A Review of Hot Deck Imputation for Survey Non-response

Health conditions among farmworkers in the Southwest: An analysis of the National Agricultural Workers Survey

Agricultural jobs pose many challenges to the health and wellbeing of a disadvantaged population. In the Southwest region, the socioeconomic factors of living along the U.S.-Mexico border, migration patterns, lack of access to health care, low utilization of health care services, lack of health insurance, and highly demanding occupation may uniquely affect health outcomes for farmworkers. This paper presents descriptive information for professionals to improve access to care by tackling barriers afforded by the agricultural industry. The National Agricultural Worker Survey (NAWS) is an employment-based, random-sample survey of U.S. agricultural workers in six regions: East, Southeast, Midwest, Southwest, Northwest, and California. We examined farmworkers' self-reported health conditions, including asthma, diabetes, high blood pressure, other chronic conditions, or any condition by region from 2013 to 2016. We used logistic regression to determine differences in lifetime prevalence of health conditions between farmworkers in the Southwest region (n = 727) and farmworkers in other regions (n = 8,850) using weighted data. After adjusting for age, gender, income, insurance status, and English-speaking ability, the odds of high blood pressure and other condition were similar in all regions. The prevalence of diabetes was almost double in the Southwest (114.2 per 1,000 farmworkers). The odds of diabetes were 1.31 (95% CI 0.99, 1.74) times greater in the Southwest region than in the other regions. Asthma was the only condition that was lower in the Southwest (22 per 1,000 farmworkers) compared to the other regions. The odds of asthma were 0.61 (95% CI 0.36, 1.03) times lower in the Southwest region than in other regions. The results follow previous studies on the prevalence of asthma among the farmworker population and elevated probability of chronic diseases including diabetes among the Latino population in the U.S.

COVID-19 Vaccine Knowledge, Attitudes, and Practices in Alabama: The Case for Primary Health Care Providers

OBJECTIVE

To examine knowledge, attitudes, and practices about COVID-19 in Alabama, with a primary focus on vaccination perception and utilization.

DESIGN

We used a COVID-19 Knowledge, Attitudes, and Practices survey and recruited adult Alabama residents in April-May 2021.

PARTICIPANTS

Initial surveys from 1324 Alabamian participants were considered for analysis; after careful review of incomplete responses, 953 were ultimately included for analysis.

MAIN OUTCOME MEASURE

Vaccine behavior and hesitancy comprise a self-reported response contained in the survey instrument. Three primary vaccine groups were used to assess differences in demographic characteristics, health status, perception of susceptibility and severity of COVID-19, sources of information, and trust about COVID-19.

RESULTS

Of the 953 survey participants included for analysis, 951 had self-identified vaccine status in which 153 (16.1%) reported to have received the vaccine at the time of the survey, 375 (39.4%) were very likely or somewhat likely to get an approved COVID-19 vaccine if it was offered, and 423 (44.5%) were somewhat unlikely or very unlikely to get an approved COVID-19 vaccine. Health care providers were the most trusted sources of information, regardless of vaccine status. For participants unlikely to receive a vaccine, social media and local news sources were consistently more trusted and utilized than those who were vaccinated or were likely to be.

CONCLUSIONS

The perceptions among unvaccinated participants are actionable and provide teachable opportunities to decrease vaccine apprehension.

Item non-response imputation in the Korea National Health and Nutrition Examination Survey

OBJECTIVES

The Korea National Health and Nutrition Examination Survey (KNHANES) is a public health survey that assesses individuals' health and nutritional status and monitors the prevalence of major chronic diseases. In general, sampling weights are adjusted for unit non-responses and imputation is conducted for item non-responses. In this study, we proposed strategies for imputing item non-responses in the KNHANES in order to improve the usefulness of data, minimize bias, and increase statistical power.

METHODS

After applying logical imputation, we adopted 2 separate imputation methods for each variable type: unweighted sequential hot-deck imputation for categorical variables and sequential regression imputation for continuous variables. For variance estimation, multiple imputations were applied to the continuous variables. To evaluate the performance of the proposed strategies, we compared the marginal distributions of imputed variables and the results of multivariable regression analysis for the complete-case data and the expanded data with imputed values, respectively.

RESULTS

When comparing the marginal distributions, most non-responses were imputed. The multivariable regression coefficients presented similar estimates; however, the standard errors decreased, resulting in statistically significant p-values. The proposed imputation strategies may cope with the loss of precision due to missing data, thus enhancing statistical power in analyses of the KNHANES by providing expanded data with imputed values.

CONCLUSIONS

The proposed imputation strategy may enhance the utility of data by increasing the number of complete cases and reducing the bias in the analysis, thus laying a foundation to cope with the occurrence of item non-responses in further surveys.

Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI

BACKGROUND

DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing.

RESULTS

We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. XAI approaches have allowed us to explain model predictions from both populational and individual perspectives.

CONCLUSIONS

We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole-blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.

Sensor-based outcomes to monitor everyday life motor activities of children and adolescents with neuromotor impairments: A survey with health professionals

In combination with appropriate data processing algorithms, wearable inertial sensors enable the measurement of motor activities in children's and adolescents' habitual environments after rehabilitation. However, existing algorithms were predominantly designed for adult patients, and their outcomes might not be relevant for a pediatric population. In this study, we identified the needs of pediatric rehabilitation to create the basis for developing new algorithms that derive clinically relevant outcomes for children and adolescents with neuromotor impairments. We conducted an international survey with health professionals of pediatric neurorehabilitation centers, provided them a list of 34 outcome measures currently used in the literature, and asked them to rate the clinical relevance of these measures for a pediatric population. The survey was completed by 62 therapists, 16 doctors, and 9 nurses of 16 different pediatric neurorehabilitation centers from Switzerland, Germany, and Austria. They had an average work experience of 13 ± 10 years. The most relevant outcome measures were the duration of lying, sitting, and standing positions; the amount of active self-propulsion during wheeling periods; the hand use laterality; and the duration, distance, and speed of walking periods. The health profession, work experience, and workplace had a minimal impact on the priorities of health professionals. Eventually, we complemented the survey findings with the family priorities of a previous study to provide developers with the clinically most relevant outcomes to monitor everyday life motor activities of children and adolescents with neuromotor impairments.

