Predicting unplanned hospitalisations in older adults using routinely recorded general practice data

Context: Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. Objective: To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Study Design and Analysis: retrospective study Setting or Dataset: general practice electronic health records linked with national administrative data Population Studied: 243,129 community dwelling adults aged 65 years and over Intervention/Instrument: The dataset was geographically split into a development (58.7%) and validation (41.3%) sample. The model was developed using logistic regression with backward selection. The models were validated internally and externally. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). Predictive performance was assessed by area under the curve (AUC) and calibration plots. Outcome Measures: unplanned hospital admissions within 6 months. Results: In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration was good. Conclusions: Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety-critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers' head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety-critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

Modeling failures in smart grids by a bilinear logistic regression approach

Modeling and recognizing events in complex systems through machine learning techniques is a challenging task. Especially if the model is constrained to be explainable and interpretable, while ensuring high levels of accuracy. In this paper, we adopt a bilinear logistic regression model in which the parameters are trained in a data-driven fashion on a real-world dataset of power grid failure data. The bilinear white-box model - grounded on a specific neural architecture - has been proven effective in classifying faulty states with a performance comparable to several classifiers in technical literature. Additionally, the low computational complexity of the bilinear model, in terms of the number of free parameters, allows gaining insights into the fault phenomenon correlating the events that impact the power grid (exogenous causes) with its constitutive characteristics, thence eliciting the relational information hidden in the data. The proposed model is also able to estimate a vulnerability vector that can be associated, as a suitable characteristic "label", to power grid components, opening the way, as will be deeply demonstrated in the following, not only to predictive maintenance programs or condition monitoring tasks but also to risk assessment and scenario analyses in line with the explainable AI paradigm.

Risk factors and predictive model for prenatal depression: A large retrospective study in China

BACKGROUND

Prenatal depression, associated with adverse effects on mothers and fetuses, has received little attention. We conducted a large-sample study to investigate the risk factors of, and develop a predictive model for, prenatal depression in the Chinese population.

METHODS

This study enrolled 14,329 pregnant women who delivered at the West China Second University Hospital, Sichuan University from January 2017 to December 2020. Participants were divided into a training or validation cohort. Multiple variables were collected and selected using univariate logistic regression and least absolute shrinkage and selection operator penalty regression. After multivariate logistic analysis, a predictive model was developed and validated internally and externally.

RESULTS

Nine variables (employment, planned pregnancy, pregnancy number, conception methods, gestational diabetes mellitus, twin pregnancy, placenta previa, umbilical cord encirclement, and educational attainment) were identified as independent risk factors for prenatal depression. Receiver operating characteristic curves in both the training and validation cohorts showed excellent discrimination of the predictive model (the area under the curve: 0.746 and 0.732, respectively).

LIMITATIONS

The results of this retrospective study may be affected by confounding and information bias. Some important variables were excluded, such as family history of mental disorders. The study was conducted in China; its results may not be generalizable to other regions.

CONCLUSION

Our study identified nine significant risk factors for prenatal depression and constructed an accurate predictive model. This model could be applied as a clinical decision aid for individualized risk estimates and prevention of prenatal depression.

Heterogeneity in crash data: A cross comparison between ordered probability model and its variant using crashes at suburban type arterial

Transportation researchers have long been using the statistical analysis of traffic crash data to create a proactive awareness of traffic safety, make important decisions about the design of vehicles and highways, and develop and implement safe preventive strategies to improve safety. Despite significant progress toward maintaining and analyzing traffic crash data, researchers still encounter several challenges and methodological barriers when conducting statistical analysis. One of these challenges is dealing with the issue of unobserved heterogeneity in crash data. This study uses state-of-the-art methodologies to model the injury severity of traffic crashes that occurred on a specific road segment, namely, a suburban-type road (STR), simultaneously addressing issues related to unobserved heterogeneity in data. Multiple heterogeneity ordered probit models are evaluated against Ohio crash data from the Highway Safety Information System (HSIS). The findings reveal the heterogeneous nature of some variables, such as the nighttime indicator, and demonstrate the distinctive feature of each model to capture the effect of unobserved heterogeneity in analyzing data with such variables. Furthermore, the result helps comprehend the contextual scenarios of crashes at STRs and formulate practical plans to lower the severity of such crashes.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

Impact of suicidality on delayed-onset post-traumatic stress disorder is modified by age: A two-year longitudinal study

OBJECTIVES

This study aimed to investigate the relationship between early suicidality and post-traumatic stress disorder (PTSD) onset in patients with physical injuries, focusing on age as a modifying factor.

METHODS

At baseline, 1014 patients were evaluated for suicidality, age divided into younger (<60 years) vs. older (≥60 years) groups, and potential covariates. PTSD was diagnosed at follow-up at 3, 6, 12, and 24 months, and then were categorized into early-onset (within the first six months after trauma) and delayed-onset (more than six months after trauma). Logistic regression models were used after adjusting for covariates.

RESULTS

Presence of suicidality at baseline was significantly associated with delayed-onset PTSD in older but not younger patients with significant interaction terms, whereas it was significantly associated with early-onset PTSD across all age groups.

CONCLUSION

Age-specific associations were found between suicidality and PTSD onset. The findings highlight the importance of early suicidality assessment, especially in older patients for ongoing monitoring and support, and underscore the critical need for early PTSD identification and intervention for all ages.

Contribution of individual and cumulative social determinants of health underlying gender disparities in periodontitis in a representative US population: A cross-sectional NHANES study

AIM

To examine the impact of both individual and cumulative social determinants of health (SDoH) on the likelihood of developing periodontitis, while also exploring any gender disparities in this relationship.

MATERIALS AND METHODS

Data of self-reported SDoH domains and sub-items based on Healthy People 2030 were obtained from the U.S. National Health and Nutrition Examination Surveys between 1999 and 2014. Logistic regression models, weighted by survey responses, were used to examine the relationship between SDoH (including eight sub-items and the cumulative number of unfavourable SDoH) and periodontitis. The results were further analysed by gender.

RESULTS

A total of 18,075 participants (8867 males and 9208 females) were included in the main analysis, of which 5814 (32.2%) had periodontitis. The study found that certain unfavourable SDoH were individually associated with higher odds of periodontitis, and the cumulative number of unfavourable SDoH was positively linked to the odds of developing periodontitis. Furthermore, males exposed to more unfavourable SDoH appeared to be more susceptible to developing periodontitis than females.

CONCLUSIONS

The findings suggest that unfavourable SDoH, especially when they accumulate, are associated with an increased odds of periodontitis and contribute to gender disparities within the U.S.

POPULATION

Comparing Preferences for Disease Profiles: A Discrete Choice Experiment from a US Societal Perspective

OBJECTIVES

There is increasing interest in expanding the elements of value to be considered when making health policy decisions. To help inform value frameworks, this study quantified preferences for disease attributes in a general public sample and examined which combination of attributes (disease profiles) are considered most important for research and treatment.

METHODS

A discrete choice experiment (DCE) was conducted in a US general population sample, recruited through online consumer panels. Respondents were asked to select one of a set of health conditions they believed to be most important, characterized by attributes defined by a previous qualitative study: onset age; cause of disease; life expectancy; caregiver requirement; symptom burden (characterized by the Health Utilities Index with varying levels of ambulation independence, dexterity limitations, and degree of pain and discomfort); and disease prevalence. A fractional factorial DCE design was implemented using R, and 60 choice sets were generated (separated into blocks of 10 per participant). Data were analyzed using a mixed-logit regression model, and results used to assess the likelihood of preferring disease profiles. Based on individual attribute preferences, overall preferences for disease profiles, including a profile aligned with Duchenne muscular dystrophy (DMD), were compared.

