Categories Of Variables Research Articles

Likert Scale Variables in Personal Finance Research: The Neutral Category Problem

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights.

"The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study".

Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting. For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes. A total of 56 mortality prediction models containing 204 unique predictor and outcome variables were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking. Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.

An Evaluation of the Estimated Aligners Needed to Correct Malocclusion Traits Using Invisalign ClinCheck™ Pro Software: A Retrospective Study.

Background: This study evaluated the number of aligners that Invisalign ClinCheck™ Pro Software estimates for correcting different malocclusion traits. Methods: This retrospective study included 157 non-extraction patients over the age of 12 years old with easy to mild malocclusions who were treated with Invisalign aligners. The Index of Complexity, Outcome, and Need (ICON) was used to evaluate the malocclusion complexity level. The number of aligners (upper, lower, and total) required to correct the malocclusion was compared based on sex, ICON level, molar and canine class, occlusal asymmetry, overbite, overjet, crowding, incisor inclination, and Bolton discrepancy. A Mann-Whitney U test (for comparisons between two groups) or a Kruskal-Wallis test (for comparisons between three or more groups) (p < 0.05) was used to evaluate differences in the number of aligners across variable categories. Results: ICON, molar class, overbite, and overjet presented significant differences (p < 0.05) in the number of aligners (upper, lower, and total) required to correct a malocclusion. Canine class and lower dental crowding showed significant differences in the lower and total number of aligners (p < 0.05). Conclusions: The number of aligners increases when the malocclusion presents any of the following elements: the absence of molar or canine class I, an altered overjet or overbite, severe lower crowding, or a higher complexity level. The clinician should consider these malocclusion traits when estimating the number of aligners needed for correction.

Service Quality and Passenger Satisfaction on the Coastal Routes of Southwest Crete

The aim of this paper is to validate the correctness of the defining variables (categories) used by Sitzimis (2024a) to measure the quality of services provided on the coastal lines of southwest Crete. In this context it tries to determine both how much they affect the overall passenger satisfaction and which one is the most important. This research is both novel and distinct and could hopefully be expanded to include comparable coastal routes around the world and other means of transport. It is also expected to benefit shipowners, passengers, and policymakers by maintaining the level of services provided at a satisfactory level. Methodologically, we have used confirmatory factor analysis to determine the real factors affecting the quality of coastal services in southwest Crete. A non-parametric correlation test has been applied between the degree of satisfaction and the above factors. It has shown that there is a moderate positive correlation. We have tested through multiple regression both the extent to which these factors have an impact on passengers’ satisfaction and their relative significance. The resulting evaluation factors are "comfort, safety, land and on-board services", "pricing policy" and "route reliability". They have predicted 63.5% of passengers’ satisfaction variance and the first factor is the most significant. Their improvement is expected to stimulate the users’ satisfaction. Ιt appears that the five categories used by Sitzimis, and their hierarchical classification, have not been fully confirmed by the factor analysis.

Machine learning's effectiveness in evaluating movement in one-legged standing test for predicting high autistic trait.

Research supporting the presence of diverse motor impairments, including impaired balance coordination, in children with autism spectrum disorder (ASD) is increasing. The one-legged standing test (OLST) is a popular test of balance. Since machine learning is a powerful technique for learning predictive models from movement data, it can objectively evaluate the processes involved in OLST. This study assesses machine learning's effectiveness in evaluating movement in OLST for predicting high autistic trait. In this study, 64 boys and 62 girls participated. The participants were instructed to stand on one leg on a pressure sensor while facing the experimenter. The data collected in the experiment were time-series data pertaining to pressure distribution on the sole of the foot and full-body images. A model to identify the participants belonging to High autistic trait group and Low autistic trait group was developed using a support vector machine (SVM) algorithm with 16 explanatory variables. Further, classification models were built for the conventional, proposed, and combined explanatory variable categories. The probabilities of High autistic trait group were calculated using the SVM model. For proposed and combined variables, the accuracy, sensitivity, and specificity scores were 1.000. The variables shoulder, hip, and trunk are important since they explain the balance status of children with high autistic trait. Further, the total Social Responsiveness Scale score positively correlated with the probability of High autistic trait group in each category of explanatory variables. Results indicate the effectiveness of evaluating movement in OLST by using movies and machine learning for predicting high autistic trait. In addition, they emphasize the significance of specifically focusing on shoulder and waist movements, which facilitate the efficient predicting high autistic trait. Finally, studies incorporating a broader range of balance cues are necessary to comprehensively determine the effectiveness of utilizing balance ability in predicting high autistic trait.

