Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.

Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data.

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intracellular calcium signals. An ongoing challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time series. In this article, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a dataset from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.

BC Clinical impact of medication reviews with follow-up in cardiovascular older patients in primary care: A cluster-randomized controlled trial.

AIMS: Cardiovascular diseases (CVD) are the primary cause of death in Chile. Pharmacist-led medication review with follow-up (MRF) has improved CVD risk factors control in Europe and North America. However, their healthcare systems differ from Chile's, precluding generalizability. This trial aimed to determine the effect of MRF on CVD risk factor control among older patients with polypharmacy attending public primary care centres in Chile. METHODS: A cluster-randomized controlled trial was conducted in 24 centres. Patients older than 65 years with moderate-to-high CVD risk, five or more medications, hypertension, type 2 diabetes or dyslipidaemia, received MRF in addition to usual care or usual care alone for 12 months. Primary outcome measures were clinical goal achievement for hypertension, type 2 diabetes and dyslipidaemia, as well as medication adherence, medication number and CVD risk score. Adjusted generalized estimating equations were used, with odds ratios (ORs) for binary measures and mean differences for continuous measures. RESULTS: In total, 324 patients from 12 centres (174 MRF group, 150 usual care group, six centres each) received four pharmacist visits. Significant improvements were found for goal achievement in hypertension (OR 4.37, 95% confidence interval [CI] 2.54 to 7.51, P = .001), LDL cholesterol (OR 3.67, 95% CI 2.13 to 6.33, P = .001), type 2 diabetes (OR 6.97, 95% CI 3.69 to 13.2, P = .001), medication adherence (OR 6.60, 95% CI 1.36 to 31.9, P = .022), medications number (-0.86, 95% CI -1.14 to -0.58, P < .001) and CVD risk score (-2.27, 95% CI -2.84 to -1.69, P < .001). CONCLUSION: Pharmacist-led medication review with follow-up improved cardiovascular disease risk factor control and medication adherence. This study supports pharmacists' inclusion in primary care teams.

Premorbid vulnerability and disease severity impact on Long-COVID cognitive impairment.

BACKGROUND: Cognitive deficits have been increasingly reported as possible long-term manifestations after SARS-CoV-2 infection. AIMS: In this study we aimed at evaluating the factors associated with cognitive deficits 6 months after hospitalization for Coronavirus Disease 2019 (COVID-19). METHODS: One hundred and six patients, discharged from a pneumology COVID-19 unit between March 1 and May 30 2020, accepted to be evaluated at 6 months according to an extensive neurological protocol, including the Montreal Cognitive Assessment (MoCA). RESULTS: Abnormal MoCA scores at 6 months follow-up were associated with higher pre-hospitalization National Health System (NHS) score (Duca et al. in Emerg Med Pract 22:1-2, 2020) (OR 1.27; 95% CI 1.05-1.6; p = 0.029) and more severe pulmonary disease expressed by the Brescia-COVID Respiratory Severity Scale (Duca et al. in Emerg Med Pract 22:1-2, 2020) (BCRSS > 1OR 4.73; 95% CI 1.53-14.63; p = 0.003) during the acute phase of the disease. DISCUSSION: This longitudinal study showed that the severity of COVID-19, indicated by BCRSS, and a complex score given by age and premorbid medical conditions, expressed by NHS, play a major role in modulating the long-term cognitive consequences of COVID-19 disease. CONCLUSIONS: These findings indicate that the association of age and premorbid factors might identify people at risk for long-term neurological consequences of COVID-19 disease, thus deserving longer and proper follow-up.

Is amyloid involved in acute neuroinflammation? A CSF analysis in encephalitis.

INTRODUCTION: Several investigations have argued for a strong relationship between neuroinflammation and amyloid metabolism but it is still unclear whether inflammation exerts a pro-amyloidogenic effect, amplifies the neurotoxic effect of amyloid, or is protective. METHODS: Forty-two patients with acute encephalitis (ENC) and 18 controls underwent an extended cerebrospinal fluid (CSF) panel of inflammatory, amyloid (Aß40, 42, and 38, sAPP-α, sAPP-ß), glial, and neuronal biomarkers. Linear and non-linear correlations between CSF biomarkers were evaluated studying conditional independence relationships. RESULTS: CSF levels of inflammatory cytokines and neuronal/glial markers were higher in ENC compared to controls, whereas the levels of amyloid-related markers did not differ. Inflammatory markers were not associated with amyloid markers but exhibited a correlation with glial and neuronal markers in conditional independence analysis. DISCUSSION: By an extensive CSF biomarkers analysis, this study showed that an acute neuroinflammation state, which is associated with glial activation and neuronal damage, does not influence amyloid homeostasis.