RESULTS

Fifty-two percent of respondents (n = 537) were female, and 70.6% were aged 18-54 years. Attributes considered most important were those related to life expectancy (odds ratio [OR], 95% confidence interval [CI] 1.88 [1.56-2.27] for a 50% reduction in remaining life expectancy vs no impact), and symptom burden (OR [95% CI] 1.84 [1.47-2.31] for severe vs mild burden). Greater importance was also found for pediatric onset, caregiver requirement, and diseases affecting more people. As an example of disease profile preferences, a DMD-like pediatric inherited disease with 50% reduction in life expectancy, extensive caregiver requirement, severe symptom burden, and 1:5000 prevalence had 2.37-fold higher odds of being selected as important versus an equivalent disease with adult onset and no life expectancy reduction.

CONCLUSIONS

Of disease attributes included in this DCE, respondents valued higher prevalence of disease, life expectancy and symptom burden as most important for prioritizing research and treatment. Based on expressed attribute preferences, a case study of an inherited pediatric disease involving substantial reductions to length and quality of life and requiring caregiver support has relatively high odds of being identified as important compared to diseases reflecting differing attribute profiles. These findings can help inform expansions of value frameworks by identifying important attributes from the societal perspective.

Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

ABO blood type and thromboembolic complications after intracerebral hemorrhage: An exploratory analysis

BACKGROUND AND PURPOSE

Non-O blood types are known to be associated with thromboembolic complications (TECs) in population-based studies. TECs are known drivers of morbidity and mortality in intracerebral hemorrhage (ICH) patients, yet the relationships of blood type on TECs in this patient population are unknown. We sought to explore the relationships between ABO blood type and TECs in ICH patients.

METHODS

Consecutive adult ICH patients enrolled into a prospective observational cohort study with available ABO blood type data were analyzed. Patients with cancer history, prior thromboembolism, and baseline laboratory evidence of coagulopathy were excluded. The primary exposure variable was blood type (non-O versus O). The primary outcome was composite TEC, defined as pulmonary embolism, deep venous thrombosis, ischemic stroke or myocardial infarction, during the hospital stay. Relationships between blood type, TECs and clinical outcomes were separately assessed using logistic regression models after adjusting for sex, ethnicity and ICH score.

RESULTS

Of 301 ICH patients included for analysis, 44% were non-O blood type. Non-O blood type was associated with higher admission GCS and lower ICH score on baseline comparisons. We identified TECs in 11.6% of our overall patient cohort. . Although TECs were identified in 9.9% of non-O blood type patients compared to 13.0% in O blood type patients, we did not identify a significant relationship of non-O blood type with TECs (adjusted OR=0.776, 95%CI: 0.348-1.733, p=0.537). The prevalence of specific TECs were also comparable in unadjusted and adjusted analyses between the two cohorts. In additional analyses, we identified that TECs were associated with poor 90-day mRS (adjusted OR=3.452, 95% CI: 1.001-11.903, p=0.050). We did not identify relationships between ABO blood type and poor 90-day mRS (adjusted OR=0.994, 95% CI:0.465-2.128, p=0.988).

CONCLUSIONS

We identified that TECs were associated with worse ICH outcomes. However, we did not identify relationships in ABO blood type and TECs. Further work is required to assess best diagnostic and prophylactic and treatment strategies for TECs to improve ICH outcomes.

Sick leave and disability pension following delivery in women with systemic lupus erythematosus

OBJECTIVE

To investigate sickness benefits following delivery in mothers with systemic lupus erythematosus (SLE) and mothers without SLE.

METHOD

SLE and non-SLE mothers, matched by age and month of delivery, with a singleton liveborn (2004-2008), were identified from the Swedish Lupus Linkage cohort. Work loss (sum of sick leave and disability pension) was studied from 1 year prenatally to 3 years postpartum. Adjusted logistic regression models of covariates associated with > 30 days of work loss in the first and second years postpartum were estimated in SLE mothers.

RESULTS

Among 130 SLE mothers and 440 non-SLE mothers, SLE mothers were more likely to have work loss from the prenatal year (42% vs 16%) to 3 years postpartum (49% vs 15%). In SLE mothers, work loss was on average 61 ± 112 days (mean ± sd) in the prenatal year and 38 ± 83 days in the first year postpartum, which increased to 71 ± 114 days in the third year postpartum. Having > 30 days of sick leave in the year of delivery [odds ratio (OR) 4.4, 95% confidence interval (CI) 1.5-12.9] and ≤ 12 years of education (OR 2.6, 95% CI 1.1-6.0) were associated with work loss in the first year postpartum. No covariates were associated with work loss in the second year postpartum.

CONCLUSION

SLE mothers more often had work loss in the prenatal year to 3 years postpartum compared to non-SLE mothers. Lower education and sick leave in the year of delivery were associated with a higher odds of work loss in the first year postpartum in SLE.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35-44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents.

Type-O Blood Is Not Associated With Elevated Mortality After Trauma: A North American Cohort Study

BACKGROUND

Recent studies have presented contradictory findings on the relationship between blood type and mortality in trauma patients. Using the largest population in a study of this type to date, we hypothesized that ABO genotype and Rhesus status would influence trauma-related mortality and morbidity given the relationship between blood type and hemostasis.

METHODS

Data from all trauma patients admitted to level I and level II trauma centers in one city over a five-year period was retrospectively analyzed. Patients were stratified by ABO type. Patient demographics and outcomes were then assessed. Chi-squared and Fisher's exact tests were used to analyze categorical variables. Continuous variables were analyzed using ANOVA or Kruskal-Wallis tests as appropriate. Logistic regression was used to determine independent associations for 28-day mortality and complications.

RESULTS

Of 5249 patients, severe injury (ISS >15) was present in 1469. Approximately one-quarter of patients with severe injury received blood products within the first 24 hours. There were no significant variations in demographics or complications between patients of different blood types. Univariate and multivariable regression analysis showed no association between blood type and mortality. However, penetrating injury, lower GCS, higher ISS, blood transfusion within 24 hours, and Asian descent were associated with higher overall mortality.

CONCLUSIONS

In contrast to previous studies, we found no evidence of an association between blood type and mortality. However, our findings suggest that patients of Asian descent may be at higher risk for mortality following trauma. Further research is warranted to explore this observation.

Gender-Diverse Patients' Awareness and Utilization of Gender-Affirming Laryngological Services

OBJECTIVE

Despite gender-affirming laryngological services (GALS; eg, voice therapy or surgery) being available nationwide, there is a discrepancy between the number of transgender and gender diverse (TGD) people with vocal incongruence and those who pursue services. Primary care is an important setting for accessing gender-affirming care, including learning about GALS. The purpose of this study was to understand the relationship between access to primary care and utilization of GALS.

METHODS

An anonymous cross-sectional online survey was developed in REDCap. Between June-November 2022, 187 TGD respondents answered all questions related to this analysis. We assessed the relationship between having a primary care provider (PCP) and use of GALS via logistic regression.

RESULTS

Of the 167 individuals who reported having a PCP, 54% reported familiarity with GALS, compared to 45% of individuals without a PCP. Compared to the group without a PCP, a greater proportion of individuals with a PCP had received professional voice therapy (21% vs 5%) and voice surgery (3% vs 0%). Logistic regression models did not demonstrate a significant effect of primary care access on either familiarity with, or use of, GALS.

CONCLUSION

Most respondents (89%) were linked to the medical community through a PCP. A greater proportion of respondents with a PCP had accessed professional voice therapy and voice surgery compared with respondents without a PCP, though this difference was not statistically significant. Increased communication between GALS providers and PCPs would raise awareness of available services and may strengthen the referral pipeline to increase access to vocal care.

LEVEL OF EVIDENCE

Level III.

Racial Distribution and Associated Outcomes for Patients With and Without Severe-Isolated Traumatic Brain Injuries Following Venous Thromboembolism Prophylaxis

INTRODUCTION

Disparities in venous thromboembolism (VTE) incidence and prophylaxis have been observed across racial groups. This study investigates the relationship between race, injury type, and the timing of VTE prophylaxis in severe trauma patients, both with and without isolated traumatic brain injuries. The primary goal is to analyze how these factors interact and their potential impact on clinical outcomes.