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery.

Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist. Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined. In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy. Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

Suitability mapping for alternative crops: A consistent, high-resolution approach for the United States

Alternative crops are a key aspect of many nature-based solutions in agriculture, and there is a need for more consistent geographic information on biophysical suitability to aid in both farm-level planning and larger-scale analyses. Here we describe an approach for generating consistent, replicable, high-resolution suitability maps for any alternative crop species across the USA. The method employs a criteria-based approach to map potential species performance using a simple suitability index. Criteria for suitable and ideal ranges of values for influential biophysical variables are created using data collected from published sources and are reviewed by experts. Publicly available soil and climate map data are used with the criteria to map suitability for each variable which are integrated into an overall suitability index map. Maps of unsuitable locations are combined to produce a limiting variable map showing which category of environmental variables is most limiting. We demonstrate the application of this approach for two alternative perennial crops, apple (Malus spp.) and Chinese chestnut (Castanea mollissima) and validate its accuracy using known farm locations. Maps of apple and chestnut in four distant study regions show varying patterns of suitability and potential performance depending on the climate and geophysical characteristics of the region. The maps can be used to identify areas of high suitability and compare across regions or between species and be combined with socio-economic and environmental datasets for further analysis. Compared to other approaches, ours can be applied to multiple species with a range of pre-existing knowledge in a consistent way, allowing for reliable mapping and subsequent research and planning for alternative crops and nature-based solutions in agriculture.

UroARC: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement

ObjectivesTo develop a surgical risk calculator for older adults undergoing suprapubic tube (SPT) placement that specifically factors in frailty, a key predictor of surgical risk in this vulnerable and heterogenous population. MethodsMedicare MedPAR, Outpatient, and Carrier files for beneficiaries undergoing SPT placement between 2014-2016 were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty, was used to calculate baseline frailty for each beneficiary. Stepwise regression models were used for each variable within the CFI and Charlson Comorbidity Index to determine the variables most highly predictive of postoperative complications. The most highly predictive variables were then combined into parsimonious categories. To ensure the prognostic accuracy for each outcome, calibration curves and tests of model fit, including C-statistics, Brier scores, and Spiegelhalter p-values were calculated. ResultsA total of 26,999 beneficiaries were included. Among these, 39.1% were pre-frail, 36.8% were mildly frail, and 12.3% were moderately-to-severely frail. Thirteen prognostic variable categories were deemed highly predictive of postoperative complications of interest. All models demonstrated low Brier scores, indicating high model accuracy, and high C-statistic and Spiegelhalter p-values, consistent with excellent model discrimination and calibration, respectively. Excellent model fit was seen on calibration curves for each outcome. ConclusionsUroARC is a novel surgical tool for older adults undergoing SPT placement that specifically factors in frailty. This risk calculator has high accuracy, calibration, and discrimination, and serves as a valuable resource to patients and clinicians for those undergoing consideration for SPT placement.

Deep Contrast Clustering Analysis to Distinguish Diabetic Complications in Elderly Chinese Patients.

To establish an innovative clustering method for predicting variable categories of diabetic complications in Chinese ≥65 with diabetes. We selected and extracted data from elderly patients with diabetes (n=4980) from a medical examination group of 51,400 people followed up annually from 2014 to date in Kunshan, China. A deep contrast clustering approach was used to cluster and predict diabetic complications. The clustering approach was further validated using data from elderly patients with diabetes (n=397) from one medical examination cohort of 20,000 people followed up yearly from 2014 to date in Beijing Jiuhua Hospital. The patients were clustered into 6 categories by analysing 20 indicators. Cluster 1-Heavy smoking and a high cardiovascular disease (CVD) risk; Cluster 2-High alcohol consumption, high aminotransferase levels, the highest risk of stroke complications, and a high fatty liver disease (FLD) risk; Cluster 3-High blood lipid levels and a risk of FLD and stroke complications; Cluster 4-Good health indicators and a low risk of FLD, stroke, and CVD complications; Cluster 5-Older age, higher uric acid concentration and creatinine level, and the highest risk of CVD complications; Cluster 6-Large waist circumference, high BMI, high blood pressure, and the highest risk of FLD complications. The gene for nonalcoholic fatty liver disease in cluster 2 had the highest risk coefficient. This was consistent with cluster 2, which had a higher FLD prevalence. A new clustering method was developed from two large Chinese cohorts of older patients with diabetes, which may effectively predict complications by clustering into different categories.

