Why might urban tree canopy reduce dementia risk? A causal mediation analysis of 109,688 adults with 11 years of hospital and mortality records.

Urban tree canopy is associated with lower dementia risk, but no mediation analysis has been attempted to reveal potential mechanisms. We examined 3,639 dementia diagnoses in 109,688 participants of the Sax Institute's 45 and Up Study. Adjusted models indicated ≥20% tree canopy lowered the odds of developing dementia by 14% over 11 years (Odds Ratio = 0.86, 95%CI = 0.79-0.93). Association between tree canopy and dementia was partially mediated by physical activity (4.5%) and absences of psychological distress (5.7%), social support (2.9%), sleep duration (2.3%) and diabetes (1.8%). Social loneliness and absence of heart disease or hypertension did not mediate the tree canopy-dementia association.

Leafier Communities, Healthier Hearts: An Australian Cohort Study of 104,725 Adults Tracking Cardiovascular Events and Mortality Across 10 Years of Linked Health Data.

OBJECTIVE: Green space reduces cardiovascular disease (CVD) risk, but few studies examine what types of green space matter, which is an important consideration as cities densify and apartments become more common. METHOD: Participants were 86,727 in houses and 17,998 in apartments from the 45 and Up Study (Sax Institute) baseline survey with 10 years of linked hospitalisation and death data used to define: (i) all-cause; and (ii) CVD-mortality; (iii) fatal and non-fatal CVD events; and (iv) acute myocardial infarction (AMI). Associations with total green space, tree canopy cover and open grass within 1.6 km buffers were assessed using survival analysis adjusted for potential confounders. RESULTS: Mean percentage green space indicators were all higher among participants in houses than in apartments. Among residents of houses, a 10% increase in total green space was associated with reduced risk of CVD mortality (HR 0.97, 95%CI 0.95-1.00). A 10% increase in tree canopy cover was associated with reduced risks of all-cause mortality (HR 0.97, 95%CI 0.95-0.99), CVD mortality (HR 0.96, 95%CI 0.93-0.98), and fatal or non-fatal AMI (HR 0.93, 95%CI 0.89-0.96). In contrast, a 10% increase in open grass was associated with an increased risk of fatal or non-fatal AMI (HR 1.15, 95%CI 1.09-1.20) in residents of houses. Among residents of apartments, a 10% increase in total green space was associated with increased risk of all-cause mortality (HR 1.04, 95%CI 1.00-1.08) and CVD mortality (HR 1.03, 95%CI 1.00-1.08). CONCLUSIONS: Urban reforestation may be a population-level intervention to protect cardiovascular health, especially for people living in houses. The intersection of urban greening and cardiovascular health among residents of apartments warrants further investigation.

Green space and cardiovascular health in people with type 2 diabetes.

Associations between green space type and 9-year risk of incident cardiovascular disease (CVD) hospitalisations and deaths were analysed in 4166 people with type 2 diabetes in the Sax Institute's 45 and Up Study. Incidence of all-cause mortality, cardiovascular mortality, fatal or non-fatal CVD events and acute myocardial infarctions (AMI) were 14.67%, 7.23%, 47.36%, and 4.51%, respectively. After full adjustment, more tree canopy was associated with lower CVD mortality, lower fatal or non-fatal CVD events, and lower AMI risk. More open grass was associated with lower all-cause mortality, lower CVD mortality and lower fatal or non-fatal CVD events, but higher AMI risk.

Multilevel modeling of geographic variation in general practice consultations.