Nearest neighbor ratio imputation with incomplete multinomial outcome in survey sampling

Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbor imputation method is used to handle such incomplete multinomial data. In this article, we investigate the nearest neighbor ratio imputation estimator, in which auxiliary variables are used to identify the closest donor and the vector of proportions from the donor is applied to the total of the recipient to implement ratio imputation. To estimate the asymptotic variance, we first treat the nearest neighbor ratio imputation as a special case of predictive matching imputation and apply the linearization method of Yang and Kim (2020). To account for the non-negligible sampling fractions, parametric and generalized additive models are employed to incorporate the smoothness of the imputation estimator, which results in a valid variance estimator. We apply the proposed method to estimate expenditures detail items based on empirical data from the 2018 collection of the Service Annual Survey, conducted by the United States Census Bureau. Our simulation results demonstrate the validity of our proposed estimators and also confirm that the derived variance estimators have good performance even when the sampling fraction is non-negligible.

Housing Stability and Access to General Healthcare and Reproductive Healthcare Among Women in Ohio

Background

The relationship between housing instability and reproductive healthcare is understudied. We examined the association between housing insecurity and access and utilization of general healthcare, contraceptive healthcare, and abortion care.

Methods

Using data from a population-representative survey of adult reproductive-age Ohio women (N = 2,529), we assessed housing insecurity (not paying rent/mortgage on time in the past year). We examined associations between housing insecurity and the following outcomes: (1) not being able to access general healthcare in the past year; (2) experiencing delays or difficulties in accessing contraceptive healthcare in the past year; and (3) ever having an abortion. We used unadjusted and adjusted logistic regression models. We selected confounders a priori and included age, socioeconomic status, and healthcare status.

Results

Overall, 10.6% of Ohio women of adult reproductive age experienced housing insecurity. Approximately 27.5% of respondents were not able to access general healthcare and 10.4% experienced delays or difficulties in accessing contraceptive care. Compared to housing-secure respondents, housing-insecure women were less able to access general healthcare (adjusted odds ratio [aOR]:2.16; 95% confidence interval [CI]:1.45–3.23) and more likely to experience delays or difficulties when accessing contraceptive care (aOR:1.74; 95% CI:1.00-3.04). Insecure housing was not statistically associated with ever having an abortion (aOR:1.76; 95% CI:0.93–3.34).

Conclusions

In this study, recent housing insecurity was associated with poorer access to general and contraceptive healthcare. Studies utilizing multidimensional measures of housing insecurity and other material insecurity measures are needed to further explore the relationship between material insecurity and access to general and contraceptive care.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10995-022-03492-5.

The association of sexual orientation with prostate, breast, and cervical cancer screening and diagnosis

PURPOSE

Data on heterogeneity in cancer screening and diagnosis rates among lesbians/gays and bisexuals (LGBs) is lacking. Recent studies showed that LGBs have decreased healthcare utilization compared to heterosexual counterparts. Few studies have examined how sexual orientation impacts cancer screening and prevalence. We, therefore, investigated the association between sexual orientation and prevalent sex-specific cancer including prostate (PCa), breast (BC), and cervical (CC) cancer.

METHODS

This was a cross-sectional survey-based US study, including men and women aged 18 + from the Health Information National Trends Survey (HINTS) database between 2017 and 2019. The primary endpoint was individual-reported prostate, breast, and cervical cancer screening and prevalence rates among heterosexual and LGB men and women. Multivariable logistic regression analyses assessed association of various covariates with undergoing screening and diagnosis of these cancers.

RESULTS

Overall, 4,441 and 6,333 heterosexual men and women, respectively, were compared to 225 and 213 LGB men and women, respectively. LGBs were younger and less likely to be screened for PCa, BC, and CC than heterosexuals. A higher proportion of heterosexual women than lesbian and bisexual women were screened for CC with pap smears (95.36% vs. 90.48% and 86.11%, p ≤ 0.001) and BC with mammograms (80.74% vs. 63.81% and 45.37%, p ≤ 0.001). Similarly, a higher proportion of heterosexual men than gay and bisexual men were screened for PCa with PSA blood tests (41.27% vs. 30.53% and 27.58%, p ≤ 0.001).

CONCLUSION

There were more heterosexuals than LGBs screened for CC, BC, and PCa. However, no association between sexual orientation and cancer diagnosis was found. Healthcare professionals should be encouraged to improve cancer screening among LGBs.

Using structured implementation interventions to improve referral to substance use treatment among justice-involved youth: Findings from a multisite cluster randomized trial

INTRODUCTION

Youth involved in the justice system have high rates of alcohol and other drug use, but limited treatment engagement. JJ-TRIALS tested implementation activities with community supervision (CS) and behavioral health (BH) agencies to improve screening, identification of substance use service need, referral, and treatment initiation and engagement, guided by the BH Services Cascade and EPIS frameworks. This paper summarizes intervention impacts on referrals to treatment among youth on CS.

METHODS

This multisite cluster-randomized trial involved 18 matched pairs of sites in 36 counties in seven states randomly assigned to core or enhanced conditions after implementing the core intervention at all sites for six months. Enhanced sites received external facilitation for local change team activities to reduce unmet treatment needs; Core sites were encouraged to form interagency workgroups. The dependent variable was percentage referred to treatment among youth in need (N = 14,012). Two-level Bayesian regression assessed factors predicting referral across all sites and time periods. Generalized linear mixed models using logit transformation tested two hypotheses: (H1) referrals will increase from baseline to the experimental period, (H2) referral increases will be larger in enhanced sites than in core sites.

RESULTS

Although the intervention significantly increased referral, condition did not significantly predict referral across all time periods. Youth who tested drug positive, had an alcohol/other drug-related or felony charge, were placed in secure detention or assigned more intensive supervision, or who were White were more likely to be referred. H1 (p < .05) and H2 (p < .0001) were both significant in the hypothesized direction. Interaction analyses comparing site pair differences showed that findings were not consistent across sites.

CONCLUSIONS

The percentage of youth referred to treatment increased compared with baseline overall, and enhanced sites showed larger increases in referrals over time. However, variations in effects suggest that site-level differences were important. Researchers should carry out mixed methods studies to further understand reasons for the inconsistent findings within randomized site pairs, and how to further improve treatment referrals across CS and BH systems. Findings also highlight that even when CS agencies work collaboratively with BH providers to improve referrals, most justice-involved youth who need SU services are not referred.

Hardship in the Heartland: Associations Between Rurality, Income, and Material Hardship

One in three U.S. households has experienced material hardship. The inadequate provision of basic needs, including food, healthcare, and transportation, is more typical in households with children or persons of color, yet little is known about material hardship in rural spaces. The aim of this study is to describe the prevalence of material hardships in Iowa and examine the relationship between rurality, income, and material hardship. Using data from the 2016 State Innovation Model Statewide Consumer Survey, we use logistic regression to examine the association between rurality, income, and four forms of material hardship. Rural respondents incurred lower odds than non-rural respondents for all four hardship models. All four models indicated that lower income respondents incurred greater odds for having material hardship. Material hardship was reported across all groups, with rurality, income, race, and age as strong predictors of material hardship among our sample.