METHODS

A retrospective cohort study of the American College of Surgeons Trauma Quality Program Participant Use File (ACS-TQIP-PUF) from 2018 to 2021. Patient demographics, injury categories, VTE prophylaxis timing, injury severity, and in-hospital complications were collected. Multivariable regression models explored associations between race, injury type, VTE prophylaxis, and in-hospital mortality. Groups were analyzed by injury profile (isolated TBI vs non-TBI) and then by VTE prophylaxis timing (early ≤24 hours, late >24 hours).

RESULTS

Of 68,504 trauma patients analyzed, the majority were non-Hispanic or Latino (83.3%), White (71.2%), and male (69.6%). Patients receiving late VTE prophylaxis had higher rates of DVT and PE across race groups than patients with early prophylaxis. Logistic regression showed Asian patients with TBI receiving early prophylaxis were significantly more likely to have in-hospital mortality (OR 16.27, CI = 1.11-237.43, P = .04) than other races.

CONCLUSION

Patients who received late prophylaxis had higher VTE rates than early prophylaxis, independent of injury pattern or race. Additionally, assessing the implications of race in early VTE prophylaxis for isolated TBI showed that adult Asian patients had 16 times higher odds of in-hospital mortality compared to other races.

Neonatal and maternal outcomes at early vs. full term following induction of labor; A secondary analysis of the OBLIGE randomized trial

INTRODUCTION

Birth at early term (37+0-38+6 completed gestational weeks [GW] and additional days) is associated with adverse neonatal outcomes compared with waiting to ≥39 GW. Most studies report outcomes after elective cesarean section or a mix of all modes of births; it is unclear whether these adverse outcomes apply to early-term babies born after induction of labor (IOL). We aimed to determine, in women with a non-urgent induction indication (elective/planned >48 h in advance), if IOL at early and late term was associated with adverse neonatal and maternal outcomes compared with IOL at full term.

MATERIAL AND METHODS

An observational cohort study as a secondary analysis of a multicenter randomized controlled trial of 1087 New Zealand women with a planned IOL ≥37+0 GW. Multivariable logistic regression was used to analyze neonatal and maternal outcomes in relation to gestational age; 37+0-38+6 (early term), 39+0-40+6 (full term) and ≥41+0 (late term) GW. Neonatal outcome analyses were adjusted for sex, birthweight, mode of birth and induction indication, and maternal outcome analyses for parity, age, body mass index and induction method. The primary neonatal outcome was admission to neonatal intensive care unit (NICU) for >4 hours; the primary maternal outcome was cesarean section.

RESULTS

Among the 1087 participants, 266 had IOL at early term, 480 at full term, and 341 at late term. Babies born following IOL at early term had increased odds for NICU admission for >4 hours (adjusted odds ratio [aOR] 2.16, 95% confidence intervals (CI) 1.16-4.05), compared with full term. Women having IOL at early term had no difference in emergency cesarean rates but had an increased need for a second induction method (aOR 1.70, 95% CI 1.15-2.51) and spent 4 h longer from start of IOL to birth (Hodges-Lehmann estimator 4.10, 95% CI 1.33-6.95) compared with those with IOL at full term.

CONCLUSIONS

IOL for a non-urgent indication at early term was associated with adverse neonatal and maternal outcomes and no benefits compared with IOL at full term. These findings support international guidelines to avoid IOL before 39 GW unless there is an evidence-based indication for earlier planned birth and will help inform women and clinicians in their decision-making about timing of IOL.

Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) is a critical condition with significant clinical implications, yet there is a need for a predictive model that can reliably assess the risk of its development. This study is undertaken to bridge a gap in healthcare by creating a predictive model for SA-AKI with the goal of empowering healthcare providers with a tool that can revolutionize patient care and ultimately lead to improved outcomes.

METHODS

A cohort of 615 patients afflicted with sepsis, who were admitted to the intensive care unit, underwent random stratification into 2 groups: a training set (n = 435) and a validation set (n = 180). Subsequently, a multivariate logistic regression model, imbued with nonzero coefficients via LASSO regression, was meticulously devised for the prognostication of SA-AKI. This model was thoughtfully rendered in the form of a nomogram. The salience of individual risk factors was assessed and ranked employing Shapley Additive Interpretation (SHAP). Recursive partition analysis was performed to stratify the risk of patients with sepsis.

RESULTS

Among the panoply of clinical variables examined, hypertension, diabetes mellitus, C-reactive protein, procalcitonin (PCT), activated partial thromboplastin time, and platelet count emerged as robust and independent determinants of SA-AKI. The receiver operating characteristic curve analysis for SA-AKI risk discrimination in both the training set and validation set yielded an area under the curve estimates of 0.843 (95% CI: 0.805 to 0.882) and 0.834 (95% CI: 0.775 to 0.893), respectively. Notably, PCT exhibited the most conspicuous influence on the model's predictive capacity. Furthermore, statistically significant disparities were observed in the incidence of SA-AKI and the 28-day survival rate across high-risk, medium-risk, and low-risk cohorts (P < .05).

CONCLUSION

The composite predictive model, amalgamating the quintet of SA-AKI predictors, holds significant promise in facilitating the identification of high-risk patient subsets.

Service Quality and Residents' Preferences for Facilitated Self-Service Fundus Disease Screening: Cross-Sectional Study

BACKGROUND

Fundus photography is the most important examination in eye disease screening. A facilitated self-service eye screening pattern based on the fully automatic fundus camera was developed in 2022 in Shanghai, China; it may help solve the problem of insufficient human resources in primary health care institutions. However, the service quality and residents' preference for this new pattern are unclear.

OBJECTIVE

This study aimed to compare the service quality and residents' preferences between facilitated self-service eye screening and traditional manual screening and to explore the relationships between the screening service's quality and residents' preferences.

METHODS

We conducted a cross-sectional study in Shanghai, China. Residents who underwent facilitated self-service fundus disease screening at one of the screening sites were assigned to the exposure group; those who were screened with a traditional fundus camera operated by an optometrist at an adjacent site comprised the control group. The primary outcome was the screening service quality, including effectiveness (image quality and screening efficiency), physiological discomfort, safety, convenience, and trustworthiness. The secondary outcome was the participants' preferences. Differences in service quality and the participants' preferences between the 2 groups were compared using chi-square tests separately. Subgroup analyses for exploring the relationships between the screening service's quality and residents' preference were conducted using generalized logit models.

RESULTS

A total of 358 residents enrolled; among them, 176 (49.16%) were included in the exposure group and the remaining 182 (50.84%) in the control group. Residents' basic characteristics were balanced between the 2 groups. There was no significant difference in service quality between the 2 groups (image quality pass rate: P=.79; average screening time: P=.57; no physiological discomfort rate: P=.92; safety rate: P=.78; convenience rate: P=.95; trustworthiness rate: P=.20). However, the proportion of participants who were willing to use the same technology for their next screening was significantly lower in the exposure group than in the control group (P<.001). Subgroup analyses suggest that distrust in the facilitated self-service eye screening might increase the probability of refusal to undergo screening (P=.02).

CONCLUSIONS

This study confirms that the facilitated self-service fundus disease screening pattern could achieve good service quality. However, it was difficult to reverse residents' preferences for manual screening in a short period, especially when the original manual service was already excellent. Therefore, the digital transformation of health care must be cautious. We suggest that attention be paid to the residents' individual needs. More efficient man-machine collaboration and personalized health management solutions based on large language models are both needed.

Multivariate mixed-effects ordinal logistic regression models with difference-in-differences estimator of the impact of WORTH Yetu on household hunger and socioeconomic status among OVC caregivers in Tanzania

BACKGROUND

Although most of the livelihood programmes target women, those that involve women and men have been evaluated as though men and women were a single homogenous population, with a mere inclusion of gender as an explanatory variable. This study evaluated the impact of WORTH Yetu (an economic empowerment intervention to improve livelihood outcomes) on household hunger, and household socioeconomic status (SES) among caregivers (both women and men) of orphaned and vulnerable children (OVC) in Tanzania. The study hypothesized that women and men respond to livelihood interventions differently, hence a need for gender-disaggregated impact evaluation of such interventions.

