Generalized Linear Regression Model Research Articles

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact

BackgroundCancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine.ResultsIn the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods.ConclusionsGSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Machine learning-based model for prediction of deep vein thrombosis after gynecological laparoscopy: A retrospective cohort study.

Successful monitoring of deep vein thrombosis (DVT) remains a challenging problem after gynecological laparoscopy. Thus, this study aimed to create and validate predictive models for DVT with the help of machine learning (ML) algorithms. A total of 489 patients from the Cancer Biology Research Center, Tongji Hospital were included in the study between January 2017 and February 2023, and 35 clinical indicators from electronic health records (EHRs) were collected within 24h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. Then, the three commonly used DVT prediction models are random forest model (RFM), generalized linear regression model (GLRM), and artificial neural network model (ANNM). In addition, the predictive performance of various prediction models (i.e. the robustness and accuracy of predictions) is evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively. We found postoperative DVT in 41 (8.38%) patients. Based on the ML algorithm, a total of 13 types of clinical data were preliminarily screened as candidate variables for DVT prediction models. Among these, age, body mass index (BMI), operation time, intraoperative pneumoperitoneum pressure (IPP), diabetes, complication and D-Dimer independent risk factors for postoperative DVT and can be used as variables in ML prediction models. The RFM algorithm can achieve the optimal DVT prediction performance, with AUC values of 0.851 (95% CI: 0.793-0.909) and 0.862 (95% CI: 0.804-0.920) in the training and validation sets, respectively. The AUC values of the other two prediction models (ANNM and GLRM) range from 0.697 (95% CI: 0.639-0.755) and 0.813 (95% CI: 0.651-0.767). In summary, we explored the potential risk of DVT after gynecological laparoscopy, which helps clinicians identify high-risk patients before gynecological laparoscopy and make nursing interventions. However, external validation will be needed in the future.

Association between the dietary antioxidant index and relative telomere length of leucocytes in the Chinese population.

Dietary antioxidant indices (DAI) may be potentially associated with relative telomere length (RTL) of leucocytes. This study aimed to investigate the relationship between DAI and RTL. A cross-sectional study involving 1656 participants was conducted. A generalised linear regression model and a restricted cubic spline model were used to assess the correlation of DAI and its components with RTL. Generalised linear regression analysis revealed that DAI (β = 0·005, P = 0·002) and the intake of its constituents vitamin C (β = 0·043, P = 0·027), vitamin E (β = 0·088, P < 0·001), Se (β = 0·075, P = 0·003), and Zn (β = 0·075, P = 0·023) were significantly and positively correlated with RTL. Sex-stratified analysis showed that DAI (β = 0·006, P = 0·005) and its constituents vitamin E (β = 0·083, P = 0·012), Se (β = 0·093, P = 0·006), and Zn (β = 0·092, P = 0·034) were significantly and positively correlated with RTL among females. Meanwhile, among males, only vitamin E intake (β = 0·089, P = 0·013) was significantly and positively associated with RTL. Restricted cubic spline analysis revealed linear positive associations between DAI and its constituents' (vitamin E, Se and Zn) intake and RTL in the total population. Sex-stratified analysis revealed a linear positive correlation between DAI and its constituents' (vitamin E, Se and Zn) intake and RTL in females. Our study found a significant positive correlation between DAI and RTL, with sex differences.

Costa Rican Propolis Chemical Compositions: Nemorosone Found to Be Present in an Exclusive Geographical Zone.

The chemistry of Costa Rican propolis from Apis mellifera remains underexplored despite its potential applications. This study identified its chemical composition, linking chemotypes to antioxidant potential. Proton nuclear magnetic resonance (1H NMR) spectra were obtained for 119 propolis extracts and analyzed using multivariate analyses. In parallel, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay was used to assess antioxidant activity. A generalized linear regression model (GLM) correlated this with its chemical profiles and geographical origin. Chromatographic methods were used to isolate active and inactive compounds, which were identified using nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). Principal component analysis (PCA) revealed three chemical profile groups for the 119 propolis extracts, explaining 73% of the total variance with two components. Radical scavenging activity was found to correlate with chemical composition. Isolation yielded n-coniferyl benzoate in type I (EC50 = 190 µg/mL, ORAC = 0.60 µmol TE/µmol) and nemorosone in type II (EC50 = 300 µg/mL, ORAC = 0.7 µmol TE/µmol). Type III was represented in terpene-like components, which exhibited lower antioxidant activity. This study categorizes Costa Rican propolis into three chemical types and identifies two key components linked to antioxidant activity. Notably, nemorosone, a valuable natural product, was found to be highly concentrated in a particular region of Costa Rica.