Prostate-specific Antigen Testing in Men with Disabilities: A Cross-sectional Analysis of the Health Information National Trends Survey

BACKGROUND

Patients with disabilities represent a unique minority population. The incidence of prostate-specific antigen (PSA) testing among this population is unknown.

OBJECTIVE

To compare PSA testing rates and associated predictors among men with and without reported disabilities in the USA.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional study of the Health Information National Trends Survey (HINTS) for the years 2012, 2013, 2017 and 2019 was conducted in men with reported disabilities.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Baseline demographics of the entire cohort were stratified based on their reported disabilities (none, disabled, deaf, and blind). Each disability was compared separately and in combination with the cohort without disabilities. Multivariable logistic regression models determined clinically significant predictors of PSA testing in men with disabilities compared with those without.

RESULTS AND LIMITATIONS

Overall, 782 (15%) men with disabilities were compared with 4569 (85%) men without disabilities. The former cohort was older with a median (interquartile range) age of 65 (56-75) versus 57 (43-67) yr (p < 0.001). On multivariable analysis, men with any disability were less likely to undergo PSA testing (odds ratio 0.77, 95% confidence interval 0.62-0.96, p = 0.018). Variables associated with increased PSA testing included age, having a health care provider, health insurance, and living with a partner.

CONCLUSIONS

Inequalities in PSA testing exist among men with disabilities in the USA, especially among the deaf and blind, being less likely to undergo PSA testing. Further research is required to identify and deal with any obstacles in the implementation of equal PSA testing in this unique population.

PATIENT SUMMARY

In the USA, men with reported disabilities are less likely to undergo PSA testing than patients without reported disabilities.

Burden and Correlates of Mental Health Symptoms Among Young Black Gay, Bisexual, and Other Men Who Have Sex with Men Engaged in HIV Care in Atlanta

Mental health comorbidities are prevalent among young Black gay, bisexual, and other men who have sex with men (YB-GBMSM) living with HIV and can adversely impact HIV-related outcomes. We conducted a cross-sectional survey study with 100 YB-GBMSM recruited from two HIV care centers in Atlanta, and constructed multivariable logistic and linear regression models to examine correlates of depression, anxiety, trauma symptoms, and general well-being. In adjusted models, full-time employment was associated with fewer depressive symptoms, while HIV stigma and substance use were associated with higher levels of depressive symptoms. Younger age and full-time employment were negatively associated with severe anxiety, while HIV stigma was positively associated with severe anxiety and trauma symptoms. Trust in physicians, lower HIV stigma, full-time employment, and lack of substance use were associated with higher average general well-being scores. In conclusion, we found high frequency of depressive, anxiety, and trauma-related symptoms among this sample of YB-GBMSM living with HIV. Unemployment, substance use, and HIV stigma emerged as particularly salient correlates of psychological morbidity, suggesting a need for structural and community-level interventions to address mental health in this population.

Financial Toxicity and Its Association With Prostate and Colon Cancer Screening

BACKGROUND

The term "financial toxicity" or "hardship" is a patient-reported outcome that results from the material costs of cancer care, the psychological impacts of these costs, and the coping strategies that patients use to deal with the strain that includes delaying or forgoing care. However, little is known about the impact of financial toxicity on cancer screening. We examined the effects of financial toxicity on the use of screening tests for prostate and colon cancer. We hypothesized that greater financial hardship would show an association with decreased prevalence of cancer screening.

METHODS

This cross-sectional survey-based US study included men and women aged ≥50 years from the National Health Interview Survey database from January through December 2018. A financial hardship score (FHS) between 0 and 10 was formulated by summarizing the responses from 10 financial toxicity dichotomic questions (yes or no), with a higher score associated with greater financial hardship. Primary outcomes were self-reported occurrence of prostate-specific antigen (PSA) blood testing and colonoscopy for prostate and colon cancer screening, respectively.

RESULTS

Overall, 13,439 individual responses were collected. A total of 9,277 (69.03%) people had undergone colonoscopies, and 3,455 (70.94%) men had a PSA test. White, married, working men were more likely to undergo PSA testing and colonoscopy. Individuals who had not had a PSA test or colonoscopy had higher mean FHSs than those who underwent these tests (0.70 and 0.79 vs 0.47 and 0.61, respectively; P≤.001 for both). Multivariable logistic regression models demonstrated that a higher FHS was associated with a decreased odds ratio for having a PSA test (0.916; 95% CI, 0.867-0.967; P=.002) and colonoscopy (0.969; 95% CI, 0.941-0.998; P=.039).

CONCLUSIONS

Greater financial hardship is suggested to be associated with a decreased probability of having prostate and colon cancer screening. Healthcare professionals should be aware that financial toxicity can impact not only cancer treatment but also cancer screening.

Nationally representative social contact patterns among U.S. adults, August 2020-April 2021

The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic, few have reported differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.

Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.

Nonparametric kernel estimation of the probability of cure in a mixture cure model when the cure status is partially observed

Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.

Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030

We examined time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030 by sex, race/skin color, educational attainment, and state capitals. Self-reported body weight and height of 730,309 adults (≥ 18 years) from the Vigitel study were collected by telephone interview between 2006 and 2019. A multinomial logistic regression model was used to predict the prevalence of body mass index (BMI) categories as a function of time by 2030. The prevalence of obesity increased from 11.8% in 2006 to 20.3% in 2019. The projected prevalences by 2030 are estimated to be 68.1% for overweight, 29.6% for obesity, and 9.3% for obesity classes II and III. Women, black and other minority ethnicities, middle-aged adults, adults with ≤ 7 years of education, and in Northern and Midwestern capitals are estimated to have higher obesity prevalence by 2030. Our findings indicate a sustained increase in the obesity epidemic in all sociodemographic subgroups and across the country. Obesity may reach three out of 10 adults by 2030.

Association of Neighborhood Characteristics With Pediatric Asthma

OBJECTIVE

To examine associations between neighborhood characteristics and asthma prevalence and severity among low-income children in a large nationally representative sample.

