Unrepresentative Samples Research Articles

The connection between social capital and Chinese public health: an analysis of six Chinese general social surveys

This study examines the connection between social capital and the perceived health status for the Chinese public. Extending from previous research that uses unrepresentative samples, the current project analyzes nationally representative samples of approximately 60,000 respondents. The data are drawn from six waves of the Chinese General Social Survey conducted in 2010, 2012, 2013, 2015, 2017, and 2018, respectively. The multilevel regression approach is used for statistical estimation. Findings show a consistent pattern that social capital, encompassing social network and generalized trust, is positively related to one’s perceived health status across these years. In addition, the moderating impact of personal capital on the link between social capital and perceived health status is identified. Social capital has a greater health impact on those with lower personal capital than those with higher personal capital. This study complements current literature and has implications for public health policies.

Reconstructing redshift distributions with photometric galaxy clustering

The accurate determination of the true redshift distributions in tomographic bins is critical for cosmological constraints from photometric surveys. The proposed redshift self-calibration method, which utilizes the photometric galaxy clustering alone, is highly convenient and avoids the challenges from incomplete or unrepresentative spectroscopic samples in external calibration. However, the imperfection of the theoretical approximation on broad bins as well as the flaw of the algorithm in previous work [1] risk the accuracy and application of the method. In this paper, we propose the improved self-calibration algorithm that incorporates novel update rules, which effectively accounts for heteroskedastic weights and noisy data with negative values. The improved algorithm greatly expands the application range of self-calibration method and accurately reconstructs the redshift distributions for various mock data. Using the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) survey, we find that the reconstructed results are comparable to the state-of-the-art external calibration. This suggests the exciting prospect of using photometric galaxy clustering to reconstruct redshift distributions in the cosmological analysis of survey data.

Health and socioeconomic well-being of women with endometriosis and provoked vestibulodynia: Longitudinal insights from Swedish registry data.

Endometriosis and provoked vestibulodynia (PVD) are prevalent pain conditions among women of reproductive age, significantly impacting their quality of life and psychological well-being. However, comprehensive evidence regarding the lifelong health and socioeconomic outcomes for these individuals remains scarce. Additionally, many prior studies rely on limited and sometimes unrepresentative samples. This study aims to inform on the long-term consequences of these disorders by examining health, fertility, and employment outcomes in a cohort of women diagnosed with endometriosis and/or PVD, tracing their experiences from childhood to their 40s. Leveraging nationwide administrative data from Sweden and employing a matched case-control design, we investigate both similarities and differences between women with these diagnoses and those without. Our findings indicate that women diagnosed with endometriosis and/or PVD demonstrate elevated healthcare utilization patterns, commencing in their early teenage years and progressively increasing over time. Notably, disparities in labor market outcomes emerge in their 20s, showcasing lower labor earnings and a rise in sickness benefit receipt. Moreover, our results show a higher likelihood among these women to experience mental health disorders and concurrent chronic pain diseases, as well as infertility. While the association between endometriosis and infertility is well-documented, this study offers novel insights into a potential similar link between PVD and infertility. Our study informs healthcare professionals and policymakers about the considerable burden of compromised health, adverse psychosocial well-being, and reduced productivity in the labor market faced by young women with these common pain conditions. These findings underscore the urgency of addressing the multifaceted challenges encountered by individuals diagnosed with endometriosis and PVD across their lifespan.

On Combining Instance Selection and Discretisation: A Comparative Study of Two Combination Orders

Data discretisation focuses on converting continuous attribute values to discrete ones which are closer to a knowledge-level representation that is easier to understand, use, and explain than continuous values. On the other hand, instance selection aims at filtering out noisy or unrepresentative data samples from a given training dataset before constructing a learning model. In practice, some domain datasets may require processing with both discretisation and instance selection at the same time. In such cases, the order in which discretisation and instance selection are combined will result in differences in the processed datasets. For example, discretisation can be performed first based on the original dataset, after which the instance selection algorithm is used to evaluate the discrete type of data for selection, whereas the alternative is to perform instance selection first based on the continuous type of data, then using the discretiser to transfer the attribute type of values of a reduced dataset. However, this issue has not been investigated before. The aim of this paper is to compare the performance of a classifier trained and tested over datasets processed by these combination orders. Specifically, the minimum description length principle (MDLP) and ChiMerge are used for discretisation, and IB3, DROP3 and GA for instance selection. The experimental results obtained using ten different domain datasets show that executing instance selection first and discretisation second perform the best, which can be used as the guideline for the datasets that require performing both steps. In particular, combining DROP3 and MDLP can provide classification accuracy of 0.85 and AUC of 0.8, which can be regarded as the representative baseline for future related researches.