Dietary acid load and its interaction with CETP TaqB1 polymorphisms on lipid profile among patients with Type 2 diabetes mellitus

ObjectiveGene-diet interaction plays a key role in the inter-individual differences in lipid abnormalities as a major risk factor for cardiovascular diseases (CVDs). Thus, we explored the interaction between CETP TaqB1 polymorphism with dietary acid load (DAL) on lipid profile among type 2 diabetes mellitus (T2DM).MethodThis cross-sectional study conducted on 220 Iranian patients with T2DM. Dietary acid load (PRAL and NEAP) was calculated via a validated food-frequency questionnaire (FFQ). The polymerase chain reaction (PCR) used for genotyping Taq1B polymorphism. Biochemical markers were measured by standard protocol. The interaction between CETP Taq1B polymorphism and DAL (PRAL and NEAP) on lipid profile was performed by a generalized linear regression model (GLM).ResultsThe overall prevalence of rs708272 genotypes was 8.6%, 72.7% and 18.6% for B1B1, B1B2 and B2B2 genotype respectively. This study showed that people with the B1B1 genotype had greater LDL, TC, LDL/HDL, and TG when they consumed diets that scored higher on the NEAP and PRAL indexes than those with the B1B2 and B2B2 genotypes. Besides, carriers of the B1B1 allele who were in the highest tertile of NEAP, had lower HDL (P Interaction < 0.05).ConclusionsIn summary, the lipid profile might be improved in B1B1 homozygotes by less adherence to DAL indexes, however, the findings should be validated in high-quality interventional studies.

Sex Specificity in the Mixed Effects of Blood Heavy Metals and Cognitive Function on Elderly: Evidence from NHANES

The way that males and females react to environmental exposures and negative impacts on their neurological systems is often different. Although previous research has examined the cognitively impairing effects of solitary metal exposures, the relationship between metal mixtures and cognitive function, particularly when considering an individual’s sex, remains elusive. This study aimed to investigate the sex differences in the association between multiple metal combinations and cognitive function in older Americans. This research employed the 2011–2014 NHANES survey of elderly Americans. The association between five mixed metals and four cognitive tests (the animal fluency test (AFT), the digit symbol substitution test (DSST), the instant recall test (IRT), and the delayed recall test (DRT)) were investigated with generalized linear regression model (GLM), Bayesian kernel machine regression model (BKMR), weighted quantile sum regression model (WQS), and quantile g-computation regression model (Qgcomp). A total of 1833 people, including 883 males and 950 females, enrolled in this cross-sectional study. We discovered that blood lead and blood cadmium were negatively associated with cognitive performance, while blood selenium demonstrated a positive association with cognitive function in older people. The negative relationship of heavy metal combinations on cognitive function might be somewhat reduced or even reversed via selenium. The IRT, AFT, and DSST are three of the four cognitive tests where men had more dramatic positive or negative results. There was a sex-specific connection between blood metal ratios and cognitive function among older Americans, as evidenced by the more significant relationship between mixed metals and cognitive performance in men (either positively or negatively). These results emphasize the impacts of ambient heavy metal exposure on cognitive function by employing sex-specific methods.

Mapping the Peds QLTM 4.0 onto CHU-9D: a cross-sectional study in functional dyspepsia population from China.