METHODS

Data source: 2018 National Survey of Children's Health, limited to low-income children, ages 0-17 years. We grouped parent responses about neighborhood characteristics into 5 scores: neighborhood support, safety, resources and quality, and a total score. Logistic regression compared rates and severity of asthma by neighborhood scores, adjusting for age, sex, race, and income.

RESULTS

Of 8,653 low-income children, those living in neighborhoods with better total neighborhood scores were significantly less likely to have parent-reported asthma; OR = 0.9, 95% CI: 0.8-1.0; P = .02, with similar findings for children living in neighborhoods with higher support, safety, and quality scores. We found no associations between neighborhood scores and asthma severity in this population.

CONCLUSIONS

Favorable neighborhoods are associated with lower parent-reported asthma prevalence in low-income children but not asthma severity. These data may support providers and policy makers interested in child asthma in addressing neighborhood improvement.

missForest with feature selection using binary particle swarm optimization improves the imputation accuracy of continuous data

BACKGROUND

Missing data are a common problem in large-scale datasets and its appropriate handling is crucial for data analyses. Missingness can be categorized as (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR). Different missingness mechanisms require different imputation strategies. Multiple imputation, an approach for averaging outcomes across multiple imputed data, is more suitable than single imputation for dealing with various missing mechanisms. missForest, a nonparametric missing value imputation strategy using random forest, is one of the most prevalent multiple imputation methods for missing-data because it can be applied to mixed-type data and does not require distributional assumptions. However, a recent study found that missForest can produce biased results for non-normal data. In addition, missForest is computationally expensive.

OBJECTIVE

Therefore, we aimed to further develop the missForest algorithm by combining a binary particle swarm optimization (BPSO)-based feature-selection strategy.

METHODS

The BPSO is an evolutionary algorithm that is well known for global optimization and computational efficiency. By using the BPSO-based feature selection step prior to imputing missing values with missForest, the imputation accuracy for continuous variables could be increased by pruning redundant variables.

RESULTS

In this study, missForest with BPSO (BPSOmf) showed better imputation accuracy than missForest alone with respect to continuous variables by feature selection prior to the imputation step.

CONCLUSIONS

BPSOmf is an appropriate and robust method when the imputation target data consist mainly of continuous variables.

The role of alexithymia and perceived stress in mental health responses to COVID-19: A conditional process model

BACKGROUND

Little is known about the psychological mechanisms underlying the mental health problems related to the COVID-19 pandemic. Hypothetically, perceived stress and alexithymia may be factors involved in the mental distress response to the pandemic; however, this remains largely unstudied. This study aims to explore the moderating role of alexithymia and the moderated mediation effects of perceived stress on the mental health change due to the pandemic.

METHODS

The conditional process model was used to examine the moderated mediation. The sample consists of 659 parents from the FinnBrain Birth Cohort Study who completed the Toronto Alexithymia Scale (TAS-20) at 6 months after delivery, the Edinburgh Postnatal Depression Scale (EPDS) and the Symptom Checklist-90 (SCL-90) at 2 or 4 years postpartum between 2014 and 2019; and a questionnaire for pandemic events, a brief 4-item version of the Perceived Stress Scale (PSS-4) and the follow-up EPDS/SCL-90 in 2020 after 3 months from the outbreak of COVID-19 pandemic in Finland.

RESULTS

Alexithymia moderated the perceived stress-mediated relations between the pandemic events and the changes of depressive and anxiety symptoms through enhancing the detrimental effect of perceived stress on mental health.

LIMITATIONS

This study was mainly limited by the causality and generalizability of the findings.

CONCLUSIONS

Our findings indicate the moderated mediation effects of alexithymia and perceived stress on the psychological symptoms, which has implications for understanding how and when stressful situations translate to mental health problems, identifying vulnerable individuals, and tailoring preventive and psychotherapeutic interventions.

Handling high-dimensional data with missing values by modern machine learning techniques

High-dimensional data have been regarded as one of the most important types of big data in practice. It happens frequently in practice including genetic study, financial study, and geographical study. Missing data in high dimensional data analysis should be handled properly to reduce nonresponse bias. We discuss some modern machine learning techniques including penalized regression approaches, tree-based approaches, and deep learning (DL) for handling missing data with high dimensionality. Specifically, our proposed methods can be used for estimating general parameters of interest including population means and percentiles with imputation-based estimators, propensity score estimators, and doubly robust estimators. We compare those methods through some limited simulation studies and a real application. Both simulation studies and real application show the benefits of DL and XGboost approaches compared with other methods in terms of balancing bias and variance.

Severe Acute Respiratory Syndrome Coronavirus 2 Cumulative Incidence, United States, August 2020-December 2020

BACKGROUND

Reported coronavirus disease 2019 (COVID-19) cases underestimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We conducted a national probability survey of US households to estimate cumulative incidence adjusted for antibody waning.

METHODS

From August-December 2020 a random sample of US addresses were mailed a survey and self-collected nasal swabs and dried blood spot cards. One adult household member completed the survey and mail specimens for viral detection and total (immunoglobulin [Ig] A, IgM, IgG) nucleocapsid antibody by a commercial, emergency use authorization-approved antigen capture assay. We estimated cumulative incidence of SARS-CoV-2 adjusted for waning antibodies and calculated reported fraction (RF) and infection fatality ratio (IFR). Differences in seropositivity among demographic, geographic, and clinical subgroups were explored.

RESULTS

Among 39 500 sampled households, 4654 respondents provided responses. Cumulative incidence adjusted for waning was 11.9% (95% credible interval [CrI], 10.5%-13.5%) as of 30 October 2020. We estimated 30 332 842 (CrI, 26 703 753-34 335 338) total infections in the US adult population by 30 October 2020. RF was 22.3% and IFR was 0.85% among adults. Black non-Hispanics (Prevalence ratio (PR) 2.2) and Hispanics (PR, 3.1) were more likely than White non-Hispanics to be seropositive.

CONCLUSIONS

One in 8 US adults had been infected with SARS-CoV-2 by October 2020; however, few had been accounted for in public health reporting. The COVID-19 pandemic is likely substantially underestimated by reported cases. Disparities in COVID-19 by race observed among reported cases cannot be attributed to differential diagnosis or reporting of infections in population subgroups.

Comparing single and multiple imputation strategies for harmonizing substance use data across HIV-related cohort studies

Background

Although standardized measures to assess substance use are available, most studies use variations of these measures making it challenging to harmonize data across studies. The aim of this study was to evaluate the performance of different strategies to impute missing substance use data that may result as part of data harmonization procedures.