METHODS

This is a secondary analysis of longitudinal data, involving caregivers' baseline (2016-2019) and follow-up (2019-2020) data from the USAID Kizazi Kipya project in 25 regions of Tanzania. Two dependent variables (ie, outcomes) were assessed; household hunger which was measured using the Household Hunger Scale (HHS), and Socioeconomic Status (SES) using the Principal Component Analysis (PCA). WORTH Yetu, a livelihood intervention implemented by the USAID Kizazi Kipya project was the main independent variable whose impact on the two outcomes was evaluated using multivariate analysis with a multilevel mixed-effects, ordinal logistic regression model with difference-in-differences (DiD) estimator for impact estimation.

RESULTS

The analysis was based on 497,293 observations from 249,655 caregivers of OVC at baseline, and 247,638 of them at the follow-up survey. In both surveys, 70% were women and 30% were men. Their mean age was 49.3 (±14.5) years at baseline and 52.7 (±14.8) years at the follow-up survey. Caregivers' membership in WORTH Yetu was 10.1% at the follow-up. After adjusting for important confounders there was a significant decline in the severity of household hunger by 46.4% among WORTH Yetu members at the follow-up compared to the situation at the baseline (adjusted Odds Ratio (aOR) = 0.536, 95% Confidence Interval (CI) [0.521, 0.553]). The decline was 45.7% among women (aOR = 0.543 [0.524, 0.563]) and 47.5% among men (aOR = 0.525 [0.497, 0.556]). Regarding SES, WORTH Yetu members were 15.9% more likely to be in higher wealth quintiles at the follow-up compared to the situation at the baseline (aOR = 1.159 [1.128, 1.190]). This impact was 20.8% among women (aOR = 1.208 [1.170, 1.247]) and 4.6% among men (aOR = 1.046 [0.995, 1.101]).

CONCLUSION

WORTH Yetu was associated with a significant reduction in household hunger, and a significant increase in household SES among OVC caregivers in Tanzania within an average follow-up period of 1.6 years. The estimated impacts differed significantly by gender, suggesting that women and men responded to the WORTH Yetu intervention differently. This implied that the design, delivery, and evaluation of such programmes should happen in a gender responsive manner, recognising that women and men are not the same with respect to the programmes.

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

Frailty and spatialization of older adults in the city of Uberlândia with IVCF-20

OBJECTIVE

To describe the functional clinical profile of elderly people linked to primary health care, using the Functional Clinical Vulnerability Index (IVCF-20) and to spatialize those with the greatest functional decline by primary health care units in the municipality of Uberlândia, in the state of Minas Gerais (MG), in the year 2022.

METHODS

A cross-sectional study with secondary data from the Municipal Health Department of Uberlândia-MG. The variables were compared using Student's t-test, Mann Whitney test, Pearson's chi-square, and multinomial logistic regression to obtain the independent effect of each variable. The significance level adopted was 5% (p < 0.05). The georeferenced database in ArcGIS® was used.

RESULTS

47,182 older adults were evaluated with a mean age of 70.3 years (60 to 113 years), 27,138 of whom were women (57.52%), with a clear predominance of low-risk or robust older adults (69.40%). However, 11.09% are high-risk older adults and 19.52% are at risk of frailty. Older men had independently lower odds of moderate and high risk compared to older women (OR = 0.53; p < 0.001). A high prevalence of polypharmacy was observed, 21.40% of the older adult population, particularly in frail older adults, with a prevalence of 63.08%. There was a greater distribution of frail older adults around the central region of the municipality and in health units with a larger coverage area. The IVCF-20 made it possible to screen frailty in primary health care.

CONCLUSION

The instrument is capable of stratifying the risk of older adults in health care networks through primary health care, enabling the application of individualized preventive, promotional, palliative, or rehabilitative interventions, according to the clinical functional stratum of the older adult and the compromised functional domains. Risk stratification and spatial distribution of the frailest older adults can be a good strategy for qualifying health professionals with the aim of maximizing the autonomy and independence of the older adults.

Multivariate logistic regression analysis of risk factors for birth defects: a study from population-based surveillance data

OBJECTIVE

To explore risk factors for birth defects (including a broad range of specific defects).

METHODS

Data were derived from the Population-based Birth Defects Surveillance System in Hunan Province, China, 2014-2020. The surveillance population included all live births, stillbirths, infant deaths, and legal termination of pregnancy between 28 weeks gestation and 42 days postpartum. The prevalence of birth defects (number of birth defects per 1000 infants) and its 95% confidence interval (CI) were calculated. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (ORs) were used to identify risk factors for birth defects. We used the presence or absence of birth defects (or specific defects) as the dependent variable, and eight variables (sex, residence, number of births, paternal age, maternal age, number of pregnancies, parity, and maternal household registration) were entered as independent variables in multivariate logistic regression analysis.

RESULTS

Our study included 143,118 infants, and 2984 birth defects were identified, with a prevalence of 20.85% (95%CI: 20.10-21.60). Multivariate logistic regression analyses showed that seven variables (except for parity) were associated with birth defects (or specific defects). There were five factors associated with the overall birth defects. The risk factors included males (OR = 1.49, 95%CI: 1.39-1.61), multiple births (OR = 1.44, 95%CI: 1.18-1.76), paternal age < 20 (OR = 2.20, 95%CI: 1.19-4.09) or 20-24 (OR = 1.66, 95%CI: 1.42-1.94), maternal age 30-34 (OR = 1.16, 95%CI: 1.04-1.29) or > = 35 (OR = 1.56, 95%CI: 1.33-1.81), and maternal non-local household registration (OR = 2.96, 95%CI: 2.39-3.67). Some factors were associated with the specific defects. Males were risk factors for congenital metabolic disorders (OR = 3.86, 95%CI: 3.15-4.72), congenital limb defects (OR = 1.34, 95%CI: 1.14-1.58), and congenital kidney and urinary defects (OR = 2.35, 95%CI: 1.65-3.34). Rural areas were risk factors for congenital metabolic disorders (OR = 1.21, 95%CI: 1.01-1.44). Multiple births were risk factors for congenital heart defects (OR = 2.09, 95%CI: 1.55-2.82), congenital kidney and urinary defects (OR = 2.14, 95%CI: 1.05-4.37), and cleft lip and/or palate (OR = 2.85, 95%CI: 1.32-6.15). Paternal age < 20 was the risk factor for congenital limb defects (OR = 3.27, 95%CI: 1.10-9.71), 20-24 was the risk factor for congenital heart defects (OR = 1.64, 95%CI: 1.24-2.17), congenital metabolic disorders (OR = 1.56, 95%CI: 1.11-2.21), congenital limb defects (OR = 1.61, 95%CI: 1.14-2.29), and congenital ear defects (OR = 2.13, 95%CI: 1.17-3.89). Maternal age < 20 was the risk factor for cleft lip and/or palate (OR = 3.14, 95%CI: 1.24-7.95), 30-34 was the risk factor for congenital limb defects (OR = 1.37, 95%CI: 1.09-1.73), >=35 was the risk factor for congenital heart defects (OR = 1.51, 95%CI: 1.14-1.99), congenital limb defects (OR = 1.98, 95%CI: 1.41-2.78), and congenital ear defects (OR = 1.82, 95%CI: 1.06-3.10). Number of pregnancies = 2 was the risk factor for congenital nervous system defects (OR = 2.27, 95%CI: 1.19-4.32), >=4 was the risk factor for chromosomal abnormalities (OR = 2.03, 95%CI: 1.06-3.88) and congenital nervous system defects (OR = 3.03, 95%CI: 1.23-7.47). Maternal non-local household registration was the risk factor for congenital heart defects (OR = 3.57, 95%CI: 2.54-5.03), congenital metabolic disorders (OR = 1.89, 95%CI: 1.06-3.37), congenital limb defects (OR = 2.94, 95%CI: 1.86-4.66), and congenital ear defects (OR = 3.26, 95%CI: 1.60-6.65).

CONCLUSION

In summary, several risk factors were associated with birth defects (including a broad range of specific defects). One risk factor may be associated with several defects, and one defect may be associated with several risk factors. Future studies should examine the mechanisms. Our findings have significant public health implications as some factors are modifiable or avoidable, such as promoting childbirths at the appropriate age, improving the medical and socio-economic conditions of non-local household registration residents, and devoting more resources to some specific defects in high-risk groups, which may help reducing birth defects in China.