The study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China. A sample of 2,152 patients with FD completed both the CHU-9D and Peds QL 4.0 instruments. A total of six regression models were used to develop the mapping algorithm, including ordinary least squares regression (OLS), the generalized linear regression model (GLM), MM-estimator model (MM), Tobit regression (Tobit) and Beta regression (Beta) for direct mapping, and multinomial logistic regression (MLOGIT) for response mapping. Peds QL 4.0 total score, Peds QL 4.0 dimension scores, Peds QL 4.0 item scores, gender, and age were used as independent variables according to the Spearman correlation coefficient. The ranking of indicators, including the mean absolute error (MAE), root mean squared error (RMSE), adjusted R2, and consistent correlation coefficient (CCC), was used to assess the predictive ability of the models. The Tobit model with selected Peds QL 4.0 item scores, gender and age as the independent variable predicted the most accurate. The best-performing models for other possible combinations of variables were also shown. The mapping algorithm helps to transform Peds QL 4.0 data into health utility value. It is valuable for conducting health technology evaluations within clinical studies that have only collected Peds QL 4.0 data.

Dose-response relationship of elements with blood lipids and the potential interaction: A cross-sectional study from four areas with different pollution levels in China

BackgroundA growing number of researches indicated the association between plasma trace elements and blood lipids. However, the potential interaction and dose-response relationship were less frequently reported. MethodsIn this study, a total of 3548 participants were recruited from four counties in Hunan Province, South China. Demographic characteristics were collected by face-to-face interviews and inductively coupled plasma mass spectrometry (ICPMS) was used to determine the levels of 23 trace elements in plasma. We applied a fully adjusted generalized linear regression model (GLM) and a multivariate restricted cubic spline (RCS) to estimate the correlation, dose-response relationship and possible interaction between 23 trace elements and four blood lipid markers. ResultsThe results indicated positive dose-response relationships of plasma 66zinc with triglycerides (TG) and low density lipoprotein cholesterol (LDL-C), plasma 78selenium with LDL-C and total cholesterol (TCH), and plasma 59cobalt with high-density lipoprotein cholesterol (HDL-C). There was a negative dose-response relationship between 59cobalt and LDL-C. Further analysis found that 66zinc and 59cobalt had an antagonistic effect on the risk of increased LDL-C level. ConclusionsThis study added new evidence for the potential adverse effects of 66Zn and 78Se on blood lipids, and provided new insight into the threshold value setting for metals as well as the intervention strategy for dyslipidemia.

CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R.

Statistical causal inference of mixed exposures has been limited by reliance on parametric models and, until recently, by researchers considering only one exposure at a time, usually estimated as a beta coefficient in a generalized linear regression model (GLM). This independent assessment of exposures poorly estimates the joint impact of a collection of the same exposures in a realistic exposure setting. Marginal methods for mixture variable selection such as ridge/lasso regression are biased by linear assumptions and the interactions modeled are chosen by the user. Clustering methods such as principal component regression lose both interpretability and valid inference. Newer mixture methods such as quantile g-computation (Keil et al., 2020) are biased by linear/additive assumptions. More flexible methods such as Bayesian kernel machine regression (BKMR)(Bobb et al., 2014) are sensitive to the choice of tuning parameters, are computationally taxing and lack an interpretable and robust summary statistic of dose-response relationships. No methods currently exist which finds the best flexible model to adjust for covariates while applying a non-parametric model that targets for interactions in a mixture and delivers valid inference for a target parameter. Non-parametric methods such as decision trees are a useful tool to evaluate combined exposures by finding partitions in the joint-exposure (mixture) space that best explain the variance in an outcome. However, current methods using decision trees to assess statistical inference for interactions are biased and are prone to overfitting by using the full data to both identify nodes in the tree and make statistical inference given these nodes. Other methods have used an independent test set to derive inference which does not use the full data. The CVtreeMLE R package provides researchers in (bio)statistics, epidemiology, and environmental health sciences with access to state-of-the-art statistical methodology for evaluating the causal effects of a data-adaptively determined mixed exposure using decision trees. Our target audience are those analysts who would normally use a potentially biased GLM based model for a mixed exposure. Instead, we hope to provide users with a non-parametric statistical machine where users simply specify the exposures, covariates and outcome, CVtreeMLE then determines if a best fitting decision tree exists and delivers interpretable results.

