A comparative investigation of methods for logistic regression with separated or nearly separated data

Investigating the associations between early labour onset symptoms and self-diagnosed labour onset in a cohort study of primiparas

OBJECTIVE

The early recognition of possible labour onset symptoms may be pivotal to identifying the beginning of early labour and are usually recognised by the birthing women themselves. The present study illustrates the interrelationship among five labour-onset symptoms and explores the association of these labour-onset symptoms with the self-diagnosed labour onset of primiparas.

METHODS

A prospective cohort study on a sample of 69 primigravida in Giessen, Germany, expecting spontaneous onset of labour at term. The participants filled in a diary from ≥37 + 0 weeks gestation until self-diagnosed labour onset. Descriptive, bivariate and inferential analysis explored association of labour onset symptoms with self-diagnosed labour onset while accounting for maternal and newborn characteristics.

RESULTS

Self-diagnosed labour onset was positively associated with all symptoms and clinical characteristics, apart from irregular pain and maternal weight and age. Moreover, regular pain was negatively correlated with irregular pain; having regular pain increased the odds of self-diagnosed labour onset substantially (OR: 10.18, 95 % CI: 2.39-66.27), followed by gastrointestinal symptoms (OR: 2.07, 95 % CI: 0.40-13.10) and emotional symptoms (OR: 2.05, 95% CI: 0.30-13.98).

CONCLUSION

Being the initiator of intrapartum care without any birth experience, primiparas are prone to experiencing dissatisfaction in care and may enter professional care too late or too early. The present study showed that regular pain may signify primiparas to self-diagnose labour onset within 24 h and indicate early labour symptoms that may be relevant for a self-diagnosed labour onset.

Rare coding variant analysis for human diseases across biobanks and ancestries

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients

The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.

Age at natural menopause and development of chronic diseases in the female population of Kharameh, Iran: A historical cohort study

Background and Aims

Declines in estradiol levels after menopause have been reported to be associated with several health outcomes. This study aimed to determine the effect of age at natural menopause (ANM) on some of the most common chronic diseases.

Methods

This historical cohort study was performed on 2636 postmenopausal women aged 40-70 years participating in phase one of the PERSIAN cohort study in Kharameh, Iran, during 2015-2017. The effect of early (<45 years), intermediate (45-53 years), and late menopause (>53 years) on chronic diseases such as hypertension, diabetes, ischemic heart diseases, stroke, thyroid diseases, and depression was assessed using classic logistic regression for diseases with an incidence rate of more than 10% and Firth's logistic regression for diseases with an incidence of less than this amount.

Results

The mean age of women was 53.48 ± 8.59. Respectively, early and intermediate menopause was associated with ischemic heart disease (odds ratio [OR = 1.61, 95% confidence interval [CI]: 1.08-2.42; p = 0.020), (OR = 1.57, 95% CI: 1.13-2.21; p = 0.008) and thyroid diseases (OR = 3.10, 95% CI: 1.64-6.24; p < 0.001), (OR = 1.83, 95% CI: 1.02-3.57; p = 0.042). furthermore, early menopause was a risk factor for diabetes (OR = 1.46, 95% CI: 1.07-2.00; p = 0.018), depression (OR = 4.79, 95% CI: 2.20-11.79; p = <0.001) and stroke (OR = 3.00, 95% CI: 1.08-9.32; p = 0.034).

Conclusions

In this study, women with diabetes, ischemic heart diseases, stroke, thyroid disorders, and depression had a younger ANM compared to their healthy counterparts. Therefore, applying appropriate strategies to postpone the age of menopause, can reduce the incidence of these types of chronic diseases.

Antibiotic overuse, poor antimicrobial stewardship, and low specificity of syndromic case management in a cross section of men with urethral discharge syndrome in Kampala, Uganda

OBJECTIVE

High prevalence of sexually transmitted infections (STIs) combined with poor antimicrobial stewardship are drivers of STI antimicrobial resistance (AMR) especially in resource-limited settings where syndromic case management (SCM) is the norm. We characterized patterns of antibiotic use prior to clinic attendance and study enrollment in Ugandan men with urethral discharge syndrome (UDS), evaluated in-clinic prescribing, and the performance characteristics of SCM.

METHODS

Participants were recruited from government clinics participating in an existing gonococcal surveillance program in Kampala, Uganda. Questionnaires including antimicrobial use prior to attendance, prior episodes of UDS, penile swabs, and blood samples were collected. Bivariable and multivariable logistic regression models were used to estimate odds ratios (OR) for preselected factors likely to be associated with antibiotic use. In-clinic antibiotic treatment data were extracted from clinical notes, and the performance of SCM against laboratory-based STI diagnoses was evaluated.

FINDINGS

Between October 2019 and November 2020, 100(40%) of 250 men with UDS reported taking antibiotics in the 14days prior to attending the clinic. Of these 210(84%) had at least one curable STI and 20% had a reactive point-of-care HIV test. Multivariable analysis demonstrated significant associations between recent antimicrobial use and duration of UDS symptoms <6 days (OR 2.98(95%CI 1.07,8.36), p = 0.038), and sex with women only (OR 0.08(95%CI 0.01,0.82),p = 0.038). The sensitivity of SCM ranged from 80.0% to 94.4%; specificity was low between 5.6% and 33.1%. The positive predictive value of SCM ranged from 2.4(95%CI 0.7,6.0) for trichomoniasis to 63.4(95%CI 56.5,69.9) for gonorrhea.

CONCLUSION

Pre-enrollment antibiotic use was common in this population at high risk of STI and HIV. Combined with the poor specificity of SCM for male UDS, extensive antibiotic use is a likely driver of STI-AMR in Ugandan men. Interventions to improve antimicrobial stewardship and deliver affordable diagnostics to augment SCM and decrease overtreatment of STI syndromes are required.

COVID-19 restrictions promoted the newly occurring loneliness in older people - a prospective study in a memory clinic population

Introduction

A high burden and many negative outcomes for older people were associated with the COVID-19 pandemic. Social isolation and loneliness are prevalent health problems impacting well-being and quality of life and may have increased due to pandemic-related restrictions. Methods: This study investigate the influence of the COVID-19 pandemic on loneliness in people visiting a mem40ory clinic between March 2020 and September 2022. We conducted a prospective, single-center, questionnaire-based observational follow-up study to assess potential predictors of newly occurring, pandemic-related loneliness. Next to a newly developed COVID-19 questionnaire, a comprehensive neuropsychological test battery, the Neuropsychiatric Inventory and the Geriatric Depression Scale were used.

Results

In total 426 people (mean age: 76.48 years, 12.9% cognitively intact, 33.1% diagnosed with Mild Cognitive Impairment, 49.8% diagnosed with dementia, and 4.2% diagnosed with depression) completed the COVID-19 questionnaire at baseline and 166 at follow-up. Newly occurring loneliness was indicated by 22.3% of baseline participants and by 24.1% of follow-up participants. Results of logistic regression analysis showed that living alone (OR 5.452) and having less contact with friends (OR 2.771) were most predictive of the occurrence of loneliness. The use of digital communication media as an alternative strategy for social interaction was lowest in dementia patients (6-13%).

Discussion

In conclusion, personal contacts and a close friendship network appear to be more decisive to prevent loneliness in older people than does the use of digital communication media. However, promoting an intensified use of digital communication media may be useful to counteract loneliness, especially in dementia patients.