Methods

We used self-reported substance use data collected between August 2014 and June 2019 from 528 participants with 2,389 study visits in a cohort study of substance use and HIV. We selected a low (heroin), medium (methamphetamine), and high (cannabis) prevalence drug and set 10–50% of each substance to missing. The data amputation mimicked missingness that results from harmonization of disparate measures. We conducted Monte Carlo simulations to evaluate the comparative performance of single and multiple imputation (MI) methods using the relative mean bias, root mean square error (RMSE), and coverage probability of the 95% confidence interval for each imputed estimate.

Results

Without imputation (i.e., listwise deletion), estimates of substance use were biased, especially for low prevalence outcomes such as heroin. For instance, even when 10% of data were missing, the complete case analysis underestimated the prevalence of heroin by 33%. MI, even with as few as five imputations produced the least biased estimates, however, for a high prevalence outcome such as cannabis with low to moderate missingness, performance of single imputation strategies improved. For instance, in the case of cannabis, with 10% missingness, single imputation with regression performed just as well as multiple imputation resulting in minimal bias (relative mean bias of 0.06% and 0.07% respectively) and comparable performance (RMSE = 0.0102 for both and coverage of 95.8% and 96.2% respectively).

Conclusion

Our results from imputation of missing substance use data resulting from data harmonization indicate that MI provided the best performance across a range of conditions. Additionally, single imputation for substance use data performed comparably under scenarios where the prevalence of the outcome was high and missingness was low. These findings provide a practical application for the evaluation of several imputation strategies and helps to address missing data problem when combining data from individual studies.

Suicidal behavior in Mexican adolescents: A test of a latent class model using two independent probability samples

Suicidal behavior is a serious health issue that affects the adolescent population and that may be prevented through evidence-based approaches. There are many risk factors involved in suicidal behavior, but there are few studies encompassing the complex relationships between them. This study tested the similarity between two latent class models from two different epidemiological samples of middle-school students and evaluated if the multinomial regression model replicated the associations between the classes and the psychosocial variables. Data of 4013 adolescents from the Mexican states of Campeche and Querétaro were included in LCA to characterize suicide behavior and associated factors. The most likely latent class membership was used as the outcome in multinomial regression models. The model with data from Querétaro was consistent with the previous LC model from Campeche. The four latent classes were nearly identical for both populations and can be defined as follows: 1) "No Problems," included 73% of the adolescents; 2) "Drug Use Only," comprised 7% of the adolescents; 3) "Suicidal Behavior, No Depression," captured 8% of the adolescents (25% reported binge drinking in the past month, 50% self-inflicted injuries, and 43% low-lethality suicide attempt); 4) "Depression and Suicidal Behavior," comprised 12% of the sample (66% met criteria for Major Depressive Episode, 28% reported binge drinking in the past month, and 11% had a high-lethality suicide attempt). The congruence between the models and the consistency with identified factors highlight the need for appropriate and effective prevention strategies to minimize risk factors and reinforce protective factors in the adolescent population.

The Association Between Hormone Receptor Status and End-of-Life Care Among Patients With Metastatic Breast Cancer

BACKGROUND

In metastatic breast cancer (MBC), positive estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status allow for more long-term, sequential treatment options compared to ER-negative and HER2-negative diseases. It is unclear if end-of-life care (timely integration of palliative care, discontinuation of chemotherapy, and enrollment into hospice) in MBC is now tailored to the ER and HER2 status.

OBJECTIVES

This article explores the association between ER and HER2 status and the quality of end-of-life care received among patients with MBC.

METHODS

A 20-year MBC clinical database captured demographics, tumor characteristics, and treatment histories of deceased patients with MBC (N = 1,258) at a tertiary hospital located in Pittsburgh, Pennsylvania. Descriptive and inferential statistics were used.

FINDINGS

Patients with ER-positive MBC had greater odds of receiving quality end-of-life care than those with ER-negative MBC. HER2 status was not associated with differences in the quality of end-of-life care.

Spinal pain in childhood: prevalence, trajectories, and diagnoses in children 6 to 17 years of age

This study aimed to investigate the trajectories of spinal pain frequency from 6 to 17 years of age and describe the prevalence and frequency of spinal pain and related diagnoses in children following different pain trajectories. First through fifth-grade students from 13 primary schools were followed for 5.5 years. Occurrences of spinal pain were reported weekly via text messages. Children reporting spinal pain were physically evaluated and classified using International Classification of Disease criteria. Trajectories of spinal pain frequency were modeled from age 6 to 17 years with latent class growth analysis. We included data from 1556 children (52.4% female, mean (SD) baseline age = 9.1 (1.9) years) and identified 10,554 weeks of spinal pain in 329,756 weeks of observation. Sixty-three percent of children reported one or more occurrences of spinal pain. We identified five trajectories of spinal pain frequency. Half the children (49.8%) were classified as members of a "no pain" trajectory. The remaining children followed "rare" (27.9%), "rare, increasing" (14.5%), "moderate, increasing" (6.5%), or "early-onset, decreasing" (1.3%) spinal pain trajectories. The most common diagnoses in all trajectory groups were non-specific (e.g., "back pain"). Tissue-specific diagnoses (e.g., muscle strain) were less common and pathologies (e.g., fracture) were rare.  Conclusion: From childhood through adolescence, spinal pain was common and followed heterogeneous courses comprising stable, increasing, and early-onset trajectories. These findings accord with recommendations from adult back pain guidelines that most children with spinal pain can be reassured that they do not have a serious disease and encouraged to stay active. What is Known: • Spinal pain imposes a large burden on individuals and society. • Although many people first experience the condition in childhood, little is known about the developmental trajectories of spinal pain from childhood to adolescence. What is New: • Data from 1556 children and 329,756 participant weeks showed five unique spinal pain trajectories from 6 to 17 years: most children rarely reported spinal pain, while one in five followed increasing or early-onset trajectories. • Most pain occurrences were non-specific; pathological diagnoses were rare.

Study of the epidemiology of COVID-19 in Ontario elementary and secondary school education workers: an interim analysis following the first school year

Objective

To estimate the incidence of SARS-CoV-2 in education workers and the factors associated with infection between March 2020 and July 2021.

Methods

A prospective cohort study of education workers working ≥8 h per week in Ontario, Canada. Participants self-reported results of tests for SARS-CoV-2 and completed online surveys about demographic information, exposures, and vaccinations against SARS-CoV-2. Participants submitted self-collected dried blood spots. Antibodies to SARS-CoV-2 spike, the receptor binding domain of spike, and nucleocapsid were assessed. Multivariable regression was used to assess risk factors for infection.

