Informing Equitable Prevention Practices: A Statewide Disaggregated Analysis of Suicide for Ethnoracially Minoritized Adolescents

The increase in adolescent suicide rates in the United States is a pervasive public health issue, and ethnoracial youth with diverse identities are disproportionately impacted, yet less studied. National planning efforts reinforce state-level approaches to suicide prevention through an equitable lens to prevent adolescent suicide. This study examined disaggregated state-level data over time to determine changes to suicide outcomes based on race/ethnicity, sex, sexual orientation, and the intersection of these identities and determined which sub-groups had higher odds of suicide outcomes. Data from the 1991-2019 Centers for Disease Control and Prevention Youth Risk Behavioral Surveillance System were analyzed for 17,419 ethnoracially minoritized high school adolescents in North Carolina. Descriptive analyses and multinominal logistic regression models were employed. Findings indicated that subgroups within categories of ethnoracial populations, specifically Black female adolescents unsure of their sexual orientation, reported higher rates of suicide attempts. Additionally, Multiracial adolescents reported higher means for suicide consideration and attempts over time. Recommendations for investigating state-level suicide data by focusing on diverse intersecting identities to illuminate areas for potential prevention efforts and support health equity are provided.

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.

Substantial but spatially heterogeneous progress in male circumcision for HIV prevention in South Africa

BACKGROUND

Voluntary medical male circumcision (VMMC) reduces the risk of male HIV acquisition by 60%. Programmes to provide VMMCs for HIV prevention have been introduced in sub-Saharan African countries with high HIV burden. Traditional circumcision is also a long-standing male coming-of-age ritual, but practices vary considerably across populations. Accurate estimates of circumcision coverage by age, type, and time at subnational levels are required for planning and delivering VMMCs to meet targets and evaluating their impacts on HIV incidence.

METHODS

We developed a Bayesian competing risks time-to-event model to produce region-age-time-type specific probabilities and coverage of male circumcision with probabilistic uncertainty. The model jointly synthesises data from household surveys and health system data on the number of VMMCs conducted. We demonstrated the model using data from five household surveys and VMMC programme data to produce estimates of circumcision coverage for 52 districts in South Africa between 2008 and 2019.

RESULTS

Nationally, in 2008, 24.1% (95% CI: 23.4-24.8%) of men aged 15-49 were traditionally circumcised and 19.4% (18.9-20.0%) were medically circumcised. Between 2010 and 2019, 4.25 million VMMCs were conducted. Circumcision coverage among men aged 15-49 increased to 64.0% (63.2-64.9%) and medical circumcision coverage to 42% (41.3-43.0%). Circumcision coverage varied widely across districts, ranging from 13.4 to 86.3%. The average age of traditional circumcision ranged between 13 and 19 years, depending on local cultural practices.

CONCLUSION

South Africa has made substantial, but heterogeneous, progress towards increasing medical circumcision coverage. Detailed subnational information on coverage and practices can guide programmes to identify unmet need to achieve national and international targets.

Behavioral intentions assessment of a disposable e-cigarette among adult current, former, and non-smokers in the United States

Modeling the public health effects of e-cigarettes requires estimates of the likelihood that different individuals and population subgroups will start using e-cigarettes and subsequently transition to and from combustible cigarette use. To begin to generate input values for modeling efforts, this study assessed adults' behavioral intentions in relation to a disposable e-cigarette, "BIDI® Stick." An online questionnaire assessed intentions to try and use a BIDI® Stick regularly in 11 flavor variants among United States (U.S.) nationally representative samples of adult (21+ years) non-smokers (n = 2284), current smokers (n = 2391), former smokers (n = 2241), and young adult (21-24 years) non-smokers (n = 1140) of combustible cigarettes following exposure to product information and images. Current smokers rated their intentions to use a BIDI® Stick to partially or completely replace cigarettes. Positive intention to try a BIDI® Stick at least once was, for each flavor variant, highest among current smokers (22.4%-28.1%), lower among former smokers (6.0%-9.7%) and non-smokers (3.4%-5.2%), and lowest among never-smokers (1.0%-2.4%). Among current smokers, former smokers, and non-smokers, trial and regular use intentions were lowest among e-cigarette non-users and e-cigarette never-users. Approximately 23.6% of current smokers reported an intention to use a BIDI® Stick in at least one flavor to completely switch from cigarettes and/or to reduce cigarette consumption. Low trial and regular use intentions suggest that U.S. adults who do not currently smoke cigarettes and/or use e-cigarettes are unlikely to initiate use of the BIDI® Stick e-cigarette. Trial and regular use intentions are highest among adults who currently smoke cigarettes and/or use e-cigarettes. A moderate proportion of current smokers may try using a BIDI® Stick e-cigarette as a partial or complete replacement for combustible cigarettes.

An integrative analytical framework to identify healthy, impactful, and equitable foods: a case study on 100% orange juice

To identify healthy, impactful, and equitable foods, we combined health scores from six diverse nutrient profiling systems (NPS) into a meta-framework (meta-NPS) and paired this with dietary guideline adherence assessment via multilevel regression and poststratification. In a case-study format, a commonly debated beverage formulation - 100% orange juice (OJ) - was chosen to showcase the utility and depth of our framework, systematically scoring high across multiple food systems (i.e. a Meta-Score percentile = 93rd and Stability percentile = 75th) and leading to an expected increase of US dietary fruit guideline adherence by ∼10%. Moreover, the increased adherence varies across the 300 sociodemographic strata, with the benefit patterns being sensitive to absolute or relative quantification of the difference of adherence affected by OJ. In sum, the adaptable, integrative framework we established deepens the science of nutrient profiling and dietary guideline adherence assessment while shedding light on the nuances of defining equitable health effects.

Multimorbidity patterns and health-related quality of life in Jamaican adults: a cross sectional study exploring potential pathways

Introduction

Multimorbidity and health-related quality of life (HRQoL) are intimately linked. Multiple chronic conditions may adversely affect physical and mental functioning, while poorer HRQoL may contribute to the worsening course of diseases. Understanding mechanisms through which specific combinations of diseases affect HRQoL outcomes can facilitate identification of factors which are amenable to intervention. Jamaica, a middle-income country with high multimorbidity prevalence, has a health service delivery system dominated by public sector provision via a broad healthcare network. This study aims to examine whether multimorbidity classes differentially impact physical and mental dimensions of HRQoL in Jamaicans and quantify indirect effects on the multimorbidity-HRQoL relationship that are mediated by health system factors pertaining to financial healthcare access and service use.

