in south-eastern Australia than in the north and west. We identified four species clusters that differed with respect to species traits and climate drivers. Species within bird clusters responded in similar ways to local climate variables, particularly the number of raindays and rainfall. The strength of phenological trends was more strongly related to local climate variables than to broad-scale drivers (Southern Oscillation Index), highlighting the importance of precipitation as a driver of movement in Australian birds.

Using Self-Organising Maps (SOMs) to assess synchronies: an application to historical eucalypt flowering records.

Self-Organising Map (SOM) clustering methods applied to the monthly and seasonal averaged flowering intensity records of eight Eucalypt species are shown to successfully quantify, visualise and model synchronisation of multivariate time series. The SOM algorithm converts complex, nonlinear relationships between high-dimensional data into simple networks and a map based on the most likely patterns in the multiplicity of time series that it trains. Monthly- and seasonal-based SOMs identified three synchronous species groups (clusters): E. camaldulensis, E. melliodora, E. polyanthemos; E. goniocalyx, E. microcarpa, E. macrorhyncha; and E. leucoxylon, E. tricarpa. The main factor in synchronisation (clustering) appears to be the season in which flowering commences. SOMs also identified the asynchronous relationship among the eight species. Hence, the likelihood of the production, or not, of hybrids between sympatric species is also identified. The SOM pattern-based correlation values mirror earlier synchrony statistics gleaned from Moran correlations obtained from the raw flowering records. Synchronisation of flowering is shown to be a complex mechanism that incorporates all the flowering characteristics: flowering duration, timing of peak flowering, of start and finishing of flowering, as well as possibly specific climate drivers for flowering. SOMs can accommodate for all this complexity and we advocate their use by phenologists and ecologists as a powerful, accessible and interpretable tool for visualisation and clustering of multivariate time series and for synchrony studies.

Modeling and control of the agitation-sedation cycle for critical care patients.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, is damaging to patient health, and increases length of stay and healthcare costs. The mathematical model presented captures the essential dynamics of the agitation-sedation system for the first time, and is statistically validated using recorded infusion data for 37 patients. Constant patient-specific patient parameters are used, illustrating the commonality of these fundamental dynamics over a broad range of patients. The validated model serves as a basis for comparison of sedation administration methods, devices, therapeutics and protocols. Heavy derivative feedback control is shown to be an effective means of managing agitation, given consistent agitation measurement. The improved agitation management reduces the modeled mean and peak agitation levels 68.4% and 52.9% on average, respectively. Some patients showed over 90% reduction in mean agitation level through increased control gains. This improved agitation management is achieved via heavy derivative feedback control of sedation administration, which provides an essentially bolus-driven management approach, aligned with recent sedation practices.