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

What Is Pharmacoepidemiology and How Does It Inform Clinical Care?

Pharmacoepidemiology (PE) is a hybrid of epidemiology and pharmacy that studies the patterns and associated benefits and risks of medicines used in a given population. This discipline evolved from the lack of clinically relevant information derived from small and unrepresentative human samples in clinical trials (usually randomized control trials), meaning that once a medicine reaches the market for use in a more diverse population, little is known about the efficacy and safety in the “real‐world.”

Mapping large European aspen (Populus tremula L.) in Finland using airborne lidar and image data

European aspen is a keystone species in boreal forests, which support numerous ecologically important and endangered species. As detection of those species by remote sensing is impossible, we instead investigated the detection of large aspen trees using airborne laser scanning and aerial image data. However, this is a challenge due to their low quantity and scattered occurrence. The performance was assessed with representative and unrepresentative (where aspens were over-represented) samples of the population. First, we detected individual trees and then the random forest (RF) classifier was used to identify large aspens. The RF classification was implemented with and without synthetic minority oversampling technique (SMOTE) to balance the training data due to the rarity of large aspens. At the tree-level, the best F1-score (0.44) was obtained when the unrepresentative plot data were used with SMOTE. However, the F1-score decreased to 0.21 when the representative data were used. The best plot-level (plots with at least one aspen tree) F1-score with the representative plot data was 0.41. We conclude that although data augmentation may improve the result, it is difficult to detect large aspen trees in genuine populations.

THE IMPORTANCE OF VACCINATION AGAINST MEASLES, RUBELLA AND MUMPS IN PREVENTING NEUROLOGICAL COMPLICATIONS IN CHILDREN

Childhood vaccination against measles, rubella and mumps has been a crucial measure in preventing neurological complications. These potentially debilitating and, in extreme cases, fatal diseases have significant impacts on public health. Among the most serious complications, the neurological consequences that can affect the cognitive and motor development of children stand out. With the introduction of large-scale immunization programs, there has been a substantial reduction in the incidence of these diseases, providing not only individual protection, but also contributing to the eradication of outbreaks in communities. Objective: To analyze scientific literature from the last 10 years to comprehensively understand how vaccination against measles, rubella and mumps impacts the prevention of neurological complications in children. Methodology: The review was conducted following the PRISMA checklist guidelines. We used the PubMed, Scielo and Web of Science databases to search for relevant articles. The descriptors were "vaccination", "measles", "rubella", "mumps" and "neurological complications". Inclusion criteria covered studies published in the last 10 years, focused on children and addressing post-vaccination neurological effects. Exclusion criteria included studies with unrepresentative samples and lack of detailed information on neurological outcomes. Results: The analysis revealed a significant reduction in neurological complications associated with measles, rubella and mumps after the implementation of vaccination programs. Studies have highlighted the effectiveness of vaccines in preventing encephalitis and other neurological sequelae. Additionally, a decline in hospitalizations related to these complications was observed. Conclusion: This systematic review reinforces the importance of vaccination in preventing neurological complications in children resulting from measles, rubella and mumps. Analysis of the last 10 years reinforces the effectiveness of vaccines, highlighting not only the individual benefits, but also the public health gains in preventing outbreaks and reducing the serious consequences associated with these diseases.

Pathways to Success, or Unfulfilled Dreams? An Examination of Pathway-Based Disparities in Graduation Rates

Research on pathway-based disparities in postsecondary education (PSE) has mounted in recent decades, indicating that nonlinear pathways may hamper students’ academic success. In Ontario, Canada, research on this topic remains largely inconclusive given the historically low quality of available data sources, with researchers commonly relying on small and unrepresentative samples, short observation windows, and a restricted range of academic and demographic controls. Through this study we reexamine pathway-based disparities in graduation rates using a custom administrative linkage that constitutes the best available evidence to study this topic in the province of Ontario. Our statistical analyses confirm the existence of a statistically significant relationship between the PSE pathways traveled by students and their odds of graduation, particularly among community college-to-university transfers. We conclude by considering the implications of these findings for both policymakers and future research in this space in Ontario and comparable jurisdictions.