Materials and methods

Latent class analysis (LCA) was used to estimate associations between multimorbidity classes and HRQoL outcomes, using latest available data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 (N = 2,551). Multimorbidity measurement was based on self-reported presence/absence of 11 non-communicable diseases (NCDs). HRQoL was measured using the 12-item short-form (SF-12) Health Survey. Mediation analyses guided by the counterfactual approach explored indirect effects of insurance coverage and service use on the multimorbidity-HRQoL relationship.

Results

LCA revealed four profiles, including a Relatively Healthy class (52.7%) characterized by little to no morbidity and three multimorbidity classes characterized by specific patterns of NCDs and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Compared to the Relatively Healthy class, Vascular-Inflammatory class membership was associated with lower physical functioning (β = -5.5; p < 0.001); membership in Vascular-Inflammatory (β = -1.7; p < 0.05), and Respiratory (β = -2.5; p < 0.05) classes was associated with lower mental functioning. Significant mediated effects of health service use, on mental functioning, were observed for Vascular-Inflammatory (p < 0.05) and Respiratory (p < 0.05) classes.

Conclusion

Specific combinations of diseases differentially impacted HRQoL outcomes in Jamaicans, demonstrating the clinical and epidemiological value of multimorbidity classes for this population, and providing insights that may also be relevant to other settings. To better tailor interventions to support multimorbidity management, additional research is needed to elaborate personal experiences with healthcare and examine how health system factors reinforce or mitigate positive health-seeking behaviours, including timely use of services.

Comparison of Cognitive and Physical Decline as Predictors of Depression Among Older Adults

Activities of daily living (ADL) limitations and cognitive impairment have been identified as key risk factors for depression among older adults. However, little has been done to compare the strength of these relationships. The current study describes the prevalence and compares the independent and joint associations of ADL and cognitive limitations with depression among older adults in the US. Analyses are based on a sample of 30,923 observations on 13,545 unique respondents from three waves (2012, 2014, and 2016) of the Health and Retirement Study. Linear and logistic multivariate regression models with random and individual fixed effects were estimated. Findings indicate that both cognitive and ADL limitations are associated with depression; however, across all models, ADL limitations have a much stronger association. Further, in our most rigorous models, having both limitations is not significantly different from having just ADL, and not cognitive, limitations.

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005-2012

BACKGROUND

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated.

OBJECTIVES

To discover obesity-related mixed chemical exposure patterns in the general U.S.

METHODS

Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR).

RESULTS

Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity.

CONCLUSION

Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

Differential richness inference for 16S rRNA marker gene surveys

Background

Individual and environmental health outcomes are frequently linked to changes in the diversity of associated microbial communities. Thus, deriving health indicators based on microbiome diversity measures is essential. While microbiome data generated using high-throughput 16S rRNA marker gene surveys are appealing for this purpose, 16S surveys also generate a plethora of spurious microbial taxa.

Results

When this artificial inflation in the observed number of taxa is ignored, we find that changes in the abundance of detected taxa confound current methods for inferring differences in richness. Experimental evidence, theory-guided exploratory data analyses, and existing literature support the conclusion that most sub-genus discoveries are spurious artifacts of clustering 16S sequencing reads. We proceed to model a 16S survey’s systematic patterns of sub-genus taxa generation as a function of genus abundance to derive a robust control for false taxa accumulation. These controls unlock classical regression approaches for highly flexible differential richness inference at various levels of the surveyed microbial assemblage: from sample groups to specific taxa collections. The proposed methodology for differential richness inference is available through an R package, Prokounter.

Conclusions

False species discoveries bias richness estimation and confound differential richness inference. In the case of 16S microbiome surveys, supporting evidence indicate that most sub-genus taxa are spurious. Based on this finding, a flexible method is proposed and is shown to overcome the confounding problem noted with current approaches for differential richness inference.

Package availability: https://github.com/mskb01/prokounter

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02722-x.

Association between living arrangements and cognitive decline in older adults: A nationally representative longitudinal study in China

Background

Living arrangements are critical to the survival and well-being of older people, especially in China where the filial piety culture demands adult children care for and serve their parents. The study aimed to explore the association between living arrangements and cognitive decline among older people in China.

Methods

Participants included 6,074 older adults over 60 years old (49.65% male, mean age 67.2 years [range 60–98]) from four waves (2011–2018) of the China Health and Retirement Longitudinal Study. Two to four assessments were conducted over a follow-up of an average of 5.3 years (range, 2–7). Cognitive function was assessed using an adapted Chinese version of Mini-Mental State Examination (MMSE). Living arrangements were classified as follows: living alone, living with spouse, living with adult children, living with spouse and adult children and living with others. Multilevel models were used to investigate the relationship between living arrangements and cognitive decline, as well as the gender difference.

Results

As the main type of living arrangements of the study participants (44.91%), living with spouse was taken as the reference group. Compared to the reference group, living alone (β=-0.126, P < 0.001), living with adult children (β=-0.136, P < 0.001), living with spouse and adult children (β=-0.040, P < 0.05) and living with others (β=-0.155, P < 0.05) were all related to a faster rate of cognitive decline. Further, the association between living arrangements and cognitive decline varied by gender. Living alone (β=-0.192, P < 0.001) was associated with a faster cognitive decline only in older men. Living with spouse and adult children (β=-0.053, P < 0.05) and living with others (β=-0.179, P < 0.05) were associated with faster cognitive decline only in older women.

Conclusion

This study suggests that living arrangements in older people in China were associated with cognitive decline, and these associations varied by gender. Greater attention to living arrangements might yield practical implications for preserving the cognitive function of the older population.

Virtual Care and Electronic Patient Communication During COVID-19: Cross-sectional Study of Inequities Across a Canadian Tertiary Cancer Center

Background

Virtual care (VC) visits (telephone or video) and email-based patient communication have been rapidly adopted to facilitate cancer care during the COVID-19 pandemic. Inequities in access and patient experience may arise as these digital health tools become prevalent.

Objective

We aimed to characterize inequities in access and patient-reported experience following adoption of digital health tools at a tertiary cancer center during the COVID-19 pandemic.

Methods

We designed a cross-sectional study of outpatients with visits from September to December 2020. Patient characteristics and responses to an email-based patient-experience survey were collated. Inequities in access were assessed across three pairs of comparison groups: (1) patients with VC and in-person visits, (2) patients with and without documented email addresses, and (3) responders and nonresponders to the survey. Inequities in patient-reported experience were assessed among survey responders. Demographics were mapped to area-level averages from national census data. Socioeconomic status was mapped to area-level dimensions of the Canadian Index of Multiple Deprivation. Covariate balance between comparison groups was assessed using standardized mean differences (SMDs), with SMD≥0.2 indicating differences between groups. Associations between patient experience satisfaction scores and covariates were assessed using multivariable analyses, with P<.05 indicating statistical significance.

