Power laws, Pareto distributions and Zipf's law

When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf's law or the Pareto distribution. Power laws appear widely in physics, biology, earth and planetary sciences, economics and finance, computer science, demography and the social sciences. For instance, the distributions of the sizes of cities, earthquakes, solar flares, moon craters, wars and people's personal fortunes all appear to follow power laws. The origin of power-law behaviour has been a topic of debate in the scientific community for more than a century. Here we review some of the empirical evidence for the existence of power-law forms and the theories proposed to explain them.Comment: 28 pages, 16 figures, minor corrections and additions in this versio

Prescribing as affective clinical practice: Transformations in sexual health consultations through HIV pre-exposure prophylaxis

New medicines can transform routines and priorities in clinical practice, but how do clinicians think and feel about these changes, and how does it affect their work? In Australia, the HIV prevention regimen pre-exposure prophylaxis (PrEP) has been rapidly rolled out, transforming the sexual cultures and practices of users, but less attention has been given to the ways PrEP has reconfigured clinical practice. This paper draws on 28 qualitative semi-structured interviews conducted between 2019 and 2020 with PrEP-providing doctors and nurses in Australia to consider how they have affectively engaged with PrEP and put it into practice. Through a reflexive thematic analysis, we explore how clinicians adapted to PrEP, how the field of HIV prevention has been transformed, and how these developments have changed how clinicians approach patients. While the introduction of PrEP was initially received with uncertainty and shock, clinicians described PrEP as enjoyable to prescribe, and better aligned with the moral duties of sexual health consultations than existing HIV prevention strategies like condoms. Through approaching clinical work as an ‘affective practice’, we argue for attending not only to how new interventions change expectations and practices, but also how these changes are felt and valued by clinicians

Should adjustment for covariates be used in prevalence estimations?

Background Adjustment for covariates (also called auxiliary variables in survey sampling literature) is commonly applied in health surveys to reduce the variances of the prevalence estimators. In theory, adjusted prevalence estimators are more accurate when variance components are known. In practice, variance components needed to achieve the adjustment are unknown and their sample estimators are used instead. The uncertainty introduced by estimating variance components may overshadow the reduction in the variance of the prevalence estimators due to adjustment. We present empirical guidelines indicating when adjusted prevalence estimators should be considered, using gender adjusted and unadjusted smoking prevalence as an illustration. Methods We compare the accuracy of adjusted and unadjusted prevalence estimators via simulation. We simulate simple random samples from hypothetical populations with the proportion of males ranging from 30% to 70%, the smoking prevalence ranging from 15% to 35%, and the ratio of male to female smoking prevalence ranging from 1 to 4. The ranges of gender proportions and smoking prevalences reflect the conditions in 1999–2003 Behavioral Risk Factors Surveillance System (BRFSS) data for Massachusetts. From each population, 10,000 samples are selected and the ratios of the variance of the adjusted prevalence estimators to the variance of the unadjusted (crude) ones are computed and plotted against the proportion of males by population prevalence, as well as by population and sample sizes. The prevalence ratio thresholds, above which adjusted prevalence estimators have smaller variances, are determined graphically. Results In many practical settings, gender adjustment results in less accuracy. Whether or not there is better accuracy with adjustment depends on sample sizes, gender proportions and ratios between male and female prevalences. In populations with equal number of males and females and smoking prevalence of 20%, the adjusted prevalence estimators are more accurate when the ratios of male to female prevalences are above 2.4, 1.8, 1.6, 1.4 and 1.3 for sample sizes of 25, 50, 100, 150 and 200, respectively. Conclusion Adjustment for covariates will not result in more accurate prevalence estimator when ratio of male to female prevalences is close to one, sample size is small and risk factor prevalence is low. For example, when reporting smoking prevalence based on simple random sampling, gender adjustment is recommended only when sample size is greater than 200

Engaging Stigmatised Communities in Australia with Digital Health Systems: Towards Data Justice in Public Health