OBJECTIVE: To test relatively simple and complex models for examining model fit, higher-level variation in, and correlates of, GP consultations, where known nonhierarchical data structures are present. SETTING: New South Wales (NSW), Australia. DESIGN: Association between socioeconomic circumstances and geographic remoteness with GP consultation frequencies per participant was assessed using single-level, hierarchical, and multiple membership cross-classified (MMCC) models. Models were adjusted for age, gender, and a range of socioeconomic and demographic confounds. DATA COLLECTION/EXTRACTION METHODS: A total of 261,930 participants in the Sax Institute's 45 and Up Study were linked to all GP consultation records (Medicare Benefits Schedule; Department of Human Services) within 12 months of baseline (2006-2009). PRINCIPAL FINDINGS: Deviance information criterion values indicated the MMCC negative binomial regression was the best fitting model, relative to an MMCC Poisson equivalent and simpler hierarchical and single-level models. Between-area variances were relatively consistent across models, even when between GP variation was estimated. Lower rates of GP consultation outside of major cities were only observed once between-GP variation was assessed simultaneously with between-area variation in the MMCC models. CONCLUSIONS: Application of the MMCC model is necessary for estimation of variances and effect sizes in sources of big data on primary care in which complex nonhierarchical clustering by geographical area and GP is present.

Behavioural change, weight loss and risk of dementia: A longitudinal study.

Recent meta-analysis reported higher dementia risks associated with lower body mass index (BMI) and decreasing BMI. We examined to what extent these associations were attenuated by changes in behaviours and local environment. Multilevel discrete time-to-event models examined associations between baseline and change in BMI with dementia detected through prescription medications (source: Department of Human Services), hospitalisations and death certificates among 144,456 participants in the Sax Institute's 45 and Up Study. Models were adjusted for socioeconomic factors and measures of change in adherence to published guidelines for moderate to vigorous physical activity, sleep duration, alcohol, and fruit and vegetable consumption, as well as incidence of cardiometabolic diseases, and indicators of area-level disadvantage and rurality. Data was analysed in 2020. Higher dementia risks (albeit with imprecision) were found among participants who were underweight (Incidence Hazard Ratio (IHR) 1.30, 95%CI=0.86-1.86) and lower risks among those who were overweight (IHR=0.78, 95%CI=0.70-0.86) or obese (IHR=0.72, 95%CI=0.62-0.83) compared with 'normal' BMI. A ≥0.8 kg/m2 reduction in BMI associated with IHR=1.81 (95%CI=1.64-2.01) higher dementia risk relative to those with stable BMI. Higher dementia risk with decreasing BMI was fairly consistent relative to baseline BMI category. Adherence to physical activity and sleep duration guidelines were associated with reduced dementia risk, but neither these, nor adjustment for other behaviours and local factors, explained the BMI-dementia association. In conclusion, we replicated the BMI-dementia findings from a recent meta-analysis and provide further support to preventive strategies focussed on increasing physical activity and improving sleep duration. Other potential environmental risk factors outside of socioeconomic and urban/rural circumstances warrant investigation.

Urban green space, tree canopy and 11-year risk of dementia in a cohort of 109,688 Australians.

INTRODUCTION: Urban greening is a climate change-related policy with considerable health benefits. But do these benefits extend to prevention of dementia and, if so, which types of green space matter? METHOD: Multilevel discrete time-to-event cohort study of incident Alzheimer's disease over 11 years among a baseline recruited between January 1, 2006 and December 31, 2009 (the Sax Institute's 45 and Up Study). Sampled participants for this study (N=109,688) were aged 45 years or older with no record of dementia up to 6 years before baseline, living in the cities of Sydney, Wollongong and Newcastle, Australia. Exposures were percentage total green space, tree canopy and open grass within 1.6-km road network distance buffers at baseline. Outcomes were time-to-first anti-dementia medication prescription (Department of Human Services) or dementia detected during hospitalisation or death up to 31 December 2016 (up to 11 years follow-up). Outcomes were analysed in parallel to triangulate on associations with green space, while testing for bias due to potential under-prescribing of anti-dementia medications. Models were adjusted for baseline person-level factors and area-level socioeconomic disadvantage. RESULTS: Dementia detection varied by case ascertainment method. 1.55% (1,703/109,688) persons were detected using prescribed anti-dementia medications. 3.32% (3,639/109,688) persons were detected during hospitalisation or death via ICD-10 codes. Dementia incidence irrespective of outcome measurement was lower among females, younger participants, those living in couples, with higher qualifications and higher incomes. Dementia risk was lower with more tree canopy when the outcome was measured using hospital and death records (≥30% vs <10% tree canopy incidence hazard ratio (IHR) = 0.86, 95%CI 0.75, 0.99), after adjusting for person-level factors. The opposite association was observed when anti-dementia medications were used to detect dementia (≥30% vs <10% tree canopy IHR = 1.33, 95%CI 1.07, 1.66). Anti-dementia medication-based detection also indicated lower dementia risk with more open grass (≥20% vs <5% IHR = 0.83, 95%CI 0.67, 1.03). Anti-dementia medication prescribing was lower in the highest vs. lowest area-level disadvantage tertile (29.8% vs. 43.7%) among people diagnosed with dementia, indicating potential bias from geographic differences in prescribing practices. Adjusting for area-level disadvantage explained associations between tree canopy, open grass and dementia when detected by anti-dementia medication, but had negligible impact on negative (i.e. potentially protective) association between tree canopy and dementia detected by hospital and death records (≥30% vs <10% tree canopy hazard ratio 0.84, 95%CI 0.72, 0.99). CONCLUSIONS: Increasing urban tree canopy cover may help to reduce the risk of dementia. Replication in contrasting contexts and mediation studies to assess pathways are warranted.

