Individual-level Assessment Research Articles

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients

BackgroundMachine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. MethodsThis study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. ResultsFrom a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. ConclusionML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.

Profiling population-wide exposure to environmental chemicals: A case study of naphthalene

Long-term exposure to environmental chemicals can detrimentally impact human health, and understanding the relationship between age distribution and levels of external and internal exposure is crucial. Nonetheless, existing methods for assessing population-wide exposure across age groups are limited. To bridge this research gap, we introduced a modeling approach designed to assess both chronic external and internal exposure to chemicals at the population level. The external and internal exposure assessments were quantified in terms of the average daily dose (ADD) and steady-state blood concentration of the environmental chemical, respectively, which were categorized by age and gender groups. The modeling process was presented within a spreadsheet framework, affording users the capability to execute population-wide exposure analyses across a spectrum of chemicals. Our simulation outcomes underscored a salient trend: younger age groups, particularly infants and children, exhibited markedly higher ADD values and blood concentrations of environmental chemicals compared to their older counterparts. This observation is due to the elevated basal metabolic rate per unit of body weight characteristic of younger individuals, coupled with their diminished biotransformation kinetics of xenobiotics within their livers. These factors collectively contribute to increased intake rates of environmental chemicals per unit of body weight through air and food consumption, along with heightened bioaccumulation of these chemicals within their bodies (e.g., blood). Furthermore, we augmented the precision of the external and internal exposure assessment by incorporating the age distribution across the population. The simulation outcomes unveiled that, to estimate the central tendency of the population's exposure levels, employing the baseline value group (age group 21–30) or the surrogate age of 25 serves as a simple yet dependable approach. However, for comprehensive population protection, our recommendation aligns with conducting exposure assessments for the younger age groups (age group 0–11). Future studies should integrate individual-level exposure assessment, analyze vulnerable population groups, and refine population structures within our developed model.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Safer Bars: A cluster-randomized effectiveness evaluation of alcohol-related sexual violence prevention through bar staff bystander training

BackgroundSexual assault is consistently associated with social contexts that support high levels of alcohol consumption such as alcohol-serving establishments (i.e., bars). The significant rates of alcohol-involved sexual assault among college students demonstrate the critical need for evidence-based efforts to reduce alcohol-involved sexual assault in this population. Although bystander approaches have demonstrated some promise for reducing alcohol-involved sexual assault, to date no published studies have examined the effectiveness of implementing bystander prevention approaches with bar staff. Given the robust evidence indicating that bars serve as hot spots for sexual aggression, interventions that improve bar staff's ability to identify and intervene in sexually aggressive situations may offer a useful approach for reducing rates of alcohol-involved sexual assault. MethodsThe Safer Bars study utilizes a cluster-randomized trial design that randomizes participants at the bar level into intervention and waitlist control arms. The sample includes bars (Nbars = 56) within a three-mile proximity to the three major public Arizona universities, with an average of 10 staff members per bar (Nstaff = 564). Assessments of individual-level and bar-level outcomes occur at baseline, training completion, and 3-months post-training, with an additional individual-level assessment at 6 months. Community-level effects are assessed using GIS data regarding police dispatches. ConclusionSafer Bars represents a novel, theory-driven approach to promote effective bystander behavior among bar staff working in close proximity to university campuses to reduce rates of alcohol-involved sexual assault.

Validation of remote assessment of preschool children's anthropometrics and motor skills.

Remotely delivered treatment and research procedures were rapidly adopted in response to the COVID-19 pandemic. However, it is unclear if these measures are valid. The purpose of this study was to compare the validity of anthropometry and motor skill proficiency measurements collected in a remote-setting to in-person setting among a sample of children ages 3-4 years. Child anthropometry and motor skill performance were measured in-person by trained assessors and by parents at home with remote supervision via videoconference by trained assessors. The following measures from the National Institutes of Health Toolbox were collected: anthropometry (height and weight), manual dexterity/manipulation (9-hole pegboard), motor coordination and agility (supine timed up and go), lower body strength (standing long jump), and postural stability (one-leg standing balance). Differences in expert and parent-based measurements were assessed using Bland-Altman plots, paired samples t-tests, and Pearson correlations. A total of n = 14 children completed the assessments. No significant differences were observed between measurement locations for weight and motor skills (p > .05). Remote measurement of height (M = 101.1 cm, SD = 5.40) was significantly greater than in-person measurements (M = 98.2 cm, SD = 5.16); p < .0001. Remote measurements of motor skills and weight are valid assessments for researchers and clinicians to utilize in young children. Remote assessment with guidance offers comparable and valid estimates as in-person assessment, potentially offering a solution to resource-constricted barriers in research and access to care. There is an opportunity for researchers to fine-tune remote height and individual-level assessment strategies.