USE OF ATHEROGENIC INDICES AS ASSESSMENT METHODS FOR CLINICAL ATHEROSCLEROTIC DISEASES

Accurate assessment of clinical atherosclerotic diseases is essential to guide effective therapeutic interventions, and atherogenic indices have emerged as valuable methods in this setting. The complexity of these pathologies demands approaches that go beyond the simple measurement of total cholesterol, requiring tools that consider the interaction between different lipoproteins and other risk factors. In this context, the use of atherogenic indices appears as a promising approach, providing a more comprehensive and refined assessment of atherosclerotic conditions. Objective: To comprehensively analyze scientific studies published in the last 10 years that investigated the use of atherogenic indices as methods of evaluating clinical atherosclerotic diseases. The review seeks to consolidate the available evidence by examining the effectiveness of these indices in early identification, risk stratification and monitoring the progress of atherosclerotic diseases. Methodology: The systematic review was conducted following the PRISMA guidelines. The PubMed, Scielo and Web of Science databases were consulted to identify relevant studies published in the last 10 years. The descriptors used were "atherogenic indices", "atherosclerotic diseases", "clinical assessment", "lipoproteins" and "cardiovascular risk factors". Inclusion criteria considered original studies that investigated the use of atherogenic indices in clinical populations, while exclusion criteria involved studies with unrepresentative samples and inadequate atherosclerotic assessment methods. Results: The results of the review highlight the diversity of available atherogenic indices and their usefulness in evaluating different aspects of atherosclerotic diseases, including prediction of cardiovascular events, risk stratification and treatment monitoring. The analysis identified indices that proved to be particularly sensitive and specific in different clinical contexts. Conclusion: In summary, the systematic review highlights the relevance of atherogenic indices as valuable tools in the assessment of clinical atherosclerotic diseases. The diversity of these indices and their ability to provide comprehensive information highlights their importance in clinical practice, contributing to a more refined and personalized approach to the management of these conditions.

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists.

Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of "auxiliary variables." A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment-subsampling, quasirandomization, poststratification, superpopulation modeling, a "doubly robust" procedure, and multilevel regression and poststratification-to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.

Exhaled breath of children swimmers conveniently collected in Teflon bags and used for trihalomethane determination by SPME-GC-MS.

Trihalomethanes (THMs) are the most common disinfection by-products in swimming pools; however, they exhibit strong cytotoxicity and genotoxicity, posing health risks. Children are more vulnerable to swimming-related health risks than adults; therefore, a rapid and accurate assessment of internal THM exposure in children swimmers is important for health risk assessment. For internal exposure measurement, collecting exhaled breath samples is more convenient, non-invasive, and easier to perform than collecting blood and urine. Therefore, this study aimed to develop a rapid, accurate, and reproducible method for determining THMs in children swimmers' exhaled breath using solid-phase microextraction (SPME)-gas chromatography-mass spectrometry (GC-MS). The factors influencing the pretreatment procedure, including selecting SPME fibers, extraction temperature, and time, were systematically evaluated. Under the optimized conditions, the instrumental linearity range was 1-200 ng L-1 with correlation coefficients >0.998. The limit of detection for this method was 0.3-0.5 ng L-1. The recovery values ranged between 76.87 and 111.49%. Detecting THMs at three different calibration levels using this method had an intra-day precision of 1.31-5.07%, while the inter-day precision was 1.59-11.10% (n = 6). Additionally, the SPME-GC-MS method was used to detect the concentration of THMs in children swimmers' exhaled breath before and after swimming. Trichloromethane was the most abundant THM in the air around the pool and children's exhaled breath, and THM concentrations in the children swimmers' exhaled breath increased significantly after swimming. This study found no significant differences in the concentrations of THMs in the children swimmers' exhaled breath with different swimming durations or frequencies, which may be a result of the unrepresentative sample population and small sample size, and more in-depth and comprehensive studies are needed to verify this conclusion.

Association between hours of work and subjective well-being. How do physicians compare to lawyers and accountants?

Analyses of physician well-being typically rely on small and unrepresentative samples. In April 2011, the UK Office for National Statistics incorporated subjective well-being metrics (SWB) into the Annual Population Survey (APS), a well-established survey. This survey includes variables from the labor market, making APS an ideal source for measuring the association between work hours and SWB metrics and comparing among different professionals. Using APS data from 2011/12 to 2014/15, this study examined the association between SWB levels and work hours using multiple linear models for physicians (primary care physicians and hospital doctors), lawyers, and accountants. Of the 11,810 observations, physicians were more satisfied, happier, and less anxious; females were more stressed (10.7%); and age was negatively associated with happiness and satisfaction. Incorporating information on preferences to work more hours (underemployment) did not affect physicians’ but worsened the well-being of other professionals (lawyers and accountants). Surveyed physicians were less anxious, happier, and more satisfied than lawyers or accountants before Covid. Although the total work hours did not alter the SWB metrics, overtime hours for other professionals did. Increasing the working hours of underemployed physicians (with appropriate compensation) could be a relatively inexpensive solution to tackle the shortage of health workers in the short run.

