Understanding non-recreational prescription medication-sharing behaviours: a systematic review

BACKGROUND

Prescription medication sharing refers to the lending or borrowing of prescription medications where the recipient is someone other than the person for whom the prescription is intended. Sharing prescription medication can cause significant harm. Adverse consequences include an increased risk of side effects, delayed health seeking, and severity of disease. Prevalence estimates vary across different populations and people's reasons for, and perceptions of risks from, sharing are poorly understood.

AIM

To better understand prescription medication-sharing behaviours and practices - specifically, the prevalence, types of medications, reasons, perceived benefits and risks, and factors associated with medication sharing.

DESIGN AND SETTING

This systematic review included primary studies in any setting, focusing on people who engage in medication sharing.

METHOD

Electronic databases were searched from inception of databases to February 2023.

RESULTS

In total, 19 studies were included. Prevalence of lifetime sharing ranged from 13% to 78%. All 19 studies reported that analgesics were the most shared, followed by antibiotics (n = 12) and allergy medication (n = 9). Common reasons for sharing were running out of medication (n = 7), cost (n = 7), and emergency (n = 6). Perceived benefits included resolution of the problem and convenience. Perceived risks included adverse drug reactions and misdiagnosis. Characteristics associated with sharing included age, female sex, having asthma, and unused medicines stored at home.

CONCLUSION

Findings suggest that medication-sharing behaviour is common and involves a range of medicines for a variety of reasons. Data on the prevalence and predictors of prescription medication sharing are inconsistent. A better understanding of non-modifiable and potentially modifiable behavioural factors that contribute to sharing is needed to support development of effective interventions aimed at mitigating unsafe sharing practices.

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design

Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof-of-concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno-inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint.

Borrowing from supplemental sources to estimate causal effects from a primary data source

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.

Differences in the Pattern of Non-Recreational Sharing of Prescription Analgesics among Patients in Rural and Urban Areas

Introduction: This study aimed to analyze differences in sharing of prescription analgesics between rural and urban populations. Methods: We surveyed 1000 participants in outpatient family medicine settings in Croatia. We used a 35-item questionnaire to analyze patients’ characteristics, pain intensity, prescription analgesic sharing behavior, and perception of risks regarding sharing prescription medications. Results: Prescription analgesic sharing was significantly more frequent in the rural (64%) than in the urban population 55% (p = 0.01). Participants from rural areas more commonly asked for verbal or written information than those from urban areas when taking others’ prescription analgesics (p < 0.001) or giving such analgesics (p < 0.001). Participants from rural areas more commonly informed their physician about such behavior compared to those from urban areas (p < 0.01), and they were significantly more often asked about such behavior by their physician (p < 0.01). Perceptions about risks associated with sharing prescription medication were similar between rural and urban populations. Conclusions: There are systematic differences in the frequency of prescription analgesics and associated behaviors between patients in family medicine who live in rural and urban areas. Patients from rural areas were more prone to share prescription analgesics. Future studies should examine reasons for differences in sharing prescription analgesics between rural and urban areas.

A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods

A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.

Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing

Assessment of efficacy in important subgroups - such as those defined by sex, age, race and region - in confirmatory trials is typically performed using separate analysis of the specific subgroup. This ignores relevant information from the complementary subgroup. Bayesian dynamic borrowing uses an informative prior based on analysis of the complementary subgroup and a weak prior distribution centred on a mean of zero to construct a robust mixture prior. This combination of priors allows for dynamic borrowing of prior information; the analysis learns how much of the complementary subgroup prior information to borrow based on the consistency between the subgroup of interest and the complementary subgroup. A tipping point analysis can be carried out to identify how much prior weight needs to be placed on the complementary subgroup component of the robust mixture prior to establish efficacy in the subgroup of interest. An attractive feature of the tipping point analysis is that it enables the evidence from the source subgroup, the evidence from the target subgroup, and the combined evidence to be displayed alongside each other. This method is illustrated with an example trial in severe asthma where efficacy in the adolescent subgroup was assessed using a mixture prior combining an informative prior from the adult data in the same trial with a non-informative prior.

Bayesian semiparametric meta-analytic-predictive prior for historical control borrowing in clinical trials

When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof-of-concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta-analytic-predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.

Exploring the determinants of distress health financing in Cambodia

Borrowing is a common coping strategy for households to meet healthcare costs in countries where social health protection is limited or non-existent. Borrowing with interest, hereinafter termed distress health financing or distress financing, can push households into heavy indebtedness and exacerbate the financial consequences of healthcare costs. We investigated distress health financing practices and associated factors among Cambodian households, using primary data from a nationally representative household survey of 5000 households. Multivariate logistic regression was used to determine factors associated with distress health financing. Results showed that 28.1% of households consuming healthcare borrowed to pay for that healthcare with 55% of these subjected to distress financing. The median loan was US$125 (US$200 for loans with interest and US$75 for loans without interest). Approximately 50.6% of healthcare-related loans were to pay for the costs of outpatient care in the past month, 45.8% for inpatient care and 3.6% for preventive care in the past 12 months. While the average period to pay off the loan was 8 months, 78% of households were still indebted from loans taken over 12 months before the survey. Distress financing is strongly associated with household poverty-the poorer the household the more likely it is to borrow, fall into debt and unable to pay off the debt-even for members of the health equity funds, a national scheme designed to improve financial access to health services for the poor. Other determinants of distress financing were household size, use of inpatient care and outpatient consultations with private providers or with both private and public providers. In order to ensure effective financial risk protection, Cambodia should establish a more comprehensive and effective social health protection scheme that provides maximum population coverage and prioritizes services for populations at risk of distress financing, especially poorer and larger households.

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient-level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.