Associations between Life's Essential 8 and post-stroke depression and all-cause mortality among US adults

BACKGROUND

Depression is the common mental disease after stroke. Our objective was to investigate the correlation of Life's Essential 8 (LE8), the recently updated evaluation of cardiovascular health, with the occurrence of post-stroke depression (PSD) and all-cause mortality among United States (US) adults.

METHODS

Participants with stroke were chosen from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The relationship between LE8 and the risk of PSD was assessed through weighted multiple logistic models. A restricted cubic spline was employed for the examination of correlations. To demonstrate the stability of the results, sensitivity analysis and subgroup analysis were carried out. Furthermore, Cox regression models were used for the correlation between LE8 and all-cause mortality.

RESULTS

In this study, a total of 1071 participants were included for analysis. It was observed that LE8 score and PSD risk shared an inverse relationship in per 10 points increase [OR = 0.62 (0.52-0.74, P < 0.001)] in logistic regression models. The analysis of restricted cubic spline demonstrated approximately a noticeable inverse linear association between LE8 score and PSD risk. Sensitivity analysis verified the stability of the findings. Moreover, no statistically significant interactions were identified in subgroup analysis. A reverse association between LE8 score and all-cause mortality was also observed with a 10-point increase [HR = 0.85 (0.78-0.94, P < 0.001)] in cox regression models.

CONCLUSIONS

A negative correlation was discovered between LE8 score and PSD and all-cause mortality risk among US adults. We need to conduct large-scale prospective studies to further validate our results.

Developing a risk prediction model for death at first suicide attempt-Identifying risk factors from Thailand's national suicide surveillance system data

More than 60% of suicides globally are estimated to take place in low- and middle-income nations. Prior research on suicide has indicated that over 50% of those who die by suicide do so on their first attempt. Nevertheless, there is a dearth of knowledge on the attributes of individuals who die on their first attempt and the factors that can predict mortality on the first attempt in these regions. The objective of this study was to create an individual-level risk-prediction model for mortality on the first suicide attempt. We analyzed records of individuals' first suicide attempts that occurred between May 1, 2017, and April 30, 2018, from the national suicide surveillance system, which includes all of the provinces of Thailand. Subsequently, a risk-prediction model for mortality on the first suicide attempt was constructed utilizing multivariable logistic regression and presented through a web-based application. The model's performance was assessed by calculating the area under the receiver operating curve (AUC), as well as measuring its sensitivity, specificity, and accuracy. Out of the 3,324 individuals who made their first suicide attempt, 50.5% of them died as a result of that effort. Nine out of the 21 potential predictors demonstrated the greatest predictive capability. These included male sex, age over 50 years old, unemployment, having a depressive disorder, having a psychotic illness, experiencing interpersonal problems such as being aggressively criticized or desiring plentiful attention, having suicidal intent, and displaying suicidal warning signals. The model demonstrated a good predictive capability, with an AUC of 0.902, a sensitivity of 84.65%, a specificity of 82.66%, and an accuracy of 83.63%. The implementation of this predictive model can assist physicians in conducting comprehensive evaluations of suicide risk in clinical settings and devising treatment plans for preventive intervention.

SLESIS-R: an improved score for prediction of serious infection in patients with systemic lupus erythematosus based on the RELESSER prospective cohort

OBJECTIVE

To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort.

METHODS

We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance.

RESULTS

A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777-0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48.

CONCLUSIONS

SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures.

Association of serum soluble α‑klotho with risk of kidney stone disease: a population-based cross-sectional study

BACKGROUND

The aim of the study was to explore the association of serum soluble klotho with kidney stone disease (KSD) in the general population over the age of 40 years in the United States.

METHODS

We integrated the data in National Health and Nutrition Examination Survey from 2007 to 2016 years. The relationship between serum soluble α‑klotho and prevalence of KSD was analyzed by constructing weighted multivariable logistic regression model, restricted cubic spline (RCS) curve, and subgroup analyses.

RESULTS

In the study, a total of 13,722 individuals were included in our study. A U-shaped association between serum soluble klotho and the risk of KSD was shown by the RCS curve (P value for nonlinear < 0.05). In the full adjusted model, compared with the lowest quartile of serum soluble α‑klotho, the adjusted odd ratios (95% confidence intervals) for KSD across the quartiles were (0.999 (0.859, 1.164), 1.005 (0.858, 1.176), and 1.061 (0.911, 1.235)). Subgroup analyses also showed that the U-shaped association of serum soluble α‑klotho with KSD was found among subjects who were age < 60 years, female or male, with or without hypertension, and BMI ≥ 30 kg/m2.

CONCLUSIONS

Our findings suggested that serum klotho levels had a U-shaped correlation with risk of KSD. When the Klotho level is at 818.66 pg/mL, prevalence of KSD is lowest. Therefore, maintaining a certain level of serum soluble α‑klotho could prevent the occurrence of KSD.

Determinants of coexistence of undernutrition and anemia among under-five children in Rwanda; evidence from 2019/20 demographic health survey: Application of bivariate binary logistic regression model

BACKGROUND

Undernutrition and anemia are significant public health issues among under-5 children, with potential long-term consequences for growth, development, and overall health. Thus, this study aims to conduct a bivariate binary logistic regression model by accounting for the possible dependency of childhood undernutrition and anemia.

METHODS

The data came from the DHS program's measurement. A total of 3,206 under-five children were involved in this study. A single composite index measure was calculated for stunting, wasting, and underweight using principal component analysis. A bivariate binary logistic regression model is used to assess the association between undernutrition and anemia given the effect of other predictors.

RESULTS

Among 3,206 under-five children considered in this study, 1482 (46.2%) and 658 (20.5%) children were agonized by anemia and undernutrition, respectively. In bivariate binary logistic regression model; Urban children [AOR = 0.751, 96% CI: 0.573-0.984; AOR = 0.663, 95% CI: 0.456-0.995] and anemic mothers [AOR = 1.160, 95% CI: 1.104-1.218; AOR = 1.663, 95% CI: 1.242-2.225] were significantly associated with both childhood anemia and undernutrition, respectively. Improved water sources [AOR = 0.681, 95% CI: 0.446-0.996], average-sized children [AOR = 0.567, 95% CI: 0.462-0.696], and diarrhea [AOR = 1.134, 95% CI: 1.120-2.792] were significantly associated with childhood anemia. Large-sized children [AOR = 0.882, 95% CI: 0.791-0.853] and those with fever [AOR = 1.152, 95% CI: 1.312-2.981] were significantly associated with under-five children's undernutrition.

CONCLUSION

The prevalence of both undernutrition and anemia among under-five-year-old children was high in Rwanda. The following determinants are statistically associated with both childhood undernutrition and anemia: place of residence; source of drinking water; maternal anemia; being a twin; birth size of children; diarrhea; fever; and child age. Anemia and nutritional deficiencies must be treated concurrently under one program, with evidence-based policies aimed at vulnerable populations.

AI technology specialization and national competitiveness

This study investigates the factors influencing specialization in artificial intelligence (AI) technology, a critical element of national competitiveness. We utilized a revealed comparative advantage matrix to evaluate technological specialization across countries and employed a three-way fixed-effect panel logit model to examine the relationship between AI specialization and its determinants. The results indicate that the development of AI technology is strongly contingent on a nation's pre-existing technological capabilities, which significantly affect AI specialization in emerging domains. Additionally, this study reveals that scientific knowledge has a positive impact on technological specialization, highlighting the necessity of integrating scientific advancements with technological sectors. Although complex technologies positively influence AI specialization, their effect is less pronounced than that of scientific knowledge. This suggests that in rapidly advancing fields, such as AI, incorporating new scientific knowledge into related industries may be more advantageous than simply advancing existing technologies to outpace competitors. This insight points nations toward enhancing AI competitiveness in new areas, emphasizing the vital importance of both scientific and technological capabilities, and the integration of novel AI knowledge with established sectors. This research offers critical guidance for policymakers in less technologically and economically developed countries, as these nations may not have the technological infrastructure required to foster AI specialization through increased technical complexity.

