Imputed Datasets Research Articles

Effect of a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial.

Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. We aim to determine whether a self-administered smartphone-based intervention, the "S-Check app" can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. χ2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; χ21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [β] -0.04, P=.02). The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true.

Forecasting potential invaders to prevent future biological invasions worldwide.

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history. Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e., generalising typical attributes of invasive alien species). We implemented a workflow based on 'Multiple Imputation with Chain Equation' to estimate invasion syndromes from imputed datasets of species' life-history and ecological traits and macroecological patterns. Importantly, our models disentangle the factors explaining (i) transport and introduction and (ii) establishment. We showcase our tool by modelling the invasion syndromes of 466 amphibians and reptile species with invasion history. Then, we project these models to amphibians and reptiles worldwide (16,236 species [c.76% global coverage]) to identify species with a risk of being unintentionally transported and introduced, and risk of establishing alien populations. Our invasion syndrome models showed high predictive accuracy with a good balance between specificity and generality. Unintentionally transported and introduced species tend to be common and thrive well in human-disturbed habitats. In contrast, those with established alien populations tend to be large-sized, are habitat generalists, thrive well in human-disturbed habitats, and have large native geographic ranges. We forecast that 160 amphibians and reptiles without known invasion history could be unintentionally transported and introduced in the future. Among them, 57 species have a high risk of establishing alien populations. Our reliable, reproducible, transferable, statistically robust and scientifically defensible quantitative invasion risk assessment tool is a significant new addition to the suite of decision-support tools needed for developing a future-proof preventative biosecurity globally.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on "within" and "across" methods.

In epidemiology and the social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the "within" approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the "across" approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers.

Data driven approach to characterize rapid decline in autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a genetic kidney disease with high phenotypic variability. Furthering insights into patients' ADPKD progression could lead to earlier detection, management, and alter the course to end stage kidney disease (ESKD). We sought to identify patients with rapid decline (RD) in kidney function and to determine clinical factors associated with RD using a data-driven approach. A retrospective cohort study was performed among patients with incident ADPKD (1/1/2002-12/31/2018). Latent class mixed models were used to identify RD patients using differences in eGFR trajectories over time. Predictors of RD were selected based on agreements among feature selection methods, including logistic, regularized, and random forest modeling. The final model was built on the selected predictors and clinically relevant covariates. Among 1,744 patients with incident ADPKD, 125 (7%) were identified as RD. Feature selection included 42 clinical measurements for adaptation with multiple imputations; mean (SD) eGFR was 85.2 (47.3) and 72.9 (34.4) in the RD and non-RD groups, respectively. Multiple imputed datasets identified variables as important features to distinguish RD and non-RD groups with the final prediction model determined as a balance between area under the curve (AUC) and clinical relevance which included 6 predictors: age, sex, hypertension, cerebrovascular disease, hemoglobin, and proteinuria. Results showed 72%-sensitivity, 70%-specificity, 70%-accuracy, and 0.77-AUC in identifying RD. 5-year ESKD rates were 38% and 7% among RD and non-RD groups, respectively. Using real-world routine clinical data among patients with incident ADPKD, we observed that six variables highly predicted RD in kidney function.

#236 Immediate or delayed initiation of renal replacement therapy in patients with leptospirosis and acute kidney injury: a target trial emulation

