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Institution Leaders' Experiences upon Managing Pupils Confronted with Domestic Physical violence.
OBJECTIVE Derivation of service areas is an important methodology for evaluating healthcare variation, which can be refined to more robust, condition-specific, and empirically-based automated regions, using cancer service areas as an exemplar. DATA SOURCES/STUDY SETTING Medicare claims (2014-2015) for the nine-state Northeast region were used to develop a ZIP-code-level origin-destination matrix for cancer services (surgery, chemotherapy, and radiation). This population-based study followed a utilization-based approach to delineate cancer service areas (CSAs) to develop and test an improved methodology for small area analyses. DATA COLLECTION/EXTRACTION METHODS Using the cancer service origin-destination matrix, we estimated travel time between all ZIP-code pairs, and applied a community detection method to delineate CSAs, which were tested for localization, modularity, and compactness, and compared to existing service areas. PRINCIPAL FINDINGS Delineating 17 CSAs in the Northeast yielded optimal parameters, with a mean localization index (LI) of 0.88 (min 0.60, max 0.98), compared to the 43 Hospital Referral Regions (HRR) in the region (mean LI 0.68; min 0.18, max 0.97). Modularity and compactness were similarly improved for CSAs vs. HRRs. CONCLUSIONS Deriving cancer-specific service areas with an automated algorithm that uses empirical and network methods showed improved performance on geographic measures compared to more general, hospital-based service areas. Terephthalic mw Spatial analyses using data from geographic areas that change shape and location over time, like US ZIP codes, produce biased results to the extent that unit misalignments are related to covariate effects. To address this issue, one method has incorporated a fixed effect measure of population shifts and a spatial structure as a block-diagonal neighborhood adjacency matrix within a Besag-York-Mollié (BYM) model. However, this approach assumes that spatial relationships among units change with time and precludes the assessment of temporal dynamic effects. Here, we assume that a continuous Gaussian random field underlies misaligned data and apply a stochastic partial differential equation (SPDE) approach to modeling area outcomes. We compare SPDE and BYM methods and show that both provide similar estimates of covariate effects. Importantly, we demonstrate that the SPDE approach can additionally identify autoregressive processes underlying the development of problematic health outcomes using data observed across Pennsylvania over 11 years. This tutorial describes the basic implementation of Bayesian hierarchical models for spatial health data using the R package nimble. To quote the nimble R description A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. Examples of the use of the package for a small range of Bayesian Disease Mapping (BDM) models is explored and focus on different approaches to model fitting and analysis are discussed. Examples of publicly available small area health data is used throughout. In urban health studies where spatial and temporal changes are of importance, spatio-temporal variations are usually neglected. For the Heinz Nixdorf Recall Study, we investigate spatio-temporal variation in analyses of effects of urban greenness on depression by including spatio-temporal random effect terms in a Poisson model on district level. Our results show negative associations between greenness and depression. The findings suggest strong temporal autocorrelation and weak spatial effects. Even if the weak spatial effects are suggestive of neglecting them, as in our case, spatio-temporal random effects should be taken into account to provide reliable inference in urban health studies. BACKGROUND The most commonly used illicit substance worldwide is cannabis. To date, no national level study of cannabis-related death has been undertaken in Australia. The current study aimed to investigate the rates, characteristics and manner of cannabis-related deaths recorded in Australia (2000-2018). METHODS A retrospective case review of medicolegal files was undertaken through the National Coronial Information System (NCIS) (1/07/2000-31/12/2018). RESULTS A total of 559 cases were identified, with a mean age of 35.8 years, 81.2% were male. The crude mortality rate per 100,000 people ranged between 0.10 (CI = 0.06-0.15) and 0.23 (CI = 0.17-0.30). The manner of deaths were accidental injury (29.9%), suicide (25.0%), polysubstance toxicity (17.0%), natural disease (16.1 %), natural disease and drug effect/toxicity (7.9%), assault (3.0%) and unascertained (1.1%). No deaths were solely due to cannabis toxicity. Men were over-represented in this group and were three times as likely to die of accidental injury than women who died from cannabis-related deaths. Motor vehicle accidents were the leading cause of accidental injury. Cardiovascular (14.3%) and respiratory conditions (9.7%) were the most common disease types recorded in cause of death. The median Δ-9-tetrahydrocannabinol blood concentration was 0.008 mg/L (range 0.0005-19.00 mg/L). Other drugs were cited in the cause of death alongside cannabis (81.4%), the most common being alcohol (47.2%). CONCLUSIONS Low all-cause crude mortality rates remained relatively stable over the study period. No deaths were due to direct cannabis toxicity, but death due to accidental injury was prominent. BACKGROUND Although previous studies have shown that opioid agonist therapy (OAT) is linked to reductions in illicit opioid use, less is known about how OAT impacts the use of other psychoactive substances. We aimed to examine the changes in use of different substances by comparing patterns before and after initiating OAT. METHODS Data for this study was derived from three ongoing prospective cohorts involving people who use drugs in Vancouver, Canada from 1996 to 2018. We assessed use patterns for heroin, illicit prescription opioid, cocaine, crack cocaine, crystal methamphetamine, cannabis, daily alcohol use, and benzodiazepines. Segmented regression was conducted to compare the trends of substance use between pre-treatment and post-treatment periods. RESULTS The study included 1107 participants. After OAT engagement, we observed an immediate decline in the proportion as well as a decreasing trend for heroin (Adjusted Odds Ratio (AOR) 0.80, 95% confidence interval (CI) 0.77, 0.83), illicit prescription opioid (AOR 0.
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