Long-term neurological manifestations of COVID-19: prevalence and predictive factors.

BACKGROUND: Clinical investigations have argued for long-term neurological manifestations in both hospitalised and non-hospitalised COVID-19 patients. It is unclear whether long-term neurological symptoms and features depend on COVID-19 severity. METHODS: From a sample of 208 consecutive non-neurological patients hospitalised for COVID-19 disease, 165 survivors were re-assessed at 6 months according to a structured standardised clinical protocol. Prevalence and predictors of long-term neurological manifestations were evaluated using multivariate logistic regression analyses. RESULTS: At 6-month follow-up after hospitalisation due to COVID-19 disease, patients displayed a wide array of symptoms; fatigue (34%), memory/attention (31%) and sleep disorders (30%) were the most frequent. At neurological examination, 40% of patients exhibited neurological abnormalities, such as hyposmia (18.0%), cognitive deficits (17.5%), postural tremor (13.8%) and subtle motor/sensory deficits (7.6%). Older age, premorbid comorbidities and severity of COVID-19 were independent predictors of neurological manifestations in logistic regression analyses. CONCLUSIONS: Premorbid vulnerability and severity of SARS-CoV-2 infection impact on prevalence and severity of long-term neurological manifestations.

Halogen-Mediated Partial Oxidation of Polyvinyl Alcohol for Tissue Engineering Purposes.

Partial oxidation of polyvinyl alcohol (PVA) with potassium permanganate turned out to be an efficient method to fabricate smart scaffolds for tissue engineering, endowed with biodegradation and protein delivery capacity. This work considered for the first time the use of halogens (bromine, chlorine and iodine) as less aggressive agents than potassium permanganate to perform controlled PVA oxidation, in order to prevent degradation of polymer molecular size upon chemical modification. Oxidized PVA solutions were chemically characterized (i.e., dinitrophenylhydrazine assay, viscosity measurements, molecular size distribution) before preparing physically cross-linked hydrogels. Scaffolds were assessed for their mechanical properties and cell/tissue biocompatibiliy through cytotoxic extract test on IMR-90 fibroblasts and subcutaneous implantation into BALB/c mice. According to chemical investigations, bromine and iodine allowed for minor alteration of polymer molecular weight. Uniaxial tensile tests demonstrated that oxidized scaffolds had decreased mechanical resistance to deformation, suggesting tunable hydrogel stiffness. Finally, oxidized hydrogels exhibited high biocompatibility both in vitro and in vivo, resulting neither to be cytotoxic nor to elicit severe immunitary host reaction in comparison with atoxic PVA. In conclusion, PVA hydrogels oxidized by halogens were successfully fabricated in the effort of adapting polymer characteristics to specific tissue engineering applications.

Convex mixture regression for quantitative risk assessment.

There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus in these studies is inference on dose levels associated with a given increase in risk relative to a baseline. In addressing this goal, popular methods either dichotomize the continuous response or focus on modeling changes with the dose in the expectation of the outcome. Such choices may lead to information loss and provide inaccurate inference on dose-response relationships. We instead propose a Bayesian convex mixture regression model that allows the entire distribution of the health outcome to be unknown and changing with the dose. To balance flexibility and parsimony, we rely on a mixture model for the density at the extreme doses, and express the conditional density at each intermediate dose via a convex combination of these extremal densities. This representation generalizes classical dose-response models for quantitative outcomes, and provides a more parsimonious, but still powerful, formulation compared to nonparametric methods, thereby improving interpretability and efficiency in inference on risk functions. A Markov chain Monte Carlo algorithm for posterior inference is developed, and the benefits of our methods are outlined in simulations, along with a study on the impact of dde exposure on gestational age.

Robustifying Bayesian nonparametric mixtures for count data.

Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure.

Barriers and facilitators to implementing priority setting and resource allocation tools in hospital decisions: A systematic review.