Results

Of 2834 participants, 85% were female, 81% were teaching staff, and 86% had received at least one dose of SARS-CoV-2 vaccine. Of the 1983 who had been tested via a respiratory specimen, 4.9% reported a positive test. Five additional participants had serologic testing suggestive of a previous infection (3.6% overall incidence). In multivariable regression analysis, risk factors for infection included exposure to a SARS-CoV-2 infected adult (adjusted incidence rate ratio (aIRR) 13.6; 95% confidence interval 8.6, 21.3) or child (aIRR 2.3; 1.3, 4.2) in the household, or school student (aIRR 1.9; 1.2, 3.2), or travel outside the province within 14 days of testing (aIRR 6.0; 1.5, 23.6).

Conclusion

In the first 18 months of the pandemic, education workers had a similar risk of infection with SARS-CoV-2 as other Ontario residents. Practicing protective measures whenever any household member has been exposed to a possible case and at all times when exposed to anyone from outside the home would help reduce the risk of infection.

Nationally Representative Social Contact Patterns among U.S. adults, August 2020-April 2021

The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic., few have reported on differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.

Retrospective analysis to describe trends in first-ever prostate-specific antigen (PSA) testing for primary healthcare facilities in the Gauteng Province, South Africa, between 2006 and 2016

OBJECTIVES

The objective of our study was to use laboratory data to describe prostate-specific antigen (PSA) testing trends for primary healthcare (PHC) services from a single province. PHC is a basic package of services offered to local communities, serving as the first point of contact within the health system. These services are offered at clinics and community health centres (CHC), the latter providing additional maternity, accident and emergency services.

DESIGN

The retrospective descriptive study design was used.

METHODS

We analysed national laboratory data between 2006 and 2016 for men ≥30 years in the Gauteng Province. We used the probabilistic matching algorithm to create first-ever PSA cohort. We used the hot-deck imputation to assign missing race group values and the district health information system facility descriptors to identify PHC testing. We reported patient numbers by calendar year, age category and race group as well as descriptive statistics. We used multivariable logistic regression to assess any association for race group and age with a PSA ≥4 µg/L.

RESULTS

Between 2006 and 2016, numbers of men tested increased from 1782 to 67 025, respectively, with 186 984/239 506 (78.1%) tests were from clinics. The majority of testing was for men in the 50-59 age category (31.5%) and Black Africans (86.4%). We reported a median of 0.9 µg/L that increased with age. A PSA ≥4 µg/L was reported for 11.7% of men, increasing to 35.5% for the ≥70 age category. The logistic regression reported that the adjusted odds of having a PSA ≥4 µg/L was significantly lower for Indian/Asians, multiracials and whites than for Black Africans (p value<0.0001).

CONCLUSIONS

Our study has shown a marked increase in PSA testing from clinics and CHC suggestive of screening for prostate cancer. The approaches reported in this study can be extended for national data.

[Longitudinal care: factors associated with adherence to postpartum follow-up according to data from PMAQ-AB]

The aim was to identify factors of longitudinal care associated with women´s adherence to postpartum consultation in Brazil. This was a cross-sectional study of data from 19,177 postpartum women who participated in the external assessment of the third cycle of the Brazilian National Program for Improvement of Access and Quality of Basic Care (PMAQ-AB), 2017. The dependent variable was postpartum consultation, and the independent variables, grouped hierarchically, were sociodemographic and economic at the distal level and issues analogous to longitudinal care at the proximal level. Multiple logistic regression analysis was performed with hierarchical entry of variables, where sociodemographic and economic variables were used to adjust the model. The results showed that 53% of women had undergone postpartum follow-up. The odds of adherence to postpartum follow-up were higher in women who received a home visit by a community health agents in the first week after childbirth (OR = 4.81), those with seven or more prenatal consultations (OR = 2.74), those who had sought care at the health unit in question (OR = 1.21), and those who had been seen by the same physician (OR = 1.14). In conclusion, the proportion of postpartum consultations was low (53%), and adherence to postpartum follow-up was higher when women received visits by community health agents, were accompanied by the same physician, had regular prenatal care, and had a specific healthcare unit as their regular source of care. Consistent longitudinal care was identified as an attribute of primary care that should be strengthened to improve postpartum care.

MaWGAN: A Generative Adversarial Network to Create Synthetic Data from Datasets with Missing Data

The creation of synthetic data are important for a range of applications, for example, to anonymise sensitive datasets or to increase the volume of data in a dataset. When the target dataset has missing data, then it is common to just discard incomplete observations, even though this necessarily means some loss of information. However, when the proportion of missing data are large, discarding incomplete observations may not leave enough data to accurately estimate their joint distribution. Thus, there is a need for data synthesis methods capable of using datasets with missing data, to improve accuracy and, in more extreme cases, to make data synthesis possible. To achieve this, we propose a novel generative adversarial network (GAN) called MaWGAN (for masked Wasserstein GAN), which creates synthetic data directly from datasets with missing values. As with existing GAN approaches, the MaWGAN synthetic data generator generates samples from the full joint distribution. We introduce a novel methodology for comparing the generator output with the original data that does not require us to discard incomplete observations, based on a modification of the Wasserstein distance and easily implemented using masks generated from the pattern of missing data in the original dataset. Numerical experiments are used to demonstrate the superior performance of MaWGAN compared to (a) discarding incomplete observations before using a GAN, and (b) imputing missing values (using the GAIN algorithm) before using a GAN.

Major ambulatory surgery among US adults with inflammatory bowel disease, 2017

Background

Patients with inflammatory bowel disease (IBD) have higher health services use than those without IBD. We investigated patient and hospital characteristics of major ambulatory surgery encounters for Crohn’s disease (CD) or ulcerative colitis (UC) vs non-IBD patients.

Methods

We conducted a cross-sectional study using 2017 Nationwide Ambulatory Surgery Sample.

Major ambulatory surgery encounters among patients aged ≥18 years with CD (n = 20,635) or UC (n = 9,894) were compared to 9.4 million encounters among non-IBD patients. Weighted percentages of patient characteristics (age, sex, median household income, primary payers, patient location, selected comorbidities, discharge destination, type of surgeries) and hospital-related characteristics (hospital size, ownership, location and teaching status, region) were compared by IBD status (CD, UC, and no IBD). Linear regression was used to estimate mean total charges, controlling for these characteristics.