Introduction: In 2018, following government policy changes to Australia’s national electronic health record system, ‘My Health Record’, consumer advocates—including organisations representing people living with HIV, people who use drugs and sex workers—raised concerns about privacy and data security. Responding to these controversies, this study explores the practical, ethical and political complexities of engaging stigmatised communities with digital health systems. Methods: We conducted 16 qualitative semi-structured interviews in 2020 with key informants representing communities who experience stigma, discrimination and marginalisation in Australia. These communities included people living with HIV, sex workers, people who inject drugs, gay and bisexual men and transgender and gender diverse people. We conducted a reflexive thematic analysis. Results: Key informants were sceptical of proposed benefits of electronic health records for their communities, and concerned about privacy risks and the potential for discrimination. Meaningful consultation, consent mechanisms and tackling structural stigma were raised as solutions for engaging communities. Conclusions: Although communities could benefit from being included in digital health systems, significant cultural, legal and social reforms from government were believed to be necessary to build trust in digital health systems. We argue that these forms of data justice are necessary for effective future systems. Policy Implications: Engaging stigmatised communities—including in relation to gender, sexuality, sex work, drug use, HIV—requires a commitment to data justice. The design and implementation of digital health systems requires investment in ongoing and meaningful consultation with communities and representative organisations

Is sex lost in translation? Linguistic and conceptual issues in the translation of sexual and reproductive health surveys

Translated questionnaires are increasingly used in population health research. Nevertheless, translation is often not conducted with the same rigour as the process of survey development in the original language. This has serious limitations and may introduce bias in question relevance and meaning. This article describes and reflects on the process of translating a large and complex sexual and reproductive health survey from English into Simplified Chinese. We interrogated assumptions embedded in taken-for-granted translation practice to locate the sociocultural origins of these assumptions. We discuss how terminology and expression related to sexual and reproductive health may lose their conceptual or linguistic significance during translation in three different ways. Firstly, meanings can be lost in the negotiation of meanings associated with linguacultural and geographical variations of terminology. Secondly, meanings can be lost in the clash between everyday and professional sexual and reproductive health discourses. Thirdly, meanings can be lost due to the design of the source questionnaire and the intended mode of survey administration. We discuss ways to help overcome the unavoidable translation challenges that arise in the process of translating English sexual and reproductive health surveys for migrants from non-English speaking backgrounds

Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

A 19-SNP coronary heart disease gene score profile in subjects with type 2 diabetes: the coronary heart disease risk in type 2 diabetes (CoRDia study) study baseline characteristics

Background: The coronary risk in diabetes (CoRDia) trial (n = 211) compares the effectiveness of usual diabetes care with a self-management intervention (SMI), with and without personalised risk information (including genetics), on clinical and behavioural outcomes. Here we present an assessment of randomisation, the cardiac risk genotyping assay, and the genetic characteristics of the recruits. / Methods: Ten-year coronary heart disease (CHD) risk was calculated using the UKPDS score. Genetic CHD risk was determined by genotyping 19 single nucleotide polymorphisms (SNPs) using Randox’s Cardiac Risk Prediction Array and calculating a gene score (GS). Accuracy of the array was assessed by genotyping a subset of pre-genotyped samples (n = 185). / Results: Overall, 10-year CHD risk ranged from 2–72 % but did not differ between the randomisation groups (p = 0.13). The array results were 99.8 % concordant with the pre-determined genotypes. The GS did not differ between the Caucasian participants in the CoRDia SMI plus risk group (n = 66) (p = 0.80) and a sample of UK healthy men (n = 1360). The GS was also associated with LDL-cholesterol (p = 0.05) and family history (p = 0.03) in a sample of UK healthy men (n = 1360). / Conclusions: CHD risk is high in this group of T2D subjects. The risk array is an accurate genotyping assay, and is suitable for estimating an individual’s genetic CHD risk. / Trial registration: This study has been registered at ClinicalTrials.gov; registration identifier NCT0189178