Estimation of the causal effects of time-varying treatments in nested case-control studies using marginal structural Cox models

When estimating the causal effects of time-varying treatments on survival in nested case-control (NCC) studies, marginal structural Cox models (Cox-MSMs) with inverse probability weights (IPWs) are a natural approach. However, calculating IPWs from the cases and controls is difficult because they are not random samples from the full cohort, and the number of subjects may be insufficient for calculation. To overcome these difficulties, we propose a method for calculating IPWs to fit Cox-MSMs to NCC sampling data. We estimate the IPWs using a pseudo-likelihood estimation method with an inverse probability of sampling weight using NCC samples, and additional samples of subjects who experience treatment changes and subjects whose follow-up is censored are required to calculate the weights. Our method only requires covariate histories for the samples. The confidence intervals are calculated from the robust variance estimator for the NCC sampling data. We also derive the asymptotic properties of the estimator of Cox-MSM under NCC sampling. The proposed methods will allow researchers to apply several case-control matching methods to improve statistical efficiency. A simulation study was conducted to evaluate the finite sample performance of the proposed method. We also applied our method to a motivating pharmacoepidemiological study examining the effect of statins on the incidence of coronary heart disease. The proposed method may be useful for estimating the causal effects of time-varying treatments in NCC studies.

Evaluation of an injectable monthly extended-release buprenorphine program in a low-barrier specialty addiction medicine clinic

INTRODUCTION

Monthly injectable extended-release buprenorphine (XR-BUP) can address several systemic and individual barriers to consistent sublingual buprenorphine treatment for patients with opioid use disorder (OUD). Real-world evaluations of XR-BUP in the outpatient addiction treatment setting are limited. The purpose of this study was to compare 6-month treatment retention and urine drug tests between patients who initiated XR-BUP compared to those who were prescribed but did not initiate XR-BUP in a low-barrier addiction medicine specialty clinic.

METHODS

We conducted a retrospective cohort study of adults with OUD prescribed XR-BUP between 12/1/2018 and 12/31/2020 in a low-barrier addiction medicine specialty clinic to compare 6-month treatment retention between patients who initiated XR-BUP and those who were prescribed but did not initiate XR-BUP (comparison group). Secondary outcomes included percent of urine toxicology tests negative for non-prescribed opioids. Multivariable logistic regression models evaluated factors associated with 6-month treatment retention and XR-BUP initiation.

RESULTS

Of the 233 patients prescribed XR-BUP, 148 (63.8 %) identified as non-Hispanic white, 218 (93.6 %) were insured by public insurance (Medicare/Medicaid), and nearly two-thirds were prescribed XR-BUP due to unstable OUD. Approximately 50 % of patients initiated XR-BUP treatment (mean number of injections = 3.7). About 60 % of XR-BUP-treated patients received supplemental sublingual buprenorphine and nearly two-thirds received a 300 mg maintenance dose. Six-month treatment retention was greater in the XR-BUP treatment versus comparison group (70.3 % vs. 36.5 %, p < 0.001). The XR-BUP treatment group had a higher percentage of opioid-negative urine toxicology tests versus the comparison group (67.2 % vs. 36.3 %, p < 0.001). Receipt of XR-BUP was an independent predictor of 6-month treatment retention (OR 5.40, 95 % CI 2.18-13.38). Those prescribed XR-BUP due to unstable OUD had lower odds of treatment retention (OR 0.41, 95 % CI 0.24-0.98) after controlling for receipt of XR-BUP and other variables known to impact retention.

CONCLUSIONS

XR-BUP improved 6-month treatment retention and resulted in a greater proportion of opioid-negative urine toxicology tests compared to a comparison group of patients who were prescribed but did not initiate XR-BUP. Patients with unstable OUD had lower odds of XR-BUP initiation, suggesting the need for targeted interventions to increase XR-BUP uptake in this high-risk population.

Firth's penalized logistic regression: A superior approach for analysis of data from India's National Mental Health Survey, 2016

The National Mental Health Survey of India (NMHS) was a ground-breaking nationwide study that harnessed a uniform, standardized methodology blending quantitative and qualitative approaches. Covering data from 12 states across diverse regions, its mission was to gauge the prevalence of psychiatric disorders, bridge treatment gaps, explore service utilization, and gauge the socioeconomic repercussions of these conditions. This initiative provided pivotal insights into the intricate landscape of mental health in India. One of the analyses planned for NMHS data was to undertake a logistic regression analysis with an aim to unravel how various sociodemographic factors influence the presence or absence of specific psychiatric disorders. Within this pursuit, two substantial challenges loomed. The first pertained to data separation, a complication that could perturb parameter estimation. The second challenge stemmed from the existence of disorders with lower prevalence rates, which resulted in datasets of limited density, potentially undermining the statistical reliability of our analysis. In response to these data-driven hurdles, NMHS recognized the critical necessity for an alternative to conventional logistic regression, one that could adeptly navigate these complexities, ensuring robust and dependable insights from the collected data. Traditional logistic regression, a widely prevalent method for modeling binary outcomes, has its limitations, especially when faced with limited datasets and rare outcomes. Here, the problem of "complete separation" can lead to convergence failure in traditional logistic regression estimations, a conundrum frequently encountered when handling binary variables. Firth's penalized logistic regression emerges as a potent solution to these challenges, effectively mitigating analytical biases rooted in small sample sizes, rare events, and complete separation. This article endeavors to illuminate the superior efficacy of Firth's method in managing small datasets within scientific research and advocates for its more widespread application. We provide a succinct introduction to Firth's method, emphasizing its distinct advantages over alternative analytical approaches and underscoring its application to data from the NMHS 2015-2016, particularly for disorders with lower prevalence.

Risk of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis Associated With Antibiotic Use: A Case-Crossover Study

BACKGROUND

Evidence is lacking on the association between antibiotic use and risk of Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in Asians.

OBJECTIVE

We assessed the risk of SJS/TEN associated with different antibiotic classes in Japanese.

METHODS

We conducted a case-crossover study using a claims database. Firth conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of SJS/TEN associated with antibiotic use in a 56-day hazard period versus 3 control periods. We created 18 cohorts for each antibiotic class and calculated 56-day cumulative incidence per 100,000 new users. The association between antibiotic class and SJS/TEN was also evaluated in each case using the ALgorithm of Drug causality for Epidermal Necrolysis (ALDEN).

RESULTS

Our case-crossover study included 170 SJS/TEN cases. Increased ORs were observed for lincomycins (OR, 33.00 [95% CI, 3.74-4332.05]), trimethoprim-sulfamethoxazole (21.20 [6.73-105.98]), penicillins (14.39 [6.95-34.21]), glycopeptides (14.37 [3.17-136.10]), cephalosporins (7.06 [4.25-12.21]), aminoglycosides (6.55 [1.97-26.84]), quinolones (5.98 [3.34-11.20]), fosfomycin (5.40 [1.20-30.97]), carbapenems (5.09 [1.85-15.64]), tetracyclines (4.95 [1.78-15.27]), and macrolides (3.78 [2.13-6.83]). Cumulative incidence of SJS/TEN was 67.4 for trimethoprim-sulfamethoxazole, 86.2 for glycopeptides, and below 10.0 for the others. Despite the high incidence, only 2 cases had a probable causal relationship with glycopeptides.

CONCLUSION

Some antibiotic classes, including lincomycins, glycopeptides, aminoglycosides, fosfomycin, and carbapenems, were newly suggested to be associated with risk of SJS/TEN; considered together with the high incidence for trimethoprim-sulfamethoxazole and glycopeptides, these findings warrant caution in clinical practice.

Exploring the complex spectrum of dominance and recessiveness in genetic cardiomyopathies

Discrete categorization of Mendelian disease genes into dominant and recessive models often oversimplifies their underlying genetic architecture. Cardiomyopathies (CMs) are genetic diseases with complex etiologies for which an increasing number of recessive associations have recently been proposed. Here, we comprehensively analyze all published evidence pertaining to biallelic variation associated with CM phenotypes to identify high-confidence recessive genes and explore the spectrum of monoallelic and biallelic variant effects in established recessive and dominant disease genes. We classify 18 genes with robust recessive association with CMs, largely characterized by dilated phenotypes, early disease onset and severe outcomes. Several of these genes have monoallelic association with disease outcomes and cardiac traits in the UK Biobank, including LMOD2 and ALPK3 with dilated and hypertrophic CM, respectively. Our data provide insights into the complex spectrum of dominance and recessiveness in genetic heart disease and demonstrate how such approaches enable the discovery of unexplored genetic associations.

