A Data Science Approach to Analyze the Association of Socioeconomic and
Environmental Conditions With Disparities in Pediatric Surgery
- URL: http://arxiv.org/abs/2104.04058v1
- Date: Tue, 16 Mar 2021 20:01:41 GMT
- Title: A Data Science Approach to Analyze the Association of Socioeconomic and
Environmental Conditions With Disparities in Pediatric Surgery
- Abstract summary: The prevalence of poor preoperative condition is significantly higher among African Americans compared to whites.
No statistically significant difference in surgery outcome was present when adjusted by surgical severity and neighborhood quality.
- Score: 3.4806267677524896
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Scientific evidence confirm that significant racial disparities exist in
healthcare, including surgery outcomes. However, the causal pathway underlying
disparities at preoperative physical condition of children is not
well-understood. This research aims to uncover the role of socioeconomic and
environmental factors in racial disparities at the preoperative physical
condition of children through multidimensional integration of several data
sources at the patient and population level. After the data integration process
an unsupervised k-means algorithm on neighborhood quality metrics was developed
to split 29 zip-codes from Memphis, TN into good and poor-quality
neighborhoods. An unadjusted comparison of African Americans and white children
showed that the prevalence of poor preoperative condition is significantly
higher among African Americans compared to whites. No statistically significant
difference in surgery outcome was present when adjusted by surgical severity
and neighborhood quality. The socioenvironmental factors affect the
preoperative clinical condition of children and their surgical outcomes.
Related papers
- Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers [0.0]
White Europeans are 81% increased risk of ASD and Middle Easterners are at 79% reduced risk of ASD compared to Asians.<n> neonatal jaundice is a significant predictor of ASD, while male children are at much higher risk of ASD compared to female children.
arXiv Detail & Related papers (2026-05-31T13:10:41Z) - Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children's Health [0.2538209532048867]
We analyze 18,792 children aged 10-17 years from the National Survey of Children's Health.<n>Overweight/obesity is defined using BMI categories. Predictors included diet, physical activity, sleep, parental stress, socioeconomic conditions, adverse experiences, and neighborhood characteristics.<n>Performance is evaluated using AUC, accuracy, precision, recall, F1 score, and Brier score.
arXiv Detail & Related papers (2026-02-23T19:31:44Z) - MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis [40.3028468133626]
Multiple instance learning (MIL) has emerged as the dominant paradigm for whole slide image (WSI) analysis in computational pathology.<n>textbfMeCaMIL, a causality-aware MIL framework, explicitly models demographic confounders through structured causal graphs.<n>MeCaMIL achieves superior fairness -- demographic disparity variance drops by over 65% relative reduction on average across attributes.
arXiv Detail & Related papers (2025-11-14T06:47:21Z) - Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring [50.164756034797136]
Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons.<n>When dropout is informative, it introduces censoring bias, because of which treatment effect estimates are also biased.<n>We propose an assumption-lean framework to assess the robustness of conditional average treatment effect estimates in survival analysis when facing censoring bias.
arXiv Detail & Related papers (2025-10-15T10:51:17Z) - Partial Identification Approach to Counterfactual Fairness Assessment [50.88100567472179]
We introduce a Bayesian approach to bound unknown counterfactual fairness measures with high confidence.<n>Our results reveal a positive (spurious) effect on the COMPAS score when changing race to African-American (from all others) and a negative (direct causal) effect when transitioning from young to old age.
arXiv Detail & Related papers (2025-09-30T18:35:08Z) - An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes [36.54657524599473]
We investigate racial and ethnic disparities in intensive care unit (ICU) outcome between majority and minority groups in Australia and the U.S.
Minority patients are younger at admission, have worse chronic health, are more likely to be admitted for urgent and non-elective reasons, and have higher illness severity.
We find a protective direct effect of belonging to a minority group, with minority patients showing improved survival compared to their majority counterparts, with all other variables kept equal.
arXiv Detail & Related papers (2025-01-09T12:48:15Z) - Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes [12.493423568689801]
preoperative cognitive status as measured by the clock drawing test contributed to predicting length of hospital stay, hospital charges, average pain experienced during follow-up.