COVID-19 and Masking Disparities: Qualitative Analysis of Trust on the CDC’s Facebook Page

This exploratory paper examines individual levels of risk assessment as impacting institutional trust in the CDC while also contributing to disparities in expressed willingness to mask early in the COVID-19 pandemic. Using both content and thematic analysis of the CDC’s Facebook (FB) page from April 2020 and Gidden’s modern risk society theory, I consider how social media (SM) users retrospectively perceived a dramatic change in public health (PH) advisory—from the CDC advising against masking in February 2020 (Time 1) to advising the use of “do-it-yourself” (DIY) cloth masking in April 2020 (Time 2)—through a lens of prior, self-guided research. Expressed “knowledge” of masking as preventative (or not) yielded unwavering and sometimes increasing distrust in the CDC based on user perception of the “correct” advisory, regardless of the CDC’s position at Time 1 or Time 2. Simultaneously, disparities in masking behaviors appeared to be driven not by CDC guidance but by this same self-guided research. I show this via three themes: (1) claims of ineffectiveness for DIY masking (do not trust CDC now—no masking from the start); (2) conflict between the first and second CDC advisories on masking (do not trust CDC—either already masking anyway or will now); (3) disappointed in the CDC for length of time taken to make a DIY mask recommendation (do not trust CDC—either already masking anyway or will mask now). I discuss the imperative nature of two-way engagement with SM users by PH rather than using SM as a one-way mode of advisory dissemination. This and other recommendations may decrease disparities in preventative behaviors based on individual-level risk assessment as well as increase institutional trust and transparency.

Assessment is contagious: the social contagion of formative assessment practices and self-efficacy among teachers

ABSTRACT Social contagion has been documented across various domains. However, this phenomenon has not been explored in relation to formative assessment in schools. This study examines the social contagion of school teachers’ formative assessment practices and self-efficacy. A sample of 296 teachers from 12 Hong Kong primary and secondary schools participated in this study. All participants completed the same questionnaire twice, one year apart. The longitudinal results showed that formative assessment practices and self-efficacy were socially contagious. When teachers had colleagues who engaged in formative assessment practices and had high self-efficacy, they were more likely to engage in formative assessment and become more efficacious themselves. Also, school-level formative assessment self-efficacy predicted individual-level formative assessment practices demonstrating the importance of collective efficacy. The findings can inform the provision of school support and the design of teacher professional development programmes for promoting formative assessment in schools.

Family-Level Multimorbidity among Older Adults in India: Looking through a Syndemic Lens.

Most evidence on multimorbidity is drawn from an individual level assessment despite the fact that multimorbidity is modulated by shared risk factors prevailing within the household environment. Our study reports the magnitude of family-level multimorbidity, its correlates, and healthcare expenditure among older adults using data from the Longitudinal Ageing Study in India (LASI), wave-1. LASI is a nationwide survey amongst older adults aged ≥45 years conducted in 2017–2018. We included (n = 22,526) families defined as two or more members coresiding in the same household. We propose a new term, “family-level multimorbidity”, defined as two or more members of a family having multimorbidity. Multivariable logistic regression was used to assess correlates, expressed as adjusted odds ratios with a 95% confidence interval. Family-level multimorbidity was prevalent among 44.46% families, whereas 41.8% had conjugal multimorbidity. Amongst siblings, 42.86% reported multimorbidity and intergenerational (three generations) was 46.07%. Family-level multimorbidity was predominantly associated with the urban and affluent class. Healthcare expenditure increased with more multimorbid individuals in a family. Our findings depict family-centred interventions that may be considered to mitigate multimorbidity. Future studies should explore family-level multimorbidity to help inform programs and policies in strategising preventive as well as curative services with the family as a unit.

Diagnostic yield of screening for SARS-CoV-2 among patients admitted to hospital for alternate diagnoses: an observational cohort study

ObjectivesTo determine the diagnostic yield of screening patients for SARS-CoV-2 who were admitted with a diagnosis unrelated to COVID-19 and to identify risk factors for positive tests.DesignCohort from the Canadian...