Results

Among the 42,194 patients who had outpatient visits, 62.65% (n=26,435) had at least one VC visit and 31.15% (n=13,144) were emailable. Access to VC and email was similar across demographic and socioeconomic indices (SMD<0.2). Among emailable patients, 21.84% (2870/13,144) responded to the survey. Survey responsiveness was similar across indices, aside from a small difference by age (SMD=0.24). Among responders, 24.4% received VC and were similar to in-person responders across indices (SMD<0.2). VC and in-person responders had similar satisfaction levels with all care domains surveyed (all P>.05). Regardless of visit type, patients had variable satisfaction with care domains across demographic and socioeconomic indices. Patients with higher ethnocultural composition scores were less satisfied with the cultural appropriateness of their care (odds ratio [OR] 0.70, 95% CI 0.57-0.86). Patients with higher situational vulnerability scores were less satisfied with discussion of physical symptoms (OR 0.67, 95% CI 0.48-0.93). Patients with higher residential instability scores were less satisfied with discussion of both physical (OR 0.81, 95% CI 0.68-0.97) and emotional (OR 0.86, 95% CI 0.77-0.96) symptoms, and also with the duration of their visit (OR 0.85, 95% CI 0.74-0.98; P=.02). Male patients were more satisfied with how their health care provider had listened to them (OR 1.64, 95% CI 1.11-2.44; P=.01).

Conclusions

Adoption of VC and email can equitably maintain access and patient-reported experience in cancer care across demographics and socioeconomic indices. Existing health inequities among structurally marginalized patients must continue to be addressed to improve their care experience.

Examining Adult E-cigarette Use in Kentucky and Its Appalachian Region Using the Behavioral Risk Factor Surveillance System, 2016-2017

OBJECTIVES

Electronic cigarette (e-cigarette) use has increased steadily in the United States, but little research has examined its prevalence in states comprising Appalachia, a rural region known for high rates of tobacco use. This study assessed lifetime and current e-cigarette use among adults by sociodemographic characteristics, geographic region, and cigarette smoking in Kentucky, with a focus on the Appalachian region.

METHODS

We used data from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) surveys to calculate the prevalence of lifetime and current e-cigarette use, and we used weighted multivariable logistic regression analyses to examine the relative influence of other factors.

RESULTS

Among adults in Kentucky, 5.8% (95% CI, 5.2%-6.4%) were current e-cigarette users and 27.0% (95% CI, 25.9%-28.0%) were lifetime users, compared with state medians of 4.6% (95% CI, 4.0%-5.1%) and 21.4% (95% CI, 19.4%-23.5%) for the United States. Multivariable regression models showed similar patterns for all regions: higher prevalence odds of current e-cigarette use among adults aged 18-24, current conventional smokers, and adults unable to work. Generally, Appalachian residents of Kentucky did not have significantly higher rates of lifetime or current e-cigarette use as compared with other non-Appalachian residents of Kentucky. Hispanic residents of Appalachian Kentucky, however, had higher rates of e-cigarette use than Hispanic residents of other regions of Kentucky.

CONCLUSIONS

Rates of e-cigarette use were higher in Kentucky than in the United States but were not further elevated in Kentucky's Appalachian region. High rates of e-cigarette use among Hispanic residents of Appalachia indicate a need to focus future interventions in the region.

Subjective Freedom of Speech: Why Do Citizens Think They Cannot Speak Freely?

We provide the first systematic research into the origins of subjective freedom of speech in Germany. Relying on the GLES 2021 Cross-Section Pre-Election Survey, which includes a newly designed survey item on subjective freedom of speech, we evaluate a whole range of plausible candidate hypotheses. First, we contribute to cumulative research by testing the explanatory factors in Gibson (1993)-citizens' social class, their political involvement and political preferences, and their personality dispositions-for the German case. Second, we move beyond the state of the art and test three new hypotheses that reflect more recent political developments and arguments in the free speech debate: the role of social media, increasing political and social polarization, and the rise of populism. Importantly, all hypothesis tests reported in this paper have been preregistered prior to data collection. Our results reveal that three explanatory factors are significantly, consistently, and substantively related to subjective free speech in Germany: political preferences, populist attitudes, and identification with the Alternative for Germany party.

SUPPLEMENTARY INFORMATION

The online version of this article (10.1007/s11615-022-00414-6) contains supplementary material, which is available to authorized users.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

How do Europeans quit using tobacco, e-cigarettes and heated tobacco products? A cross-sectional analysis in 28 European countries

OBJECTIVES

While smoking tobacco remains a substantial cause of harm in Europe, novel products such as electronic cigarettes or e-cigarettes (ECs) and heated tobacco products (HTPs) have entered the market recently. While debate still persists over the role of these novel products, they are now in widespread use. This study aimed to explore the prevalence and methods of attempts to quit EC and HTP.

SETTING

We analysed the 2020 Eurobarometer survey, which collected data in 28 European countries.

PARTICIPANTS

A representative sample of individuals residing in these countries aged ≥15 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Multilevel regression analyses were performed to assess differences in quit attempts and cessation methods among tobacco smokers and exclusive EC/HTP users separately.

RESULTS

51.1% of current tobacco smokers and 27.1% of exclusive EC or HTP users reported having ever made a quit attempt. The majority of former and current smokers (75.8%) who made a quit attempt did so unassisted, with 28.8% reporting at least one attempt using a cessation aid. The most popular cessation aids were nicotine replacement therapy or other medication (13.4%) and ECs (11.3%). 58.8% of exclusive EC or HTP users who had made a quit attempt did so unassisted, with 39.5% reporting the use of a cessation aid.

CONCLUSION

Most EC and HTP users in Europe try to quit unassisted, although more of them report the use of a cessation aid compared with tobacco smokers. Cessation support services should take into consideration the increasing numbers of users of EC and HTP who may be trying to quit.

Validation and generalizability of machine learning prediction models on attrition in longitudinal studies

Attrition in longitudinal studies is a major threat to the representativeness of the data and the generalizability of the findings. Typical approaches to address systematic nonresponse are either expensive and unsatisfactory (e.g., oversampling) or rely on the unrealistic assumption of data missing at random (e.g., multiple imputation). Thus, models that effectively predict who most likely drops out in subsequent occasions might offer the opportunity to take countermeasures (e.g., incentives). With the current study, we introduce a longitudinal model validation approach and examine whether attrition in two nationally representative longitudinal panel studies can be predicted accurately. We compare the performance of a basic logistic regression model with a more flexible, data-driven machine learning algorithm—gradient boosting machines. Our results show almost no difference in accuracies for both modeling approaches, which contradicts claims of similar studies on survey attrition. Prediction models could not be generalized across surveys and were less accurate when tested at a later survey wave. We discuss the implications of these findings for survey retention, the use of complex machine learning algorithms, and give some recommendations to deal with study attrition.