Machine learning models were trained to classify postoperative outcomes in hold-out test sets.
arXiv Detail & Related papers (2024-10-29T23:42:51Z) - I-SIRch: AI-Powered Concept Annotation Tool For Equitable Extraction And Analysis Of Safety Insights From Maternity Investigations [0.8609957371651683]
Most current tools for analysing healthcare data focus only on biomedical concepts, overlooking the importance of human factors.
We developed I-SIRch, using artificial intelligence to automatically identify and label human factors concepts.
I-SIRch was trained using real data and tested on both real and simulated data to evaluate its performance in identifying human factors concepts.
arXiv Detail & Related papers (2024-06-08T16:05:31Z) - Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study [41.94295877935867]
Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities.
Within an intersectional framework, patient SES, race, and sex were found to have significant interactions.
Increased SES is associated with a higher probability of obtaining a schizophrenia diagnosis in Black Americans.
Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans.
arXiv Detail & Related papers (2024-05-30T19:10:35Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery [4.870691561986669]
Congenital heart defects (CHD) are the most prevalent birth defects in the United States.
The outcomes of treatment for CHD differ for specific patient subgroups, with non-Hispanic Black and Hispanic populations experiencing higher rates of mortality and morbidity.
We propose a causal inference framework for outcome assessment and leverage advances in transfer learning to incorporate data from both target and source populations.
arXiv Detail & Related papers (2024-03-21T17:20:23Z) - Operationalizing Counterfactual Metrics: Incentives, Ranking, and
Information Asymmetry [62.53919624802853]
We analyze the incentive misalignments that arise from such average treated outcome metrics.
We show how counterfactual metrics can be modified to behave reasonably in patient-facing ranking systems.
arXiv Detail & Related papers (2023-05-24T00:24:38Z) - Advancing Community Engaged Approaches to Identifying Structural Drivers
of Racial Bias in Health Diagnostic Algorithms [0.0]
Much attention has been raised recently about bias and the use of machine learning algorithms in healthcare.
This paper highlights the importance of centering the discussion of data and healthcare on people and their experiences with healthcare and science.
Collective memory of community trauma, through deaths attributed to poor healthcare, and negative experiences with healthcare are endogenous drivers of seeking treatment and experiencing effective care.
arXiv Detail & Related papers (2023-05-22T20:58:15Z) - Predicting Adverse Neonatal Outcomes for Preterm Neonates with
Multi-Task Learning [51.487856868285995]
We first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.
In particular, the MTL framework contains shared hidden layers and multiple task-specific branches.
arXiv Detail & Related papers (2023-03-28T00:44:06Z) - Association Between Neighborhood Factors and Adult Obesity in Shelby
County, Tennessee: Geospatial Machine Learning Approach [0.966840768820136]
The objective of this study was to investigate the effects of social determinants of Health (SDoH) on obesity prevalence among adults in Shelby County, Tennessee, USA.
Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA.
Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County.
arXiv Detail & Related papers (2022-08-09T15:28:43Z) - Treatment Effect Risk: Bounds and Inference [58.442274475425144]
Since the average treatment effect measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population.
In this paper we consider how to nonetheless assess this important risk measure, formalized as the conditional value at risk (CVaR) of the ITE distribution.
Some bounds can also be interpreted as summarizing a complex CATE function into a single metric and are of interest independently of being a bound.
arXiv Detail & Related papers (2022-01-15T17:21:26Z) - Predictive Modeling of ICU Healthcare-Associated Infections from
Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling
Approach [55.41644538483948]
This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units.
The aim is to support decision making addressed at reducing the incidence rate of infections.
arXiv Detail & Related papers (2020-05-07T16:13:12Z) - 1-D Convlutional Neural Networks for the Analysis of Pupil Size
Variations in Scotopic Conditions [79.71065005161566]
1-D convolutional neural network models are trained for classification of short-range sequences.
Model provides prediction with high average accuracy on a hold out test set.
arXiv Detail & Related papers (2020-02-06T17:25:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.