A longitudinal investigation of non-suicidal self-injury persistence patterns, risk factors, and clinical outcomes during the college period

BACKGROUND

Although non-suicidal self-injury (NSSI) is known typically to begin in adolescence, longitudinal information is lacking about patterns, predictors, and clinical outcomes of NSSI persistence among emerging adults. The present study was designed to (1) estimate NSSI persistence during the college period, (2) identify risk factors and high-risk students for NSSI persistence patterns, and (3) evaluate the association with future mental disorders and suicidal thoughts and behaviors (STB).

METHODS

Using prospective cohorts from the Leuven College Surveys (n = 5915), part of the World Mental Health International College Student Initiative, web-based surveys assessed mental health and psychosocial problems at college entrance and three annual follow-up assessments.

RESULTS

Approximately one in five (20.4%) students reported lifetime NSSI at college entrance. NSSI persistence was estimated at 56.4%, with 15.6% reporting a high-frequency repetitive pattern (≥five times yearly). Many hypothesized risk factors were associated with repetitive NSSI persistence, with the most potent effects observed for pre-college NSSI characteristics. Multivariate models suggest that an intervention focusing on the 10-20% at the highest predicted risk could effectively reach 34.9-56.7% of students with high-frequency repetitive NSSI persistence (PPV = 81.8-93.4, AUC = 0.88-0.91). Repetitive NSSI persistence during the first two college years predicted 12-month mental disorders, role impairment, and STB during the third college year, including suicide attempts.

CONCLUSIONS

Most emerging adults with a history of NSSI report persistent self-injury during their college years. Web-based screening may be a promising approach for detecting students at risk for a highly persistent NSSI pattern characterized by subsequent adverse outcomes.

Randomized pilot trial of the "Mom Power" trauma- and attachment-informed multi-family group intervention in treating and preventing postpartum symptoms of depression among a health disparity sample

Introduction

Perinatal depression, a common complication related to childbearing, impacts mothers, children, and families. Efficacious interventions reduce perinatal depression symptoms; effort is needed to prevent the onset of perinatal depression. To determine feasibility and preliminary efficacy in reducing perinatal depression, we conducted a community-based, randomized parallel open pilot trial of Mom Power, a group-based intervention to improve mental health and parenting in mothers with young children.

Methods

Mom Power consists of 10 group sessions, focused on parenting, child development and self-care and three individual sessions, to build rapport and provide personalized referrals. Control group participants received psychoeducational mailings. Computer-based urn randomization assigned mothers with experiences of interpersonal violence, depression, or other traumatic experiences to Mom Power (68) or control (54).

Results

At 3-months post-treatment, the 31 retained women assigned to Mom Power were half as likely to meet criteria for probable depression (26%) as the 22 women retained in the control group (55%), with treatment predicting lower incidence of probable depression (OR = 0.13, p = 0.015). Moreover, among the 23 women who did not meet criteria for depression diagnosis at baseline, no women in the treatment group developed depression (n = 0, 0%) compared to control group women (n = 3, 30%). Logistic regression controlling for selective attrition confirmed the treatment effect on preventing new onset of depression (OR = 0.029, p = 0.012).

Conclusion

These findings support the use of Mom Power for both treatment and prevention of perinatal depression.

Clinical trial registration

https://classic.clinicaltrials.gov/ct2/show/NCT01554215, NCT01554215.

Maternal cyclobenzaprine exposure and risk of birth defects in the National Birth Defects Prevention Study (1997-2011) and Birth Defects Study to Evaluate Pregnancy exposureS (2014-2018)

PURPOSE

Cyclobenzaprine is a muscle relaxant indicated for acute pain. Little is known about cyclobenzaprine's safety during pregnancy. We explored the association between maternal cyclobenzaprine exposure and risk of birth defects among offspring.

METHODS

We combined data from two large, multi-site, population-based case-control studies in the United States. Cases were identified from birth defects registries across 10 states; controls were liveborn infants without birth defects randomly selected from the same catchment areas. Participants reported cyclobenzaprine use during the month before conception through the third month of pregnancy ("periconception") via computer-assisted telephone interview. We used logistic regression to assess associations between periconceptional cyclobenzaprine exposure and selected structural birth defects. We calculated crude odds ratios (OR) with corresponding 95% confidence intervals (CI).

RESULTS

Our study included 33 615 cases and 13 110 controls. Overall, 51 case (0.15%) and 9 control (0.07%) participants reported periconceptional cyclobenzaprine use. We observed increased risk for all seven defects with ≥3 exposed cases: cleft palate (OR = 4.79, 95% CI 1.71-13.44), cleft lip (OR = 2.50, 95% CI 0.89-7.02), anorectal atresia/stenosis (OR = 6.91, 95% CI 1.67, 28.65), d-transposition of the great arteries (OR = 6.97, 95% CI 2.17-22.36), coarctation of the aorta (OR = 5.58, 95% CI 1.88-16.58), pulmonary valve stenosis (OR = 4.55, 95% CI 1.10-18.87), and secundum atrial septal defect (OR = 3.08, 95% CI 0.83-11.45).

CONCLUSIONS

Even in our large sample, cyclobenzaprine use was rare. Our estimates are unadjusted and imprecise so should be interpreted cautiously. These hypothesis-generating results warrant confirmation and further research to explore possible mechanisms.

The impact of rare protein coding genetic variation on adult cognitive function

Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.

Mobility is Associated with Higher-risk Sexual Partnerships Among Both Men and Women in Co-resident Couples in Rural Kenya and Uganda: A Longitudinal Cohort Study

Population mobility is associated with higher-risk sexual behaviors in sub-Saharan Africa and is a key driver of the HIV epidemic. We conducted a longitudinal cohort study to estimate associations between recent mobility (overnight travel away from home in past six months) or migration (changes of residence over defined geopolitical boundaries) and higher-risk sexual behavior among co-resident couples (240 couples aged ≥ 16) from 12 rural communities in Kenya and Uganda. Data on concurrent mobility and sexual risk behaviors were collected every 6-months between 2015 and 2020. We used sex-pooled and sex-stratified multilevel models to estimate associations between couple mobility configurations (neither partner mobile, male mobile/female not mobile, female mobile/male not mobile, both mobile) and the odds of higher-risk (casual, commercial sex worker/client, one night stand, inherited partner, stranger) and concurrent sexual partnerships based on who was mobile. On average across all time points and subjects, mobile women were more likely than non-mobile women to have a higher-risk partner; similarly, mobile men were more likely than non-mobile men to report a higher-risk partnership. Men with work-related mobility versus not had higher odds of higher-risk partnerships. Women with work-related mobility versus not had higher odds of higher-risk partnerships. Couples where both members were mobile versus neither had greater odds of higher-risk partnerships. In analyses using 6-month lagged versions of key predictors, migration events of men, but not women, preceded higher-risk partnerships. Findings demonstrate HIV risks for men and women associated with mobility and the need for prevention approaches attentive to the risk-enhancing contexts of mobility.

Genetic diversity of the oipA gene among Helicobacter pylori isolates and clinical outcome in Vietnam

Outer inflammatory protein A (OipA), which is encoded by the oipA gene, can induce interleukin-8 secretion in gastric epithelial cells. The functional status of the oipA gene is regulated by the slipped-strand mispairing mechanism based on the CT dinucleotide repeat number in the 5' region. This study aimed to investigate the oipA functional status ("on/off") of Helicobacter pylori (H. pylori) and its association with gastroduodenal diseases in southwestern Vietnam. The cross-sectional study was conducted on 173H. pylori isolates from 173 patients with gastroduodenal diseases. Sanger sequencing was used to determine the functional status of oipA. Multivariable logistic regression analysis was performed to identify the association between oipA status and gastroduodenal diseases. The oipA "on" status accounted for 96% of H. pylori isolates. Twenty-five CT repeat patterns of the oipA 5' signal region were observed, five of which were novel CT repeat patterns. The oipA "on" status was found in 100%, 97.8%, and 86.8% of H. pylori isolates from patients with peptic ulcer, precancerous lesions, and chronic gastritis, respectively (p < 0.01). The oipA "on" status was related to gastric precancerous lesions versus chronic gastritis (adjusted OR = 7.39, 95% CI: 1.35-40.59, p = 0.021) and peptic ulcers versus chronic gastritis (adjusted OR = 12.79, 95% CI: 1.19-1760.32, p = 0.033). Our data show a high prevalence of the oipA "on" status, which was associated with precancerous gastric lesions and peptic ulcers. Moreover, genetic diversity in the number and pattern of CT dinucleotide repeat of oipA among Vietnamese H. pylori strains was identified.

