Exploring the main and moderating effects of individual-level characteristics on consumer responses to sugar taxes and front-of-pack nutrition labels in an experimental marketplace

InterventionThis study examined whether the impacts of sugar taxes and front-of-pack (FOP) nutrition labels differ across socio-demographic subgroups.Research questionWhat are the main and moderating effects of individual-level characteristics on the nutrient content of participants’ purchases in response to varying taxation levels and FOP labels?MethodsData from an experimental marketplace were analyzed. A sample of 3584 Canadians aged 13 years and older received $5 to purchase an item from a selection of 20 beverages and 20 snack foods. Participants were shown products with one of five FOP labels and completed eight within-subject purchasing tasks with different tax conditions. Linear mixed models were used to estimate the main and moderating effects of 11 individual-level variables on the sugars, sodium, saturated fats, and calorie content of participants’ purchases.ResultsParticipants who were younger, male, and more frequent consumers of sugary drinks purchased products containing more sugars, sodium, saturated fats, and calories. Sex and age moderated the relationship between tax condition and sugars or calories purchased: female participants were more responsive than males to a tax that included fruit juice, and younger participants were more responsive to all sugar tax conditions than older participants. Reported thirst and education level also moderated the relationship between tax condition and calories purchased. No individual-level characteristics moderated the effects of FOP labels.ConclusionA small proportion (7 of 176) of the moderating effects tested in this study were significant. Sugar taxes and FOP labelling policies may therefore produce similar effects across key socio-demographic groups.Supplementary InformationThe online version contains supplementary material available at 10.17269/s41997-021-00475-x.     

Universal HIV testing and treatment and HIV stigma reduction: a comparative thematic analysis of qualitative data from the HPTN 071 (PopART) trial in South Africa and Zambia

Despite continued development of effective HIV treatment, expanded access to care and advances in prevention modalities, HIV‐related stigma persists. We examine how, in the context of a universal HIV‐testing and treatment trial in South Africa and Zambia, increased availability of HIV services influenced conceptualisations of HIV. Using qualitative data, we explore people’s stigma‐related experiences of living in ‘intervention’ and ‘control’ study communities. We conducted exploratory data analysis from a qualitative cohort of 150 households in 13 study communities, collected between 2016 and 2018. We found that increased availability of HIV‐testing services influenced conceptualisations of HIV as normative (non‐exceptional) and the visibility of people living with HIV (PLHIV) in household and community spaces impacted opportunities for stigma. There was a shift in community narratives towards individual responsibility to take up (assumingly) widely available service – for PLHIV to take care of their own health and to prevent onward transmission. Based on empirical data, we show that, despite a growing acceptance of HIV‐related testing services, anticipated stigma persists through the mechanism of shifting responsibilisation. To mitigate the responsibilisation of PLHIV, heath implementers need to adapt anti‐stigma messaging and especially focus on anticipated stigma.     

Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.     

Exploratory assessment of treatment‐dependent random‐effects distribution using gradient functions

In analyzing repeated measurements from randomized controlled trials with mixed‐effects models, it is important to carefully examine the conventional normality assumption regarding the random‐effects distribution and its dependence on treatment allocation in order to avoid biased estimation and correctly interpret the estimated random‐effects distribution. In this article, we propose the use of a gradient function method in modeling with the different random‐effects distributions depending on the treatment allocation. This method can be effective for considering in advance whether a proper fit requires a model that allows dependence of the random‐effects distribution on covariates, or for finding the subpopulations in the random effects.     

A framework for considering the risk‐benefit trade‐off in designing noninferiority trials using composite outcome approaches

When a new treatment regimen is expected to have comparable or slightly worse efficacy to that of the control regimen but has benefits in other domains such as safety and tolerability, a noninferiority (NI) trial may be appropriate but is fraught with difficulty in justifying an acceptable NI margin that is based on both clinical and statistical input. To overcome this, we propose to utilize composite risk‐benefit outcomes that combine elements from domains of importance (eg, efficacy, safety, and tolerability). The composite outcome itself may be analyzed using a superiority framework, or it can be used as a tool at the design stage of a NI trial for selecting an NI margin for efficacy that balances changes in risks and benefits. In the latter case, the choice of NI margin may be based on a novel quantity called the maximum allowable decrease in efficacy (MADE), defined as the marginal difference in efficacy between arms that would yield a null treatment effect for the composite outcome given an assumed distribution for the composite outcome. We observe that MADE: (1) is larger when the safety improvement for the experimental arm is larger, (2) depends on the association between the efficacy and safety outcomes, and (3) depends on the control arm efficacy rate. We use a numerical example for power comparisons between a superiority test for the composite outcome vs a noninferiority test for efficacy using the MADE as the NI margin, and apply the methods to a TB treatment trial.     

Sampling‐based estimation for massive survival data with additive hazards model

For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample‐based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample‐based procedure can largely reduce the computational cost compared with the full data method. In numerical simulations, our method has low bias and satisfactory coverage probabilities. We provide an illustrative example on the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program.     

Impact of COVID‐19 on access to healthcare in low‐ and middle‐income countries: Current evidence and future recommendations

The COVID‐19 pandemic continues to be a major public health threat globally and low‐ and middle‐income countries (LMICs) are not an exception. The impact of the COVID‐19 pandemic is far‐reaching on many areas including but not limited to global health security, economic and healthcare delivery with a potential impact on access to healthcare in LMICs. We evaluate the impact of the COVID‐19 pandemic on access to healthcare in LMICs, as well as plausible strategies that can be put in place to ensure that the delivery of healthcare is not halted. In order to mitigate the devastating effect of the COVID‐19 pandemic on the already weak health systems in LMICs, it is much necessary to reinforce and scale up interventions and proactive measures that will ensure that access to healthcare is not disrupted even in course of the pandemic.     

Development of a composite indicator to prioritize districts for implementation of human immunodeficiency virus programmes in Maharashtra,India

A key recommendation of the National AIDS Control Programme‐IV of India was to develop new strategies for geo‐prioritization of the human immunodeficiency virus (HIV) epidemic. We conducted this study to categorize the districts in Maharashtra (India) based on a multidimensional framework for geo‐prioritization of services. Programmatic data on trends of HIV prevalence, coverage of marginalized populations and vulnerability factors were included. A composite indicator based on these was developed, and the cumulative score was calculated for each district. HIV prevalence among general population has declined steadily from 0.60% in 2007 to 0.33% in 2017. The programme coverage was stable but inadequate for men who have sex with men (MSM). The coverage for female sex workers (FSWs) was inadequate and reduced over time. Nine districts were categorized as high priority, 13 as moderate priority and 11 were classified as low‐priority districts based on burden and vulnerability for HIV. The high‐priority districts were Pune, Solapur and Yavatmal for FSW interventions and Pune, Thane and Latur for MSM interventions. This multidimensional indicator is based on existing programmatic data, dynamic and can be made state‐specific. It is useful to categorize and prioritize districts for allocation of resources and geo‐prioritization of services in resource limited settings.