Effect of Gestational Weight Gain during the First Half of Pregnancy on the Incidence of GDM, Results from a Pregnant Cohort in Northern Greece.

The aim of this study was to evaluate the effect of gestational weight gain (GWG) up to 23+6 weeks of gestation on the incidence of Gestational Diabetes Mellitus (GDM). A pregnant cohort of 5948 women in Northern Greece was recruited. Anthropometric features before and during pregnancy were recorded, the GWG by 23+6 weeks was calculated and a Generalized Linear Regression Model (GLM) with subgroup analyses based on weight status were computed. GDM was diagnosed in 5.5% of women. GLM results showed that GDM likelihood increased with maternal age (MA) and pre-pregnancy BMI (aOR: 1.08, 95%CI: [1.06, 1.11] and aOR: 1.09, 95%CI: [1.09, 1.11], respectively). Ιn the normal pre-pregnancy weight group, when the extra weight gain was >8 kgs, the odds of GDM increased (OR: 2.13, 95%CI: [0.98, 4.21], p = 0.03). Women with pre-pregnancy level 2 clinical obesity (OB2 pre) (BMI > 35 and <40 kg/m2) that shifted to OB3 category (BMI ≥ 40 kg/m2) had an increased GDM likelihood (OR: 4.85, 95%CI: [1.50, 15.95]). Women of higher MA may require stricter monitoring for GDM from early pregnancy, while in obese women, recommended GWG may need to be re-evaluated, since refraining from any weight gain may have a preventive effect for GDM.

Association between mixed aldehydes and bone mineral density based on four statistical models.

Aldehydes as an environmental pollutant may lead to oxidative stress, which is an important mechanism in the development of osteoporosis. This suggests a possible link between aldehyde exposure and osteoporosis. Considering the mixed nature of aldehyde exposure and the interactions between different aldehydes, we explored for the first time the associations between mixed six aldehydes (benzaldehyde, butyraldehyde, heptanal, hexanal, isovaleraldehyde, and propionaldehyde) and BMD in three populations (men, premenopausal women, and postmenopausal women) by applying four statistical models: quantile g-computation (qgcomp) model, Bayesian kernel machine regression (BKMR) model, generalized linear regression model (GLM), and generalized additive model (GAM), based on the National Health and Nutrition Examination Survey (NHANES) 2013-2014. We found that mixed aldehydes could significantly reduce BMD in men, with hexanaldehyde and propanaldehyde having the greatest negative qgcomp model and BKMR model weights, also confirmed by GLM. The associations between isopentanaldehyde and propanaldehyde and femoral BMD in men were non-linear and had threshold effects as derived from the BKMR model and GAM. The associations turned positive when the concentrations of isopentanaldehyde and propanaldehyde exceeded their respective inflection points. To conclude, our study might provide new ideas for the prevention and treatment of osteoporosis, and hexanaldehyde and propanaldehyde should be more regulated to prevent osteoporosis.

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

BackgroundThe effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. ObjectivesTo discover obesity-related mixed chemical exposure patterns in the general U.S. population. MethodsSparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). ResultsEight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. ConclusionOur study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

Association between Perfluoroalkyl and Polyfluoroalkyl Substances and Women's Infertility, NHANES 2013-2016.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are widely used in consumer products. However, the role of PFAS in infertility is still poorly understood. A total of 788 women from the 2013-2016 nationally representative NHANES were included to explore the association between PFAS exposure and self-reported infertility. Six PFAS, including PFDE, PFNA, PFHxS, n-PFOA, n-PFOS, and Sm-PFOS, were detected by online SPE-HPLC-TIS-MS/MS. We used the generalized linear regression model (GLM), generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) to assess the single effects, non-linear relationships, and mixed effects on women's infertility, respectively. The prevalence of self-reported infertility was 15.54% in this study. In GLM, n-PFOA showed a negative association with self-reported infertility in women for the Q3 (OR: 0.396, 95% CI: 0.119, 0.788) and Q4 (OR: 0.380, 95% CI: 0.172-0.842) compared with Q1 (p for trend = 0.013). A negative trend was also observed in n-PFOS and ∑PFOS (p for trend &lt; 0.05). In GAM, a non-linear relationship was revealed in Sm-PFOS, which exhibits a U-shaped relationship. The BKMR model indicated that there might be a joint effect between PFAS and women's infertility, to which PFNA contributed the highest effect (PIP = 0.435). Moreover, age stratification analysis showed a different dose-response curve in under and above 35 years old. Women under the age of 35 have a more noticeable U-shaped relationship with infertility. Therefore, the relatively low level of mixed PFAS exposure was negatively associated with self-reported infertility in women in general, and the impact of PFAS on infertility may vary among women of different age groups. Further studies are needed to determine the etiological relationship.