A comparison of bias-adjusted generalized estimating equations for sparse binary data in small-sample longitudinal studies

Using a generalized estimating equation (GEE) can lead to a bias in regression coefficients for a small sample or sparse data. The bias-corrected GEE (BCGEE) and penalized GEE (PGEE) were proposed to resolve the small-sample bias. Moreover, the standard sandwich covariance estimator leads to a bias of standard error for small samples; several modified covariance estimators have been proposed to address this issue. We review the modified GEEs and modified covariance estimators, and evaluate their performance in sparse binary data from small-sample longitudinal studies. The simulation results showed that GEE and BCGEE often failed to achieve convergence, whereas the convergence proportion for PGEE was quite high. The bias for the regression coefficients was generally in the ascending order of PGEE < $$ < $$ BCGEE < $$ < $$ GEE. However, PGEE and BCGEE did not sufficiently remove the bias involving 20-30 subjects with unequal exposure levels with a 5% response rate. The coverage probability (CP) of the confidence interval for BCGEE was relatively poor compared with GEE and PGEE. The CP with the sandwich covariance estimator deteriorated regardless of the GEE methods under the small sample size and low response rate, whereas the CP with the modified covariance estimators-such as Morel's method-was relatively acceptable. PGEE will be the reasonable way for analyzing sparse binary data in small-sample studies. Instead of using the standard sandwich covariance estimator, one should always apply the modified covariance estimators for analyzing these data.

An exact regression-based approach for the estimation of natural direct and indirect effects with a binary outcome and a continuous mediator

In the causal mediation framework, a number of parametric regression-based approaches have been introduced in recent years for estimating natural direct and indirect effects for a binary outcome in an exact manner, without invoking simplifying assumptions based on the rareness or commonness of the outcome. However, most of these works have focused on a binary mediator. In this article, we aim at a continuous mediator and introduce an exact approach for the estimation of natural effects on the odds ratio, risk ratio, and risk difference scales. Our approach relies on logistic and linear models for the outcome and mediator, respectively, and uses numerical integration to calculate the nested counterfactual probabilities underlying the definition of natural effects. Formulas for the delta method standard errors for all effects estimators are provided. The performance of our proposed exact estimators was evaluated in simulation studies that featured scenarios with different levels of outcome rareness/commonness, including a marginally but not conditionally rare outcome scenario. Furthermore, we evaluated the merit of Firth's penalization to mitigate the bias in the logistic regression coefficients estimators for the smallest outcome prevalences and sample sizes investigated. Using a SAS macro provided, we implemented our approach to assess the effect of placental abruption on low birth weight mediated by gestational age. We found that our exact natural effects estimators worked properly in both simulated and real data applications.

The effect of preoperative statins on postoperative mortality, renal, and neurological complications in patients undergoing cardiac surgeries: a retrospective cohort study

BACKGROUND

Cardiac surgery is performed worldwide to treat severe cases of cardiovascular diseases. Statins have shown controversial effects on complications after cardiac surgeries. We aimed to investigate the effect of preoperative statin therapy on the frequency of postoperative mortality, renal, and neurological complications.

METHODS

In a retrospective cohort study, the database of patients operated on in two hospitals in southern Iran during 2008-2019 was used to compare preoperative statin use with no use on the composite outcome of mortality, renal, and neurological complications as well as on each component of the composite, separately. Effects of low dose (<40 mg simvastatin equivalence) vs. high dose (≥40 mg) statins were also evaluated. Confounders that could affect the outcomes were considered in the logistic regression model, and multiple imputation techniques were used to categorize patients with unknown statin dose use as either high or low-dose users.

RESULTS

Of total 7329 patients, 17.6% of statin users and 17% of non-statin users developed the composite outcome (P=0.51). Statin use had no statistically significant association with the composite outcome (aRR 1.01 [95% CI: 0.88-1.16]). There was no significant association with mortality [aRR: 0.75 (95% CI: 0.34-1.69)], neurological [aRR: 1.25 (95% CI: 0.77-2.12)], or renal complications [aRR: 1.03 (95% CI 0.90-1.19)] after surgery. Neither low nor high doses had any statistically significant effect on the composite or any of its components.

CONCLUSIONS

In this large study, preoperative statin use, either high dose or low dose, did not affect short-term postoperative mortality, neurological, or renal complications.

The Association between Delirium and In-Hospital Falls: A Cross-Sectional Analysis of a Delirium Screening Program

Methods

A cross-sectional study using delirium screening and falls reports was used to measure the association between delirium and falls. All inpatient data from August, 2018, to January, 2020, at a large academic medical center were analyzed. A multivariable logistic regression of 29,655 hospital admissions was used to understand the association between in-hospital delirium and falls.

Results

Analysis revealed a delirium rate of 12.5% (n = 3,707) of all admissions and 286 (0.9%) admissions with falls; of the falls studied, 37.6% of these patients screened positive for delirium during their admission. Relative to those who screened negative for delirium, admissions that screened positive for delirium had a 2.81 increased odds of falling.

Conclusions

Delirium and falls are related. This strong association should motivate health systems to look closely at both problems. Falls and delirium can both have immense impacts on the patient and the health system. The powerful association between them provides a window to reduce these additional patient harms. More specifically, a modern delirium screening tool should be used as part of routine risk assessment focused on reducing in-hospital falls.

Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

BACKGROUND

In emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients' chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early identification models for diseases.

OBJECTIVE

This study aims to propose a systematic approach to deal with the problems of missing, imbalanced, and sparse features for developing sudden-death prediction models using emergency medicine (or ED) data.

METHODS

We proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. For continuous and discrete variables, the decision coefficient R² and the κ coefficient were used to evaluate performance, respectively. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to estimate the model's performance. To further evaluate the proposed approach, we carried out a case study using an ED data set obtained from the Hainan Hospital of Chinese PLA General Hospital. A logistic regression (LR) prediction model for patient condition worsening was built.

RESULTS

A total of 1085 patients with rescue records and 17,959 patients without rescue records were selected and significantly imbalanced. We extracted 275, 402, and 891 variables from laboratory tests, medications, and diagnosis, respectively. After data preprocessing, the median R² of the RF continuous variable interpolation was 0.623 (IQR 0.647), and the median of the κ coefficient for discrete variable interpolation was 0.444 (IQR 0.285). The LR model constructed using the initial diagnostic data showed poor performance and variable separation, which was reflected in the abnormally high odds ratio (OR) values of the 2 variables of cardiac arrest and respiratory arrest (201568034532 and 1211118945, respectively) and an abnormal 95% CI. Using processed data, the recall of the model reached 0.746, the F₁-score was 0.73, and the AUROC was 0.708.

CONCLUSIONS

The proposed systematic approach is valid for building a prediction model for emergency patients.

Bridge or Barrier? The Impact of Network Capital on the Receipt of Long-Term Care Services in Germany

OBJECTIVES

In Germany, roughly 8.3 million people are in need of care, but only one-third of them receive state care benefits. The study investigates whether the individual network of a care-seeking person, as well as its resources, interact with health status on the likelihood of accessing formal care services.