Comment on Gruntkowski, L.M.; Martinez, L.F. Online Grocery Shopping in Germany: Assessing the Impact of COVID-19. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 984–1002

Gruntkowski and Martinez examined the impact of factors such as perceived risk and perceived usefulness on German consumers’ intention to purchase groceries online once the COVID-19 pandemic had subsided. They also compared consumer perceptions before and during the COVID-19 outbreak. This comment shows that Gruntkowski and Martinez’s research suffers from a number of problems, the most important of which is the use of an unrepresentative sample. They should therefore have refrained from generalizing their findings to the German population.

Cybersickness as the virtual reality sickness questionnaire (VRSQ) measures it!? –an environment-specific revision of the VRSQ

Background: Virtual Reality (VR) does not only include the use of stereoscopic images, but also possibilities for an interaction with and participation in a computer-generated environment. However, laboratory studies primarily focus on the first part of the definition only. In this context, comparing results from different VR applications with diverging goals becomes difficult. This is especially true in the field of cybersickness research (visually induced motion sickness in VR), as self-report symptom questionnaires are used. The prominent Simulator Sickness Questionnaire (SSQ) is criticized for the lack of specificity, the double factorial loadings, the outdatedness, and the unrepresentative sample. VR-specific revisions like the Virtual Reality Sickness Questionnaire (VRSQ) address these criticisms but lack generalizability.Methods: The current paper uses a Confirmatory Factor Analysis of the VRSQ with data from three different VR environments and a sample size of N = 244. The environments had different setups, visual complexities, and interaction possibilities. These characteristics influenced the factorial structure of the VRSQ as a moderator. Furthermore, to control for VR-unrelated effects Baseline ratings were taken into account.Results: The Confirmatory Factor Analysis indicated a moderate fit for the global model, but a misspecification for two of the three environments. Only the environment similar to the original VRSQ paper converged with the model.Conclusions: In conclusion, a detailed description of the VR environment is required in scientific method reports. Focusing on VR accessibility for physically impaired in addition to healthy subjects, an added Baseline measurement can address the discriminant validity. Until generalizable VR-specific revisions of the SSQ are validated, the paper suggests using the Δ-SSQ in aggregated raw format.

Investigating Potential Racial Differences in Dementia‐Related Plasma Biomarkers

AbstractBackgroundBlack self‐identified individuals are disproportionately affected by dementia, a phenomenon best attributable to differences in social determinants of health (SDOH). Plasma protein levels can serve as antemortem indicators of dementia pathology; recent findings indicate potential race‐specific differences in these biomarkers of tau and non‐tau pathologies between Black self‐identified or White individuals.MethodsThis sample included 568 participants (77, 13.56% Black self‐identified, Table 1) from the Boston University Alzheimer’s Disease and Research Center (BUADRC). Participants completed a battery of neuropsychological tests and cognitive diagnoses were made during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using ELISA methods for YKL‐40 levels and using Simoa platform for phosphorylated tau (p‐tau 181, p‐tau231), total‐tau, glial fibrillary acidic (GFAP), and neurofilament light chain (NfL) levels. ANCOVAs tested for a cross‐sectional association between racial identity and each plasma biomarker and stratified by cognitive impairment status with covariates for age, education (in years), sex assigned at birth, and APOE ε4 carrier status. Cox proportional‐hazards models were used for longitudinal analyses of the interaction effect between racial identity and plasma biomarkers in the risk for conversion from normal cognition (NC) to mild cognitive impairment (MCI) and reversion from MCI to NC.ResultsBaseline YKL‐40 was lower among Black self‐identified participants (adjusted mean diff = 0.37 SD, p<.001); this association was particularly salient within the MCI group (adjusted mean diff = 0.62 SD, p<.001). The longitudinal analysis revealed interaction effects between race and GFAP for risk for conversion from NC to MCI and reversion from MCI to NC. Among White participants, higher GFAP predicted increased risk for conversion from NC to MCI and lower levels predicted reversion. However, no significant effects were found among Black self‐identified participants; these findings might be limited by sample size (n = 18) resulting from the high occurrence of baseline MCI diagnoses and subsequent lack of conversion or reversion among Black self‐identified participants.ConclusionIn the context of an unrepresentative sample, we found trivial differences in plasma biomarkers between Black self‐identified and White participants. Recruitment of Black self‐identified participants representative of the population and further exploration of SDOH is essential in understanding the disproportionate risk for dementia carried by Black self‐identified individuals.

