Defining access without excess: expanding appropriate use of antibiotics targeting multidrug-resistant organisms.

Antimicrobial resistance remains a significant global public health threat. Although development of novel antibiotics can be challenging, several new antibiotics with improved activity against multidrug-resistant Gram-negative organisms have recently been commercialised. Expanding access to these antibiotics is a global public health priority that should be coupled with improving access to quality diagnostics, health care with adequately trained professionals, and functional antimicrobial stewardship programmes. This comprehensive approach is essential to ensure responsible use of these new antibiotics.

Reimbursement models to tackle market failures for antimicrobials: Approaches taken in France, Germany, Sweden, the United Kingdom, and the United States.

INTRODUCTION: The pipeline of new antibacterials remains limited. Reasons include low research investments, limited commercial prospects, and scientific challenges. To complement existing initiatives such as research grants, governments are exploring policy options for providing new market incentives to drug developers. MATERIALS AND METHODS: Reimbursement interventions for antibacterials in France, Germany, Sweden, US, and UK were reviewed and analysed by the authors. RESULTS: In France, Germany, and the US, implemented interventions centre on providing exceptions in cost-containment mechanisms to allow higher prices for certain antibacterials. In the US, also, certain antibacterials are granted additional years of protection from generic competition (exclusivity) and faster regulatory review. The UK is piloting a model that will negotiate contracts with manufacturers to pay a fixed annual fee for ongoing supply of as many units as needed. Sweden is piloting a model that will offer manufacturers of selected antibacterials contracts that would guarantee a minimum annual revenue. A similar model of guaranteed minimal annual revenues is under consideration in the US (PASTEUR Act). CONCLUSIONS: The UK and Sweden are piloting entirely novel procurement and reimbursement models. Existing interventions in the US, France, and Germany represent important, but relatively minor interventions. More countries should explore the use of novel models and international coordination will be important for 'pull' incentives to be effective. If adopted, the PASTEUR legislation in the US would constitute a significant 'pull' incentive.

A health professional's guide to the intersection of public health with intellectual property rights in trade and investment: the case of tobacco plain packaging.

Intellectual property law is a crucial determinant of global public health, capable of both endangering and facilitating advances in the health of populations. This Viewpoint explains the most important aspects of the interaction between intellectual property law and public health. We use the plain packaging of tobacco products to illustrate how public health policies may be subject to scrutiny under existing trade and investment law structures. Plain packaging of tobacco products is challenging to implement due to legal complexities and uncertainties surrounding the status of mandated plain packaging for consumer products. While the tobacco industry and its proponents once relied on the denial of scientific evidence to delay legislation and influence consumers, its tactics have shifted to the use of trade threats and investment disputes, directly challenging the sovereignty of governments to enact bona fide public health measures to improve the health of their population.

A Smart Mobile Health Tool Versus a Paper Action Plan to Support Self-Management of Chronic Obstructive Pulmonary Disease Exacerbations: Randomized Controlled Trial.