METHODS

German data from the Survey of Health, Ageing and Retirement in Europe (SHARE) from 2015 were used in several Firth logistic regressions with interaction terms.

RESULTS

Health limitations are a significant predictor for the probability of receiving formal care benefits. As moderating factors, caregivers from the immediate family as well as caregivers with lower levels of education tend to contribute to an increase in this probability.

DISCUSSION

Findings are based on a limited data set and indicate the importance of further research in this area to examine the mechanisms of access to formal care more precisely.

Benefits and challenges of using logistic regression to assess neuropsychological performance validity: Evidence from a simulation study

Logistic regression (LR) is recognized as a promising method for making decisions about neuropsychological performance validity by integrating information across multiple measures. However, this method has yet to be widely adopted in clinical practice, likely because several open questions remain about its utility relative to simpler methods, its effectiveness across different clinical contexts, and its feasibility at sample sizes common in the field. The current study addresses these questions by assessing classification performance of logistic regression and alternative methods across an array of simulated data sets.

We simulated scores of valid and invalid performers on 6 tests designed to mimic the psychometric and distributional properties of real performance validity measures. Out-of-sample predictive performance of LR and a commonly used alternative ("vote counting") was assessed across different base rates, validity measure properties, and sample sizes.

LR improved classification accuracy by 2%-12% across simulation conditions, primarily by improving sensitivity. False positives and negatives can be further reduced when LR predictions are interpreted as continuous, rather than binary. LR made robust predictions at sample sizes feasible for neuropsychology research (N = 307) and when as few as 2 tests with good psychometric properties were used.

Although training and test data sets of at least several hundred individuals may be required to develop and evaluate LR models for use in clinical practice, LR promises to be an efficient and powerful tool for improving judgements about performance validity. We offer several recommendations for model development and LR interpretation in a clinical setting.

Influence factors on registration of three-dimensional contrast-enhanced ultrasound fusion imaging in evaluating the ablative margin - A phantom study

BACKGROUND

Registration of three-dimensional contrast-enhanced ultrasound fusion imaging (3DCEUS-FI) is time-consuming to obtain high success rate.

OBJECTIVE

To investigate the influence factors on registration success rate of 3DCEUS-FI.

METHODS

Water tank phantoms were made to obtain mimicked pre- and post- radiofrequency ablation three-dimensional contrast-enhanced ultrasound (3DCEUS) and CT images. Orthogonal trials were designed according to factors including size, depth, enhancement level of mimicked tumor, diameter and number of mimicked adjacent vessels. Mimicked pre- and post-RFA 3DCEUS images of 72 trials were fused to assess ablative margin (AM) by two radiologists. With CT images as standard, 3DCEUS-FI accuracy was considered as the consistency of AM evaluation. The inter-observer agreement and the influence factors on registration success rates were analyzed.

RESULTS

The intraclass correlation coefficient (ICC) for the consistency of AM evaluation between CT and 3DCEUS-FI in x-axis, y-axis or z-axis was 0.840∼0.948 (P < 0.001). The ICC for inter-observer agreement was 0.840∼0.948 (P < 0.001). The success rates of registration within mimicked vessels with diameter of 2 mm were significantly lower than those with diameter of 3 mm and 4 mm.

CONCLUSIONS

The mimicked AM measured by 3DCEUS-FI had high accuracy and inter-observer agreement. Diameter of the mimicked adjacent vessels was significantly related to success rate of registration.

Unplanned visits and midwife-led antenatal care

INTRODUCTION

Midwives provide antenatal care to women to ensure the health of both mother and baby, according to women's needs. This study aims to investigate demographic and social, clinical and obstetrical factors that may be associated with unplanned visits to the emergency by nulliparous and multiparous women who received midwifery care during the antenatal period.

METHODS

This was a retrospective cohort study with data collection from medical records of the CHU Saint-Pierre hospital. A total of 971 women gave birth between 1 January and 31 December 2017 and received midwifery-led care during their pregnancy. Descriptive statistics and multivariable logistic regression models with 95% confidence intervals (95% CI) were performed separately for nulliparous and multiparous women.

RESULTS

For nulliparae (n=246), the odds of visiting emergency services during pregnancy were 1.45 times (95% CI: 1.08-2.27) higher in women with more previous pregnancies than women with less previous pregnancies, 3.57 times (95% CI: 1.43-11.11) more likely in women without than with high-level hypertension, and 1.09 times (95% CI: 1.01-1.25) more likely in women with less previous midwifery-led visits than women with more previous midwifery-led visits. For multiparae (n=444), the odds of visiting emergency services during pregnancy were 2.12 times (95% CI: 1.06-6.07) higher in women presenting risk factors at first consultation than women without such factors.

CONCLUSIONS

For nulliparous and multiparous women, some characteristics seem to be associated with unplanned visits. Spontaneous visits may be driven by a need for care perceived by women and/or their partner but not specifically by urgent or unfavorable medical conditions.

Linking leaf economic and hydraulic traits with early-age growth performance and survival of Eucalyptus pauciflora

Selection on plant functional traits may occur through their direct effects on fitness (or a fitness component), or may be mediated by attributes of plant performance which have a direct impact on fitness. Understanding this link is particularly challenging for long-lived organisms, such as forest trees, where lifetime fitness assessments are rarely achievable, and performance features and fitness components are usually quantified from early-life history stages. Accordingly, we studied a cohort of trees from multiple populations of Eucalyptus pauciflora grown in a common-garden field trial established at the hot and dry end of the species distribution on the island of Tasmania, Australia. We related the within-population variation in leaf economic (leaf thickness, leaf area and leaf density) and hydraulic (stomatal density, stomatal length and vein density) traits, measured from two-year-old plants, to two-year growth performance (height and stem diameter) and to a fitness component (seven-year survival). When performance-trait relationships were modelled for all traits simultaneously, statistical support for direct effects on growth performance was only observed for leaf thickness and leaf density. Performance-based estimators of directional selection indicated that individuals with reduced leaf thickness and increased leaf density were favoured. Survival-performance relationships were consistent with size-dependent mortality, with fitness-based selection gradients estimated for performance measures providing evidence for directional selection favouring individuals with faster growth. There was no statistical support for an effect associated with the fitness-based quadratic selection gradient estimated for growth performance. Conditional on a performance measure, fitness-based directional selection gradients estimated for the leaf traits did not provide statistical support for direct effects of the focal traits on tree survival. This suggested that, under the environmental conditions of the trial site and time period covered in the current study, early-stage selection on the studied leaf traits may be mediated by their effects on growth performance, which in turn has a positive direct influence on later-age survival. We discuss the potential mechanistic basis of the direct effects of the focal leaf traits on tree growth, and the relevance of a putative causal pathway of trait effects on fitness through mediation by growth performance in the studied hot and dry environment.

Sexual partnership concurrency and age disparities associated with sexually transmitted infection and risk behavior in rural communities in Kenya and Uganda

OBJECTIVES

We examined sex-specific associations of partner age disparity and relationship concurrency with Neisseria gonorrhoeae and/or Chlamydia trachomatis (NG/CT) infection, higher-risk relationships, and condom use as proxies for HIV risk.

METHODS

Data were collected in 2016 from 2179 adults in 12 communities in Uganda and Kenya. Logistic regression models examined associations of age disparity and relationship concurrency with NG/CT infection, condom use, and higher-risk (commercial sex and other higher-risk) relationships in the past 6 months, controlling for covariates.

RESULTS

Partner age and relationship concurrency were associated with NG/CT infection in women but not men. Relative to women in age-disparate relationships, women in both age-disparate and age-homogeneous relationships had higher odds of NG/CT infection (adjusted odds ratio [aOR]=3.82, 95% confidence interval [CI]: 1.46-9.98). Among men and women, partnership concurrency was associated with higher-risk partnerships. In addition, relative to those with a single age-homogeneous partner, those with concurrent age-homogeneous partners had higher odds of condom use (men: aOR=2.85, 95% CI: 1.89-4.31; women: aOR=2.99, 95% CI: 1.52-5.89). Concurrent age-disparate partnerships were associated with condom use among men only (aOR=4.02, 95% CI: 2.54-6.37).

