Anti-cholinergic load, health care utilization, and survival in people with advanced cancer: a pilot study.

INTRODUCTION: Anti-cholinergic medications have been associated with increased risks of cognitive impairment, premature mortality and increased risk of hospitalisation. Anti-cholinergic load associated with medication increases as death approaches in those with advanced cancer, yet little is known about associated adverse outcomes in this setting. METHODS: A substudy of 112 participants in a randomised control trial who had cancer and an Australia modified Karnofsky Performance Scale (AKPS) score (AKPS) of 60 or above, explored survival and health service utilisation; with anti-cholinergic load calculated using the Clinician Rated Anti-cholinergic Scale (modified version) longitudinally to death. A standardised starting point for prospectively calculating survival was an AKPS of 60 or above. RESULTS: Baseline entry to the sub-study was a mean 62 +/- 81 days (median 37, range 1-588) days before death (survival), with mean of 4.8 (median 3, SD 4.18, range 1 - 24) study assessments in this time period. Participants spent 22% of time as an inpatient. There was no significant association between anti-cholinergic score and time spent as an inpatient (adjusted for survival time) (p = 0.94); or survival time. DISCUSSION: No association between anti-cholinergic load and survival or time spent as an inpatient was seen. Future studies need to include cognitively impaired populations where the risks of symptomatic deterioration may be more substantial.

Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues.

For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.

The Effect of Slum Redevelopment on Child Health Outcomes: Evidence from Mumbai

As the population of urban poor living in slums increases, governments are trying to relocate people into government-provided free housing. Slum redevelopment affects every part of a household’s livelihood, but most importantly the health and wellbeing of younger generations. This paper investigates the effect of slum redevelopment schemes on child stunting levels. Data was collected in forty-one buildings under the slum-redevelopment program in Mumbai. The study demonstrates through a fixed effect regression analysis that an additional year of living in the building is associated with an increase in the height-for-age Z-score by 0.124 standard deviations. Possible explanations include an improvement in the overall hygienic environment, sanitation conditions, indoor air pollution, and access to health and water facilities. However, anecdotal evidence suggests that water contamination, loss of livelihood and increased expenses could worsen health outcomes for residents. This study prompts more research on the health effects of slum redevelopment projects, which are becoming increasingly common in the rapidly urbanizing developing world.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.

Role of patient factors, preferences, and distrust in health care and access to liver transplantation and organ donation.

Despite major improvements in access to liver transplantation (LT), disparities remain. Little is known about how distrust in medical care, patient preferences, and the origins shaping those preferences contribute to differences surrounding access. We performed a single-center, cross-sectional survey of adults with end-stage liver disease and compared responses between LT listed and nonlisted patients as well as by race. Questionnaires were administered to 109 patients (72 nonlisted; 37 listed) to assess demographics, health care system distrust (HCSD), religiosity, and factors influencing LT and organ donation (OD). We found that neither HCSD nor religiosity explained differences in access to LT in our population. Listed patients attained higher education levels and were more likely to be insured privately. This was also the case for white versus black patients. All patients reported wanting LT if recommended. However, nonlisted patients were significantly less likely to have discussed LT with their physician or to be referred to a transplant center. They were also much less likely to understand the process of LT. Fewer blacks were referred (44.4% versus 69.7%; P = 0.03) or went to the transplant center if referred (44.4% versus 71.1%; P = 0.02). Fewer black patients felt that minorities had as equal access to LT as whites (29.6% versus 57.3%; P < 0.001). For OD, there were more significant differences in preferences by race than listing status. More whites indicated OD status on their driver's license, and more blacks were likely to become an organ donor if approached by someone of the same cultural or ethnic background (P < 0.01). In conclusion, our analysis demonstrates persistent barriers to LT and OD. With improved patient and provider education and communication, many of these disparities could be successfully overcome. Liver Transplantation 22 895-905 2016 AASLD.