Abstract Background and Aims Leptospirosis is a widespread zoonosis responsible for 60,000 deaths annually worldwide [1]. Acute kidney injury (AKI) stands out as a hallmark of the disease. Leptospirosis-induced AKI (AKI-L) has been entertained as a potential cause of chronic kidney injury of unknown etiology, which is a significant burden in tropical rural settings [2]. Anecdotal evidence suggests that early renal replacement therapy (RRT) may improve mortality associated with AKI-L [3]. Conversely, several randomized controlled trials (RCTs) conducted in intensive care units have refuted the positive impact of early RRT on mortality in patients with other causes of AKI and sepsis [4]. Given these contradicting results, the optimal timing for initiating RRT in patients with leptospirosis and severe AKI remains uncertain. In this emulated RCT, we aimed to evaluate the impact of the timing of RRT on mortality and worsening renal failure (WRF) in patients hospitalized with leptospirosis, stage 3 AKI on admission, and no immediate need for RRT. Method We conducted a retrospective cohort study in two academic centers in Réunion island between 2010 and 2020. During this period, we included adult patients with documented leptospirosis and stage 2 or 3 AKI. Then we selected patients with stage 3 AKI to perform a target trial emulation, adapted from the AKIKI trial's design. Patients were categorized according to the allocated treatment strategy. Patients assigned to the “early” group received RRT within 48 hours following hospital admission while patients in the “delayed” group did not. A propensity score was derived for each imputed dataset using multivariate logistic regression. The propensity score estimates the probability of treatment allocation as a function of covariates assessed upon hospital entry. These covariates included those used in the SAPSII score and patients' baseline renal function. The primary outcome was a composite outcome assessed at one year: death or WRF. Results The cohort included 380 patients. We selected 295 patients with stage 3 AKI to perform the emulated RCT. Eight two (28%) patients were started on RRT within 48 hours of admission and were assigned to the early group. The delayed group included the remaining 213 patients, with 25% undergoing subsequent RRT and 75% not requiring such intervention. The median time to RRT initiation was 2 (1-3) days. Fifty-nine patients (20%) met the primary outcome: 14 patients (4.7%) died during their hospital stay or within 1 year after discharge, and 45 (15.3%) developed WRF. In the main analysis, the adjusted odds ratio (OR) for primary outcome occurrence was of 2.12 (95% confidence interval: 1.03 to 4.37, p = 0.04; reference group: “delayed” strategy). In secondary analyses, there was a significantly higher probability of WRF at one year in patients assigned to the “early strategy” (OR = 2.79 [1.28-6.11], p = 0.010). Mortality at one year according to RRT timing strategy did not differ (p = 0.6). Conclusion Early initiation of RRT was not found to improve the combined outcome of death and development of WRF in patients with leptospirosis and stage 3 AKI. A strategy based on a delayed initiation of RRT should be formally recommended for patients with severe AKI-L as in the general population of critically ill patients.

Predictive modelling and identification of key risk factors for stroke using machine learning

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study.

The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis. A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated. There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001). The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

Filling the gaps: A multiple imputation approach to estimating aging curves in baseball

In sports, an aging curve depicts the relationship between average performance and age in athletes’ careers. This paper investigates the aging curves for offensive players in Major League Baseball. We study this problem in a missing data context and account for different types of dropouts of baseball players during their careers. We employ a multiple imputation framework for multilevel data to impute the player performance associated with the missing seasons, and estimate the aging curves based on the imputed datasets. We then evaluate the effects of different dropout mechanisms on the aging curves through simulation, before applying our method to analyze MLB player data from past seasons. Results suggest an overestimation of the aging curves constructed without considering the unobserved seasons, whereas estimates obtained from multiple imputation address this shortcoming.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS)

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4–8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.Graphical

A hybrid stacking classifier with feature selection for handling imbalanced data

Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.

Progressive feature reduction with varied missing data and feature selection for arthritis disease prediction

In the expansive domain of data-driven research, the curse of dimensionality poses challenges such as increased computational complexity, noise sensitivity, and the risk of overfitting models. Dimensionality reduction is vital to handle high-dimensional datasets effectively. The pilot study disease dataset (PSD) with 53 features contains patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Our work aims to reduce the dimension of the features in the PSD dataset, identify a suitable feature selection technique for the reduced-dimensional dataset, analyze an appropriate Machine Learning (ML) model, select significant features to predict the RA and OA disease and reveal significant features that predict the arthritis disease. The proposed study, Progressive Feature Reduction with Varied Missing Data (PFRVMD), was employed to reduce the dimension of features by using PCA loading scores in the random value imputed PSD dataset. Subsequently, notable feature selection methods, such as backward feature selection, the Boruta algorithm, the extra tree classifier, and forward feature selection, were implemented on the reduced-dimensional feature set. The significant features/biomarkers are obtained from the best feature selection technique. ML models such as the K-Nearest Neighbour Classifier (KNNC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Random Forest Classifier (RFC) and Support Vector Classifier (SVC) are used to determine the best feature selection method. The results indicated that the Extra Tree Classifier (ETC) is the promising feature selection method for the PSD dataset because the significant features obtained from ETC depicted the highest accuracy on SVC.