Effects of seatback angle, seat rotation, and impact speed on injury risk of occupants in highly automated vehicles in frontal crashes

Objective The objective of this study is to examine the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in highly automated vehicles. Methods The study utilized the Global Human Body Models Consortium midsize male (M50-OS+B) simplified occupant model in a simplified vehicle model (SVM) to simulate frontal crashes. The M50-OS+B model was gravity-settled and belted into the driver and left rear passenger seat. To investigate the effects of seatback angle, seat rotation, and impact speed on occupant kinematics and injury risk in frontal crashes, a design of experiments (DOE) was conducted. The DOE incorporated four seatback angles (13°, 23°, 45°, and 57.5° about vertical), four seat rotation angles (0°, 25°, 45°, and 90°), three impact speeds (25, 35, and 45 kph), and four frontal crash type configurations. All four seatback angles were used with 0° seat rotation, whereas 13° seatback angle was used with the remaining seat rotation configurations because of cabin fit considerations. Injury risks were estimated for the head, neck, shoulder, thorax, pelvis, and lower extremities for both occupants for each simulation (n=588). Results Statistically significant differences between all the groups within each independent variable category were observed based on the analysis of variance. HIC-based head injury risk and chest injury risk decreased and femur force for the driver and tibia force for the passenger increased with an increase in seatback angles. The head injury risk increased with seat rotation. All the injury risks increased with an increase in impact speed. The driver airbag was able to safeguard the driver from head injuries for all seat rotations except at 90° of seat rotation. Conclusion This is the first vehicle modeling study that collectively looked at the effects of seatback angle, seat rotation, and impact speed along with the interaction of occupants on the risk of injury in frontal crashes. The rear passenger experienced higher seatbelt loads than the driver. More reclined seats decreased head and chest injury risk, but increased driver femur injury risk and rear passenger tibia injury risk. Results underscore the necessity for additional anti-submarining mechanisms and driver airbag designs adapted for the anticipated occupant positions.

Novel characterisation of sheep flocks in Wales: A description of principal flock typologies and antimicrobial use patterns

There is increasing pressure to reduce and refine antimicrobial use (AMU) in farmed livestock, to slow the development of antimicrobial resistance (AMR) and preserve the efficacy of antimicrobials (AMs) in both humans and animals. Developing strategies to help drive the prudent use of AMs requires an understanding of the direct and indirect factors influencing the between-farm variation in AMU typically observed. Given limited evidence of risk factors in sheep, this exploratory study aimed to investigate whether sheep flocks could be classified into farm types based on farm characteristics, health parameters and management practices, and whether important differences existed in AMU between these flock types. This study was conducted on 22 sheep flocks in Wales, United Kingdom as part of a wider longitudinal study on AMU and AMR. Comprehensive surveys were administered to flocks where 147 variables regarding farm characteristics, flock health parameters and management practices were captured. AMU data for each flock were also collated. A Multiple Correspondence Analysis (MCA), followed by a Hierarchical Clustering on Principal Components (HCPC) analysis, were used to classify the flocks. The top 10 dimensions yielded by MCA explained 67.4 % of the total variance. Nine partitions of relatively homogeneous flocks, derived from three typologies produced from the first three cut-points of the HCPC dendrogram, were visualised and described. These nine partitions were characterised by 70 variable categories, principally the implementation or neglect of best-practice lameness management practices. Partitions of flocks that neglected best-practice lameness managements – characterised by delayed treatments of lame sheep, footbathing and bleeding when foot trimming – reported higher lameness prevalence and fewer lame ewes recovering within five days of treatment. These flocks had higher total AMU (mg/PCU) and used a higher mass of injectable AMs than other partitions of flocks. Flock traits, lambing practices, disease challenges and other management factors also described partitions derived in later dendrogram cuts. Findings from this study confirm good AM stewardship in sheep flocks to be a complex picture, given the typologies of flocks described and the range of factors likely to influence AMU. Opportunities for targeted strategies for sustainable reductions in AMU can be directed towards specific flock types identified, specifically within the context of lameness treatment and control. We highlight the importance of understanding flock heterogeneity, through the construction of typologies, as a means to fine-tune appropriate interventions to specific flock types in order to help drive more prudent use of AMs.