Health care budgets in high-income countries are having issues coping with unsustainable growth in demand, particularly in the hospital setting. Despite this, implementing tools systematising priority setting and resource allocation decisions has been challenging. This study answers two questions: (1) what are the barriers and facilitators to implementing priority setting tools in the hospital setting of high-income countries? and (2) what is their fidelity? A systematic review using the Cochrane methods was conducted including studies of hospital-related priority setting tools reporting barriers or facilitators for implementation, published after the year 2000. Barriers and facilitators were classified using the Consolidated Framework for Implementation Research (CFIR). Fidelity was assessed using priority setting tool's standards. Out of thirty studies, ten reported program budgeting and marginal analysis (PBMA), twelve multi-criteria decision analysis (MCDA), six health technology assessment (HTA) related frameworks, and two, an ad hoc tool. Barriers and facilitators were outlined across all CFIR domains. Implementation factors not frequently observed, such as 'evidence of previous successful tool application', 'knowledge and beliefs about the intervention' or 'external policy and incentives' were reported. Conversely, some constructs did not yield any barrier or facilitator including 'intervention source' or 'peer pressure'. PBMA studies satisfied the fidelity criteria between 86% and 100%, for MCDA it varied between 36% and 100%, and for HTA it was between 27% and 80%. However, fidelity was not related to implementation. This study is the first to use an implementation science approach. Results represent the starting point for organisations wishing to use priority setting tools in the hospital setting by providing an overview of barriers and facilitators. These factors can be used to assess readiness for implementation or to serve as the foundation for process evaluations. Through our findings, we aim to improve the uptake of priority setting tools and support their sustainable use.

Breast Cancer Screening Should Embrace Precision Medicine: Evidence by Reviewing Economic Evaluations in China.

The cost-effectiveness of conventional population-based breast cancer screening strategies (e.g. mammography) has been found controversial, while evidence shows that genetic testing for early detection of pathogenic variants is cost-effective. We aimed to review the economic evaluations of breast cancer screening in China to provide an information summary for future research on this topic. We searched the literature to identify the economic evaluations that examined breast cancer screening and testing in China, supplemented by hand-searching the reference lists of the included studies. We finally included five studies satisfying our inclusion criteria. Four articles examined mammography while the rest investigated multigene testing. The existing breast cancer screening programmes were found to be cost-effective among urban Chinese women, but one study concluded that they might cause harm to women in rural areas. Contextual factors, such as data absence, urban-rural disparity, willingness-to-pay threshold, and model design, imposed barriers to cost-effectiveness analysis. Multigene testing was found to be cost-effective and has a promising population impact among all women with breast cancer in China. Future research should investigate the cost-effectiveness of screening and identifying breast cancer through precision medicine technologies, including genetic testing, genome sequencing, cascade testing, and the return of secondary findings.

Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study.

INTRODUCTION: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. METHODS AND ANALYSIS: The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. ETHICS AND DISSEMINATION: Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). TRIAL REGISTRATION NUMBER: ACTRN12621001223820.

Medication review with follow-up for cardiovascular outcomes: a trial based cost-utility analysis.

Aim: To assess the trial-based cost-effectiveness of medication review with follow-up compared with usual care in primary care. Materials & methods: A cluster randomized controlled trial included patients if they were independent older adults, receiving five or more prescriptions, with moderate or high cardiovascular risk. Costs were estimated from the public healthcare sector perspective, and health benefits were measured as quality-adjusted life years. Both of which were used to calculate the incremental cost-effectiveness ratio. Results: Twelve centers completed the study, six (146 patients) in the intervention group and six (145 patients) in the control group. The base-case analysis showed an incremental cost-effectiveness ratio of US$ (2019) 434.4/quality-adjusted life year (95% CI 64.20-996.03). Conclusion: The intervention was cost-effective in the public primary care setting.

Cost-utility analysis of medication review with follow-up for cardiovascular outcomes: A microsimulation model.

BACKGROUND: Cardiovascular diseases are the leading cause of death. Pharmacist-led medication review with follow-up might be cost-effective preventing cardiovascular diseases. OBJECTIVE: To undertake a cost-utility analysis of the addition of pharmacist-led medication review with follow-up to usual care compared to usual care alone for cardiovascular outpatients. MATERIALS AND METHODS: A state-transition microsimulation model was built to project outcomes over a lifetime time horizon. Inputs from a cluster randomized controlled trial conducted in primary health care centers in Chile with full-time pharmacists were used. Probabilities were estimated using patient-level data. Utilities and costs associated with each health state were obtained from the literature, whereas the intervention costs were retrieved from the trial. The public third-party payer perspective was used. Uncertainty was evaluated through one-way and probabilistic sensitivity analyses. RESULTS: For the base case analysis, an incremental cost-effectiveness ratio of $963 per quality-adjusted life-year was observed which was considered cost-effective. The results were robust to sensitivity analyses and were driven by decreased cardiovascular events resulting in lower mortality. CONCLUSIONS: Medication review with follow-up was deemed a cost-effective addition to usual care with low uncertainty.

