The Makings of an Evidence-Based Local Health Department: Identifying Administrative and Management Practices

There is a gap in knowledge about how to best organize and administer practice in local health departments to implement sustained evidence-based policies, programs, and interventions. This report identifies administrative and management evidence-based practices to inform ongoing initiatives in local public health system quality improvement, accreditation processes, and performance. The article presents administrative elements in workforce development, leadership, organizational climate and culture, relationships and partnerships, and financial processes that local health departments can address at modest cost within a few years or less. Local public health systems can further identify, implement and evaluate evidence-based administrative practices

Developing a Tool to Assess Administrative Evidence-Based Practices in Local Health Departments

There is need for assessing the practices undertaken by local health departments in order to improve the implementation of evidence-based actions. This paper describes the development and testing of a survey instrument for assessing Administrative Evidence-Based Practices (A-EBPs) in Local Health Departments. A-EBPs identified through a review of the literature were used to develop a survey composed of nine sections and tested in a sample of local health department practitioners. The resulting tool showed adequate test-retest reliability and internal consistency. Practitioners and researchers may apply this tool in practice-based and evaluation research

What Influences the Use of Administrative Evidence-Based Practices in Local Health Departments?

Evidence based public health (EBPH) in local health departments (LHDs) is a process that involves translating the best available scientific evidence into practice. However, EBPH and implementation of evidence based programs and policies in LHDs are not widespread. This report outlines the patterns and predictors of the use of administrative evidence based practices (A-EBPs) in a national sample of LHD directors. LHDs can improve performance, prepare for accreditation and ultimately improve community health by utilizing an administrative evidence based process

Counseling versus antidepressant therapy for the treatment of mild to moderate depression in primary care: economic analysis.

OBJECTIVE: To compare the cost-effectiveness of generic psychological therapy (counseling) with routinely prescribed antidepressant drugs in a naturalistic general practice setting for a follow-up period of 12 months. METHODS: Economic analysis alongside a randomized clinical trial with patient preference arm. Comparison of depression-related health service costs at 12 months. Cost-effectiveness analysis of bootstrapped trial data using net monetary benefits and acceptability curves. RESULTS: No significant difference between the mean observed costs of patients randomized to antidepressants or to counseling (342 pounds sterling vs 302 pounds sterling , p = .56 [t test]). If decision makers are not willing to pay more for additional benefits (value placed on extra patient with good outcome, denoted by K, is zero), then we find little difference between the treatment modalities in terms of cost-effectiveness. If decision makers do place value on additional benefit (K > 0 pounds sterling), then the antidepressant group becomes more likely to be cost-effective. This likelihood is in excess of 90% where decision makers are prepared to pay an additional 2,000 pounds sterling or more per additional patient with a good global outcome. The mean values for incremental net monetary benefits (INMB) from antidepressants are substantial for higher values of K (INMB = 406 pounds sterling when K = 2,500 pounds sterling). CONCLUSION: For a small proportion of patients, the counseling intervention (as specified in this trial) is a dominant cost-effective strategy. For a larger proportion of patients, the antidepressant intervention (as specified in this trial) is the dominant cost-effective strategy. For the remaining group of patients, cost-effectiveness depends on the value of K. Since we cannot observe K, acceptability curves are a useful way to inform decision makers

Here Versus There: Creating British Sexual Politics Elsewhere

This reflection draws upon two recent ‘moments’ in British sexuality politics—a series of Parliamentary debates on Global LGBT rights and Brighton Pride’s campaign to ‘Highlight Global LGBT Communities’. It contrasts these two moments in order to demonstrate how, at a time when LGBT rights have ostensibly been ‘won’ in the UK, there is an increasing tendency to shift focus to the persecution of SOGI minorities elsewhere in the world. This shift in focus sets up a binary of here versus there that is politically persuasive but ultimately limited and limiting. By reflecting on the way that this growing trend of creating sexual politics elsewhere occurs in two very different locations in British politics and activism, we seek to begin a conversation about the relational affects of placing sexual politics ‘elsewhere’

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

The importance of different frailty domains in a population based sample in England

Background: The aim was to estimate the prevalence of frailty and relative contribution of physical/balance, nutritive, cognitive and sensory frailty to important adverse health states (falls, physical activity levels, outdoor mobility, problems in self-care or usual activities, and lack of energy or accomplishment) in an English cohort by age and sex. Methods: Analysis of baseline data from a cohort of 9803 community-dwelling participants in a clinical trial. The sample was drawn from a random selection of all people aged 70 or more registered with 63 general practices across England. Data were collected by postal questionnaire. Frailty was measured with the Strawbridge questionnaire. We used cross sectional, multivariate logistic regression to estimate the association between frailty domains and known correlates and adjusted for age. Some models were stratified by sex. Results: Mean age of participants was 78 years (sd 5.7), range 70 to 101 and 47.5% (4653/9803) were men. The prevalence of overall frailty was 20.7% (2005/9671) and there was no difference in prevalence by sex (Odds Ratio 0.98; 95% Confidence Interval 0.89 to 1.08). Sensory frailty was the most common and this was reported by more men (1823/4586) than women (1469/5056; Odds Ratio for sensory frailty 0.62, 95% Confidence Interval 0.57 to 0.68). Men were less likely than women to have physical or nutritive frailty. Physical frailty had the strongest independent associations with adverse health states. However, sensory frailty was independently associated with falls, less frequent walking, problems in self-care and usual activities, lack of energy and accomplishment. Conclusions: Physical frailty was more strongly associated with adverse health states, but sensory frailty was much more common. The health gain from intervention for sensory frailty in England is likely to be substantial, particularly for older men. Sensory frailty should be explored further as an important target of intervention to improve health outcomes for older people both at clinical and population level.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.08/14/41/DH_/Department of Health/United Kingdom Project number 08/14/41/Health Technology Assessment Programmepre-print, post-print, publisher's version/PD

Discovery of fevipiprant (NVP-QAW039), a potent and selective DP2 receptor antagonist for treatment of asthma

Further optimization of an initial DP2 receptor antagonist clinical candidate NVPQAV680 led to the discovery of a follow-up molecule 2-(2-methyl-1-(4-(methylsulfonyl)-2- (trifluoromethyl)benzyl)-1H-pyrrolo[2,3-b]pyridin-3-yl)acetic acid (compound 11, NVP-QAW039, fevipiprant), which exhibits improved potency on human eosinophils and Th2 cells, together with a longer receptor residence time, and is currently in clinical trials for severe asthma