Drug control, human rights, and the right to the highest attainable standard of health: a reply to Saul Takahashi

A recent article in this journal challenged claims that a human rights framework should be applied to drug control. This article questions the author’s assertions and reframes them in the context of socio-legal drug scholarship, aiming to build on the discourse concerning human rights and drug use. It is submitted that a rights-based approach is a necessary, indeed obligatory, ethical and legal framework through which to address drug use and that international human rights law provides the proper scope for determining where interferences with individual human rights might be justified on certain, limited grounds.

Health care rights and NHS rationing: turning theory into practice

How should we understand the nature of patients’ right in public health care systems? Are health care rights different to rights under a private contract for car insurance? This article distinguishes between public and private rights and the relevance of community interests and notions of social solidarity. It discusses the distinction between political and civil rights, and social and economic rights and the inherently political and redistributive nature of the latter. Nevertheless, social and economic rights certainly give rise to “rights” enforceable by the courts. In the UK (as in many other jurisdictions), the courts have favoured a “procedural” approach to the question, in which the courts closely scrutinise decisions and demand high standards of rationality from decision-makers. However, although this is the general rule, the article also discusses a number of exceptional cases where “substantive” remedies are available which guarantee patients access to the care they need.

Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications

BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.