Primary health care pharmacists and vision for community pharmacy and pharmacists in Chile.

The Chilean healthcare system is composed of public and private sectors, with most of the higher-income population being covered privately. Primary healthcare in the public system is provided in more than 2,500 public primary care centers of different sizes with assigned populations within territories. Private insurance companies have their own healthcare networks or buy services from individual health providers. Patients from the public system receive most medications free of charge in primary care pharmacies embedded in each care center. Private patients must purchase their medicines from community pharmacies. Some government policies subsidize part of the cost of medications, but original medicines remain as the most expensive of Latin America. Three chain pharmacies have more than 90% of the market share, and these pharmacies have negative public perception because of price collusion court sentences. A non-profit, municipal pharmacy model was developed but has limited implementation. Most privately owned independent and chain community pharmacies do not provide pharmaceutical services as there is no remuneration or cover by insurers. The limited number of publicly owned Municipal pharmacies could implement pharmaceutical services in community settings as they are non-profit establishments and have full-time pharmacists but are not resourced for these services. A limited number of pharmaceutical services are almost exclusively provided in public primary care, including medication reviews, pharmaceutical education, home visits and pharmacovigilance services, but several barriers to their implementation remain. A risk-based multimorbidity care model was implemented in 2020 for public primary care with additional employment of part-time pharmacists to provide services. We believe that this model will help pharmacists to optimize their time by prioritizing the much-needed clinical tasks. We propose within this multimorbidity care model that the more time-consuming services are provided to higher risk patients. Pharmacy prescribing i.e. amending or approving changes in medications in primary care for chronic conditions could also be useful for the health system, but pharmacists would require additional training. The landscape for pharmaceutical services for primary care in Chile is promising, but the integration with community pharmacies will not be possible until they are funded by public and private insurance, and the public perception of these establishments is improved.

Economic evaluations of pharmacist-led medication review in outpatients with hypertension, type 2 diabetes mellitus, and dyslipidaemia: a systematic review.

OBJECTIVES: To evaluate the health economics evidence based on randomized controlled trials of pharmacist-led medication review in pharmacotherapy managed cardiovascular disease risk factors, specifically, hypertension, type-2 diabetes mellitus and dyslipidaemia in ambulatory settings and to provide recommendations for future evaluations. METHODS: A systematic review was carried out according to the Cochrane Handbook for Systematic Reviews. PubMed (Medline), Scopus, Web of Science, National Health System Economic Evaluation Database (NHS EED), Cochrane Library, and Econlit were searched and screened by two independent authors. Incremental cost-effectiveness ratio was the main outcome. Risk of bias was assessed with the Effective Practice and Organisation of Care tool by the Cochrane Collaboration. Economic evaluation quality was assessed with the he Consensus Health Economic Criteria list (CHEC list). RESULTS: 5636 records were found, and 174 were retrieved for full-text review yielding 11 articles. Eight articles deemed the intervention as cost effective and two as dominant. Two cost-utility analyses were performed yielding ICERs of $612.7 and $59.8 per QALY. Four articles were considered to perform a high-quality economic evaluation and four had a low risk of bias. Future economic evaluations should consider cost-utility analysis, to describe usual care thoroughly, and use time horizons that capture the effect of cardiovascular disease prevention, a societal perspective and uncertainty analysis. CONCLUSION: Pharmacist-led medication review has proven to be cost effective in various studies in different settings. Policy decision makers are advised to undertake local economic evaluations reflecting the gaps observed in this systematic review and published literature. If this is not possible, a transferability assessment should be conducted.

Systematic Review and Meta-Analysis of Medication Reviews Conducted by Pharmacists on Cardiovascular Diseases Risk Factors in Ambulatory Care.