Results

Compared with non-IBD patients, IBD patients were more likely to have private insurance, reside in urban areas and higher income zip codes, and undergo surgeries in hospitals that were private not-for-profit, urban teaching, and in the Northeast. Gastrointestinal surgeries were more common among IBD patients. Some comorbidities associated with increased risk of surgical complications were more prevalent among IBD patients. Total charges were 9% lower for CD patients aged <65 years (Median: $16,462 vs $18,106) and 6% higher for UC patients aged ≥65 years (Median: $16,909 vs $15,218) compared to their non-IBD patient counterparts.

Conclusions

Differences in characteristics of major ambulatory surgery encounters by IBD status may identify opportunities for efficient resource allocation and positive surgical outcomes among IBD patients.

A reinforcement learning-based approach for imputing missing data

Missing data is a major problem in real-world datasets, which hinders the performance of data analytics. Conventional data imputation schemes such as univariate single imputation replace missing values in each column with the same approximated value. These univariate single imputation techniques underestimate the variance of the imputed values. On the other hand, multivariate imputation explores the relationships between different columns of data, to impute the missing values. Reinforcement Learning (RL) is a machine learning paradigm where the agent learns by taking actions and receiving rewards in response, to achieve its goal. In this work, we propose an RL-based approach to impute missing data by learning a policy to impute data through an action-reward-based experience. Our approach imputes missing values in a column by working only on the same column (similar to univariate single imputation) but imputes the missing values in the column with different values thus keeping the variance in the imputed values. We report superior performance of our approach, compared with other imputation techniques, on a number of datasets.

Complaints about excessive use of police force in women's neighborhoods and subsequent perinatal and cardiovascular health

There are substantial, unexplained racial disparities in women’s health. Some of the most pronounced involve elevated rates of preterm delivery (PTD) and cardiovascular disease (CVD) among Black women. We hypothesized that stress associated with excessive use of force by police may contribute to these disparities. In two prospective cohorts derived from electronic health records (pregnancy cohort, N = 67,976; CVD cohort, N = 6773), we linked formal complaints of excessive police force in patients’ neighborhoods with health outcomes. Exposed Black women were 1.19 times as likely to experience PTD [95% confidence interval (CI): 1.04 to 1.35] and 1.42 times as likely to develop CVD (95% CI: 1.12 to 1.79), even after adjustment for neighborhood disadvantage and homicide. The excess risks of PTD were also observed in maternal fixed-effects analyses comparing births to the same woman. These findings suggest police violence may be an unrecognized contributor to health inequity for Black women.

A generalized class of estimators for sensitive variable in the presence of measurement error and non-response

In this paper, a general class of estimators is proposed for estimating the finite population mean for sensitive variable, in the presence of measurement error and non-response in simple random sampling. Expressions for bias and mean square error up to first order of approximation, are derived. Impact of measurement errors is examined using real data sets, including the survey conducted at Quaid-i-Azam University, Islamabad. Simulated data sets are also used to observe the performance of the proposed estimators in comparison to some other estimators. We obtain the empirical bias and MSE values for the proposed and the competing estimators.

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

Care processes and racial/ethnic differences in family reports of end-of-life care among Veterans: A mediation analysis

BACKGROUND

Bereaved family members of racial/ethnic minority Veterans are less likely than families of White Veterans to provide favorable overall ratings of end-of-life (EOL) care quality; however, the underlying mechanisms for these differences have not been explored. The objective of this study was to examine whether a set of EOL care process measures mediated the association between Veteran race/ethnicity and bereaved families' overall rating of the quality of EOL care in VA medical centers (VAMCs).

METHODS

A retrospective, cross-sectional analysis of linked Bereaved Family Survey (BFS), administrative and clinical data was conducted. The sample included 17,911 Veterans (mean age: 73.7; SD: 11.6) who died on an acute or intensive care unit across 121 VAMCs between October 2010 and September 2015. Mediation analyses were used to assess whether five care processes (potentially burdensome transitions, high-intensity EOL treatment, and the BFS factors of Care and Communication, Emotional and Spiritual Support, and Death Benefits) significantly affected the association between Veteran race/ethnicity and a poor/fair BFS overall rating.

RESULTS

Potentially burdensome transitions, high-intensity EOL treatment, and the three BFS factors of Care and Communication, Emotional and Spiritual Support, and Death Benefits did not substantially mediate the relationship between Veteran race/ethnicity and poor/fair overall ratings of quality of EOL care by bereaved family members.

CONCLUSIONS

The reasons underlying poorer ratings of quality of EOL care among bereaved family members of racial/ethnic minority Veterans remain largely unexplained. More research on identifying potential mechanisms, including experiences of racism, and the unique EOL care needs of racial and ethnic minority Veterans and their families is warranted.

Social anxiety and rumination in the context of the revised reinforcement sensitivity theory and the mediation model of social anxiety

The aim of this study was to examine the relationship between temperament, ruminative thought style and social anxiety using latent variable modeling. Before examining the integrated model that specifies the relations between the constructs, relevant measurement issues were examined. The study was conducted on a heterogeneous sample from the general population that included 1,029 participants (62.1% female) aged 19 to 79. The findings show that the Behavioural Inhibition System is the most important vulnerability factor for the development of social anxiety, and it has both a direct effect and an indirect one through the ruminative thought style. Also, Freeze has an additional contribution to the increased experience of social anxiety. The Behavioural Approach System has complex effects on social anxiety ? with a direct protective effect, and indirectly ? with a facilitation of the ruminative thought style. Thus, BAS can also act as a risk factor. The findings support the revised Reinforcement Sensitivity Theory and provide a basis for the extension of the Kimbrel?s Mediation Model of Social Anxiety.

Thirdhand Smoke Knowledge, Attitudes, and Behavior: Development of Reliable and Valid Self-report Measures

INTRODUCTION

This study sought to provide essential tobacco control tools by testing the reliability and validity of new self-report scales developed to assess thirdhand smoke (THS) (ie, toxic tobacco residue) related knowledge, attitudes, and behavior (KAB).

AIMS AND METHODS

Items for the KAB scales were adapted from established secondhand smoke (SHS) measures, reviewed to support face validity, and tested in a longitudinal online survey evaluating THS health messages. Participants were California adults at risk of THS exposure. For 7 months, the three KAB scales were completed monthly, and data from the first (n = 1086), third (n = 315), and seventh (n = 301) month surveys were used in these analyses.