Semiparametric count data regression for self-reported mental health

''For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: the data are overdispersed, zero-inflated, bounded by 30, and heaped in five- and seven-day increments. To address these challenges-which are especially common for health questionnaire data-we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (star) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an EM algorithm that is compatible with any continuous data model estimable by least squares. star regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. Using star regression, we identify key factors associated with self-reported mental health and demonstrate substantial improvements in goodness-of-fit compared to existing count data regression models.

Drinking in Mexico by Whites and Hispanics on and off the US/Mexico border in California

This paper compares drinking patterns of Whites and Hispanics who after crossing the U.S./Mexico border drink and do not drink in Mexico. Data came from a household survey of 1,209 adults 18 to 39 years of age in California. Residence near the US/Mexico border increases the likelihood of drinking in Mexico (AOR = 4.57; 95%CI = 2.45-8.52; p < .001). Hispanics (AOR = 1.91; 95%CI = 1.26-2.90; p < .01), those who drink more frequently (AOR = 1.05; 95%CI = 1.02-1.09; p < .01) and those who drink six or more drinks in day (AOR = 1.91; 95%CI = 1.26-2.29; p < .01) are more likely than Whites and lighter drinkers to report this behavior. Crossing the U.S./Mexico border to drink is influenced by living close to the border, Hispanic ethnicity, and drinking many drinks in a day.

A network examination of risky behaviours in a state-level and national epidemiological sample of high school students

AIM

Engagement in risky behaviours, including substance use, disordered eating, suicidal behaviour, and peer victimization/violence, during adolescence is becoming increasingly prevalent. These risky behaviours are highly comorbid and associated with long-term consequences for health, relationships, and socioeconomic status, representing an important public health concern. Past research has primarily investigated risky behaviours in adolescence using latent variable models, which are based on assumptions that may limit insight into the complex reality of these behaviours.

METHODS

The current study uses network analysis to examine adolescent substance use, disordered eating, suicide risk, and peer victimization/violence in a national (N = 29 008) and state-level (Kentucky; N = 3455) epidemiological dataset. We calculated central and bridge symptoms and compared network structure based on demographic factors (race, sex, grade) and sample (state vs. nation).

RESULTS

The most central symptoms were suicidal ideation and attempts, stimulant drug use, and prescription drug misuse. The most central bridge symptoms were depression, methamphetamine use, peer violence, and suicide attempts. There were no differences in network structure between samples or across demographic factors in the Kentucky sample. There were differences in network structure across sex and race in the national dataset.

CONCLUSIONS

These findings suggest stimulant use, suicidal ideation, depression, and peer violence may contribute to the high rates and co-occurrence of risky behaviours in adolescence. Based on network theory, these symptoms may represent important targets for intervention. Due to network differences, special considerations may be necessary to adapt such interventions to meet the needs of students from different backgrounds.

TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs

T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

Is Patient-Physician Gender Concordance Related to the Quality of Patient Care Experiences?

BACKGROUND

There is great interest in identifying factors that are related to positive patient experiences such as physician communication style. Documented gender-specific physician communication and patient behavior differences raise the question of whether gender concordant relationships (i.e., both the provider and patient share the same gender) might affect patient experiences.

OBJECTIVE

Assess whether patient experiences are more positive in gender concordant primary care relationships.

DESIGN

Statewide telephone surveys. Linear mixed regression models to estimate the association of CAHPS scores with patient gender and gender concordance.

SUBJECTS

Two probability samples of primary care Medicaid patients in Connecticut in 2017 (5/17-7/17) and 2019 (7/19-10/19).

MAIN MEASURES

Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) survey augmented with questions about aspects of care most salient to PCMH-designated organizations and two questions to assess access to mental health services.

KEY RESULTS

There were no significant effects of gender concordance and differences in experiences by patient gender were modest.

CONCLUSIONS

This study did not support the suggestion that patient and physician gender and gender concordance have an important effect on patient experiences.

Multilevel population and socioeconomic variation in health insurance coverage in India

OBJECTIVES

This study explores population-level variation in different types of health insurance coverage in India. We aimed to estimate the extent to which contextual factors at community, district, and state levels may contribute to place-based inequalities in coverage after accounting for household-level socioeconomic factors.

METHODS

We used data from the 2015-2016 National Family Health Survey in India, which provides the most recent and comprehensive information available on reports of different types of household health insurance coverage. We used multilevel regression models to estimate the relative contribution of different population levels to variation in coverage by national, state, and private health insurance schemes.

RESULTS

Among 601,509 households in India, 29% reported having coverage in 2015-2016. Variation in each type of coverage existed between population levels before and after adjusting for differences in the distribution of household socioeconomic and demographic factors. For example, the state level accounted for 36% of variation in national scheme coverage and 41% of variation in state scheme coverage after adjusting for household characteristics. In contrast, the community level was the largest contextual source of variation in private insurance coverage (accounting for 24%). Each type of coverage was associated with higher socioeconomic status and urban location.

CONCLUSIONS

Contextual factors at community, district, and state levels contribute to variation in household health insurance coverage even after accounting for socioeconomic and demographic factors. Opportunities exist to reduce disparities in coverage by focusing on drivers of place-based differences at multiple population levels. Future research should assess whether new insurance schemes exacerbate or reduce place-based disparities in coverage.

Design-Based Approach for Analysing Survey Data in Veterinary Research

Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.

The moderating role of social factors in the relationship between an incident of fall and depressive symptoms: a study with a national sample of older adults in South Korea

OBJECTIVES

The purpose of the study was to examine the effect of an incident of fall on depressive symptoms and the moderating role of social factors (marital status, living arrangement, family network, and friend network) in older adults in South Korea. We hypothesized that the adverse mental health effect of a fall would be pronounced among those who lack social resources (e.g., no spouse, living alone, and social disconnectedness).

METHOD

Using the 2017 National Survey of Older Koreans, data were drawn from 8,522 survey participants (aged 65 or older). Multivariate linear regression models of depressive symptoms were examined with an array of predictors: (1) demographic and health variables, (2) social factors, (3) an incident of fall, and (4) interactions between falls and social factors.