Automatic measurement of interburst interval in premature neonates using range EEG.

OBJECTIVE: To explore the direct measure of EEG amplitude (range EEG, rEEG) for detection of interburst intervals (IBIs) and bursts in neonates. METHODS: Previously described 177 two-channel EEG recordings 3-6h long from 26 preterm infants (median gestational age of 26 weeks) at 23-38 weeks post-menstrual age (PMA) without major abnormalities were used to test four definitions of IBI detection algorithms with various settings of the parameters. RESULTS: As the basis for all four algorithms we developed the aggregation of rEEG signal over the channels by taking its maximum, and method of EEG trace selection at different phases of sleep-wake cycle (with different degree of discontinuity). The two less restrictive algorithms - with one and two amplitude thresholds - turned to be the most promising definitions. There were enough IBI detections for analysis, with no substantial difference in mean and maximum values of intervals. The longest IBI were measured at the location of greater discontinuity. Values of bursts and IBI indices as well as association with PMA were close to the published normative values derived manually. CONCLUSIONS: rEEG as a direct measure of EEG amplitude can be used for detection of bursts and IBI. SIGNIFICANCE: The automatic measurement of IBI based on rEEG provides a basis for improvements in neonatal brain monitoring.

Peak-to-peak amplitude in neonatal brain monitoring of premature infants.

OBJECTIVE: To assess the strength of association between alternative measures of electroencephalographic (EEG) signal peak-to-peak amplitude (ppA) and postmenstrual age (PMA) among a cohort of extremely premature infants. METHODS: 177 Two-channel EEG recordings 3-6h long were collected from 26 infants born before 29weeks of gestation. The raw EEG was converted into four different continuous measures of ppA: amplitude-integrated EEG (aEEG), range-EEG (rEEG), Gotman and Gloor's half-wave decomposition (HWD), and root of mean squares (RMS). For each ppA-measure EEG indices including mean, median, and 5% margins; indices of spread (width, standard deviation, coefficient of variation), and asymmetry were calculated for each 1min epoch. The medians of each index for the entire recording were tested for association with PMA using linear mixed models. RESULTS: The log-transformed values of aEEG and rEEG indices of spread were highly associated with PMA (fixed effects R(ß)(2)=0.84-0.89). CONCLUSIONS: Indices of spread by aEEG or rEEG can be used as indicators of neonatal brain maturation. However, rEEG produces the absolute values that most closely approximate the raw EEG amplitudes. SIGNIFICANCE: The indices of spread and rEEG as a measure of ppA provide a basis for improvements in neonatal EEG monitoring.

The dynamics of the law of effect: a comparison of models.