Towards a better understanding of the mechanics of refactoring detection tools

Refactoring is a crucial practice used by many developers, available in popular IDEs, like Eclipse. Moreover, refactoring detection tools, such as RefDiff and RefactoringMiner, help improve the comprehension of refactoring application changes. In this article, we better understand to what extent refactoring detection tools (RefDiff and RefactoringMiner) identify refactoring operations that developers apply in practice. We survey with 53 developers of popular Java projects on GitHub. We asked them to identify six refactoring transformations applied to small programs. There is no unanimity in all questions of our survey. Refactoring detection tools do not detect many refactoring operations expected by developers. In 4 out of 6 questions, most developers prefer the Eclipse refactoring mechanics. The results highlight the importance of diving deep into the refactoring mechanics and defining a baseline. Empirical studies focused on mining refactoring operations may be limited by an incomplete or unrepresentative sample of such operations, thus posing a challenge for researchers in this field.

Female perpetrators of child sexual abuse in the United States

PurposeResearch on women who sexually abuse children is relatively scarce and tends to rely on small or unrepresentative convenience samples. The purpose of the current descriptive study is to examine characteristics female perpetrators of child sexual abuse using a large and contemporary dataset.Design/methodology/approachThis study analyzes data collected by the National Child Abuse and Neglect Data System, which is a census of all child protective services investigations or assessments conducted in all 50 states, as well as in the District of Columbia and Puerto Rico from October 1, 2018, to September 30, 2019.FindingsOnly substantiated cases of sexual abuse were analyzed (n = 51,442 cases). Overall, 7.6% of the perpetrators were female, though the percentage of female perpetrators varied dramatically across states from less than 1% to over 36%. Female perpetrators tended to have younger victims than did male perpetrators. For children aged 2 or less, female perpetrators constituted approximately 20% of the abusers. Female perpetrators were more likely to victimize male and female children, whereas male perpetrators predominantly had female victims. More female perpetrators had a prior finding of maltreatment than did male perpetrators (32.3% vs 23.5%).Research limitations/implicationsThese findings add to the limited research base on female perpetrators of child sexual abuse and highlight avenues for further research into the differences between male and female sexual abusers.Originality/valueThis study is a replication of previous research but also provides additional novel findings.

Examining Participant Adherence with Wearables in an In-the-Wild Setting.

Wearable devices offer a wealth of data for ubiquitous computing researchers. For instance, sleep data from a wearable could be used to identify an individual's harmful habits. Recently, devices which are unobtrusive in size, setup, and maintenance are becoming commercially available. However, most data validation for these devices come from brief, short-term laboratory studies or experiments which have unrepresentative samples that are also inaccessible to most researchers. For wearables research conducted in-the-wild, the prospect of running a study has the risk of financial costs and failure. Thus, when researchers conduct in-the-wild studies, the majority of participants tend to be university students. In this paper, we present a month-long in-the-wild study with 31 Japanese adults who wore a sleep tracking device called the Oura ring. The high device usage results found in this study can be used to inform the design and deployment of longer-term mid-size in-the-wild studies.

Epigenetic clocks andresearch implications ofthe lack ofdata onwhom they have been developed: a review ofreported andmissing sociodemographic characteristics.

Epigenetic clocks are increasingly being used as a tool to assess the impact of a wide variety of phenotypes and exposures on healthy ageing, with a recent focus on social determinants of health. However, little attention has been paid to the sociodemographic characteristics of participants on whom these clocks have been based. Participant characteristics are important because sociodemographic and socioeconomic factors are known to be associated with both DNA methylation variation and healthy ageing. It is also well known that machine learning algorithms have the potential to exacerbate health inequities through the use of unrepresentative samples - prediction models may underperform in social groups that were poorly represented in the training data used to construct the model. To address this gap in the literature, we conducted a review of the sociodemographic characteristics of the participants whose data were used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilizing data provided by individuals from different ages, sexes/genders, and racialized groups, sociodemographic characteristics are generally poorly reported. Reported information is limited by inadequate conceptualization of the social dimensions and exposure implications of gender and racialized inequality, and socioeconomic data are infrequently reported. It is important for future work to ensure clear reporting of tangible data on the sociodemographic and socioeconomic characteristics of all the participants in the study to ensure that other researchers can make informed judgements about the appropriateness of the model for their study population.