Associations between Plasma Tocopherols and Lung Cancer Risk: Results from the Southern Community Cohort Study

BACKGROUND

Despite the various anticancer activities of tocopherols, little is known about tocopherols associated with lung cancer risk among low-income African Americans (AA) and European Americans (EA) who are disproportionately affected by the disease.

METHODS

We conducted a nested case-control study that included 209 incident lung cancer cases and 406 matched controls within the Southern Community Cohort Study. Using biospecimens collected at cohort enrollment, plasma levels of α-, β/γ-, δ-, and total-tocopherols were measured by high-performance liquid chromatography with photodiode array detection. Conditional logistic regression was used to estimate ORs and 95% confidence intervals (CI) for lung cancer risk after adjusting for potential confounders. Stratified analyses were also conducted.

RESULTS

Plasma levels of total-tocopherols were inversely associated with lung cancer risk overall [OR (95% CI) for the highest vs. lowest tertile = 0.51 (0.30-0.90)]. The inverse association remained significant among EAs [0.20 (0.06-0.65)], men [0.43 (0.21-0.90)], current smokers [0.49 (0.26-0.93)], and cases diagnosed within 2 years of blood draw [0.36 (0.15-0.86)], though we did not find a significant risk reduction among AAs [0.75 (0.39-1.45)]. Notably, we found significant interactions between α-tocopherol and race after controlling the FDR to correct for multiple comparisons (Pinteraction = 0.02).

CONCLUSIONS

Our results indicate that plasma total-tocopherols are inversely associated with lung cancer risk, but the association may differ across specific isomeric forms of tocopherols, race, or other individuals' characteristics. Further large-scale studies are warranted to confirm our findings.

IMPACT

Recommendations on tocopherols for lung cancer prevention should take isomers, race, and smoking behaviors into consideration.

An observational study to identify causative factors for not using hydroxychloroquine in systemic lupus erythematosus

Hydroxychloroquine (HCQ) use is indicated for patients with systemic lupus erythematosus (SLE). Nevertheless, reports discussing the reasons for not prescribing HCQ are limited. We identified the factors that interfere with HCQ use in patients with SLE. This observational, single-center study included data from 265 patients with SLE in 2019. The patients were categorized into groups with and without a history of HCQ use. Between these groups, clinical characteristics were compared using univariate analysis and logistic regression models. Among the 265 patients, 133 (50.2%) had a history of HCQ use. Univariate analysis identified older age; longer disease duration; lower prednisolone dose, clinical SLE disease activity index 2000, and estimated glomerular filtration rate; higher C3 level; and lower anti-double-stranded DNA antibody concentration as HCQ non-use-related variables. Logistic regression models identified a positive association between HCQ non-use and longer disease duration (odds ratio [OR] 1.08), prednisolone dose ≤ 7.5 mg/day (OR 4.03), C3 level ≥ 73 mg/dL (OR 2.15), and attending physician having graduated > 10 years prior (OR 3.19). In conclusion, a longer disease duration, lower prednisolone dose, higher C3 level, and longer time since attending physicians' graduation correlated with HCQ non-use. Physicians and patients should be educated to facilitate HCQ use despite these factors.

Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening

Objective

This study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules.

Methods

The study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regression with the least absolute shrinkage and selection operator (Lasso) was applied to the complete dataset for variable selection. Binary logistic regression was used to analyze the relationship between various influencing factors and the prevalence of thyroid nodules.

Results

Based on the screening results of Lasso regression and the subsequent establishment of the Binary Logistic Regression Model on the training dataset, it was found that advanced age (OR=1.046, 95% CI: 1.033-1.060), females (OR = 1.709, 95% CI: 1.342-2.181), overweight individuals (OR = 1.546, 95% CI: 1.165-2.058), individuals with impaired fasting glucose (OR = 1.590, 95% CI: 1.193-2.122), and those with dyslipidemia (OR = 1.588, 95% CI: 1.197-2.112) were potential risk factors for thyroid nodule disease (p<0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the Binary Logistic Regression Model is 0.68 (95% CI: 0.64-0.72).

Conclusions

advanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease.

Association between early lactate-related variables and 6-month neurological outcome in out-of-hospital cardiac arrest patients

INTRODUCTION

The role of lactate measurement in out-of-hospital cardiac arrest (OHCA) survivors remains controversial. We assessed the association between early lactate-related variables, OHCA characteristics, and long-term neurological outcome.

METHODS

In OHCA patients who received targeted temperature management, lactate levels were measured at 0, 12, and 24 h after the return of spontaneous circulation. We calculated lactate clearance and time-weighted cumulative lactate (TWCL), which represent the area under the time-lactate curve. The area under the receiver operating characteristic curve (AUC) and the adjusted odds ratios (AORs) of lactate-related variables for predicting 6-month poor outcome (Cerebral Performance Category 3-5) were evaluated. Interactions between lactate variables and characteristics of OHCA were evaluated by a multivariable logistic model with interaction terms and subgroup analysis.

RESULTS

A total of 347 OHCA patients were included. After adjustment, higher lactate levels at the three time points were associated with a poor outcome (AOR 1.10 [95% CI, 1.03-1.18], AOR 1.15 [95% CI, 1.02-1.29], and AOR 1.36 [95% CI, 1.15-1.60], respectively), while TWCL was the only lactate kinetics variable associated with a poor outcome (AOR 1.29 [95% CI, 1.12-1.49]). We identified several interactions between lactate-related variables and OHCA characteristics. In particular, the AUC of TWCL was excellent in cases of noncardiac etiology (AUC 0.92 [95% CI, 0.86-0.96] but only moderate in cardiac etiology (AUC 0.69 [95% CI, 0.62-0.75]).

CONCLUSIONS

Early lactate levels, especially at 24 h, and TWCL were independent predictors of neurologic outcome in these patients, whereas lactate clearance was not. The prognostic ability of lactate-related variables varied depending on the OHCA characteristics.

BRAF V600E mutation in papillary thyroid microcarcinoma: is it a predictor for the prognosis of patients with intermediate to high recurrence risk?

OBJECTIVE

The BRAFV600E mutation is the universal genetic mutation in papillary thyroid microcarcinoma (PTMC). The present study is to estimate the role of the BRAFV600E mutation in the clinical outcome of PTMC with intermediate to high recurrence risk after radioactive iodine (RAI) therapy, which is considered to be an indolent tumor.

METHODS

We conducted a single-center retrospective study. Between May 2016 and March 2019, PTMC patients with known BRAFV600E status who received RAI therapy were reviewed at the Second Hospital of Shandong University. Treatment and follow-up were defined according to criteria used in the 2015 ATA guidelines. The association between the BRAFV600E mutation and clinicopathological characteristics, response to RAI therapy, and recurrence after a period of follow-up were analyzed. Propensity score matching (PSM) and logistic regression were used to control confounding variables.

RESULTS

Of the 322 patients with intermediate to high recurrence risk in PTMC, the mean age of the patients were 43.7 ± 12.2 years, and 72.1% were women. BRAFV600E mutation was found in 64.9% (209/322). After PSM, 112 pairs of patients were matched, and except for multifocality (P = 0.001), extrathyroidal invasion (P = 0.003) and tumor size (P = 0.03), there was no significant difference in all baseline characteristics between the two groups. An excellent response (ER) to RAI therapy was observed in 273 patients (84.7%). At the end of the study, 17(5.2%) and 6(1.8%) patients showed structural incomplete response (SIR) and biochemical incomplete response (BIR) status. The proportion of patients who achieved ER status in the BRAFV600E mutation positive and negative groups was 86.6% and 81.4%, respectively. Kaplan-Meier analyses showed that the BRAFV600E mutation was not related to lower ER reached time. The median follow-up was 51 months.

CONCLUSIONS

We found the BRAFV600E mutation was associated with multifocality, extrathyroidal invasion, and tumor size in papillary thyroid microcarcinoma. However, the BRAFV600E mutation had no significant association with clinical outcomes in patients with intermediate to high recurrence risk after RAI therapy. Furthermore, the extra-thyroid uptake results and distant metastasis had been proven to be independent factor predicting the clinical response.