Species Composition Affects the Accuracy of Stand-Level Biomass Models in Hemiboreal Forests

Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

Is the urinary iodine/creatinine ratio applicable to assess short term individual iodine status in Chinese adults? Comparison of iodine estimates from 24-h urine and timed-spot urine samples in different periods of the day

BackgroundUrinary iodine concentration (UIC) is routinely used to evaluate the population iodine status while the uniform method for the individual level assessment is uncertain.ObjectivesTo explore the 24-h urinary iodine excretion (UIE) in five different periods of the day and the corresponding prediction equations respect by the use of creatinine-corrected UIC.MethodsWe collected 24-h, spot and fasting urine in five periods of the day to estimate 24-h UIE by the six different prediction equations. We compared the estimated creatinine-corrected UIC to the collected 24-h UIE and identified the most suitable equations in each period of the day.ResultsAmong the six different prediction equations, the equation of Kawasaki T was the best to estimate the 24-h UIE by fasting urine among Chinese adults. Among the five periods of time, the equation of Knudsen N was the best to estimate the 24-h UIE in the non-morning period.ConclusionUrinary iodine status at the individual level could be estimated by different creatinine-based equations at different periods of the day.

Detecting country of residence from social media data: a comparison of methods

Identifying users’ place of residence is an important step in many social media analysis workflows. Various techniques for detecting home locations from social media data have been proposed, but their reliability has rarely been validated using ground truth data. In this article, we compared commonly used spatial and Spatio-temporal methods to determine social media users’ country of residence. We applied diverse methods to a global data set of publicly shared geo-located Instagram posts from visitors to the Kruger National Park in South Africa. We evaluated the performance of each method using both individual-level expert assessment for a sample of users and aggregate-level official visitor statistics. Based on the individual-level assessment, a simple Spatio-temporal approach was the best-performed for detecting the country of residence. Results show why aggregate-level official statistics are not the best indicators for evaluating method performance. We also show how social media usage, such as the number of countries visited and posting activity over time, affect the performance of methods. In addition to a methodological contribution, this work contributes to the discussion about spatial and temporal biases in mobile big data.

Feasibility and Reliability of the AWIN Welfare Assessment Protocol for Dairy Goats in Semi-extensive Farming Conditions.

The aim of this study was to test the feasibility and reliability of the Animal Welfare Indicators (AWIN) protocol for welfare assessment of dairy goats when applied to semi-extensive farming conditions. We recruited 13 farms located in the NW Italian Alps where three assessors individually and independently applied a modified version of the AWIN welfare assessment protocol for goats integrated with some indicators derived from the AWIN welfare assessment protocol for sheep. The applied protocol consisted of nine individual-level (body condition score, hair coat condition, abscesses, overgrown claws, udder asymmetry, fecal soiling, nasal discharge, ocular discharge, and improper disbudding) and seven group-level (severe lameness, Qualitative Behavior Assessment-QBA, thermal stress, oblivion, Familiar Human Approach Test-FHAT, synchrony at grazing, synchrony at resting) animal-based indicators. On most farms, the level of welfare was good. Many of the considered welfare problems (overgrown claws, fecal soiling, discharges, and thermal stress) were never recorded. However, oblivion, severe lameness, hair coat condition and abscesses were detected on some farms, with percentages ranging from 5 to 35%. The mean percentage of animals with normal body condition was 67.9 ± 5.7. The level of synchronization during resting was on average low (14.3 ± 7.2%). The application of the whole protocol required more than 4 h/farm and 3 min/goat. The inter-observer reliability varied from excellent (udder asymmetry, overgrown claws, discharges, synchrony at resting, use of shelter) to acceptable (abscesses, fecal soiling, and oblivion), but insufficient for hair coat condition, improper disbudding, synchrony at grazing, QBA. Differences in background of the assessors and feasibility constraints (i.e., use of binoculars in unfenced pastures, individual-level assessment conducted during the morning milking in narrow and dark pens, difficulties when using the scan and instantaneous sampling method due to the high number of animals that moved at the same time) can affect the reliability of data collection. Extensive training seems necessary for properly scoring animals when applying the QBA, whereas the FHAT to evaluate the Human-Animal Relationship of goats at pasture seems promising but needs to be validated. Indicators that evaluate the synchrony of activities require to be validated to identify the best moment to perform the observations during the day.