RESULTS

More than 15% of the sample had at least one fall in the past 12 months. Higher levels of depressive symptoms were associated with an incident of fall, not married and living alone, and lack of family and friend networks. Not married and living alone and family network significantly moderated the relationship between falls and depressive symptoms. The adverse mental health effect of a fall was more pronounced among those who were not married and living alone and who reported not having any close family members than their counterparts.

CONCLUSION

The findings highlight the critical role of family and social resources in protecting older Koreans from the negative mental health consequences of a fall. Findings also provide implications for developing fall prevention and management programs, suggesting prioritizing older adults with limited social resources.

Understanding the Challenges and Uncertainties of Seroprevalence Studies for SARS-CoV-2

SARS-CoV-2 continues to widely circulate in populations globally. Underdetection is acknowledged and is problematic when attempting to capture the true prevalence. Seroprevalence studies, where blood samples from a population sample are tested for SARS-CoV-2 antibodies that react to the SARS-CoV-2 virus, are a common method for estimating the proportion of people previously infected with the virus in a given population. However, obtaining reliable estimates from seroprevalence studies is challenging for a number of reasons, and the uncertainty in the results is often overlooked by scientists, policy makers, and the media. This paper reviews the methodological issues that arise in designing these studies, and the main sources of uncertainty that affect the results. We discuss the choice of study population, recruitment of subjects, uncertainty surrounding the accuracy of antibody tests, and the relationship between antibodies and infection over time. Understanding these issues can help the reader to interpret and critically evaluate the results of seroprevalence studies.

Comparing Public Perceptions and Preventive Behaviors During the Early Phase of the COVID-19 Pandemic in Hong Kong and the United Kingdom: Cross-sectional Survey Study

BACKGROUND

Given the public health responses to previous respiratory disease pandemics, and in the absence of treatments and vaccines, the mitigation of the COVID-19 pandemic relies on population engagement in nonpharmaceutical interventions. This engagement is largely driven by risk perception, anxiety levels, and knowledge, as well as by historical exposure to disease outbreaks, government responses, and cultural factors.

OBJECTIVE

The aim of this study is to compare psychobehavioral responses in Hong Kong and the United Kingdom during the early phase of the COVID-19 pandemic.

METHODS

Comparable cross-sectional surveys were administered to adults in Hong Kong and the United Kingdom during the early phase of the epidemic in each setting. Explanatory variables included demographics, risk perception, knowledge of COVID-19, anxiety level, and preventive behaviors. Responses were weighted according to census data. Logistic regression models, including effect modification to quantify setting differences, were used to assess the association between the explanatory variables and the adoption of social distancing measures.

RESULTS

Data from 3431 complete responses (Hong Kong, 1663; United Kingdom, 1768) were analyzed. Perceived severity of symptoms differed by setting, with weighted percentages of 96.8% for Hong Kong (1621/1663) and 19.9% for the United Kingdom (366/1768). A large proportion of respondents were abnormally or borderline anxious (Hong Kong: 1077/1603, 60.0%; United Kingdom: 812/1768, 46.5%) and regarded direct contact with infected individuals as the transmission route of COVID-19 (Hong Kong: 94.0%-98.5%; United Kingdom: 69.2%-93.5%; all percentages weighted), with Hong Kong identifying additional routes. Hong Kong reported high levels of adoption of various social distancing measures (Hong Kong: 32.6%-93.7%; United Kingdom: 17.6%-59.0%) and mask-wearing (Hong Kong: 98.8% (1647/1663); United Kingdom: 3.1% (53/1768)). The impact of perceived severity of symptoms and perceived ease of transmission of COVID-19 on the adoption of social distancing measures varied by setting. In Hong Kong, these factors had no impact, whereas in the United Kingdom, those who perceived their symptom severity as "high" were more likely to adopt social distancing (adjusted odds ratios [aORs] 1.58-3.01), and those who perceived transmission as "easy" were prone to adopt both general social distancing (aOR 2.00, 95% CI 1.57-2.55) and contact avoidance (aOR 1.80, 95% CI 1.41-2.30). The impact of anxiety on adopting social distancing did not vary by setting.

CONCLUSIONS

Our results suggest that health officials should ascertain baseline levels of risk perception and knowledge in populations, as well as prior sensitization to infectious disease outbreaks, during the development of mitigation strategies. Risk should be communicated through suitable media channels-and trust should be maintained-while early intervention remains the cornerstone of effective outbreak response.

Bayesian Analysis of Finite Populations under Simple Random Sampling

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

A mixed-methods analysis of mobility behavior changes in the COVID-19 era in a rural case study

Background

As a reaction to the novel coronavirus disease (COVID-19), countries around the globe have implemented various measures to reduce the spread of the virus. The transportation sector is particularly affected by the pandemic situation. The current study aims to contribute to the empirical knowledge regarding the effects of the coronavirus situation on the mobility of people by (1) broadening the perspective to the mobility rural area's residents and (2) providing subjective data concerning the perceived changes of affected persons' mobility practices, as these two aspects have scarcely been considered in research so far.

Methods

To address these research gaps, a mixed-methods study was conducted that integrates a qualitative telephone interview study (N = 15) and a quantitative household survey (N = 301). The rural district of Altmarkkreis Salzwedel in Northern Germany was chosen as a model region.

Results

The results provide in-depth insights into the changing mobility practices of residents of a rural area during the legal restrictions to stem the spread of the virus. A high share of respondents (62.6%) experienced no changes in their mobility behavior due to the COVID-19 pandemic situation. However, nearly one third of trips were also cancelled overall. A modal shift was observed towards the reduction of trips by car and bus, and an increase of trips by bike. The share of trips by foot was unchanged. The majority of respondents did not predict strong long-term effects of the corona pandemic on their mobility behavior.

National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry

Background

The U.S. lacks a stroke surveillance system. This study develops a method to transform an existing registry into a nationally representative database to evaluate acute ischemic stroke care quality.

Methods

Two statistical approaches are used to develop post-stratification weights for the Get With The Guidelines-Stroke registry by anchoring population estimates to the National Inpatient Sample. Post-stratification survey weights are estimated using a raking procedure and Bayesian interpolation methods. Weighting methods are adjusted to limit the dispersion of weights and make reasonable epidemiologic estimates of patient characteristics, quality of hospital care, and clinical outcomes. Standardized differences in national estimates are reported between the two post-stratification methods for anchored and non-anchored patient characteristics to evaluate estimation quality. Primary measures evaluated are patient and hospital characteristics, stroke severity, vital and laboratory measures, disposition, and clinical outcomes at discharge.