Multinomial Logit Modeling of Financial Inclusion and Financial Literacy of Women MSMEs in Tangerang

This research will examine the determinants of financial literacy and inclusion using sociodemographic variables of women MSMEs in Tangerang. The sociodemographic variables used are education, age, length of business, turnover, source of financing, type of savings, type of credit, credit ceiling, financial literacy score (OJK) and financial inclusion score (OJK). In Indonesia, there has been a lot of research on socio-demographic factors as determinants of financial literacy with mixed and unsatisfactory results, making it less reliable as a research reference. Some of the errors in previous research were that the respondents were not precise enough, for example only students or MSMEs at random. These two things result in the results of entering sociodemographic data being less reliable and ineffective when tested with categorical equation models from the simplest, namely logit, probit and tobit as well as binary logistic to the most complex models such as multinomial logit. Logit, probit and tobit and binary logistics models generally only use two categories of dependent variables, namely 0 if there is no decision or certain condition and 1 if there is a certain decision or condition. Preliminary research data before the Covid 19 pandemic, namely 150 women MSMEs who had unique characteristics in terms of socio-demographic factors as well as levels of literacy and financial inclusion, can be tried to prove empirically that these three variables will play an important role in each other through the multinomial logit categorical equation model according to Bayar et research procedures. al. (2020). The important result to be achieved is that the researcher wants to prove the business potential of women MSME actors in Tangerang, which so far has not been exposed enough to BI UMKM as one of the common market places for all MSME actors in Indonesia. There is a stigma that the low literacy and financial inclusion (OJK) scores of MSME players are a negative effect of less exposure to the business potential of MSME players in the local area. The results of this research succeeded in describing the socio-demographic profile of female MSMEs in Tangerang with several distinctive characteristics, namely the predominance of those with a productive age of 40 - 55 years, the majority having a tertiary education; have a turnover of more than 10-25 million per month and most of them use access to bank and non-bank financial institutions, usually pawnshops, for storage purposes and credit applications. Furthermore, this research succeeded in proposing a categorical equation model with multinomial logistic regression using STATA 13.0 software referring to the research model from Bayar et.al. (2020) and managed to find that socio-demographic variables of financial institutions are always a determining factor in financial inclusion and literacy. The implication is that individual women MSME actors in Tangerang can make financial decisions more quickly considering that they are so close to access to existing financial institutions.

Syphilis among HIV-positive men who have sex with men in Ghana: The 2023 biobehavioral survey.

Apart from HIV acquisition, men who have sex with men are at a higher risk of sexually transmitted infections, especially syphilis. Although the intersection between syphilis and HIV poses a formidable challenge among key populations who are known to be vulnerable to many health threats, there is little known about syphilis infection among MSM living with HIV in Ghana. This study seeks to investigate the burden of syphilis and address the existing knowledge gap by exploring behavioral, healthcare access, and structural factors influencing the syphilis burden within the HIV-positive MSM population. This study was conducted in 2023 as part of the bio-behavioral survey (BBS) among men who have sex with men (MSM) in Ghana. A cross-sectional survey that used a respondent-driven sampling (RDS) approach was conducted in the old ten regions of Ghana. Data was collected on 3,420 participants, however, 857 HIV-positive MSM were included in this study since it focused on syphilis among HIV-positive MSM in Ghana. The study estimated the prevalence of syphilis among MSM living with HIV and provided a 95% confidence interval across different categories of explanatory variables. Bivariate and multivariable logistic regression models were used to identify factors associated with overall syphilis prevalence. All other analyses were weighted due to the complex design of the study. The overall prevalence of syphilis was 23.83% (95% CI: 20.44, 27.58). HIV-positive men who only had sex with men had a 29.77% (95% CI: 23.90, 36.40) prevalence of syphilis compared to a prevalence of 9.50% (95% CI: 2.56, 29.53) recorded by HIV-positive MSM who were attracted to mostly females. Participants who ever had receptive anal sex recorded a higher prevalence 26.79% (95% CI: 22.78, 31.23) than those who never had receptive anal sex 12.86% (95% CI: 8.02, 19.99). The odds of syphilis among HIV-positive MSM who never used condoms with regular sex partners in the last 6 months were 41.08 (aOR: 41.08 at 95% CI 1.24-136.42; p = 0.038). There is a high prevalence of syphilis among HIV-positive men who have sex with men in Ghana, especially among those who engage in unprotected anal sex and have multiple sexual partners. Our findings underscore the dire need for targeted interventions to address the dual brunt of HIV and syphilis among the MSM population in Ghana.