CONCLUSION

Findings underscore the importance of targeted HIV prevention efforts for couples in age-disparate and concurrent relationships.

Predictive models for first-onset and persistence of depression and anxiety among university students

BACKGROUND

Depression and anxiety are both prevalent among university students. They frequently co-occur and share risk factors. Yet few studies have focused on identifying students at highest risk of first-onset and persistence of either of these conditions.

METHODS

Multicenter cohort study among Spanish first-year university students. At baseline, students were assessed for lifetime and 12-month Major Depressive Episode and/or Generalized Anxiety Disorder (MDE-GAD), other mental disorders, childhood-adolescent adversities, stressful life events, social support, socio-demographics, and psychological factors using web-based surveys; 12-month MDE-GAD was again assessed at 12-month follow-up.

RESULTS

A total of 1253 students participated in both surveys (59.2% of baseline respondents; mean age = 18.7 (SD = 1.3); 56.0% female). First-onset of MDE-GAD at follow-up was 13.3%. Also 46.7% of those with baseline MDE-GAD showed persistence at follow-up. Childhood/Adolescence emotional abuse or neglect (OR= 4.33), prior bipolar spectrum disorder (OR= 4.34), prior suicidal ideation (OR=4.85) and prior lifetime symptoms of MDE (ORs=2.33-3.63) and GAD (ORs=2.15-3.75) were strongest predictors of first-onset MDE-GAD. Prior suicidal ideation (OR=3.17) and prior lifetime GAD symptoms (ORs=2.38-4.02) were strongest predictors of MDE-GAD persistence. Multivariable predictions from baseline showed AUCs of 0.76 for first-onset and 0.81 for persistence. 74.9% of first-onset MDE-GAD cases occurred among 30% students with highest predicted risk at baseline.

LIMITATIONS

Self-report data were used; external validation of the multivariable prediction models is needed.

CONCLUSION

MDE-GAD among university students is frequent, suggesting the need to implement web-based screening at university entrance that identify those students with highest risk.

Recovering Hidden Responder Groups in Individuals Receiving Neurofeedback for Tinnitus

The widespread understanding that chronic tinnitus is a heterogeneous phenomenon with various neural oscillatory profiles has spurred investigations into individualized approaches in its treatment. Neurofeedback, as a non-invasive tool for altering neural activity, has become increasingly popular in the personalized treatment of a wide range of neuropsychological disorders. Despite the success of neurofeedback on the group level, the variability in the treatment efficacy on the individual level is high, and evidence from recent studies shows that only a small number of people can effectively modulate the desired aspects of neural activity. To reveal who may be more suitable, and hence benefit most from neurofeedback treatment, we classified individuals into unobserved subgroups with similar oscillatory trajectories during the treatment and investigated how subgroup membership was predicted by a series of characteristics. Growth mixture modeling was used to identify distinct latent subgroups with similar oscillatory trajectories among 50 individuals suffering from chronic subjective tinnitus (38 male, 12 female, mean age = 47.1 ± 12.84) across 15 neurofeedback training sessions. Further, the impact of characteristics and how they predicted the affiliation in the identified subgroups was evaluated by including measures of demographics, tinnitus-specific (Tinnitus Handicap Inventory) and depression variables, as well as subjective quality of life subscales (World Health Organization—Quality of Life Questionnaire), and health-related quality of life subscales (Short Form-36) in a logistic regression analysis. A latent class model could be fitted to the longitudinal data with a high probability of correctly classifying distinct oscillatory patterns into 3 different groups: non-responder (80%), responder (16%), and decliner (4%). Further, our results show that the health-related wellbeing subscale of the Short Form-36 questionnaire was differentially associated with the groups. However, due to the small sample size in the Responder group, we are not able to provide sufficient evidence for a distinct responder profile. Nevertheless, the identification of oscillatory change-rate differences across distinct groups of individuals provides the groundwork from which to tease apart the complex and heterogeneous oscillatory processes underlying tinnitus and the attempts to modify these through neurofeedback. While more research is needed, our results and the analytical approach presented may bring clarity to contradictory past findings in the field of tinnitus research, and eventually influence clinical practice.

Long Term Neurodevelopmental Outcomes after Sevoflurane Neonatal Exposure of Extremely Preterm Children: A Cross-Sectional Observationnal Study

Sevoflurane, a volatile anesthetic, is used when extremely preterm neonates (EPT) undergo painful procedures. Currently, no existing studies analyze sevoflurane’s long-term effects during the EPT’s immediate neonatal period. Our primary objective was to compare the EPT’s neurocognitive development regardless of any sevoflurane exposure prior to 45 weeks corrected gestational age (GA). We analyzed those live discharges, less than 28 weeks GA, who were either exposed, unexposed, and/or multiply exposed to sevoflurane before 45 weeks GA. All data were obtained from a cross-sectional multicenter study (GPQoL study, NCT01675726). Children, both exposed and non-exposed to sevoflurane, were sampled using a propensity-guided approach. Neurological examinations (Touwen), cognitive and executive functions (WISC IV, NEPSY, Rey figure), and assessments when the children were between 7 and 10 years old, were correlated to their neonatal sevoflurane exposure. There were 139 children in the study. The mean gestational age was 26.2 weeks (±0.8) GA and the mean birth weight was 898 g (±173). The mean age of their evaluation was 8.47 years old (±0.70). Exposure to sevoflurane to the mean corrected age 27.10 (3.37) weeks GA had a significant correlation with cerebral palsy (adjusted odds ratio (aOR): 6.70 (CI 95%: 1.84–32.11)) and other major disorders (cerebral palsy and/or severe cognitive retardation) (aOR: 2.81 [95% CI: 1.13–7.35]). Our results demonstrate the possibility of long-term effects on EPT infants who had a sevoflurane exposure before 45 weeks corrected GA. However, these results will require further confirmation by randomized controlled trials.

Factors Affecting the Patient Journey in Scheduling a Specialist Appointment in a Public Healthcare System

A questionnaire was developed to evaluate the journey experienced by patients from identifying a need to see a community specialist in Israel's public healthcare system, through scheduling an appointment and attending. A telephone survey was conducted with a nationally representative group of 3751 adults, in 2019 to 2020. Fifty-seven percent needed to see a specialist in the last 6 months; among those, 82%, visited a specialist. Among the 3% who did not make an appointment, in 41 of 52 (79%) cases this was due to long waiting time. Younger and more educated patients were more likely to try to get an earlier appointment. Timeliness (55%) and wanting a specific physician (43%) were major considerations in scheduling. Reported need was greater in females, Jewish versus Arab respondents, more educated and those with chronic illness. Those who did not make an appointment sought private care, emergency treatment, or went untreated. Although a large percentage of respondents did eventually get an appointment, vulnerable patients may have more difficulty navigating the system. Following the patient journey can provide insights to help design services better suited to patients’ needs.

The burden of rare protein-truncating genetic variants on human lifespan

Genetic predisposition has been shown to contribute substantially to the age at which we die. Genome-wide association studies (GWASs) have linked more than 20 loci to phenotypes related to human lifespan¹. However, little is known about how lifespan is impacted by gene loss of function. Through whole-exome sequencing of 352,338 UK Biobank participants of European ancestry, we assessed the relevance of protein-truncating variant (PTV) gene burden on individual and parental survival. We identified four exome-wide significant (P < 4.2 × 10^-7) human lifespan genes, BRCA1, BRCA2, ATM and TET2. Gene and gene-set, PTV-burden, phenome-wide association studies support known roles of these genes in cancer to impact lifespan at the population level. The TET2 PTV burden was associated with a lifespan through somatic mutation events presumably due to clonal hematopoiesis. The overlap between PTV burden and common variant-based lifespan GWASs was modest, underscoring the value of exome sequencing in well-powered biobank cohorts to complement GWASs for identifying genes underlying complex traits.

