Competing and coexisting logics in the changing field of English general medical practice

Recent reforms, which change incentive and accountability structures in the English National Health Service, can be conceptualised as trying to shift the dominant institutional logic in the field of primary medical care (general medical practice) away from medical professionalism towards a logic of "population based medicine". This paper draws on interviews with primary care doctors, conducted during 2007–2009 and 2011–2012. It contrasts the approach of active management of populations, in line with recent reforms with responses to patients with medically unexplained symptoms. Our data suggest that rather than one logic becoming dominant, different dimensions of organisational activity reflect different logics. Although some aspects of organisational life are relatively untouched by the reforms, this is not due to ‘resistance’ on the part of staff within these organisations to attempts to ‘control’ them. We suggest that a more helpful way of understanding the data is to see these different aspects of work as governed by different institutional logics.     

Nonparametric bootstrap tests for independence of generalized errors

In this paper, we develop a general method of testing for independence when unobservable generalized errors are involved. Our method can be applied to testing for serial independence of generalized errors, and testing for independence between the generalized errors and observable covariates. The former can serve as a unified approach to testing the adequacy of time series models, as model adequacy often implies that the generalized errors obtained after a suitable transformation are independent and identically distributed. The latter is a key identification assumption in many nonlinear economic models. Our tests are based on a classical sample dependence measure, the Hoeffding–Blum–Kiefer–Rosenblatt‐type empirical process applied to generalized residuals. We establish a uniform expansion of the process, thereby deriving an explicit expression for the parameter estimation effect, which causes our tests not to be nuisance‐parameter‐free. To circumvent this problem, we propose a multiplier‐type bootstrap to approximate the limit distribution. Our bootstrap procedure is computationally very simple as it does not require a re‐estimation of the parameters in each bootstrap replication. Simulations and empirical applications to daily exchange rate data highlight the merits of our approach.     

Spillovers and Social Interaction Effects in the Demand for Preventive Healthcare: Evidence from the PROGRESA program

This paper exploits the randomized research design of a large welfare program–PROGRESA–to study the existence in rural Mexico of spillover effects in the propensity to screen for gender- and non-gender-specific conditions. I find significant evidence of increased demand for Papanicolaou cervical-cancer screening among women ineligible for the Conditional Cash Transfer, yet no evidence of similar externalities in non-gender specific tests, such as blood-pressure and blood-sugar checks. Certain pieces of evidence are suggestive of the weakening of the social norm related to husbands’ opposition to screening of their wives as one of the possible drivers of the indirect effect.     

Quality provision in hospital markets with demand inertia: The role of patient expectations

Switching costs and persistent preferences generate demand inertia and link current and future choices of hospital. Using a model of hospital competition with demand inertia, we investigate the effect of patient expectations on quality. We consider three types of expectations. Myopic patients choose a hospital based on current variables alone, forward-looking but naïve patients consider the future but assume that quality remains constant, and forward-looking and rational patients foresee the evolution of quality. We rank quality provision and show that it is higher under naïve than myopic expectations, while quality under rational expectations may be highest or lowest. This result also holds for patients’ health gains, suggesting that rationality may hurt patients. Additionally, policies to reduce switching costs lead to lower quality, possibly unless patients are rational and cost substitutability between output and quality is sufficiently strong. Finally, we show how optimal price regulation depends on expectations and switching costs.     

MiR133b is involved in endogenous hydrogen sulfide suppression of sFlt-1 production in human placenta

Increased production of soluble fms-like tyrosine kinase-1 (sFlt-1) from placenta is one of the major contributors to the development of preeclampsia. Our previous study has shown that hydrogen sulfide (H₂S) inhibits sFlt-1 release in placenta. In the present study, we sought to investigate whether endogenous H₂S affects sFlt-1 production and elucidate which H₂S-producing enzyme is responsible for its effect in placenta. It was found that, besides cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE), 3-mercaptopyruvatesulfurtransferase (3-MST) was identified in human placenta and mainly localized in syncytiotrophoblasts. There was no significant difference in expression level of 3-MST among preeclamptic and normal placentas. Treatment of cultured syncytiotrophoblasts with NaHS and l-cysteine suppressed sFlt-1 mRNA expression and caused a decrease in sFlt-1 protein content in culture media of the cells. Transfection of syncytiotrophoblasts with CBS siRNA and CSE siRNA reversed the above effects of l-cysteine. Furthermore, NaHS and l-cysteine treatment decreased the half-life of sFlt-1 mRNA and increased the expression of miR-133b targeting sFlt-1. MiR-133b expression was downregulated in preeclamptic placentas and correlated with the level of CBS and CSE. These results indicate that H₂S is an important regulatory factor in sFlt-1 production in placenta. Reduced H₂S generation in placenta contributes to development of preeclampsia by enhancing sFlt-1 production.     

Dosimetric benefits of robust treatment planning for intensity modulated proton therapy for base-of-skull cancers

PurposeThe clinical advantage of intensity modulated proton therapy (IMPT) may be diminished by range and patient setup uncertainties. We evaluated the effectiveness of robust optimization that incorporates uncertainties into the treatment planning optimization algorithm for treatment of base of skull cancers.Methods and materialsWe compared 2 IMPT planning methods for 10 patients with base of skull chordomas and chondrosarcomas: (1) conventional optimization, in which uncertainties are dealt with by creating a planning target volume (PTV); and (2) robust optimization, in which uncertainties are dealt with by optimizing individual spot weights without a PTV. We calculated root-mean-square deviation doses (RMSDs) for every voxel to generate RMSD volume histograms (RVHs). The area under the RVH curve was used for relative comparison of the 2 methods’ plan robustness. Potential benefits of robust planning, in terms of target dose coverage and homogeneity and sparing of organs at risk (OARs) were evaluated using established clinical metrics. Then the plan evaluation metrics were averaged and compared with 2-sided paired t tests. The impact of tumor volume on the effectiveness of robust optimization was also analyzed.ResultsRelative to conventionally optimized plans, robustly optimized plans were less sensitive for both targets and OARs. In the nominal scenario, robust and conventional optimization resulted in similar D_95% doses (D_95% clinical target volume [CTV]: 63.3 and 64.8 Gy relative biologic effectiveness [RBE]), P < .01]) and D_5%-D_95% (D_5%-D_95% CTV: 8.0 and 7.1 Gy[RBE], [P < .01); irradiation of OARs was less with robust optimization (brainstem V₆₀: 0.076 vs 0.26 cm³ [P < .01], left temporal lobe V₇₀: 0.22 vs 0.41 cm³, [P = .068], right temporal lobe V₇₀: 0.016 vs 0.11 cm³, [P = .096], left cochlea D_mean: 28.1 vs 30.1 Gy[RBE], [P = .023], right cochlea D_mean: 23.7 vs 25.2 Gy[RBE], [P = .059]). Results in the worst-case scenario were analogous.ConclusionsRobust optimization is effective for creating clinically feasible IMPT plans for tumors of the base of skull.     

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures,Masks, and Prediction Arrays

Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.