Results

A total of 1,388,296 acute ischemic strokes occurred between 2012 and 2014. Raking and Bayesian estimates of clinical data not available in administrative data are estimated within 5 to 10% of margin for expected values. Median weight for the raking method is 1.386 and the weights at the 99th percentile is 6.881 with a maximum weight of 30.775. Median Bayesian weight is 1.329 and the 99th percentile weights is 11.201 with a maximum weight of 515.689.

Conclusions

Leveraging existing databases with patient registries to develop post-stratification weights is a reliable approach to estimate acute ischemic stroke epidemiology and monitoring for stroke quality of care nationally. These methods may be applied to other diseases or settings to better monitor population health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01214-z.

Selection-Bias-Corrected Visualization via Dynamic Reweighting

The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.

Oral health conditions and correlates: a National Oral Health Survey of Rwanda

Background: Dental diseases are chronic, lifelong and preventable yet affect over half the world's population. Personal oral hygiene practices and socio-economic factors contribute to oral health outcomes affecting oral health quality of life. Integrating basic oral care within community level health systems increases accessibility and availability of oral health resources.Objective: National Oral Health Survey of Rwanda (NOHSR) data were investigated for associations of socio-demographic characteristics, personal oral hygiene practices, oral health outcomes, and oral health quality of life indicators.Methods: Data were analyzed and descriptive statistics calculated. Multivariable logistic regression models were developed to assess associations between untreated caries, calculus, and pain with various independent variables (demographics and personal oral hygiene practices). Additional logistic regression models examined associations between quality of life indicators and the aforementioned independent variables as well as untreated caries and pain.Results: Those who did not use a toothbrush (62.7%), or toothpaste (70.0%), and cleaned their teeth less than once per day (55.3%) had a higher prevalence of untreated caries. Approximately one-third of those in rural areas cleaned their teeth once per day or more compared to two-thirds of those in urban areas (35.4% vs. 71.2%). Those cleaning their teeth less than once daily were estimated to have 56.0% higher odds of caries than those who cleaned their teeth once a day or more (OR = 1.56, [95% CI 1.25-1.95]). Those with secondary education or higher and those with skilled jobs demonstrated more frequent teeth cleaning and higher toothbrush and toothpaste use. Quality-of-life indicators varied significantly with untreated caries and pain.Conclusion: Socio-economic, individual, and workforce characteristics are important considerations when assessing oral health outcomes. This study investigated social demographic disparities in relation to oral health related behaviors and outcomes. This information can help guide oral health care programming in Rwanda.

Regional differences in the impact of diabetes on population health in the USA

BACKGROUND

To evaluate regional disparities in the influence of diabetes on population health, we examine life expectancies at age 50 between population with diabetes and healthy population and life quality among the population with diabetes among native-born Americans by birth region and current residence.

METHODS

Using data on a cohort of 17 686 native-born individuals from the Health and Retirement Survey (1998-2014), we applied a Bayesian multistate life table method to estimate life expectancies at age 50 between population with diabetes and healthy population by each birth/current region combination. We further estimate the proportion of life remaining without either chronic conditions or disabilities as a quality of life measure and the probabilities that one region is worse than the other in terms of different health outcomes.

RESULTS

At age 50, persons with diabetes (PWD) were expected to live on average 5.8-10.8 years less than their healthy equivalents across regions. Diabetes had the greatest influence on life expectancy (LE) for older adults who lived in the South at the time of interviews. PWD born in the South were more likely to have developed chronic conditions or disabilities and spent greater proportions of life with these two issues compared to other regions.

CONCLUSION

Diabetes is a significant threat to LE and healthy LE in the USA, particularly for people born or living in the South.

Investigating the Relationship between Food Insecurity and Fertility Preferences in Tanzania

OBJECTIVE

We analyze fertility preferences among women at risk of pregnancy with children ages five or younger as a function of two food security metrics: perceptions of household hunger and child stunting (height for age z scores ≤ -2.0) in order to convey a robust picture of food insecurity.

METHODS

We use data from the 2016 Tanzania Demographic and Health Surveys to analyze this research question. Multinomial generalized logit models with cluster-adjusted standard errors are used to determine the association between different dimensions of food insecurity and individual-level fertility preferences.

RESULTS

On average, women who experience household hunger are 19% less likely to want more children compared to women who do not experience household hunger (AOR: 0.81, p = 0.02) when controlling for education, residence, maternal age, number of living children, and survey month. Adjusting for the same covariates, having at least one child ≤ 5 years old who is stunted is associated with 13% reduced odds of wanting more children compared to having no children stunted (AOR: 0.87, p = 0.06).

CONCLUSIONS FOR PRACTICE

In the context of a divided literature base, this research aligns with the previous work identifying a preference among women to delay or avoid pregnancy during times of food insecurity. The similarity in magnitude and direction of the association between food insecurity and fertility preferences across the two measures of food insecurity suggest a potential association between lived or perceived resource insecurity and fertility aspirations. Further research is needed in order to establish a mechanism through which food insecurity affects fertility preferences.

SIGNIFICANCE STATEMENT

Individual fertility preferences are sensitive to dynamic multi-level factors in a woman's life. While qualitative research has explored the effect that food insecurity and associated resource constraints have on fertility preferences, results are conflicting. Here, we quantitatively examine how individual woman's fertility preferences associate with two measures of food insecurity and qualitatively compare the associations across food insecurity measures. We establish that two food insecurity measures- household hunger and child stunting- capture similar populations and have similar associations with fertility preferences. This is a critical step forward in understanding the dynamic relationship between resource availability, child well-being, and fertility preferences.

The COVID-19 Social Monitor longitudinal online panel: Real-time monitoring of social and public health consequences of the COVID-19 emergency in Switzerland

BACKGROUND

The COVID-19 pandemic challenges societies in unknown ways, and individuals experience a substantial change in their daily lives and activities. Our study aims to describe these changes using population-based self-reported data about social and health behavior in a random sample of the Swiss population during the COVID-19 pandemic. The aim of the present article is two-fold: First, we want to describe the study methodology. Second, we want to report participant characteristics and study findings of the first survey wave to provide some baseline results for our study.

METHODS

Our study design is a longitudinal online panel of a random sample of the Swiss population. We measure outcome indicators covering general well-being, physical and mental health, social support, healthcare use and working state over multiple survey waves.

RESULTS

From 8,174 contacted individuals, 2,026 individuals participated in the first survey wave which corresponds to a response rate of 24.8%. Most survey participants reported a good to very good general life satisfaction (93.3%). 41.4% of the participants reported a worsened quality of life compared to before the COVID-19 emergency and 9.8% feelings of loneliness.