When a refusal turns into donation: the moderating effect of the initial position toward blood donation in the door-in-the-face effectiveness

This research aimed to test the moderating effect of people's initial position to blood donation on the actual acceptance to donate blood in a door-in-the-face situation. This position (attitude, self-importance, normative beliefs) was measured one month prior to the request (Study 1, N = 99) or immediately before (Study 2, N = 80). The results revealed that the door-in-the-face effect is moderated by the importance of blood donation to the self, all the more so when the position is made salient. This highlights the specific character of blood donation in France and the centrality of the importance of donating for the self at the heart of the DITF technique. These results offer new insights into the conditions that must be met to achieve acceptance to donate blood after an initial refusal.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression

Poisson regression can be challenging with sparse data, in particular with certain data constellations where maximum likelihood estimates of regression coefficients do not exist. This paper provides a comprehensive evaluation of methods that give finite regression coefficients when maximum likelihood estimates do not exist, including Firth’s general approach to bias reduction, exact conditional Poisson regression, and a Bayesian estimator using weakly informative priors that can be obtained via data augmentation. Furthermore, we include in our evaluation a new proposal for a modification of Firth’s approach, improving its performance for predictions without compromising its attractive bias-correcting properties for regression coefficients. We illustrate the issue of the nonexistence of maximum likelihood estimates with a dataset arising from the recent outbreak of COVID-19 and an example from implant dentistry. All methods are evaluated in a comprehensive simulation study under a variety of realistic scenarios, evaluating their performance for prediction and estimation. To conclude, while exact conditional Poisson regression may be confined to small data sets only, both the modification of Firth’s approach and the Bayesian estimator are universally applicable solutions with attractive properties for prediction and estimation. While the Bayesian method needs specification of prior variances for the regression coefficients, the modified Firth approach does not require any user input.

Alternations in the gut microbiota and metabolome with newly diagnosed unstable angina

Gut microbiota plays an important role in coronary heart disease, but its compositional and functional changes in unstable angina (UA) remain unexplored. We performed metagenomic sequencing of 133 newly diagnosed UA patients and 133 sex- and age-matched controls, and profiled the fecal and plasma metabolomes in thirty case-control pairs. The alpha diversity of gut microbiota was increased in UA patients: the adjusted odds ratios (ORs) per standard deviation increase in Shannon and Simpson indices were 1.30 (95% confidence interval, 1.01-1.70) and 1.36 (1.05-1.81), respectively. Two common species (depleted Klebsiella pneumoniae and enriched Streptococcus parasanguinis; P ≤ 0.002) and three rare species (depleted Weissella confusa, enriched Granulicatella adiacens and Erysipelotrichaceae bacterium 6_1_45; P ≤ 0.005) were associated with UA. The UA-associated gut microbiota was depleted in the pathway of L-phenylalanine degradation (P = 0.001), primarily contributed by Klebsiella pneumoniae. Consistently, we found increased circulating phenylalanine in UA patients (OR = 2.76 [1.17-8.16]). Moreover, S. parasanguinis was negatively correlated with fecal citrulline (Spearman's r_s = -0.470, P = 0.009), a metabolite depleted in UA patients (OR = 0.26 [0.08-0.63]). These findings are informative to help understand the metabolic connection between gut microbiota and UA.

Evidence of absence regression: a binomial N-mixture model for estimating fatalities at wind energy facilities

Estimating bird and bat fatalities caused by wind-turbine facilities is challenging when carcasses are rare and produce counts that are either exactly or very near zero. The rarity of found carcasses is exacerbated when live members of a particular species are rare and when carcasses degrade quickly, are removed by scavengers, or are not detected by observers. With few observed carcass counts, common statistical methods like logistic, Poisson, or negative binomial regression are unreliable (statistically biased) and often fail to provide answers (i.e., fail to converge). Here, we propose a binomial N-mixture model that estimates fatality rates as well as the total number of carcasses when rates are expanded. Our model extends the "evidence of absence" model by relating carcass deposition rates to study covariates and by incorporating terms that naturally scale counts from facilities of different sizes. Our model, which we call Evidence of Absence Regression (EoAR), can estimate the total number of birds or bats killed at a single wind energy facility or a fleet of wind energy facilities based on covariate values. Furthermore, with accurate prior distributions the model's results are extremely robust to sparse data and unobserved combinations of covariate values. In this paper, we describe the model, show its low bias and high precision via computer simulation, and apply it to bat carcass counts observed at 21 wind energy facilities in Iowa.

Approximate confidence intervals for the difference in proportions for partially observed binary data

We consider partially observed binary matched-pair data. We assume that the incomplete subjects are missing at random. Within this missing framework, we propose an EM-algorithm based approach to construct an interval estimator of the proportion difference incorporating all the subjects. In conjunction with our proposed method, we also present two improvements to the interval estimator through some correction factors. The performances of the three competing methods are then evaluated through extensive simulation. Recommendation for the method is given based on the ability to preserve type-I error for various sample sizes. Finally, the methods are illustrated in two real-world data sets. An R-function is developed to implement the three proposed methods.

Identifying early indicators of secondary peritonitis in critically ill patients with cirrhosis

Ascitic fluid infection (AFI) is a life-threatening complication of cirrhosis. We aimed to identify early indicators of secondary peritonitis (SP), which requires emergency surgery, and to describe the outcomes of SP and spontaneous bacterial/fungal peritonitis (SBFP). Adults with cirrhosis and AFI admitted to 16 university or university-affiliated ICUs in France between 2002 and 2017 were studied retrospectively. Cases were identified by searching the hospital databases for relevant ICD-10 codes and hospital charts for AFI. Logistic multivariate regression was performed to identify factors associated with SP. Secondary outcomes were short- and long-term mortality and survivors' functional outcomes. Of 178 included patients (137 men and 41 women; mean age, 58 ± 11 years), 21 (11.8%) had SP, confirmed by surgery in 16 cases and by abdominal computed tomography in 5 cases. Time to diagnosis exceeded 24 h in 7/21 patients with SP. By multivariate analysis, factors independently associated with SP were ascitic leukocyte count > 10,000/mm³ (OR 3.70; 95%CI 1.38-9.85; P = 0.009) and absence of laboratory signs of decompensated cirrhosis (OR 4.53; 95%CI 1.30-15.68; P = 0.017). The 1-year mortality rates in patients with SBFP and SP were 81.0% and 77.5%, respectively (Log-rank test, P = 0.92). Patients with SP vs. SBFP had no differences in 1-year functional outcomes. This multicenter retrospective study identified two indicators of SP as opposed to SBFP in patients with cirrhosis. Using these indicators may help to provide early surgical treatment.

Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue

The novel coronavirus disease 2019 (COVID-19) presents with non-specific clinical features. This may result in misdiagnosis or delayed diagnosis, and lead to further transmission in the community. We aimed to derive early predictors to differentiate COVID-19 from influenza and dengue. The study comprised 126 patients with COVID-19, 171 with influenza and 180 with dengue, who presented within 5 days of symptom onset. All cases were confirmed by reverse transcriptase polymerase chain reaction tests. We used logistic regression models to identify demographics, clinical characteristics and laboratory markers in classifying COVID-19 versus influenza, and COVID-19 versus dengue. The performance of each model was evaluated using receiver operating characteristic (ROC) curves. Shortness of breath was the strongest predictor in the models for differentiating between COVID-19 and influenza, followed by diarrhoea. Higher lymphocyte count was predictive of COVID-19 versus influenza and versus dengue. In the model for differentiating between COVID-19 and dengue, patients with cough and higher platelet count were at increased odds of COVID-19, while headache, joint pain, skin rash and vomiting/nausea were indicative of dengue. The cross-validated area under the ROC curve for all four models was above 0.85. Clinical features and simple laboratory markers for differentiating COVID-19 from influenza and dengue are identified in this study which can be used by primary care physicians in resource limited settings to determine if further investigations or referrals would be required.