Background Pharmacists-led medication reviews (MRs) are claimed to be effective for the control of cardiovascular diseases; however, the evidence in the literature is conflicting. The main objective of this meta-analysis was to analyze the impact of pharmacist-led MRs on cardiovascular disease risk factors overall and in different ambulatory settings while exploring the effects of different components of MRs. Methods and Results Searches were conducted in PubMed, Web of Science, Embase, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library Central Register of Controlled Trials database. Randomized and cluster randomized controlled trials of pharmacist-led MRs compared with usual care were included. Settings were community pharmacies and ambulatory clinics. The classification used for MRs was the Pharmaceutical Care Network Europe as basic (type 1), intermediate (type 2), and advanced (type 3). Meta-analyses in therapeutic goals used odds ratios to standardize the effect of each study, and for continuous data (eg, systolic blood pressure) raw differences were calculated using baseline and final values, with 95% CIs. Prediction intervals were calculated to account for heterogeneity. Sensitivity analyses were conducted to test the robustness of results. Meta-analyses included 69 studies with a total of 11 644 patients. Sample demographic characteristics were similar between studies. MRs increased control of hypertension (odds ratio, 2.73; 95% prediction interval, 1.05-7.08), type 2 diabetes mellitus (odds ratio, 3.11; 95% prediction interval, 1.17-5.88), and high cholesterol (odds ratio, 1.91; 95% prediction interval, 1.05-3.46). In ambulatory clinics, MRs produced significant effects in control of diabetes mellitus and cholesterol. For community pharmacies, systolic blood pressure and low-density lipoprotein values decreased significantly. Advanced MRs had larger effects than intermediate MRs in diabetes mellitus and dyslipidemia outcomes. Most intervention components had no significant effect on clinical outcomes and were often poorly described. CIs were significant in all analyses but prediction intervals were not in continuous clinical outcomes, with high heterogeneity present. Conclusions Intermediate and advanced MRs provided by pharmacists may improve control of blood pressure, cholesterol, and type 2 diabetes mellitus, as statistically significant prediction intervals were found. However, most continuous clinical outcomes failed to achieve statistical significance, with high heterogeneity present, although positive trends and effect sizes were found. Studies should use a standardized method for MRs to diminish sources of these heterogeneities.

Nonparametric spatial models for clustered ordered periodontal data.

Clinical attachment level (CAL) is regarded as the most popular measure to assess periodontal disease (PD). These probed tooth-site level measures are usually rounded and recorded as whole numbers (in mm) producing clustered (site measures within a mouth) error-prone ordinal responses representing some ordering of the underlying PD progression. In addition, it is hypothesized that PD progression can be spatially-referenced, i.e., proximal tooth-sites share similar PD status in comparison to sites that are distantly located. In this paper, we develop a Bayesian multivariate probit framework for these ordinal responses where the cut-point parameters linking the observed ordinal CAL levels to the latent underlying disease process can be fixed in advance. The latent spatial association characterizing conditional independence under Gaussian graphs is introduced via a nonparametric Bayesian approach motivated by the probit stick-breaking process, where the components of the stick-breaking weights follows a multivariate Gaussian density with the precision matrix distributed as G-Wishart. This yields a computationally simple, yet robust and flexible framework to capture the latent disease status leading to a natural clustering of tooth-sites and subjects with similar PD status (beyond spatial clustering), and improved parameter estimation through sharing of information. Both simulation studies and application to a motivating PD dataset reveal the advantages of considering this flexible nonparametric ordinal framework over other alternatives.

Primary health care pharmacists and vision for community pharmacy and pharmacists in Chile

The Chilean healthcare system is composed of public and private sectors, with most of the higher-income population being covered privately. Primary healthcare in the public system is provided in more than 2,500 public primary care centers of different sizes with assigned populations within territories. Private insurance companies have their own healthcare networks or buy services from individual health providers. Patients from the public system receive most medications free of charge in primary care pharmacies embedded in each care center. Private patients must purchase their medicines from community pharmacies. Some government policies subsidize part of the cost of medications, but original medicines remain as the most expensive of Latin America. Three chain pharmacies have more than 90% of the market share, and these pharmacies have negative public perception because of price collusion court sentences. A non-profit, municipal pharmacy model was developed but has limited implementation. Most privately owned independent and chain community pharmacies do not provide pharmaceutical services as there is no remuneration or cover by insurers. The limited number of publicly owned Municipal pharmacies could implement pharmaceutical services in community settings as they are non-profit establishments and have full-time pharmacists but are not resourced for these services. A limited number of pharmaceutical services are almost exclusively provided in public primary care, including medication reviews, pharmaceutical education, home visits and pharmacovigilance services, but several barriers to their implementation remain. A risk-based multimorbidity care model was implemented in 2020 for public primary care with additional employment of part-time pharmacists to provide services. We believe that this model will help pharmacists to optimize their time by prioritizing the much-needed clinical tasks. We propose within this multimorbidity care model that the more time-consuming services are provided to higher risk patients. Pharmacy prescribing i.e. amending or approving changes in medications in primary care for chronic conditions could also be useful for the health system, but pharmacists would require additional training. The landscape for pharmaceutical services for primary care in Chile is promising, but the integration with community pharmacies will not be possible until they are funded by public and private insurance, and the public perception of these establishments is improved

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Bayesian Kernel Mixtures for Counts.

Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.