'Sharing may not be caring' - Prescription medication sharing among adults in Saudi Arabia

OBJECTIVES

The aim of the current study was to describe prescription medication sharing behaviours (i.e. borrowing and lending) among adults living in Saudi Arabia.

METHODS

This cross-sectional survey was conducted in Saudi Arabia during December 2018. The eligibility criteria were an ability to communicate in Arabic or English and aged ≥18 years. An online survey was distributed to a convenience sample, supplemented by snowballing, by email and social media via an Internet link leading to a web-based survey platform in QuestionPro to assess their medication sharing behaviours. Bivariate and multivariate analyses were used to assess the associations.

KEY FINDINGS

A total of 667 completed the questionnaire. The prevalence of borrowing and lending prescription medication was found to be 14% and 16% in 2018 (past year), respectively. Twenty per cent of participants revealed that they had given a medication prescribed for one child to another child in their care, and 75% reported having leftover prescription medicine at home. The majority (90%) had borrowed or lent on one to three occasions. A wide range of medications were borrowed and lent mainly between immediate family members. Different reasons have been identified for medicine borrowing or lending behaviours.

CONCLUSIONS

Borrowing and lending prescription medications are a common practice in the Saudi population. Further research is warranted into the development of successful approaches or interventions to reduce medication sharing behaviour.

Risks associated with borrowing and sharing of prescription analgesics among patients observed by pain management physicians in Croatia: a qualitative study

Background

Understanding and improving patient safety is a key issue in medicine. One of the potential threats to patient safety is the sharing of medication among patients, which is a form of self-medication. This study analyzed experiences and attitudes of pain management physicians (PMPs) about sharing prescription analgesics among patients.

Methods

This qualitative study was conducted by semi-structured interviews among PMPs employed in Croatian pain clinics. The study involved two researchers and 15 PMPs.

Results

Among PMPs, 80% have seen patients who share their prescription analgesics with other patients for whom prescription is not intended. Most PMPs consider prescription analgesics sharing a risky and negative behavior. Some of them, however, found certain positive aspects associated to it, such as being a benevolent behavior, helping patients to get medications when they need them, and helping them cope with pain.

Conclusion

The majority of physicians specialized in pain management encountered patients sharing prescription analgesics. Most of them considered this as risky behavior with a number of potential consequences. It has been noted that this problem is neglected and that physicians should inquire about medication sharing. Direct-to-consumers advertising was perceived as a factor contributing to such behavior. Patient education and more involvement of physicians in identifying this behavior were cited as potential remedies for preventing sharing of prescription analgesics.

The power prior: theory and applications

The power prior has been widely used in many applications covering a large number of disciplines. The power prior is intended to be an informative prior constructed from historical data. It has been used in clinical trials, genetics, health care, psychology, environmental health, engineering, economics, and business. It has also been applied for a wide variety of models and settings, both in the experimental design and analysis contexts. In this review article, we give an A-to-Z exposition of the power prior and its applications to date. We review its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors. We review models for which it has been used, including generalized linear models, survival models, and random effects models. Statistical areas where the power prior has been used include model selection, experimental design, hierarchical modeling, and conjugate priors. Frequentist properties of power priors in posterior inference are established, and a simulation study is conducted to further examine the empirical performance of the posterior estimates with power priors. Real data analyses are given illustrating the power prior as well as the use of the power prior in the Bayesian design of clinical trials.

Networks of lexical borrowing and lateral gene transfer in language and genome evolution

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution.

Microhealth insurance and the risk coping strategies for the management of illness in Karnataka: a case study

Risk coping strategies adopted by the households in the event of illness depends on the accessibility to healthcare financing mechanisms including health insurance. The empirical evidence on the effect of microhealth insurance (MHI) on the risk coping strategies of the households is scarce. This paper evaluates the impact of Sampoorna Suraksha Program, a nongovernmental organization-initiated MHI scheme and the risk coping strategies of households faced with medical illness in Karnataka state, India. In this descriptive cross-sectional study, we collected data from 416 insured households, 366 newly insured households and 364 uninsured households in randomly selected 10 taluks in three districts of Karnataka state, India. We hypothesized that insured individuals rely less on ex post risk coping strategies (borrowing, use of savings and sale of assets) compared with uninsured and newly insured individuals. Our hypothesis was tested using logistic and linear regression analysis. A significant difference among insured, uninsured and newly insured individuals was found for borrowing but not in the use of savings or sale of assets. A positive impact of MHI on illness-induced borrowing (both incidence and amount) was evident. The evidence from this study reinforces the role of MHI as a pivotal financing alternative to out-of-pocket expenditure in India.

Use of historical control data for assessing treatment effects in clinical trials

Clinical trials rarely, if ever, occur in a vacuum. Generally, large amounts of clinical data are available prior to the start of a study, particularly on the current study's control arm. There is obvious appeal in using (i.e., 'borrowing') this information. With historical data providing information on the control arm, more trial resources can be devoted to the novel treatment while retaining accurate estimates of the current control arm parameters. This can result in more accurate point estimates, increased power, and reduced type I error in clinical trials, provided the historical information is sufficiently similar to the current control data. If this assumption of similarity is not satisfied, however, one can acquire increased mean square error of point estimates due to bias and either reduced power or increased type I error depending on the direction of the bias. In this manuscript, we review several methods for historical borrowing, illustrating how key parameters in each method affect borrowing behavior, and then, we compare these methods on the basis of mean square error, power and type I error. We emphasize two main themes. First, we discuss the idea of 'dynamic' (versus 'static') borrowing. Second, we emphasize the decision process involved in determining whether or not to include historical borrowing in terms of the perceived likelihood that the current control arm is sufficiently similar to the historical data. Our goal is to provide a clear review of the key issues involved in historical borrowing and provide a comparison of several methods useful for practitioners.