Investigating the impact of a national educational program on patient adherence to osteoporosis medications.

Interrupted time series analysis (ITS) measured improvements in osteoporosis medication adherence after a national education program. The proportion of patients who were adherent to treatment increased following the program. INTRODUCTION: The NPS MedicineWise osteoporosis program, implemented nationally in 2015-2016 in Australia, sought to improve adherence to osteoporosis medicines using evidence-based multifaceted large-scale educational interventions targeting general practitioners. METHODS: We undertook a retrospective, observational study using ITS analysis from 1 December 2011 to 31 December 2019 using a 10% sample of Pharmaceutical Benefits Scheme (PBS) dispensing data for 71,093 patients ≥ 45 years. The adherence measure was the percentage of patients with a proportion of days covered (PDC) ≥ 80%. RESULTS: The program significantly increased adherence to osteoporosis medicines. After 12 months, the estimated adherence rate with the program was 48.4% (95% CI, 47.4-49.4%). Without the program, adherence would have fallen to 43.5% (95% CI, 42.5-44.5%). There was a further increase in adherence by the end of the study period (44 months after the program). Among patients prescribed denosumab only, despite a significant increase in adherence following the program, adherence rates were overall sub-optimal (65.0% 12 months following the program). CONCLUSIONS: The NPS MedicineWise osteoporosis program significantly increased osteoporosis medicine adherence. The program changed primary care prescriber behavior and improved treatment adherence. However, some patients had a period of treatment discontinuation, placing them at increased risk of fracture. A focused program emphasizing the importance of long-term adherence with denosumab (including switching to bisphosphonates if treatment is discontinued) may be warranted to further improve the quality use of osteoporosis treatment in Australia.

The claustrum's proposed role in consciousness is supported by the effect and target localization of Salvia divinorum.

THIS ARTICLE BRINGS TOGETHER THREE FINDINGS AND IDEAS RELEVANT FOR THE UNDERSTANDING OF HUMAN CONSCIOUSNESS: (I) Crick's and Koch's theory that the claustrum is a "conductor of consciousness" crucial for subjective conscious experience. (II) Subjective reports of the consciousness-altering effects the plant Salvia divinorum, whose primary active ingredient is salvinorin A, a κ-opioid receptor agonist. (III) The high density of κ-opioid receptors in the claustrum. Fact III suggests that the consciousness-altering effects of S. divinorum/salvinorin A (II) are due to a κ-opioid receptor mediated inhibition of primarily the claustrum and, additionally, the deep layers of the cortex, mainly in prefrontal areas. Consistent with Crick and Koch's theory that the claustrum plays a key role in consciousness (I), the subjective effects of S. divinorum indicate that salvia disrupts certain facets of consciousness much more than the largely serotonergic hallucinogen lysergic acid diethylamide (LSD). Based on this data and on the relevant literature, we suggest that the claustrum does indeed serve as a conductor for certain aspects of higher-order integration of brain activity, while integration of auditory and visual signals relies more on coordination by other areas including parietal cortex and the pulvinar.

Health effects of the September 2009 dust storm in Sydney, Australia: did emergency department visits and hospital admissions increase?

BACKGROUND: During September 2009, a large dust storm was experienced in Sydney, New South Wales, Australia. Extremely high levels of particulate matter were recorded, with daily average levels of coarse matter (<10 µm) peaking over 11,000 µg/m3 and fine (<2.5 µm) over 1,600 µg/m3. We conducted an analysis to determine whether the dust storm was associated with increases in all-cause, cardiovascular, respiratory and asthma-related emergency department presentations and hospital admissions. METHODS: We used distributed-lag Poisson generalized models to analyse the emergency department presentations and hospital admissions adjusted for pollutants, humidity, temperature and day of week and seasonal effects to obtain estimates of relative risks associated with the dust storm. RESULTS: The dust storm period was associated with large increases in asthma emergency department visits (relative risk 1.23, 95% confidence interval 1.10-1.38, p < 0.01), and to a lesser extent, all emergency department visits (relative risk 1.04, 95% confidence interval 1.03-1.06, p < 0.01) and respiratory emergency department visits (relative risk 1.20, 95% confidence interval 1.15-1.26, p < 0.01). There was no significant increase in cardiovascular emergency department visits (p = 0.09) or hospital admissions for any reason. Age-specific analyses showed the dust storm was associated with increases in all-cause and respiratory emergency department visits in the ≥65 year age group; the ≤5 year group had higher risks of all-cause, respiratory and asthma-related emergency department presentations. CONCLUSIONS: We recommend public health measures, especially targeting asthmatics, should be implemented during future dust storm events.

Relationship between the population incidence of pertussis in children in New South Wales, Australia and emergency department visits with cough: a time series analysis.