REGISTRATION NUMBER

ChiCTR2200062911.

Reporting bias in breast reconstruction clinical trials: Which and when clinical trials get published

BACKGROUND

Reporting bias refers to the phenomenon in which the reporting of research findings is influenced by the nature of the results. Without the totality of evidence, clinical practice may be misguided. The objective of this work was to examine the extent of reporting bias in clinical trials of breast reconstruction surgery.

METHODS

We searched and extracted data from all completed breast reconstruction clinical trials published in ClinicalTrials.gov from database inception to August 2020. Investigators sought to identify published full manuscripts of the registered trials. The primary outcome was classified as positive or nonpositive and trials were classified as industry or nonindustry funded. Time to publication in a peer-reviewed journal was computed and compared using time-to-event analysis. Trial characteristics associated with publication were evaluated using logistic regression.

RESULTS

A total of 156 clinical trials were identified, of which, 53 trials were published. The median time to publication was 22 months (IQR, 13-35 months). Industry-funded studies were associated with a longer time to publication (HR = 2.4, p = 0.023) and publication in lower-impact journals (OR = 3.7, p = 0.048). Randomized clinical trials were associated with faster times to publication than nonrandomized studies (aHR = 3.2, p = 0.030). Statistical significance and the effect size were not associated with time to publication.

CONCLUSIONS

We found no evidence that industry-funded trials were more likely to report a positive primary outcome. However, industry-funded trials were associated with a longer time to publication and publication in lower-impact journals.

Understanding the relationship between road users and the roadway infrastructure in Ghana

Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding driver behavior at unsignalized junctions is particularly important since failure to stop or yield can seriously affect vulnerable road users. The study's objectives were to develop relationships between driver behavior and junction characteristics. Understanding the characteristics that lead to determining what factors influence a driver's behavioral response at rural junctions provides information for policy makers to determine the best strategies to address these behaviors. The study evaluated stopping behavior at rural junctions. Driver behavior was extracted from video views of ten junctions in the Ashanti Region of Ghana. A total of 3,420 vehicles were observed across all ten junctions during data collection before any analysis was conducted. The type of stop was selected as a surrogate measure of safety. Logistic regression was used to model stopping behavior at the selected junctions. The analysis showed drivers were more likely to stop when going straight (versus a left turn) and left turning vehicles were more likely to stop than right turning vehicles. Additionally, single unit trucks and tro-tros were more likely to stop than other vehicle types. Drivers were also much more likely to stop when channelization, intersection lighting, or speed humps were present. Drivers at junctions with 4-approaches were also more likely to stop than those with 3 approaches. The results from this research contribute valuable information about what factors contribute to positive safety behaviors at rural junctions. This provides guidance for safety professionals to select solutions and can be a valuable tool to predict the economical effectiveness of solutions to addressing junction safety in low- and middle-income countries (LMIC) such as Ghana. The results can also provide insight and recommendations to Ghanaian road safety agencies and launch sustainable efforts to raise community awareness toward decreasing road crash fatalities in Ghana.

Employment Preferences of Cardiologists in Türkiye: A Discrete Choice Experiment

OBJECTIVE

The homogeneous distribution of physicians is important for ensuring patients' access to health services. To encourage physicians to work in underserved areas, policymakers create incentives. Understanding physicians' employment preferences is essential when developing these incentive packages. This study aims to quantitatively reveal the preferences of cardiologists in Türkiye using a discrete choice experiment (DCE).

METHODS

A DCE questionnaire was distributed electronically to all cardiologists in Türkiye. It included 14 different pairs of hypothetical job offers based on seven parameters likely to influence their employment preferences. The data were analyzed using a conditional logit model. The coefficients (CEs) of conditional logistic regression and the willingness-to-accept (WTA) values were calculated.

RESULTS

The analysis included 278 cardiologists. It revealed that the most influential parameter was location (CE: 2.86). To move to an undesirable location, the average participant would require an earnings increase of at least 123.3% relative to the average potential earnings of a cardiologist. Other parameters included availability of suitable facilities (CE: 1.07, WTA: 46.3%), harmony with co-workers (CE: 0.92, WTA: 39.61%), working conditions (CE: 0.68, WTA: 29.26%), and the number of night shifts (CE: 0.61, WTA: 26.34%).

CONCLUSION

'Location' emerged as the most important factor in the employment preferences of cardiologists in Türkiye. However, several other monetary and non-monetary factors were also influential, suggesting that policymakers should adopt a holistic approach when developing incentives for cardiologists.

Iodine Excess May Lead to Low Exam Score in Children Aged 8-10 Years

Recent research has shown that iodine excess may damage children's intelligence. Years of monitoring results in Shanghai show the iodine status has approached the upper limit of the appropriate range for children aged 8-10 years, indicating a risk of iodine excess. We used multi-stage random sampling to select children. Sixteen districts of Shanghai were divided into five units based on geographic location, and one primary school was randomly selected from each unit. In each selected school, about 40 children aged 8-10 years were randomly recruited to measure their urinary iodine concentration (UIC), household salt iodine concentration (SIC), the score of the final unified exam of the last semester, and school canteen salt iodine concentration. The median UIC of 3213 children aged 8-10 years in Shanghai was 195.4 (122.0, 285.8) µg/L and exceeded 200 µg/L in 48.8% of the population. Household and school canteen iodized salt coverage rates were 60.3% and 82.5% respectively, and mean household and school canteen SICs were 21.51 ± 9.30 mg/kg and 25.29 ± 3.40 mg/kg respectively. By correcting for potential confounding factors, logistic regression demonstrated that compared to the adequate iodine status group, students in the slight iodine excess group were less likely to get "A" (score > 90) in math, Chinese, and English exams (Math: OR = 0.775, 95% CI = 0.660-0.911, P = 0.002; Chinese: OR = 0.707, 95% CI = 0.543-0.842, P < 0.001; English: OR = 0.720, 95% CI = 0.610-0.849, P < 0.001). In Shanghai, the iodine status of 8-10-year-old children is approaching the upper limit of the adequate range. Iodine excess in Shanghai may lead to low exam scores for students.

Health Literacy and Health-care Resource Utilization Following Gastrostomy Tube Placement in Pediatric Patients

INTRODUCTION

Parental health literacy and neighborhood socioeconomic disadvantage are associated with adverse health outcomes and increased health-care resource utilization in children. We sought to evaluate the association between community-level health literacy and neighborhood socioeconomic disadvantage and their relationships with outcomes of pediatric patients undergoing gastrostomy tube (GT) placement.

METHODS

Pediatric patients who underwent GT placement from 2000 to 2019 were identified using the IBM MarketScan Research database. Claims data were merged with the health literacy index (HLI) and area deprivation index (ADI), measures of community-level health literacy and neighborhood socioeconomic disadvantage, respectively. We used multivariate logistic regression to estimate factors associated with postoperative 30- and 90-day ED visits (EVs) and 30-day readmissions.

RESULTS

A total of 4374 pediatric patients underwent GT placement. In this cohort, 6.1% and 11.4% had 30-day and 90-day EV; and 30-day readmissions in 19.75%. HLI was lower in those with 30-(244.6 ± 6.1 versus 245.4 ± 6.1; P = 0.0482) and 90-(244.5 ± 5.8 versus 245.5 ± 6.1; P = 0.001) day EV, and 30-day readmission (244.5 ± 5.56 versus 245.4 ± 6.1; P = 0.001) related to GT. ADI was lower in those with 90-day EV (55.1 ± 13.1 versus 55.9 ± 14.6; P = 0.0244). HLI was associated with decreased odds of 30- (adjusted odds ratio: 0.968; 95% confidence interval: 0.941-0.997) and 90-day (adjusted odds ratio: 0.975; 95% confidence interval: 0.954-0.998) EV following GT placement. ADI was also significantly associated with 30 and 90-day EV following GT placement.