Combined effects of multiple metals on hearing loss: A Bayesian kernel machine regression approach

BackgroundExposure to heavy elements is considered one of the risks of hearing loss. The combined effect of multiple metals on different hearing losses has not been extensively studied. MethodsThis study deals with the association between different metals (Ba, Cd, Co, Cs, Mo, Pb, Mn, Sn, Sb, Tl, W) and hearing loss in the 2013–2018 National Health and Nutrition Examination Survey (NHANES). Associations were estimated by a generalized linear regression model (GLM) adjusting for age, gender, race/ethnicity, educational level, marital status, drinking status, hypertension, diabetes, smoking status, noise exposure, body mass index, and income-poverty ratio. The joint effects of mixed exposure were assessed by weighted quantile sum (WQS) model and Bayesian kernel machine regression (BKMR). The effect of multiple metals on speech-frequency hearing loss (SFHL) and high-frequency hearing loss (HFHL) was further calculated. The odds ratio (OR) indicates the risk of every metal exposure. ResultsA total of 2205 adult participants were enrolled from NHANES. Cd, Pb, Sb and Sn had significant effects on total hearing loss. The WQS model found that mixed exposure to heavy elements was significantly positively associated with total hearing loss (OR: 1.136; 95% CI: 1.031, 1.253) after adjusting for various covariates. The ORs of mixed exposure in the SFHL and HFHL groups were 1.066 (95% CI: 0.994, 1.143) and 1.102 (95% CI: 1.013, 1.199), respectively. BKMR found a significant positive association between multiple metals and hearing loss. The results showed that there may be potential interactions between Cd, Pb and other metals. ConclusionsMultiple metals have joint effects on hearing loss in the United States. The findings provide practitioners with important scientific evidence for possible interventions.

Associations between mixed urinary phenols and parabens metabolites and bone mineral density: Four statistical models

Phenols and parabens widely exist in personal care and consumer products and have been proved to be endocrine disrupting chemicals that could disturb bone metabolism. The current studies focusing on the associations between phenols and parabens with bone mineral density (BMD) drew contradictory conclusions. Considering the bias might be due to not considering the effects of mixed exposure, we conducted the first cross-sectional study to investigate the associations of both single and mixed metabolites of phenols and parabens with BMD in three populations by setting up four models: generalized linear regression model (GLM), weighted quantile sum (WQS) regression model, quantile g-computation (qgcomp) model and Bayesian kernel machine regression (BKMR) model, based on the US National Health and Nutrition Examination Survey (NHANES) database. We found that the association between the mixtures and total femur BMD in men was significantly negative. Bisphenol A (BPA) was shown to play the most important role in this negative association in the BKMR model, and this negative association was also confirmed in the GLM model with β coefficient (95% CI) being −0.02 (−0.04, −0.01). The relationships between the mixtures and femoral neck and trochanter BMD in postmenopausal women were significantly positive. Benzophenone-3 (BP-3) played the most significant role in the positive association with trochanter BMD, as confirmed by the WQS, qgcomp and BKMR models, and this positive association was also verified by the GLM model with β coefficient (95% CI) being 0.01 (0.00, 0.02). In conclusion, the association between the mixed phenols and parabens and BMD was negative in men while was positive in postmenopausal women, which was gender-specific. This study might provide new ideas for the prevention and treatment of osteoporosis and the control of personal care and consumer products containing phenols and parabens in the future.