BACKGROUND: Little is known about the potential of syndromic surveillance to provide early warning of pertussis outbreaks. We conducted a time series analysis to assess whether an emergency department (ED) cough syndrome would respond to changes in the incidence of pertussis in children aged under 10 years in New South Wales (NSW), Australia, and to evaluate the timing of any association. A further aim was to assess the lag between the onset of pertussis symptoms and case notification in the infectious diseases surveillance system in NSW. METHODS: Using routinely collected data, we prepared a daily count time series of visits to NSW EDs assigned a provisional diagnosis of cough. Separate daily series were prepared for three independent variables: notifications of cases of pertussis and influenza and ED visits with bronchiolitis (a proxy measure of respiratory syncytial virus (RSV) infection). The study period was 1/1/2007-31/12/2010. A negative binomial multivariate model was used to assess associations between the outcome and independent variables. We also evaluated the median delay in days between the estimated onset of a case of pertussis and the date the local public health authority was notified of that case. RESULTS: When notified pertussis increased by 10 cases in one day, ED visits with cough increased by 5.2% (95% confidence interval (CI): 0.5%-10.0%) seven days later. Daily increases in the other independent variables had a smaller impact on cough visits. When notified influenza increased by 10 cases in one day, ED visits with cough increased by 0.8% (95% CI: 0%-1.7%) seven days later. When ED visits with bronchiolitis increased by 10 visits in one day, ED visits with cough increased by 4.8% (95% CI: 1.2%-8.6%) one day earlier. The median interval between estimated onset of pertussis and case notification was seven days. CONCLUSIONS: Pertussis appears to be an important driver of ED visits with cough in children aged under 10 years. However, the median delay in notification of cases of pertussis was similar to the lag in the pertussis-associated short-term increases in ED visits with cough. Elevations in RSV and influenza activity may also explain increases in the ED cough syndrome. Real time monitoring of ED visits with cough in children is therefore unlikely to consistently detect a potential outbreak of pertussis before passive surveillance.

Sample size calculations for the design of health studies: a review of key concepts for non-statisticians.

Sample size calculations before conducting a health study or clinical trial are important to provide evidence that the proposed study is capable of detecting real associations between study factors. This review aims to clarify statistical issues related to the calculation of sample sizes and is illustrated with an example of a recent study design to improve health outcomes related to water and sewage in NSW Aboriginal communities. The effect of power, significance level and effect size on sample size are discussed. Calculations of sample sizes for individual-based studies are modified for more complex trial designs by multiplying individual-based estimates by an inflationary factor.

Associations between the use of metformin, sulphonylureas, or diet alone and cardiovascular outcomes in 6005 people with type 2 diabetes in the FIELD study.

AIMS: We aimed to determine the associations between metformin or sulphonylurea monotherapy at study entry into the FIELD diabetes trial and (1) metabolic risk factors, (2) risk of a first major cardiovascular (CVD) outcome, and (3) the effect of each therapy on the risk-modifying effect of fenofibrate. METHODS: Patients receiving metformin or sulphonylureas without insulin therapy were compared for the relative risk of CVD outcomes, adjusted for differences in baseline characteristics likely to affect risk. RESULTS: Metformin-treated patients were likely to be younger, female, or obese. Metformin was associated with higher levels of lipids (other than LDL-C) and homocysteine (P<0.001). Sulphonylurea-treated patients had a longer history of diabetes and more CVD and microvascular disease. Sulphonylurea treatment was associated with higher plasma creatinine and lower plasma HDL-C (P<0.001). The risks of all CVD outcomes were higher for those on sulphonylureas than diet alone, but were nonsignificant after adjustment for the duration and intensity of diabetes and severity of risk factors. Metformin and sulphonylureas did not significantly influence the benefits of fenofibrate on CVD outcomes. CONCLUSIONS: Apparent differences in the risk of CVD outcomes associated with oral hypoglycemics therapy were largely abolished by adjustment for diabetes and CVD risk factors.

Simulating invasion with cellular automata: connecting cell-scale and population-scale properties.

Interpretive and predictive tools are needed to assist in the understanding of cell invasion processes. Cell invasion involves cell motility and proliferation, and is central to many biological processes including developmental morphogenesis and tumor invasion. Experimental data can be collected across a wide range of scales, from the population scale to the individual cell scale. Standard continuum or discrete models used in isolation are insufficient to capture this wide range of data. We develop a discrete cellular automata model of invasion with experimentally motivated rules. The cellular automata algorithm is applied to a narrow two-dimensional lattice and simulations reveal the formation of invasion waves moving with constant speed. The simulation results are averaged in one dimension-these data are used to identify the time history of the leading edge to characterize the population-scale wave speed. This allows the relationship between the population-scale wave speed and the cell-scale parameters to be determined. This relationship is analogous to well-known continuum results for Fisher's equation. The cellular automata algorithm also produces individual cell trajectories within the invasion wave that are analogous to cell trajectories obtained with new experimental techniques. Our approach allows both the cell-scale and population-scale properties of invasion to be predicted in a way that is consistent with multiscale experimental data. Furthermore we suggest that the cellular automata algorithm can be used in conjunction with individual data to overcome limitations associated with identifying cell motility mechanisms using continuum models alone.