DISCUSSION

The COVID-19 Social Monitor is a population-based online survey which informs the public, health authorities, and the scientific community about relevant aspects and potential changes in social and health behavior during the COVID-19 emergency and beyond. Future research will follow up on the described study population focusing on COVID-19 relevant topics such as subgroup differences in the impact of the pandemic on well-being and quality of life or different dynamics of perceived psychological distress.

Cooking Frequency and Perception of Diet among US Adults Are Associated with US Healthy and Healthy Mediterranean-Style Dietary Related Classes: A Latent Class Profile Analysis

Background: Meal habits are associated with overall dietary quality and favorable dietary patterns determined by the Healthy Eating Index (HEI). However, within dietary patterns, complexities of food combinations that are not apparent through composite score determination may occur. Also, explorations of these food combinations with cooking and perceived diet quality (PDQ) remain unknown. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2007–2010 were utilized to determine the frequency of cooking at home and PDQ, along with sociodemographic variables. Latent class profile analysis was performed to determine person-centered data-driven analysis using the dietary index, HEI-2010, at both the daily and dinner meal-time levels. Multinomial logistic regression analysis was utilized to evaluate the association of dietary patterns with all covariates. Results: For daily HEI, five distinct dietary classes were identified. For dinner HEI, six classes were identified. In comparison to the standard American diet classes, home cooking was positively associated with daily (p < 0.05) and dinner (p < 0.001) dietary classes that had the highest amounts of total vegetable and greens/beans intake. PDQ was positively associated with these classes at the daily level (p < 0.001), but negatively associated with healthier classes at the dinner level (p < 0.001). Conclusion: The use of latent class profile analysis at the daily and dinner meal-time levels identified that food choices coalesce into diverse intakes, as shown by identified dietary classes. Home cooking frequency could be considered a positive factor associated with higher vegetable intake, particularly greens/beans, at the daily and dinner levels. At the same time, the perception of diet quality has a positive association only with daily choices.

Self-reported driving after marijuana use in association with medical and recreational marijuana policies

BACKGROUND

A common concern surrounding increasingly permissive marijuana policies in the US is that they will lead to more dangerous behavior, including driving after marijuana use. Although there is considerable research on the effects of marijuana policies on behaviours, few studies have examined self-reported driving after marijuana use. In this study, we use data from the Traffic Safety Culture Index (TSCI) to model self-reported past-year driving after marijuana use in association with medical and recreational marijuana policies.

METHODS

We analysed individual responses to annual administrations of TSCI from years 2013-2017 using a multiple logistic regression model. Our outcome variable was self-reported past-year driving after marijuana use (at least once vs. never), and our primary explanatory variable was the respondents' state medical marijuana (MM) and recreational marijuana (RM) policy. Additional explanatory variables include policies that specify thresholds for marijuana-intoxicated driving, year, and demographic factors.

RESULTS

Drivers in states that legalized MM but not RM had marginally higher odds of self-reporting driving after marijuana use compared to drivers in states where both RM and MM were illegal (adjusted OR 1.29; 95% CI 0.98, 1.70; p = 0.075). However, we found little evidence that drivers in states that legalized both RM and MM had higher odds of driving after marijuana use compared to drivers in states where both RM and MM were illegal (adjusted OR 1.06; 95% CI 0.71, 1.56; p = 0.784). Per-se or THC threshold laws were associated with lower self-reported driving after marijuana use (adjusted OR 0.74; 95% CI 0.57, 0.95; p = 0.018).

CONCLUSION

Although we found some evidence of an association between MM legalization and self-reported driving after marijuana use, our results provide only mixed support for the hypothesis that permissive marijuana policies are associated with higher odds of self-reported driving after marijuana use.

Informed Consent for Academic Surgeons: A Curriculum-Based Update

INTRODUCTION

The principles of consent are evolving but remain an important part of the surgeon-patient relationship. The goal of this course was a concise, contemporary review of the principles of informed consent that would be favorably received by academic surgeons.

METHODS

The curriculum consisted of ethicohistorical and legal principles, current requirements, and new consent developments. An anonymous, voluntary evaluation tool was used to assess strengths and opportunities for improvement. A short postcourse quiz was developed to assess understanding.

RESULTS

Eighty-five percent of the surgery department faculty participated. Evaluations were overwhelmingly positive, all elements having weighted averages of greater than 4.5 on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Furthermore, a majority of respondents for the posttest got the answers correct for all five questions asked on the postcourse quiz.

DISCUSSION

A proper understanding of informed consent remains critically important in the practice of surgery. This short course updating surgeons on informed consent quantitatively confirms the favorable reception of this approach in terms of attendance and satisfaction, as well as understanding of the material.

Unequal Loneliness in the Digitalized Classroom: Two Loneliness Effects of School Computers and Lessons for Sustainable Education in the E-Learning Era

Incorporating information and communication technology (ICT) into schooling has been one of the most conspicuous trends in education innovation for decades. Despite the education community’s optimistic consensus on the digitalization of the classroom, however, evidence-based research on the educational effectiveness of ICT is an unfinished task. In this situation, this study gives renewed attention to the socioemotional effects of school computers and draws lessons for sustainable education in the e-learning era. By analyzing the Trends in International Mathematics and Science Study (TIMSS) 2015, this study identifies the causal link between school computer usage time (the independent variable) and satisfaction with peer relationships (the dependent variable) among elementary and middle school students: the loneliness deepening effect. Then, considering the issue of digital divide, it finds the positive interaction between the independent variable and academic performance (the moderating variable): the loneliness inequality effect. These two findings—summarized by the term “unequal loneliness”—call for critical reflections on the current use of school computers but do not support the Ludditish claim that wholly denies ICT’s educational values and potentials. Rather, the existence of the loneliness inequality effect additionally implies an opportunity to go beyond mere technological determinism and deliberate on human users’ capabilities for effective ICT usage.