Abstract C131: Understanding the impact of social risk factors on cancer care delivery: A scoping review

Abstract In recent years, there has been a growing emphasis on understanding the relationship between individual-level social determinants of health, which include social risks and social needs, within the realm of cancer care delivery. Social risks include food insecurity, housing instability, and transportation barriers, leading to adverse health outcomes. These social risks become social needs when patients identify a desire for assistance in addressing them. As the number of cancer survivors increases and disparities in the burden of cancer persist, there is a critical need to focus efforts on ensuring all survivors experience high-quality and equitable care across the cancer continuum, from diagnosis to end-of-life care. To inform research activities and intervention development, we aim to summarize and describe cancer care delivery research focused on three social risk factors: food insecurity, housing insecurity, and transportation barriers. To achieve our research objectives, we conducted a scoping review using Covidence software to examine published research assessing and addressing housing insecurity, food insecurity, and transportation barriers among individuals diagnosed with cancer. Six databases were searched: PubMed, Scopus, Cochrane Library, CINAHL Plus, EMBASE, and PsycINFO. Inclusion criteria for this review included original research in English published between 2010 and 2023 that focused on cancer patients and survivors in the United States and included social risk factors and cancer care delivery data. Data selection and screening were performed in two phases: (1) title and abstract and (2) full text, which required two independent reviewers. Discrepancies and disagreements between the two reviewers for inclusion of studies were resolved through weekly discussions at team meetings. A data abstraction tool that contains nine general variable categories was developed. We identified 10,032 studies, of which 76 were duplicates, and 9,368 were irrelevant at the title and abstract levels. We are currently finishing the full-text screening for the 563 studies deemed potentially eligible for inclusion at the title abstract level. To date, 164 of the 335 studies screened have met our eligibility criteria. We will extract data from all eligible studies once the full-text screening is completed. Across eligible studies, transportation barriers (91.5%) were the most common social risk factor of focus, followed by studies focusing on food insecurity (6.7%) and housing insecurity (3.7%). Our scoping review findings will provide us with a wide spectrum of knowledge regarding the most prominent relationships between social risk factors and cancer care delivery, gaps in current work, and existing interventions targeting these efforts. The results of this work will enhance our understanding of how social-structural disadvantages influence cancer outcomes and inform the development of concerted efforts aimed at addressing the social factors contributing to the unequal burden of cancer among survivors. Citation Format: Carol Y. Ochoa-Dominguez, Victoria M. Telles, Janeth I. Sanchez, Brenda A. Adjei, Avertson Worthy, Daniel Sabater-Minarim, Angel Arizpe, Matthew P. Banegas. Understanding the impact of social risk factors on cancer care delivery: A scoping review [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C131.

A summary of pain and pain-related variables in the Avon Longitudinal Study of Parents and Children.

To study pain, data on pain characteristics, possible triggers and consequences - such as the impact of pain on people's lives - need to be available. When not collated, described and/or organised in a systematic manner, it can be difficult to assess how useful an existing dataset may be for one's project. This data note describes and categorises the complex and multi-modal indices of pain available in the Avon Longitudinal Study of Parents and Children (ALSPAC). Data from two generations of the ALSPAC cohort; index child participants (Generation 1, G1), their mothers and fathers/mothers' partners (Generation 0, G0) were used. Search terms such as 'pain', 'ache', 'hurt', 'sore', specific pain conditions, labour pain and methods of pain relief were used to identify pain and pain-related variables. These data were extracted from all waves of data collection. We developed pain categories and subsequently categorised variables in an iterative process. Repeated measurements of the same variables over waves of data collection were also identified. We identified 21 categories of pain variables, which were subsequently grouped into themes: pain characteristics, extended pain characteristics and causes, treatment for pain, pain interference and pain-related to specific events. Pain and pain-related data have been collected from G1 participants, G0 mothers, and G0 partners, although there are fewer data for the partners. There were some repeated measurements, most commonly, of pain location. As is typical with longitudinal birth cohort studies, maternal proxy-reports were used during participants' younger years and self-reports were utilised from adolescence onwards. Researchers interested in studying pain can feasibly do so in two generations of a regional UK population who have been followed up over 30 years. ALSPAC can be used to study pain from the early years through to young adulthood and in mothers from the perinatal period onwards.