RESULTS

All three scales demonstrated consistent reliability and single-factor loading at all three timepoints for knowledge (αrange: .87-.90), attitude (αrange: .84-.87), and behavior (αrange: .80-.86). Similarly, analyses supported scale convergent validity (scale correlations rrange: .45-.85; all p values <.001), discriminant validity between smokers and nonsmokers (knowledge Cohen's drange: .57-.61, all p values <.001; attitude Cohen's drange: .78-.82, all p values <.001; behavior Cohen's drange: .90-.99, all p values <.001), and predictive validity (range R2KAB: .41-.48; all p values <.001).

CONCLUSIONS

KAB scales about THS provide new opportunities for tobacco control advocates and scholars to identify gaps in knowledge, misperceptions, and obstacles to behavior change in order to guide the design of novel tobacco control policies and interventions.

IMPLICATIONS

Numerous scales have been vetted as reliable and valid measures for assessing SHS-related KABs. Currently, standard measures of THS KABs are not available. This study tested three THS scales to fill this gap. The present findings provide tobacco control advocates, scholars, and practitioners tools for assessing KABs related to THS. This information is critical to development, implementation, and evaluation of novel tobacco control strategies.

Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

Impact of neoadjuvant therapy followed by laparoscopic radical gastrectomy with D2 lymph node dissection in Western population: A multi-institutional propensity score-matched study

BACKGROUND AND OBJECTIVES

In the setting of a minimally invasive approach, we aimed to compare short and long-term postoperative outcomes of patients treated with neoadjuvant therapy (NAT) + surgery or upfront surgery in Western population.

METHODS

All consecutive patients from six Italian and one Serbian center with locally advanced gastric cancer who had undergone laparoscopic gastrectomy with D2 lymph node dissection were selected between 2005 and 2019. After propensity score-matching, postoperative morbidity and oncologic outcomes were investigated.

RESULTS

After matching, 97 patients were allocated in each cohort with a mean age of 69.4 and 70.5 years. The two groups showed no difference in operative details except for a higher conversion rate in the NAT group (p = 0.038). The overall postoperative complications rate significantly differed between NAT + surgery (38.1%) and US (21.6%) group (p = 0.019). NAT was found to be related to a higher risk of postoperative morbidity in patients older than 60 years old (p = 0.013) but not in patients younger (p = 0.620). Conversely, no difference in overall survival (p = 0.41) and disease-free-survival (p = 0.34) was found between groups.

CONCLUSIONS

NAT appears to be related to a higher postoperative complication rate and equivalent oncological outcomes when compared with surgery alone. However, poor short-term outcomes are more evident in patients over 60 years old receiving NAT.

Age at Glaucoma Diagnosis in Germline Myocilin Mutation Patients: Associations with Polymorphisms in Protein Stabilities

Glaucoma is the leading cause of irreversible blindness worldwide, with elevated intraocular pressure (IOP) as the only known modifiable risk factor. Trabecular meshwork (TM)-inducible myocilin (the MYOC gene) was the first to be identified and linked to juvenile and primary open-angle glaucoma. It has been suggested that mutations in the MYOC gene and the aggregation of mutant myocilin in the endoplasmic reticulum (ER) of TM may cause ER stress, resulting in a reduced outflow of aqueous humor and an increase in IOP. We selected 20 MYOC mutations with experimentally determined melting temperatures of mutated myocilin proteins. We included 40 published studies with at least one glaucoma patient with one of these 20 MYOC mutations and information on age at glaucoma diagnosis. Based on data from 458 patients, we found that a statistically significant but weak correlation was present between age and melting temperature based on various assumptions for age. We therefore conclude that genetic analysis of MYOC mutations alone cannot be used to accurately predict age at glaucoma diagnosis. However, it might be an important prognostic factor combined with other clinical factors for critical and early detection of glaucoma.

A comparison study of human examples vs. non-human examples in an evolution lesson leads to differential impacts on student learning experiences in an introductory biology course

Background

Instructors can teach evolution using any number of species contexts. However, not all species contexts are equal, and taxa choice can alter both cognitive and affective elements of learning. This is particularly true when teaching evolution using human examples, a promising method for evolution instruction that nevertheless comes with unique challenges. In this study, we tested how an evolution lesson focused on a human example may impact students’ engagement, perceived content relevance, learning gains, and level of discomfort, when compared to the same lesson using a non-human mammal example. We use this isomorphic lesson and a pre-post study design administered in a split-section introductory biology classroom to isolate the importance of the species context.

Results

For two of the four measurements of interest, the effect of using human examples could not be understood without accounting for student background. For learning gains, students with greater pre-class content knowledge benefited more from the human examples, while those with low levels of knowledge benefited from the non-human example. For perceived relevance, students who were more accepting of human evolution indicated greater content relevance from the human example. Regardless of condition, students with lower evolution acceptance reported greater levels of discomfort with the lesson.

Conclusions

Our results illustrate the complexities of using human examples to teach evolution. While these examples were beneficial for many students, they resulted in worse outcomes for students that were less accepting of evolution and those who entered the course with less content knowledge. These findings demonstrate the need to consider diverse student backgrounds when establishing best practices for using human examples to teach evolution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12052-021-00148-w.

Psychometric properties of the Child Development Assessment Questionnaire (QAD-PIPAS) for use in population studies involving Brazilian children aged 0-59 months

OBJECTIVE

To analyze the psychometric properties of the Child Development Assessment Questionnaire (QAD-PIPAS).

METHODS

This methodological study was comprised of two axes. The first one aimed to analyze the instrument's construct validity (discriminant and concurrent validity) and internal consistency, and the second one examined test-retest reliability, involving two different samples and procedures. For construct validity and internal consistency, the sample was recruited in Embu das Artes-SP, Brasilia-DF and Recife-PE during the immunization campaign in 2017, involving caregivers of 2005 children under 60 months of age (1295 under 36 and 710 from 37 to 59 months). For the test-retest analysis the sample consisted of 30 children aged 0-59 months old that attended daycare centers in Embu das Artes-SP in 2018.

RESULTS

Multivariate analyses of construct validity showed that the QAD-PIPAS was able to identify the association between the outcome (suspected child development delays) and expected risk and protective factors based on Nurturing Care Framework (OMS/UNICEF). A significant positive correlation was achieved between the scores of the QAD-PIPAS and CREDI in six of the eight age groups analyzed, with the most significant correlations being in the age groups from 25 to 30 and 31-36 months. Acceptable internal consistencies were identified in all age groups, with better performance above 36 months of age (Cronbach's alpha between 0.61 to 0.80). We also found an adequate test-retest reliability (global Kappa 0.81).

CONCLUSION

The QAD-PIPAS showed evidence of construct validity and reliability to be used in population studies involving children aged 0-59 months during multi-vaccination campaigns in Brazil.