Impact of cardiovascular events on primary and hospital care costs: findings from UK Biobank study

Abstract Background Need for primary and secondary healthcare increases following cardiovascular disease (CVD) events but there is no data on comparative increases in costs. Purpose To estimate annual primary care and hospital inpatient costs associated with key CVD and other adverse events using the UK Biobank (UKB) individual participant data. Methods UKB participants with linked primary care data (192,983 participants) or hospital inpatient episodes data (all 501,807 participants) contributed data to this study. The three categories of primary care services (patient consultations, diagnostic and monitoring tests, prescription medications), and hospital episodes were costed (2020 UK£) using the NHS England reference costs. Annual primary care costs and, separately, annual hospital inpatient costs were modelled as functions of participant characteristics at entry (socio-demographic, clinical, prior diseases) and time-updated first occurrences of myocardial infarction, stroke, coronary revascularization, incident cancer, incident diabetes, vascular death and non-vascular death during follow-up (p-value &amp;lt;0.01 in stepwise covariate selection). One-part generalized linear regression model (GLM) with Poisson distribution and identity link function was used for primary care costs, and two-part model was used for inpatient costs (part 1: logistic regression models probability of incurring costs; part 2: GLM with Poisson distribution and identity link function models costs conditional on incurring any). Separate models were fitted among participants with and without previous CVD at entry into UKB. Results Most adverse events were associated with excess primary care and hospital inpatient costs. Compared to people without previous CVD, people with previous CVD had on average larger excess primary care and hospital inpatient costs in years with myocardial infarction, stroke and vascular death; but similar excess costs in years with other events. Among both people without and with previous CVD, the excess annual primary care costs were less than 7% of the excess annual hospital inpatient costs for vascular events (Table). However, following diabetes diagnosis the excess annual primary care costs were higher than the excess annual hospital inpatient costs (Table). Conclusions These excess primary and hospital care costs associated with CVD events could inform assessments of interventions and policies to reduce CVD risks in UK. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation

The bidirectional association between depressive symptoms, assessed by the HADS, and albuminuria-A longitudinal population-based cohort study with repeated measures from the HUNT2 and HUNT3 Study.

Both albuminuria and depression are associated with cardiovascular disease, reflecting low-grade systemic inflammation and endothelial dysfunction. They share risk factors including weight, blood pressure, smoking and blood glucose levels. This longitudinal study aimed to examine bidirectional associations between depression symptoms, indexed by the Hospital Anxiety and Depression scale (HADS), and the inflammation marker albuminuria. 2909 persons provided urine samples in both the second (HUNT2, 1995-97) and third wave (HUNT3, 2006-2008) of the Trøndelag Health Survey, Norway. We used a generalized linear regression model (GLM) and ANOVA to assess the association between albuminuria levels (exposure HUNT2) with depression symptoms (outcome in HUNT3); and between depression symptoms (exposure HUNT2) with albuminuria (outcome HUNT3). Depression symptoms were measured with the HADS Depression Scale, analyzed utilising the full 7 items version and analyses restricted to the first 4 items (HADS-D and HADS-4). We accounted for confounders including baseline individual levels of the exposure variables. In this 10-years follow-up study, we found no statistical evidence for an association between baseline depression symptoms and subsequent albuminuria, nor between baseline albuminuria and subsequent depression symptoms. For albuminuria, only 0.04% was explained by prior depression, and for depression, only 0.007% was explained by previous albuminuria levels. The results were essentially the same for the shorter HADS-4 measure. There does not appear to be a longitudinal association between albuminuria and depression measured by the HADS.

The association between air pollution, meteorological factors, and daily outpatient visits for urticaria in Shijiazhuang, Hebei Province, China: a time series analysis.