CONCLUSIONS

In pediatric patients undergoing GT placement, higher ecologically-measured health literacy and neighborhood socioeconomic disadvantage are associated with decreased health-care resource utilization, as evidenced by decreased ED visits. Future studies should focus on the role of individual parental health literacy in outcomes of pediatric surgical patients.

Influence of the food environment on obesity risk in a large cohort of US veterans by community type

OBJECTIVE

The aim of this study was to examine relationships between the food environment and obesity by community type.

METHODS

Using electronic health record data from the US Veterans Administration Diabetes Risk (VADR) cohort, we examined associations between the percentage of supermarkets and fast-food restaurants with obesity prevalence from 2008 to 2018. We constructed multivariable logistic regression models with random effects and interaction terms for year and food environment variables. We stratified models by community type.

RESULTS

Mean age at baseline was 59.8 (SD = 16.1) years; 93.3% identified as men; and 2,102,542 (41.8%) were classified as having obesity. The association between the percentage of fast-food restaurants and obesity was positive in high-density urban areas (odds ratio [OR] = 1.033; 95% CI: 1.028-1.037), with no interaction by time (p = 0.83). The interaction with year was significant in other community types (p < 0.001), with increasing odds of obesity in each follow-up year. The associations between the percentage of supermarkets and obesity were null in high-density and low-density urban areas and positive in suburban (OR = 1.033; 95% CI: 1.027-1.039) and rural (OR = 1.007; 95% CI: 1.002-1.012) areas, with no interactions by time.

CONCLUSIONS

Many healthy eating policies have been passed in urban areas; our results suggest such policies might also mitigate obesity risk in nonurban areas.

When cancer survivors are also caregivers: well-being of "dual-role" cancer survivors

PURPOSE

As the life expectancy of cancer survivors continues to improve, cancer survivors start or resume their life roles as caregivers themselves. We aim to assess the associations between caregiving, cancer diagnosis, and self-reported well-being.

METHODS

Data were obtained from the Behavioral Risk Factor Surveillance System (BRFSS) 2016, 2018, and 2020. Outcomes included self-reported general health, physical health, mental health, depression, physical inactivity, and poor sleep. Weighted multivariable logistic regression models were used to calculate self-reported well-being's adjusted odds ratio (aOR).

RESULTS

Comparable to the proportion of caregivers in the general population, approximately 1 out of 5 cancer survivors were caregivers to others. Individuals with dual roles were significantly more likely to report poor general health (aOR = 2.45; 95%CI: 1.46-4.11), physical health (aOR = 2.17; 95%CI: 1.32-3.56), mental health (aOR = 2.47; 95%CI: 1.31-4.64), depression (aOR = 1.64; 95%CI: 1.15-2.41), physical inactivity (aOR = 1.56; 95%CI: 1.05-2.31), and poor sleep (aOR = 1.48; 95%CI: 1.00-2.19) than the general population. Differential impacts of an additional cancer diagnosis on the well-being of caregivers were observed by sex, race, and time since cancer diagnosis.

CONCLUSIONS

Nearly four million cancer survivors in the USA are concomitant caregivers. Individuals with dual roles reported diminished well-being across a variety of measures than caregivers only.

IMPLICATIONS FOR CANCER SURVIVORS

Significant unmet health and psychosocial needs exist among individuals with dual roles. Our findings urge for increased awareness of this additional role/responsibility in cancer survivors and provide direct evidence for healthcare providers and policymakers to develop substantial support from the structural level.

Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

IMPORTANCE

Missed appointments lead to decreased clinical productivity and poor health outcomes.

OBJECTIVES

The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments.

STUDY DESIGN

We conducted an institutional review board-approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments.

RESULTS

Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47-72 years] vs 69 years [IQR, 59-78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5-55.5 days] vs 30.5 days [IQR, 12.8-47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04-4.48], P = 0.044).

CONCLUSIONS

Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study

OBJECTIVE

Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort.

METHODS

Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders.

RESULTS

The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%).

SIGNIFICANCE

The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

Independent and combined associations of polycyclic aromatic hydrocarbons exposure and sleep disorders among adults in the U.S. adult population

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are prevalent organic pollutants in the environment; however, limited research has been conducted to explore their potential effects on sleep disorders. This study aims to investigate the relationship between single and mixed PAHs exposures and sleep disorders.

METHODS

This study analyzed National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2016, involving 7730 adult participants. To examine the relationship between PAHs exposure and sleep disorders, we employed survey-weighted multivariate logistic regression models and restricted cubic spline (RCS) models to evaluate single PAHs exposure. Additionally, we employed three mixed-exposure models to examine the relationship between combined PAHs exposure and sleep disorders.

RESULTS

After adjusting for covariates, our analyses revealed positive associations between several urinary PAHs metabolites (1-hydroxynaphthalene (1-NAP), 2-NAP, 3-hydroxyfluorene (3-FLU), 2-FLU, and 1-hydroxypyrene (1-PYR)) and sleep disturbance. Consistency across various analytical methods underscores a discernible positive correlation between simultaneous exposure to PAHs and sleep disorders. This association is predominantly influenced by the presence of NAP and FLU. Remarkably, a positive relationship between combined PAHs exposure and sleep disorders emerged within the younger and middle-aged demographic but did not manifest within the elderly population.

CONCLUSION

In conclusion, our study provides new epidemiological evidence suggesting that both single and mixed PAHs exposures may increase the risk of sleep disorders. Further prospective investigations are necessary to validate these findings.

Dyslexia is associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations of children from China: Data from the READ program

It has been found that exposure to polycyclic aromatic hydrocarbons (PAHs) is associated with the risk of certain childhood neurodevelopmental disorders. However, no research has investigated the relationship between exposure to PAHs and children's dyslexia odds. The objective of this research was to investigate whether urinary mono-hydroxylated polycyclic aromatic hydrocarbons (OH-PAHs) are associated with increased dyslexia odds in Chinese children. We recruited 1,089 children (542 dyslexic children and 547 non-dyslexic children) for this case-control study. Ten OH-PAHs were measured in the participants' urine samples, which were collected between November 2017 and March 2023. Odds ratios (ORs) of the associations between the OH-PAHs and dyslexia were calculated using logistic regression models, after adjustment for the potential confounding factors. A significant association was found between urinary concentrations of 2-hydroxynaphthalene (2-OHNap) and the elevated odds of dyslexia. The children in the highest quartile of 2-OHNap had a higher OR of dyslexia (1.87, 95% CI: 1.07-3.27) than those in the lowest quartile (P-trend = 0.02) after adjustment for the covariates. After excluding children with maternal disorders during pregnancy, logistic regression analyses showed similar results. Our results suggested a possible association between PAH exposure and the elevated odds of dyslexia.

The association between the number of pregnancies and depressive symptoms: A population-based study

BACKGROUND

Depression is a psychosomatic disorder that affects reproductive health. The number of pregnancies is an important indicator of reproductive health. Multiple pregnancies and births may aggravate the risk of depression in females. However, the evidence of the connection between the number of pregnancies and depression is unclear. We aimed to investigate the relationship between the number of pregnancies and depressive symptoms.

METHODS

We used the National Health and Nutrition Examination Survey (NHANES) data with a total of 17,216 women from 2005 to 2020. The number of pregnancies obtained from the self-report questionnaire. Depressive symptoms were measured by the nine-item patient health questionnaire (PHQ-9). Multivariate logistic regression models were used to examine the risk factors of depression. The restricted cubic spline (RCS) was applied to explore the nonlinear relationship. In addition, subgroup analysis was used to support the accuracy of our findings.

RESULTS

We found that the number of pregnancies is positively associated with the prevalence of depression. According to the multivariable logistic regression analysis, pregnant women was 1.52-fold higher than the normal group to experience depression in the fully-adjusted model. No interaction between number of pregnancies and covariates in subgroups.

LIMITATIONS

This study was cross-sectional, which limits its ability to draw conclusions about the causal relationship between the number of pregnancies and depression.

CONCLUSION

In the United States, the number of pregnancies was positively associated with the prevalence of depression. It is critical to register the number of pregnancies for monitoring depressive symptoms.