Patient-reported outcome measures in hip fracture patients.

The aims of this study were to assess quality of life after hip fractures, to characterize respondents to patient-reported outcome measures (PROMs), and to describe the recovery trajectory of hip fracture patients. Data on 35,206 hip fractures (2014 to 2018; 67.2% female) in the Norwegian Hip Fracture Register were linked to data from the Norwegian Patient Registry and Statistics Norway. PROMs data were collected using the EuroQol five-dimension three-level questionnaire (EQ-5D-3L) scoring instrument and living patients were invited to respond at four, 12, and 36 months post fracture. Multiple imputation procedures were performed as a model to substitute missing PROM data. Differences in response rates between categories of covariates were analyzed using chi-squared test statistics. The association between patient and socioeconomic characteristics and the reported EQ-5D-3L scores was analyzed using linear regression. The median age was 83 years (interquartile range 76 to 90), and 3,561 (10%) lived in a healthcare facility. Observed mean pre-fracture EQ-5D-3L index score was 0.81 (95% confidence interval 0.803 to 0.810), which decreased to 0.66 at four months, to 0.70 at 12 months, and to 0.73 at 36 months. In the imputed datasets, the reduction from pre-fracture was similar (0.15 points) but an improvement up to 36 months was modest (0.01 to 0.03 points). Patients with higher age, male sex, severe comorbidity, cognitive impairment, lower income, lower education, and those in residential care facilities had a lower proportion of respondents, and systematically reported a lower health-related quality of life (HRQoL). The response pattern of patients influenced scores significantly, and the highest scores are found in patients reporting scores at all observation times. Hip fracture leads to a persistent reduction in measured HRQoL, up to 36 months. The patients' health and socioeconomic status were associated with the proportion of patients returning PROM data for analysis, and affected the results reported. Observed EQ-5D-3L scores are affected by attrition and selection bias mechanisms and motivate the use of statistical modelling for adjustment.

Changes in trauma appraisal during brief internet-based exposure and cognitive restructuring treatment for Arabic-speaking people with PTSD

ABSTRACT Background: Maladaptive trauma appraisal plays an important role in the development and maintenance of posttraumatic stress disorder (PTSD). While studies have demonstrated the effectiveness of exposure and cognitive treatments for PTSD symptomatology, the effect of such treatments on specific trauma appraisals is still not well understood. Objective: The study investigated the effect of an exposure and a cognitive restructuring internet-based treatment on specific trauma appraisals in Arabic-speaking participants with PTSD. Method: 334 participants received either an exposure (n = 167) or a cognitive restructuring (n = 167) internet-based treatment. PTSD symptom severity (PCL-5) and specific trauma appraisals (TAQ) were assessed at pre- and post-treatment. Changes in specific trauma appraisals within and between the two treatments were analyzed using multi-group change modelling. Associations between changes in PTSD symptom severity and changes in trauma appraisals were evaluated using Pearson product-moment correlation. For both treatments, participants with versus without reliable improvement were compared regarding changes in specific trauma appraisals using Welch tests. Analyses were performed on 100 multiple imputed datasets. Results: Both treatments yielded significant changes in shame, self-blame, fear, anger, and alienation (all ps < .001). Changes in betrayal were only significant in the cognitive restructuring treatment (p < .001). There was no evidence of differences between treatments for any specific trauma appraisal. Changes in PTSD symptom severity were significantly associated with changes in trauma appraisals (all ps < .001). In both treatments, participants who experienced reliable improvement in PTSD symptom severity showed significantly larger pre- to post-treatment changes in specific trauma appraisals compared to those without reliable improvement. Again, differences in betrayal were only significant in the cognitive restructuring treatment. Conclusions: The findings indicate that both treatments are effective in reducing trauma appraisals in Arabic-speaking people with PTSD. Changes in trauma appraisal seem to be associated with changes in PTSD symptomatology. Trial registration: German Clinical Trials Register identifier: DRKS00010245.