Combining cohorts of prospectively collected and linked data to improve health after release from prison

BackgroundWhile people who experience incarceration have remarkably poor health profiles, undertaking research to inform evidence-based responses is complicated by difficulties of recruiting people in prison; high rates of socioeconomic marginalisation, study attrition; and legislative and financial barriers to linked data research. There are many advantages to pooling data from multiple studies involving people who experience incarceration, including greater statistical power and geographic and participant heterogeneity. However, there are also challenges that need to be addressed. MethodsWe combined four prospective cohort studies of adults released from prisons in four Australian states. Pre-release interviews, and validated screening assessments, were linked to primary care, medicine dispensing, hospital, alcohol and other drug treatment services, ambulatory mental health, ambulance, corrective services, and death records. Data were harmonised by team members reviewing variable definitions and categories within subject domains. ResultsThe combined cohort consists of 4,232 adults, including 1,544 Indigenous people and 905 women, with a median age of 31 years. Data linkage will enable a median of 9.3 years of prospective follow-up after release from incarceration. Differences in data structures, coding systems between and within datasets, changes over time, and grouping of records belonging to the same event were also addressed. ConclusionThis combined multi-site cohort study is an example of a complex, policy-oriented data linkage project. It was developed to underpin evidence-based, culturally appropriate interventions, health policy and service development for people who were incarcerated. This presentation will discuss the processes and pitfalls experienced while building a multi-sectoral, multi-jurisdictional data linkage project.

The influence of context on diagnostic reasoning: A narrative synthesis of experimental findings.

Contextual information which is implicitly available to physicians during clinical encounters has been shown to influence diagnostic reasoning. To better understand the psychological mechanisms underlying the influence of context on diagnostic accuracy, we conducted a review of experimental research on this topic. We searched Web of Science, PubMed, and Scopus for relevant articles and looked for additional records by reading the references and approaching experts. We limited the review to true experiments involving physicians in which the outcome variable was the accuracy of the diagnosis. The 43 studies reviewed examined two categories of contextual variables: (a) case-intrinsic contextual information and (b) case-extrinsic contextual information. Case-intrinsic information includes implicit misleading diagnostic suggestions in the disease history of the patient, or emotional volatility of the patient. Case-extrinsic or situational information includes a similar (but different) case seen previously, perceived case difficulty, or external digital diagnostic support. Time pressure and interruptions are other extrinsic influences that may affect the accuracy of a diagnosis but have produced conflicting findings. We propose two tentative hypotheses explaining the role of context in diagnostic accuracy. According to the negative-affect hypothesis, diagnostic errors emerge when the physician's attention shifts from the relevant clinical findings to the (irrelevant) source of negative affect (for instance patient aggression) raised in a clinical encounter. The early-diagnosis-primacy hypothesis attributes errors to the extraordinary influence of the initial hypothesis that comes to the physician's mind on the subsequent collecting and interpretation of case information. Future research should test these mechanisms explicitly. Possible alternative mechanisms such as premature closure or increased production of (irrelevant) rival diagnoses in response to context deserve further scrutiny. Implications for medical education and practice are discussed.

Neurodiversity and Inclusive Education: A Therapeutic and Pedagogical Approach from Music Therapy in Early Childhood Education from a Systematic Review

Neurodiversity from the perspective of inclusive education has generated intervention processes that have been addressed from music therapy in the expression of human diversity in this research addresses the therapeutic approach of intervention from music in the comprehensive care of neurodivergent students. The objective was focused on analyzing the designs and implementations of the effectiveness of music therapy programs in students who present neurodiversity situations through the therapeutic and pedagogical strategies reported from scientific evidence. The method used refers to the systematic review from the PRISMA statement through the analysis of three categories of variables. The discussion focused on the reports and evidence generated from the interventions with music at the level of increasing cognitive, emotional, social and relational development in early childhood education students. In conclusion, it was identified that the promotion of emotional regulation skills, communication dynamics and social integration lead to increased levels of adaptation and inclusion that lead to improved academic success and inclusion in neurodivergent students.