Dynamical models based on three steady-state equations for the law of effect were constructed under the assumption that behavior changes in proportion to the difference between current behavior and the equilibrium implied by current reinforcer rates. A comparison of dynamical models showed that a model based on Navakatikyan's (2007) two-component functions law-of-effect equations performed better than models based on Herrnstein's (1970) and Davison and Hunter's (1976) equations. Navakatikyan's model successfully described the behavioral dynamics in schedules with negative-slope feedback functions, concurrent variable-ratio schedules, Vaughan's (1981) melioration experiment, and experiments that arranged equal, and constant-ratio unequal, local reinforcer rates.

A model for residence time in concurrent variable interval performance.

A component-functions model of choice behavior is proposed for performance on interdependent concurrent variable-interval (VI) variable-interval schedules based on the product of two component functions, one that enhances behavior and one that reduces behavior. The model is the solution to the symmetrical pair of differential equations describing behavioral changes with respect to two categories of reinforcers: enhancing and reducing, or excitatory and inhibitory. The model describes residence time in interdependent concurrent VI VI schedules constructed from arithmetic and exponential distributions. The model describes the data reported by Alsop and Elliffe (1988) and Elliffe and Alsop (1996) with a variance accounted for of 87% compared to 64% accounted for by the Davison and Hunter (1976) model and 42% by Herrnstein's (1970) hyperbola. The model can explain matching, undermatching, and overmatching in the same subject under different procedures and has the potential to be extended to performance on concurrent schedules with more than two alternatives, multiple schedules, and single schedules. Thus, it can be considered as an alternative to Herrnstein's quantitative law of effect.

Seizure detection algorithm for neonates based on wave-sequence analysis.

OBJECTIVE: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. METHODS: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. RESULTS: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. CONCLUSIONS: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. SIGNIFICANCE: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring.

Dynamic baroreflex control of blood pressure: influence of the heart vs. peripheral resistance.

The aim in the present experiments was to assess the dynamic baroreflex control of blood pressure, to develop an accurate mathematical model that represented this relationship, and to assess the role of dynamic changes in heart rate and stroke volume in giving rise to components of this response. Patterned electrical stimulation [pseudo-random binary sequence (PRBS)] was applied to the aortic depressor nerve (ADN) to produce changes in blood pressure under open-loop conditions in anesthetized rabbits. The stimulus provided constant power over the frequency range 0-0.5 Hz and revealed that the composite systems represented by the central nervous system, sympathetic activity, and vascular resistance responded as a second-order low-pass filter (corner frequency approximately 0.047 Hz) with a time delay (1.01 s). The gain between ADN and mean arterial pressure was reasonably constant before the corner frequency and then decreased with increasing frequency of stimulus. Although the heart rate was altered in response to the PRBS stimuli, we found that removal of the heart's ability to contribute to blood pressure variability by vagotomy and beta(1)-receptor blockade did not significantly alter the frequency response. We conclude that the contribution of the heart to the dynamic regulation of blood pressure is negligible in the rabbit. The consequences of this finding are examined with respect to low-frequency oscillations in blood pressure.

A real-time algorithm for the quantification of blood pressure waveforms.

A real-time algorithm for quantification of biological oscillatory signals, such as arterial blood pressure (BP) is proposed which does not require user intervention and works on waveforms complicated by rapid changes in the mean level, frequency, or by the presence of arrhythmia. The algorithm is based on the continous independent assessment of the refractory period (RP). In the first stage, a sample of the signal is band-pass filtered. During the next stage: 1) the local maxima in the filtered signal are identified and their pulse amplitudes (PA) measured on the side opposite to the possible notch position and 2) those maxima whose PA exceeds some threshold are selected and an array of RP values is formed as a fraction of the moving estimate of the interval between successive selected peaks. Finally, the original signal is analyzed by means of two moving averages (MAs) with short and long averaging time intervals. The true peaks are determined as the maxima between intersections of MAs if the peak-to-peak or the intersection-to-intersection intervals since the previous peak and the previous intersection exceed the RP. The algorithm proved to be superior against three commercially available heartbeat detectors yielding an error rate of 0.09%.