Hospital infections and health-related quality of life after cardiac surgery: a multicenter survey

BackgroundRecent research suggested that hospital infections are a predictive marker for physical non-recovery one year after cardiothoracic surgery. The purpose of this study was to explore whether this risk factor is etiologic. Additional, the influence of a potential effect modifying factor, diabetes mellitus, was investigated.MethodsIn this multicenter study, patients underwent elective or urgent cardiothoracic surgery between 01-01-2015 and 31-12-2019, and completed pre- and one year post-operative Short Form Health Survey 36/12 quality of life questionnaires. A binary logistic regression model, in which the inverse of the propensity score for infection risk was included as a weight variable, was used. Second, this analysis was stratified for diabetes mellitus status.Results8577 patients were included. After weighing for the propensity score, the standardized mean differences of all variables decreased and indicated sufficient balance between the infection and non-infection groups. Hospital infections were found to be a risk factor for non-recovery after cardiothoracic surgery in the original and imputed dataset before weighting. However, after propensity score weighing, hospital infections did not remain significantly associated with recovery (OR for recovery = 0.79; 95% CI [0.60–1.03]; p = 0.077). No significant interaction between diabetes mellitus and hospital infections on recovery was found (p = 0.845).ConclusionsThis study could not convincingly establish hospital infections as an etiologic risk factor for non-improvement of physical recovery in patients who underwent cardiothoracic surgery. In addition, there was no differential effect of hospital infections on non-improvement of physical recovery for patients with and without diabetes mellitus.Trial registration International Clinical Trials Registry Platform ID NL9818; date of registration, 22-10-2021 (https://trialsearch.who.int/).

Predictors of HIV Testing Among Pregnant Women aged 15-49 Years in Nigeria.

The utilization of HIV testing services in Nigeria has not been optimal due to socioeconomic and demographic factors such as educational level, place of residence, and religion. For Nigeria to achieve epidemic control, pregnant women need to know their HIV status through HIV testing, which is the gateway to HIV prevention, care, support, and treatment services. Therefore, the purpose of this study was to determine the predictors of HIV testing among Nigerian pregnant women aged 15-49 years HIV testing during antenatal visits or childbirth. Secondary data analyses of 659 pregnant women randomly selected from the multiple imputation datasets of the 2013 Nigeria Demographic and Health Survey (NDHS)were done using SPSS version 25. Data on socio-demographic characteristics, HIV stigmatizing attitudes, and HIV testing were retrieved from the dataset for the study. Bivariate and multivariate regression analyses were done to determine the predictors of HIV testing. The prevalence of self-reported HIV testing and receiving results among women of reproductive age was 36.72%. Bivariate model findings showed that educational level, place of residence, and religion were statistically significant predictors of HIV testing among pregnant women. Only educational level and place of residence significantly predicted pregnant women's HIV testing in the parsimonious multivariable regression model. Pregnant women with higher levels of education (Primary Education, AOR, 1.85; 95% CI, 1.07,3.19; Secondary Education, AOR, 3.75; 95% CI:1.93, 7.28; Higher Education, AOR, 11.23; 95% CI: 4.40, 28.68) and those living in urban areas (AOR, 1.60: 95% CI:1.03, 2.47) were more likely to test for HIV than those with no education and those living in the rural areas. This study identified having a higher level of education and living in urban areas as predictors of HIV testing, therefore, more efforts are needed to have women with lower levels of education and those living in rural areas test for HIV as a strategy for the elimination of mother-to-child transmission of HIV.

The Significant Role of Alcohol in the Relationship between C-Reactive Protein and Self-Reported Osteoarthritis

BackgroundDespite the known inflammatory nature of osteoarthritis (OA) and the established role of C-reactive protein (CRP) as an inflammation marker, the influence of alcohol consumption on the CRP–OA relationship remains uncertain, with previous research providing conflicting results. ObjectivesThis study aims to examine the potential moderating effect of alcohol on the association between CRP concentrations and self-reported OA. MethodsWe conducted a cross-sectional study involving 50,259 participants, all data collected from NHANES between 2005–2010 and 2015–2018. A multivariable logistic regression model was used to analyze the relationship between CRP and OA. ResultsWe found a nonsignificant positive association between CRP concentration and prevalence of self-reported OA after adjusting for covariates in the raw dataset or 5 multiple imputed datasets. In the stratified analysis by alcohol drinking, for every 10 mg/L higher in CRP concentration, the prevalence of self-reported OA was higher by 13 % in nondrinkers (P = 0.007, adjusted for covariates). Conversely, for every 10 mg/L higher in CRP concentration, the prevalence of self-reported OA was lower by 59 % in drinkers (P = 0.005, adjusted for covariates). Furthermore, we discovered that the directions of the association between CRP concentrations (10 mg/L) and prevalence of self-reported OA [odds ratio (OR) < 1 in the drinking subgroup and OR > 1 in the no-drinking subgroup] were stable in both the main and sensitivity analyses. The significant interaction between CRP concentration and alcohol drinking on the prevalence of self-reported OA was shown in most of our analyses (P-interaction < 0.05). ConclusionAlcohol consumption may be an interaction factor between CRP and self-reported OA. To our knowledge, our findings are the first to highlight the importance of incorporating analysis of alcohol consumption differences into future studies of CRP and self-reported OA.