Pattern of admissions and needs assessment for palliative care services among in-patients in a tertiary health facility in South-Western Nigeria

BackgroundPalliative care evolution focuses on education and medication accessibility. As little as 12% of palliative care needs are met. Assessment of the domains of Palliative care and patients’ and families’ experience are essential in life-limiting conditions. The Lagos University Teaching Hospital (LUTH), have the National Cancer Centre without offering palliative care services.AimThe aim was to examine pattern of admissions and needs assessment for palliative services among patients admitted into LUTH wards.Materials and methodResponses were entered into a data sheet inputted into Epi info version 7.2. Descriptive characteristics of the participants were presented as frequencies and percentages for age, sex, pattern of disease, domains of Palliative care, Advance care Plan, Preparation for home care, death and Education about the illness and category of medical conditions (palliative and non-palliative conditions). Together for Short Lives (TfSL) tool was used to categorize respondents’ conditions into Palliative and Non-palliative conditions. Chi-square test was used to determine association between independent variables (pattern of diagnoses, stage of disease, advanced care plan, preparation for home care/ death and education on illness) and dependent variables (category of medical condition). Chi-square test was also used to explore the association between specialty of the managing doctor (independent variable) and Advance care plan (dependent variable). The level of statistical significance was P-value < 0.05.Results80.6% of the respondents had palliative care conditions, 83.7% had family members as their caregiver while 13.2% of the participants had no caregiver and 65.9% had no advance care plan. There was no preparation for home care or death in 72.1%, 70.5% had education about their illness, and 68.2% were in the advanced stage of their disease. Participants attending the surgery non-trauma unit (51.6%) were more likely to have advance care plans. Adults were more likely to have palliative care conditions (79.8%) compared to children (20.2%), and was statistically significant.ConclusionMajority of the participants need palliative care services but are unavailable and unmet and the most predominant condition was cancer. Majority had no advance care plan or preparation for home care or death despite having advanced stage of the disease. This survey emphasized the need for symptom management, communication and provision of support.

Identify Primary Risk Variables Influencing the Recurrence of Cervical Cancer in Patients, Using Non-Parametric Methods at Tikur Anbessa Specialized Hospital

&amp;lt;i&amp;gt;Background&amp;lt;/i&amp;gt;: This study aimed to identify the primary risk variables influencing the recurrence of cervical cancer in patients, at Tikur Anbessa Specialized Hospital. Cervical cancer deaths in Ethiopia reached 4,595, or 0.76% of total deaths. The age-adjusted death rate is 18.51 per 100,000 of the population in Ethiopia. &amp;lt;i&amp;gt;Method&amp;lt;/i&amp;gt;: Among patients with cervical cancer, an institution-based retrospective follow-up research was conducted from January 2015 to March 2017 at TASH and is under follow-up. Out of a population of cervical cancer patients who were taking treatment in the hospital during that period, data on 420 patients is included in this study. Non-parametric methods, such as log-rank tests and the Kaplan-Meier method, were used to compare the rate of recurrence among the different explanatory variable categories. &amp;lt;i&amp;gt;Results&amp;lt;/i&amp;gt;: After the medical cards of women were reviewed among those patients with cervical cancer, 170 (40.5%) were recurrent, and the remaining 250 (59.5%) were censored. Out of the total patients, 6.2% were at stage I, 32.6% were at stage II, 51.7% were at stage III, and 9.5% were at stage IV. The recurrence proportions of stage I, stage II, stage III, and stage IV patients were 5.88%, 27.05%, 52.35%, and 14.705%, respectively. &amp;lt;i&amp;gt;Conclusion&amp;lt;/i&amp;gt;: Finally, the findings of this study implied that age, smoking cigarettes, stage of disease, initial treatment patients took, types of treatment patients took, and place were major factors related to the recurrence time of cervical cancer patients.