Principal Component Analysis as a novel method for the assessment of the enclosure use patterns of captive Livingstone’s fruit bats (Pteropus livingstonii)

The Spread of Participation Index (SPI) is a standard tool for assessing the suitability of enclosure design by measuring how captive animals access space. This metric, however, lacks the precision to quantify individual-level space utilization or to determine how the distribution of resources and physical features within an enclosure might influence space use. Here we demonstrate how Principal Component Analysis (PCA) can be employed to address these aims and to therefore facilitate both individual-level welfare assessment and the fine-scale evaluation of enclosure design across a range of captive settings. We illustrate the application of this methodology by investigating enclosure use patterns of the Livingstone’s fruit bat (Pteropus livingstonii) population housed at Jersey Zoo. Focal sampling was used to estimate the time each of 44 individuals in the first data collection period and 50 individuals in the second period spent in each of 42 theoretical enclosure sections. PCA was then applied to reduce the 42 sections to five and seven ecologically relevant “enclosure dimensions” for the first and second data collection periods respectively. Individuals were then assigned to the dimension that most accurately represented their enclosure use patterns based on their highest dimensional eigenvalue. This assigned dimension is hereafter referred to as the individual’s Enclosure Use Style (EUS). Sex was found to be significantly correlated with an individual’s EUS in the second period, whilst age was found to significantly influence individual fidelity to assigned EUS. When assessing the effect of resource location on group-level preference for certain sections, the presence of feeders and proximity to public viewing areas in period one, and feeders and heaters in period two, were positively correlated with space use. Finally, individual EUS remained consistent between both data collection periods. We interpret these results for this species in the context of its observed behavioural ecology in the wild and evaluate the degree to which the current captive enclosure for this population allows for optimal individual welfare through the facilitation of spatial choice. We then explore how these methods could be applied to safeguard captive animal welfare across a range of other scenarios.

Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia.

Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low.

The impact of health-related supports in senior housing on ambulance transfers and visits to emergency departments: The Right Care, Right Place, Right Time Project.

To test the impact of placing a wellness team (nurse and social worker) in senior housing on ambulance transfers and visits to emergency departments over 18months. Intervention sites included seven Boston-area buildings, with five buildings at comparable settings acting as controls. Data derive from building-level ambulance data from emergency responders; building-level Medicare claims data on emergency department utilization; and individual-level baseline assessment data from participants in the intervention (n=353) and control (n=208) sites. We used a pre/postdifference in difference quasi-experimental design applying several analytic methods. The preintervention period was January 2016-March 2017, while the intervention period was July 2017-December 2018. Emergency responders provided aggregate transfer data on a daily basis for intervention and control buildings; the Quality Improvement Organization provided quarterly aggregate data on emergency department visit rates; and assessment data came from a modified Vitalize 360 assessment and coaching tool. The study found an 18.2% statistically significant decline in ambulance transfers in intervention buildings, with greater declines in buildings that had fewer services available at baseline, compared to other intervention sites. Analysis of Medicare claims data, adjusted for the proportion of residents over 75 per building, found fewer visits to emergency departments in intervention buildings. Health-related supports in senior housing sites can be effective in reducing emergency transfers and visits to emergency departments.

An analysis of within-subject and population level risk related to substance use and mental health outcomes among adolescents in the PATH study

IntroductionThe objective of this study was to understand how adolescent substance use patterns may lead to negative mental health outcomes. MethodsData from adolescents (12–17 years old at baseline, Wave 1) who participated in the first 3 waves of the Population Assessment of Health and Tobacco (PATH) study were used. Self-reported data on past 30-day substance use and internalizing/externalizing problems were used to conduct within-subject (fixed-effects model) and population-averaged (GEE model) analyses. ResultsIn both within-subject and between-subject analyses, the use of other illicit drugs (e.g., opioids, cocaine, prescription drugs for non-medical use) was positively associated with internalizing problems (within-subject estimate, AOR: 1.65, 95 % CI = 1.36–2.01; between-subject estimate, AOR: 1.53, 95 % CI = 1.32–1.78) and alcohol use was positively associated with externalizing problems (within-subject estimate, AOR: 1.66, 95 % CI = 1.43–1.93; between-subject estimate, AOR: 1.67, 95 % CI = 1.48–1.89). Additionally, within-subject analysis suggested that alcohol, marijuana, and other illicit drugs were associated with increased odds of comorbid internalizing and externalizing problems (ranging from marijuana, AOR: 1.18, - alcohol, AOR: 1.58). DiscussionUsing within-subject and between-subject comparisons, this study demonstrated associations between adolescent substance use and internalizing and externalizing problems. Results suggest not only the need for individual level assessment and early intervention, but also the development and implementation of public health policy aimed at preventing or mitigating the negative effects of substance use in adolescence to promote improved mental health outcomes for this at-risk group.