Group-based trajectory models of integrated vaccine delivery and equity in low- and middle-income countries

BackgroundIntegrated vaccine delivery – the linkage of routine vaccination with provision of other essential health services – is a hallmark of robust primary care systems that has been linked to equitable improvements in population health outcomes.MethodsWe gathered longitudinal data relating to routine immunization coverage and vaccination equity in 78 low- and middle-income countries that have ever received support from Gavi, the Vaccine Alliance, using multiple imputation to handle missing values. We then estimated several group-based trajectory models to describe the relationship between integrated vaccine delivery and vaccination equity in these countries. Finally, we used multinomial logistic regression to identify predictors of group membership.ResultsWe identified five distinct trajectories of geographic vaccination equity across both the imputed and non-imputed datasets, along with two and four trajectories of socioeconomic vaccination equity in the imputed and non-imputed datasets, respectively. Integration was associated with reductions in the slope index of inequality of measles vaccination in the countries analyzed. Integration was also associated with an increase in the percentage of districts reporting high measles vaccination coverage.ConclusionsIntegrated vaccine delivery is most strongly associated with improvements in vaccination equity in settings with high baseline levels of inequity. Continued scholarship is needed to further characterize the relationship between integration and health equity, as well as to improve measurement of vaccination coverage and integration.

Integration of Multikinds Imputation With Covariance Adaptation Based on Evidence Theory.

For incomplete data classification, missing attribute values are often estimated by imputation methods before building classifiers. The estimated attribute values are not actual attribute values. Thus, the distributions of data will be changed after imputing, and this phenomenon often results in degradation of classification performance. Here, we propose a new framework called integration of multikinds imputation with covariance adaptation (MICA) based on evidence theory (ET) to effectively deal with the classification problem with incomplete training data and complete test data. In MICA, we first employ different kinds of imputation methods to obtain multiple imputed training datasets. In general, the distributions of each imputed training dataset and test dataset will be different. A covariance adaptation module (CAM) is then developed to reduce the distribution difference of each imputed training dataset and test dataset. Then, multiple classifiers can be learned on the multiple imputed training datasets, and they are complementary to each other. For a test pattern, we can combine the multiple pieces of soft classification results yielded by these classifiers based on ET to obtain better classification performance. However, the reliabilities/weights of different imputed training datasets are usually different, so the soft classification results cannot be treated equally during fusion. We propose to use covariance difference across datasets and accuracy of imputed training data to estimate the weights. Finally, the soft classification results discounted by the estimated weights are combined by ET to make the final class decision. MICA was compared with a variety of related methods on several datasets, and the experimental results demonstrate that this new method can significantly improve the classification performance.

Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model

Background and aimsGastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records (EHR) to identify individuals at high risk for non-cardia gastric cancer (NCGC). MethodsWe included 614 patients who had a diagnosis of NCGC between ages 40-80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equation for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve (AUC) and the 0.632 estimator was used as the estimate for discrimination. ResultsThe 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR]=1.16, 95% confidence interval [CI]: 1.04 – 1.3), male sex (OR = 1.97; 95% CI: 1.64-2.36), Black (OR= 3.07; 95% CI: 2.46-3.83) or Asian race (OR=4.39; 95% CI: 2.60-7.42), tobacco use (OR=1.61; 95% CI: 1.34-1.94), anemia (OR=1.35; 95% CI: 1.09-1.68) and pernicious anemia (OR=6.12, 95% CI: 3.42-10.95). ConclusionWe demonstrate the feasibility and good performance of an EHR-based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.