The associations of air pollution and meteorological factors with the outpatient visits of urticaria remain poorly studied. This study aimed to assess the association between air pollution, meteorological factors, and daily outpatient visits for urticaria in Shijiazhuang, China, during 2014-2019. Daily recordings of air pollutant concentrations, meteorological data, and outpatient visits data for urticaria were collected during the 6years. Descriptive research methods were used to describe the distribution characteristics and demographic features of urticaria. A combination of the generalized linear regression model (GLM) and distribution lag nonlinear model (DLNM) was used to evaluate the lag association between environmental factors and daily outpatient visits for urticaria. Stratified analyses by gender (male; female) and age (< 18years; 18-39years; > 39years) were further conducted. The dose-response relationship between daily urticaria visits and CO, NO2, O3, temperature, and relative humidity was nonlinear. High concentrations of CO, NO2, O3, and high temperatures increased the risk of urticaria outpatient visits. The maximum cumulative association of high concentrations of CO, NO2, and O3 was lag 0-14days (CO: RR = 1.10, 95%CI: 1.06, 1.31; NO2: RR = 1.09, 95%CI: 1.01, 1.08; O3: RR = 1.16, 95%CI: 1.08, 1.25), and high temperatures was lag 0-7days (RR = 1.27, 95%CI: 1.14, 1.41). Low concentrations of NO2, O3, and high humidity, on the other hand, act as protective factors for urticaria outpatient. The maximum cumulative association of low concentrations of NO2 was the 0-day lag (RR = 0.97, 95%CI: 0.95, 0.99), O3 was lag 0-5days (RR = 0.94, 95%CI: 0.88, 0.99), and high humidity was lag 0-10days (RR = 0.93, 95%CI: 0.89, 0.98). Stratified analyses showed that the risk of urticaria outpatient visits was higher for the males and in the < 18years age group. In conclusion, we found that the development of urticaria in Shijiazhuang has a distinct seasonal and cyclical nature. Air pollutants and meteorological factors had varying degrees of influence on the risk of urticaria outpatient visits. This study provides indirect evidence for a link between air pollution, meteorological factors, and urticaria outpatient visits.

Are there joint effects of different air pollutants and meteorological factors on mental disorders? A machine learning approach.

Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM2.5, PM10, O3, SO2, and NO2), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10μg/m3 increase in O3, PM2.5 and PM10 exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O3 and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O3 and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.

Preconception care utilization and associated factors among reproductive age women in Mizan-Aman town, Bench Sheko zone, Southwest Ethiopia, 2020. A content analysis.

BackgroundPreconception care is highly important in reducing a number of adverse pregnancy outcomes and helps to improve maternal health. Preconception care optimizes women’s health and improves pregnancy outcomes. It is a cost-effective first-line preventive strategy for birth defects. However, preconception care utilization in Ethiopia was very low. Studies on these issues are limited in Ethiopia in general and in Mizan-Aman town in particular.ObjectiveTo assess preconception care utilization and associated factors among reproductive age women in Mizan-Aman town, Bench-Sheko Zone, Southwest Ethiopia.MethodsA community based cross-sectional study design was employed from April 16 to May 26, 2020 in Mizan-Aman town. The total study participants were 624 reproductive age women. Data were collected by using pre-tested interviewer administered questionnaires and entered into Epi-data version 3.1 then exported to STATA version 14 and analyzed accordingly. Univeriate and Bivariable analysis was done by analysis of variance (ANOVA) and independent t-test. Multivariable statistical analysis using generalized linear regression model (GLM) approach was used to classify factors of preconception care utilization. Since our response variable is measured in terms of count variable, we used a Poisson regression model with a log link function. Finally, Statistical significance between dependent and independent variables were assessed by odds ratios and 95% confidence intervals.ResultsOverall, 28.6% of the women receipt atleast one item of preconception care while only 1.5% were taken the whole recommended components of preconception care services. The most common item received in the study area was taking micronutrient supplementation (18.5%). Age of women, educational status, husbands educational status, husbands occupation, wealth status, distance from the health facility, waiting time to get services, planning to pregnancy, age at first pregnancy, previous ANC use, Previous PNC use, adverse pregnancy experience, women’s knowledge of preconception care, and attitude on preconception care were determinants of preconception service utilization.ConclusionsPreconception care component utilization was lower as compare with recommended service with different disparities. Multipurpose tailored strategies which incorporate a woman with no formal education, poor knwledge on preconception care,never take maternal services previously and distant from health facility could improve preconception care service utilization. Advocative strategies on preconception care component and planning pregnancy may elicite more women to use the services of preconception care.