Performance characteristics of five immunoassays for SARS-CoV-2: a head-to-head benchmark comparison

SummaryBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic in 2020. Testing is crucial for mitigating public health and economic effects. Serology is considered key to population-level surveillance and potentially individual-level risk assessment. However, immunoassay performance has not been compared on large, identical sample sets. We aimed to investigate the performance of four high-throughput commercial SARS-CoV-2 antibody immunoassays and a novel 384-well ELISA.MethodsWe did a head-to-head assessment of SARS-CoV-2 IgG assay (Abbott, Chicago, IL, USA), LIAISON SARS-CoV-2 S1/S2 IgG assay (DiaSorin, Saluggia, Italy), Elecsys Anti-SARS-CoV-2 assay (Roche, Basel, Switzerland), SARS-CoV-2 Total assay (Siemens, Munich, Germany), and a novel 384-well ELISA (the Oxford immunoassay). We derived sensitivity and specificity from 976 pre-pandemic blood samples (collected between Sept 4, 2014, and Oct 4, 2016) and 536 blood samples from patients with laboratory-confirmed SARS-CoV-2 infection, collected at least 20 days post symptom onset (collected between Feb 1, 2020, and May 31, 2020). Receiver operating characteristic (ROC) curves were used to assess assay thresholds.FindingsAt the manufacturers' thresholds, for the Abbott assay sensitivity was 92·7% (95% CI 90·2–94·8) and specificity was 99·9% (99·4–100%); for the DiaSorin assay sensitivity was 96·2% (94·2–97·7) and specificity was 98·9% (98·0–99·4); for the Oxford immunoassay sensitivity was 99·1% (97·8–99·7) and specificity was 99·0% (98·1–99·5); for the Roche assay sensitivity was 97·2% (95·4–98·4) and specificity was 99·8% (99·3–100); and for the Siemens assay sensitivity was 98·1% (96·6–99·1) and specificity was 99·9% (99·4–100%). All assays achieved a sensitivity of at least 98% with thresholds optimised to achieve a specificity of at least 98% on samples taken 30 days or more post symptom onset.InterpretationFour commercial, widely available assays and a scalable 384-well ELISA can be used for SARS-CoV-2 serological testing to achieve sensitivity and specificity of at least 98%. The Siemens assay and Oxford immunoassay achieved these metrics without further optimisation. This benchmark study in immunoassay assessment should enable refinements of testing strategies and the best use of serological testing resource to benefit individuals and population health.FundingPublic Health England and UK National Institute for Health Research.

Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles

Fall-related hip fractures are a major public health issue. While individual-level risk assessment tools exist, population-level predictive models could catalyze innovation in large-scale interventions. This study presents a hierarchical probabilistic model that predicts population-level hip fracture risk based on Factor of Risk (FOR) principles. Model validation demonstrated that FOR output aligned with a published dataset categorized by sex and hip fracture status. The model predicted normalized FOR for 100000 individuals simulating the Canadian older-adult population. Predicted hip fracture risk was higher for females (by an average of 38%), and increased with age (by15% per decade). Potential applications are discussed.

Life Course and Gender: Unpacking Women’s Thoughts About Access to the Superintendency

Abstract The current study utilizes feminist life course theory to examine the perspectives of women who have aspired or have entered into the superintendency in the United States. Life course theory suggests that our choices about careers are often founded upon “role histories” - near instantaneous responses to the social cues of our external world. Consequently, it offers opportunity to understand how patterns of socialization may impact real-life decisions over career possibilities (and impossibilities) and the historical conditions in which career decisions are made. Using survey responses from current and aspiring female superintendents (n=133), we engaged in descriptive statistical analyses. These findings were further contextualized through the four principles of life course theory, historical time and place, timing in lives, linked lives, and human agency. Our findings indicate women’s perspectives on the accessibility of the superintendency have shifted as narratives around women’s executive leadership roles have also changed. Importantly, the women in this study view accessibility to the superintendency as a largely contingent decision - a strategic, individual-level assessment focusing on the favorability of district work conditions to their success as leaders. Simultaneously, we see where issues of social networking, leadership “tapping”, and district “fit” emerge as normative expectations for accessing leadership roles as well as the preferred conditions upon which such choices are made. This reflects an encouraging perspective shift in which women are focusing less on “feasibility” than on “fit”. . Recommendations for practice are provided.