Impact of a community-policing initiative promoting substance use disorder treatment over criminal charges on arrest recidivism

BACKGROUND

Overdose deaths, addiction, and drug-related crime have increased in the United States over the past decade. Treatment improves outcomes, including reducing crime, but few individuals with addiction receive treatment. Here, we determine whether the Madison Addiction Recovery Initiative (MARI), a community policing program implemented by the City of Madison (Wisconsin) Police Department (MPD) that diverts adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment, reduces the risk of recidivism (i.e., an arrest) in the 6-month period following the index crime.

METHODS

Observational data were collected by the MPD for 12 months before through 6 months after an index crime from participants in the MARI program (n = 263) who referred to MARI between September 1, 2017 and August 31, 2020 and a Historical Comparison group (n = 52) who committed a comparable crime between September 1, 2015 and August 31, 2016. Average effects were estimated using intention-to-treat (ITT), a per-protocol, and a complier average causal effects (CACE) analyses, adjusted for covariates.

RESULTS

ITT analysis did not show that MARI assignment lowered adjusted odds of 6-month recidivism (aOR = 0.59 [0.32, 1.12], p = 0.11). Per-protocol analysis showed that completing MARI lowered the adjusted odds of 6-month recidivism (aOR = 0.23 [0.10, 0.52], p < 0.001). CACE analysis indicated that assignment to MARI among individuals who would complete the MARI program if assigned to the program lowered the adjusted odds of 6-month recidivism (aOR = 0.85 [0.80, 0.90], p < 0.001).

CONCLUSIONS

Diverting adults who committed a non-violent, drug use-related crime from criminal prosecution to addiction treatment may reduce 6-month recidivism.

Bias in odds ratios from logistic regression methods with sparse data sets

BACKGROUND

Logistic regression models are widely used to evaluate the association between a binary outcome and a set of covariates. However, when there are few study participants at the outcome and covariate levels, the models lead to bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method. This bias is known as sparse data bias, and the estimated OR can yield impossibly large values because of data sparsity. However, this bias has been ignored in most epidemiological studies.

METHODS

We review several methods for reducing sparse data bias in logistic regression. The primary aim is to evaluate the Bayesian methods in comparison with the classical methods, such as the ML, Firth's, and exact methods using a simulation study. We also apply these methods to a real data set.

RESULTS

Our simulation results indicate that the bias of the OR from the ML, Firth's, and exact methods is considerable. Furthermore, the Bayesian methods with hyper-g prior modeling of the prior covariance matrix for regression coefficients reduced the bias under the null hypothesis, whereas the Bayesian methods with log F-type priors reduced the bias under the alternative hypothesis.

CONCLUSION

The Bayesian methods using log F-type priors and hyper-g prior are superior to the ML, Firth's, and exact methods when fitting logistic models to sparse data sets. The choice of a preferable method depends on the null and alternative hypothesis. Sensitivity analysis is important to understand the robustness of the results in sparse data analysis.

Antimicrobial resistance among Enterobacteriaceae, Staphylococcus aureus, and Pseudomonas spp. isolates from clinical specimens from a hospital in Nairobi, Kenya

Background

Antimicrobial resistance among pathogens of public health importance is an emerging problem in sub-Saharan Africa. Unfortunately, published information on the burden and patterns of antimicrobial resistance (AMR) in this region is sparse. There is evidence that the burden and patterns of AMR vary by geography and facility. Knowledge of local epidemiology of AMR is thus important for guiding clinical decisions and mitigation strategies. Therefore, the objective of this study was to determine the burden and predictors of AMR and multidrug resistance (MDR) among bacterial pathogens isolated from specimens submitted to the diagnostic laboratory of a hospital in Nairobi, Kenya.

Methods

This retrospective study used laboratory records of 1,217 clinical specimens submitted for bacterial culture and sensitivity testing at the diagnostic laboratory of The Karen Hospital in Nairobi, Kenya between 2012 and 2016. Records from specimens positive for Enterobacteriaceae, Staphylococcus aureus, or Pseudomonas spp. isolates were included for analysis. Firth logistic models, which minimize small sample bias, were used to investigate determinants of AMR and MDR of the isolates.

Results

A total of 222 specimens had bacterial growth. Most Enterobacteriaceae isolates were resistant to commonly used drugs such as penicillin/β-lactamase inhibitor combinations (91.2%) and folate pathway inhibitors (83.7%). Resistance to extended-spectrum cephalosporins was also high (52.9%). Levels of AMR and MDR for Enterobacteriaceae were 88.5% and 51%, respectively. Among S. aureus isolates, 57.1% were AMR, while 16.7% were MDR. As many as 42.1% of the Pseudomonas spp. isolates were aminoglycoside-resistant and 15% were fluoroquinolone-resistant, but none exhibited resistance to antipseudomonal carbapenems. Half of Pseudomonas spp. isolates were AMR but none were MDR. Significant predictors of MDR among Enterobacteriaceae were organism species (p = 0.002) and patient gender (p = 0.024).

Conclusions

The high levels of extended-spectrum cephalosporin resistance and MDR among Enterobacteriaceae isolates are concerning. However, the relatively low levels of MDR S. aureus, and an absence of carbapenem resistance among Pseudomonas isolates, suggests that last-line drugs are still effective against S. aureus and Pseudomonas infections. These findings are relevant for guiding evidence-based treatment decisions as well as surveillance efforts and directions for future research, and contribute to the sparse literature on AMR in sub-Saharan Africa.

A prospective observational study of persistent opioid use after complex foot and ankle surgery

PURPOSE

The incidence of persistent postsurgical opioid use (PPOU) after complex foot and ankle surgery is unknown. We aimed to determine the incidence and characteristics of PPOU in opioid-naïve, occasional, and regular opioid users at baseline and at six weeks, three months, and six months postoperatively.

METHODS

We conducted a prospective observational study in patients undergoing complex foot and ankle surgery over an 18-month period. Daily opioid consumption was recorded at the indicated intervals. Logistic regression models were fit to predict the risk of opioid use at these intervals. The Brief Pain Inventory (BPI) was used to record pain intensity and interference. Correlations were tested between opioid use and BPI interference parameters.

RESULTS

Eighty-two out of 139 consecutively approached patients were included in the final analysis. Six percent (98.3% confidence interval [CI], 2 to 20) of patients who were not using opioids preoperatively at baseline were using opioids daily at three and six months after surgery. Fifty percent (98.3% CI, 26 to 73) of patients who were regular opioid users preoperatively continued to use opioids daily six months after surgery. All associations between BPI interference parameters and opioid use were estimated to be positive.

CONCLUSION

The probability of using opioid analgesia six months after complex foot and ankle surgery was significantly higher in patients who used opioids preoperatively. Regular preoperative opioid use was associated with a greater risk of PPOU compared with occasional or "as required" opioid use prior to surgery.

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study

BACKGROUND

Randomised controlled trials in reproductive medicine are often subject to outcome truncation, where the study outcomes are only defined in a subset of the randomised cohort. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These outcomes are typically analysed by making a comparison between treatment arms within the subgroup (for example, comparing birthweights in the subgroup who gave birth or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for the design and interpretation of reproductive trials are unclear however.

METHODS

We developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Simulation settings were informed by published assisted reproduction trials.

RESULTS

Increasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and the presence of an interaction between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation could cause separation in smaller studies, where none or all of the participants in a study arm experience the outcome event. This was found to have severe consequences for inferences.

CONCLUSION

We have provided a simulation platform that can be used by researchers in the design and interpretation of reproductive medicine trials subject to outcome truncation and have used this to conduct a simulation study. The study highlights several key factors which trialists in the field should consider carefully to protect against erroneous inferences. Standard analyses of truncated binary outcomes in small studies may be highly biassed, and it remains to identify suitable approaches for analysing data in this context.