BACKGROUND: Many patients with chronic obstructive pulmonary disease (COPD) suffer from exacerbations, a worsening of their respiratory symptoms that warrants medical treatment. Exacerbations are often poorly recognized or managed by patients, leading to increased disease burden and health care costs. OBJECTIVE: This study aimed to examine the effects of a smart mobile health (mHealth) tool that supports COPD patients in the self-management of exacerbations by providing predictions of early exacerbation onset and timely treatment advice without the interference of health care professionals. METHODS: In a multicenter, 2-arm randomized controlled trial with 12-months follow-up, patients with COPD used the smart mHealth tool (intervention group) or a paper action plan (control group) when they experienced worsening of respiratory symptoms. For our primary outcome exacerbation-free time, expressed as weeks without exacerbation, we used an automated telephone questionnaire system to measure weekly respiratory symptoms and treatment actions. Secondary outcomes were health status, self-efficacy, self-management behavior, health care utilization, and usability. For our analyses, we used negative binomial regression, multilevel logistic regression, and generalized estimating equation regression models. RESULTS: Of the 87 patients with COPD recruited from primary and secondary care centers, 43 were randomized to the intervention group. We found no statistically significant differences between the intervention group and the control group in exacerbation-free weeks (mean 30.6, SD 13.3 vs mean 28.0, SD 14.8 weeks, respectively; rate ratio 1.21; 95% CI 0.77-1.91) or in health status, self-efficacy, self-management behavior, and health care utilization. Patients using the mHealth tool valued it as a more supportive tool than patients using the paper action plan. Patients considered the usability of the mHealth tool as good. CONCLUSIONS: This study did not show beneficial effects of a smart mHealth tool on exacerbation-free time, health status, self-efficacy, self-management behavior, and health care utilization in patients with COPD compared with the use of a paper action plan. Participants were positive about the supportive function and the usability of the mHealth tool. mHealth may be a valuable alternative for COPD patients who prefer a digital tool instead of a paper action plan. TRIAL REGISTRATION: ClinicalTrials.gov NCT02553096; https://clinicaltrials.gov/ct2/show/NCT02553096.

Validation of ACCESS: an automated tool to support self-management of COPD exacerbations.

BACKGROUND: To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO2), forced expiratory volume in one second (FEV1), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS. METHODS: We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO2, FEV1, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations. RESULTS: Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0-99.3), specificity of 65.6% (95% CI 63.5-67.6), and positive and negative predictive value of 13.4% (95% CI 11.2-15.9) and 99.8% (95% CI 99.3-99.9), respectively, for ACCESS' advice to contact a health care professional in case of an exacerbation. CONCLUSION: In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation.

Learning Bayesian networks for clinical time series analysis.

INTRODUCTION: Autonomous chronic disease management requires models that are able to interpret time series data from patients. However, construction of such models by means of machine learning requires the availability of costly health-care data, often resulting in small samples. We analysed data from chronic obstructive pulmonary disease (COPD) patients with the goal of constructing a model to predict the occurrence of exacerbation events, i.e., episodes of decreased pulmonary health status. METHODS: Data from 10 COPD patients, gathered with our home monitoring system, were used for temporal Bayesian network learning, combined with bootstrapping methods for data analysis of small data samples. For comparison a temporal variant of augmented naive Bayes models and a temporal nodes Bayesian network (TNBN) were constructed. The performances of the methods were first tested with synthetic data. Subsequently, different COPD models were compared to each other using an external validation data set. RESULTS: The model learning methods are capable of finding good predictive models for our COPD data. Model averaging over models based on bootstrap replications is able to find a good balance between true and false positive rates on predicting COPD exacerbation events. Temporal naive Bayes offers an alternative that trades some performance for a reduction in computation time and easier interpretation.

Describing disease processes using a probabilistic logic of qualitative time.

BACKGROUND: Clinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty. OBJECTIVES: It is investigated whether and how Allen's temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes. METHODS: To bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful. RESULTS: The probabilistic logic extension of Allen's temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem. CONCLUSION: The combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.

An autonomous mobile system for the management of COPD.

INTRODUCTION: Managing chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We have developed and evaluated a disease management system for patients with chronic obstructive pulmonary disease (COPD). Its aim is to predict and detect exacerbations and, through this, help patients self-manage their disease to prevent hospitalisation. MATERIALS: The carefully crafted intelligent system consists of a mobile device that is able to collect case-specific, subjective and objective, physiological data, and to alert the patient by a patient-specific interpretation of the data by means of probabilistic reasoning. Collected data are also sent to a central server for inspection by health-care professionals. METHODS: We evaluated the probabilistic model using cross-validation and ROC analyses on data from an earlier study and by an independent data set. Furthermore a pilot with actual COPD patients has been conducted to test technical feasibility and to obtain user feedback. RESULTS: Model evaluation results show that we can reliably detect exacerbations. Pilot study results suggest that an intervention based on this system could be successful.