A Novel Cluster Sampling Design that Couples Multiple Surveys to Support Multiple Inferential Objectives

In the United States the number of health systems that own practices or hospitals have increased in number and complexity leading to interest in assessing the relationship between health organization factors and health outcomes. However, the existence of multiple types of organizations combined with the nesting of some hospitals and practices within health systems and the nesting of some health systems within larger health systems generates numerous analytic objectives and complicates the construction of optimal survey designs. An objective function that explicitly weighs all objectives is theoretically appealing but becomes unwieldy and increasingly ad hoc as the number of objectives increases. To overcome this problem, we develop an alternative approach based on constraining the sampling design to satisfy desired statistical properties. For example, to support evaluations of the comparative importance of factors measured in different surveys on health system performance, a constraint that requires at least one organization of each type (corporate owner, hospital, practice) to be sampled whenever any component of a system is sampled may be enforced. Multiple such constraints define a nonlinear system of equations that "couples" the survey sampling designs whose solution yields the sample inclusion probabilities for each organization in each survey. A Monte Carlo algorithm is developed to solve the simultaneous system of equations to determine the sampling probabilities and extract the samples for each survey. We illustrate the new sampling methodology by developing the constraints and solving the ensuing systems of equations to obtain the sampling design for the National Surveys of United States Health Care Systems, Hospitals and Practices. We illustrate the virtues of "coupled sampling" by comparing the proportion of eligible systems for whom the corporate owner and both a hospital and a practice that are expected to be sampled to that expected under alternative sampling designs. Comparative and descriptive analyses that illustrate features of the sampling design are also presented.

State coercion, moral attitudes, and tax compliance: Evidence from a national factorial survey experiment of income tax evasion

Why do some people comply with their obligation to pay taxes while others do not? Scholars of tax behavior, particularly economists and political scientists, have relied on models of state coercion and state reciprocity to answer this question. Neither state coercion nor state reciprocity, however, sufficiently account for individuals who voluntarily comply with their tax obligations to the state. We offer a third explanation, derived from the new sociology of morality and moral psychology, suggesting that two types of moral attitudes (moral imperatives and moral alignment) affect tax compliance. Using a factorial survey experiment of income tax evasion and a survey questionnaire administered to a nationally representative random sample of U.S. adults, we provide a systematic test of the three different models of tax compliance. The results yield strong support for moral attitudes (both moral imperatives and moral alignment) and state coercion, but little support for state reciprocity. We review the implications of our findings in the discussion and conclusion.

Smokers who have not tried alternative nicotine products: a 2019 survey of adults in Great Britain

Aims

Switching from smoking to using nicotine replacement therapy (NRT), electronic cigarettes (e-cigarettes) or heated tobacco products can reduce tobacco-related health risks. However, not all smokers in Great Britain have tried these products. This study aimed to identify and describe smokers who have never tried alternative nicotine products.

Methods

We analysed cross-sectional survey data of smokers (n = 1777) from a representative adult sample from Great Britain. The online survey was run in March 2019. The proportion of smokers who had never used alternative nicotine products was measured. A multivariate logistic regression assessed the association between never having used alternative nicotine products and sociodemographic and smoking characteristics and motivation to stop smoking.

Results

One in four smokers (27.8%, 95% CI 25.8–29.9%) had never tried NRT, e-cigarettes or heated tobacco products. These smokers were more commonly from Black and Minority than White ethnic groups (AOR = 1.55; 95% CI 1.02–2.31), were more likely to smoke up to 10 versus more cigarettes per day (AOR = 1.52; 95% CI 1.14–2.03) and to report low versus moderate or high motivation to stop smoking (AOR = 1.79; 95% CI 1.20–2.74).

Conclusion

Light smokers, those unmotivated to stop and smokers from Black and Minority ethnic groups are less likely to have ever tried alternative nicotine products. Different approaches are needed to facilitate harm reduction and smoking cessation among these groups of smokers.

Factors associated with sugar-sweetened beverage consumption in adults with children in the home after a statewide health communications program

Background

In 2016, Oklahoma launched "Shape Your Future - Rethink Your Drink" (SYF/RYD), an obesity prevention health communication program targeting parents and caregivers of children. The aims of this study are to compare sugar-sweetened beverage (SSB) consumption before and after the program and to report factors associated with SSB consumption, knowledge, and attitudes.

Methods

This repeated cross-sectional study involved 2656 Oklahoma adults with ≥ one child under 18 years in the home. Weighted prevalence estimates were calculated and the relationship between SSB consumption and covariates of interest were examined using logistic regression techniques appropriate for survey data.

Results

Following the SYF/RYD program, SSB consumption decreased 18.6% (p = 0.0232) and heavy SSB consumption, ≥ three SSB per day, decreased 42.9% (p = 0.0083). Factors associated with SSB consumption, 1 year after the launch of SYF/RYD included high school education or less (AOR = 1.33 with 95% CI = 1.02, 1.73), fair or poor health status (AOR = 2.02 with 95% CI = 1.47, 2.78), drinking less than eight cups of water daily (AOR = 1.77 with 95% CI = 1.39, 2.25), inability to afford healthy foods (AOR = 1.33 with 95% CI = 1.06, 1.67), and self-identifying as American Indian/Alaska Native (AOR = 1.59 with 95% CI = 1.10, 2.29).

Conclusions

Health communication campaigns, such as SYF/RYD, are an evidence-based strategy for health behavior change and likely contributed to the declines observed in SSB consumption. Important differences in SSB consumption by population subgroups persist and have implications for future message development.

Methods for analysis of complex survey data: an application using the Tanzanian 2015 Demographic and Health Survey and Service Provision Assessment

Background

Low-income and middle-income countries (LMICs) seek to better utilize household and health facility survey data for monitoring and evaluation, as well as for health program planning. However, analysis of this complex survey data are complicated. In Tanzania, the National Evaluation Platform project sought to analyze Demographic and Health Survey (DHS) data and Service Provision Assessment (SPA) data as part of an evaluation of the national One Plan for Maternal and Child Health. To support this evaluation, we used this survey data to answer two key methodological questions: 1) what are the benefits and costs of using sampling weights in rate estimation; and 2) what is the best method for calculating standard errors in these two surveys?

Methods

We conducted a simulation study for each methodologic question. The first simulation study assessed the benefits and costs of using sampling weights in rate estimation. This simulation used weighted and unweighted estimates and examined bias, variance, and the mean squared error (MSE). The second simulation study assessed the best method for calculating standard errors comparing cluster bootstrapped variance estimation, design based asymptotic variance with one level (svy1), and design based asymptotic variance with three levels (svy3). We compared coverage probability and confidence interval length.

Results

Our results showed that although weighted estimates were less biased, unweighted estimates were less variable. The weighted estimates had a lower MSE, indicating that the effect of the bias trade-off was greater than the effect of the variance trade-off for most indicators assessed. The best performer for variance estimation was the cluster bootstrap method, followed by the svy3 method. The svy1 method was the worst performer for most indicators assessed.

Conclusions

As complex survey data become more widely used for policymaking in LMICs, there is a need for guidance on the best methods for analyzing this data. The standard of practice has been a design-based analysis using survey weights and the single-level svy method for calculating standard errors. This study puts forth an alternative approach to analysis. In addition, this study offers practical guidance on determining the best method for analysis of complex survey data.