Graduate Student Papers

Permanent URI for this collection

https://hdl.handle.net/10919/91467

Browse

Now showing 1 - 20 of 129

Understanding the Risks and Benefits of Implementing AI-Enabled Remote Patient Monitoring Systems for Disease Management
Nabi, Junaid; Staynings, Richard; Sofi, Javaid Iqbal; Willis, Henry H. (MDPI, 2025-11-17)
Effectively managing risk is essential for fostering innovation in healthcare, especially with advancements like artificial intelligence (AI) and machine learning (ML). These technologies aim to enhance accessibility, efficiency, and equity in healthcare delivery. To assess the practical utility of AI-enabled remote patient monitoring (RPM) devices, it is crucial to identify and evaluate associated risks while distinguishing between acceptable risk, which society tolerates, and optimal risk, which balances risk reduction costs with benefits. This paper outlines how policymakers should adopt the framework of optimal risk to ensure patient safety while maximizing the advantages of these technologies.
Molecular Determinants of Per- and Polyfluoroalkyl Substances Binding to Estrogen Receptors
Mada, Sahith; Jordan, Samuel; Mathew, Joshua; Loveranes, Coby; Moran, James; Ganesh, Harrish; Dakshanamurthy, Sivanesan (MDPI, 2025-10-22)
Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent organofluorines linked to cancer, organ dysfunction, and other health problems. This study used quantitative structure–property relationship (QSPR) and quantitative structure–activity relationship (QSAR) modeling to examine the binding of PFAS to estrogen receptor alpha (ERα) and beta (ERβ). Molecular docking of 14,591 PFAS compounds was performed, and docking scores were used as a measure of receptor affinity. QSPR models were built for two datasets: the ERα and ERβ top binders (TBs), and a set of commonly exposed (CE) PFAS. These models quantified how chemical descriptors influence binding affinity. Across the models, higher density and electrophilicity indicated positive correlations with affinity, while surface tension indicated negative correlations. Electrostatic descriptors, including HOMO energy and positive Fukui index (F⁺ max), were part of the models but showed inconsistent trends. The CE QSPR models displayed correlations that conflicted with those of the TB models. Following QSPR analysis, 66 QSAR models were developed using a mix of top binders and experimental data. These models achieved strong performance, with R² values averaging 0.95 for training sets and 0.78 for test sets, that indicated reliable predictive ability. To improve generalizability, large-set QSAR models were created for each receptor. After outlier removal, these models reached R² values of 0.68–0.71, which supports their use in screening structurally diverse PFAS. Overall, QSPR and QSAR analyses reveal key chemical features that influence PFAS–ER binding. This predictive approach provides a scalable framework to assess the binding interactions of structurally diverse PFAS to ERs and other nuclear receptors. All the codes, data, and the GUI visualization of the results are freely available at sivaGU/QSPR-QSAR-Molecular-Visualization-Tool.
Differential Toxicity of Arsenic in Daphnia pulex Under Phosphorus and Food Limitation
Schultz, Anthony; Owens, Joseph; Demidenko, Eugene; Chowdhury, Priyanka Roy (Wiley, 2024-06-05)
The on-going anthropogenic degradation of freshwater habitats has drastically altered the environmental supply of both nutrients and common pollutants. Most organisms living in these altered habitats experience interactive effects of various stressors that can initiate adjustments at multiple levels impacting their fitness. Hence, studies measuring response to a single environmental parameter fail to capture the complexities of the status quo. We tested both the individual and the interactive effect of arsenic (As) exposure, food quantity, and dietary phosphorus (P)-supply on six life-history traits (Juvenile Growth Rate; Adult Growth Rate; Age and Size at Maturity, Lifespan, and Fecundity) as surrogates for organismal fitness in the keystone aquatic grazer Daphnia pulex. We also tested the effect of food quantity and P-supply on somatic As accumulation in Daphnia. Our results indicated an influence of P-supply on neonatal growth and an influence of As and food quantity on growth and maintenance later in life. Maturation was strongly influenced by all three variables, with no reproduction observed in the presence of two or more environmental stressors. We found a strong interaction between As and dietary P, with increased P-supply intensifing the toxicity effect of As. No such effects were seen between As and food quantity, indicating a differential role of quantity versus quality on As toxicity. We found a nominal effect of diet on somatic As accumulation. The results from the present study emphasize the importance of considering such interactions between co-occurring environmental stressors and the dietary status of organisms, to better predict and manage impacts and risks associated with common environmental toxicants in highly vulnerable ecosystems. Environ Toxicol Chem 2024;00:1-13. (c) 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Words to Waves: Emotion-Adaptive Music Recommendation System
Chavali, Apoorva; Menezes, Reeve (2025-10)
Current recommendation systems often tend to overlook emotional context and rely on historical listening patterns or static mood tags. This paper introduces a novel music recommendation framework employing a variant of Wide and Deep Learning architecture that takes in real-time emotional states inferred directly from natural language as inputs and recommends songs that closely portray the mood. The system captures emotional contexts from user-provided textual descriptions by using transformer-based embeddings, which were fine-tuned to predict the emotional dimensions of valence-arousal. The deep component of the architecture utilizes these embeddings to generalize unseen emotional patterns, while the wide component effectively memorizes user-emotion and emotion-genre associations through cross-product features. Experimental results show that personalized music selections positively influence the user’s emotions and lead to a significant improvement in emotional relevance.
Efficiency Evaluation in Railway System Operations: A Focus on Punctuality of Train Paths
Mashayekhy, Yas (2025-05-07)
This study addresses the operational efficiency of train paths within a railway system by applying Data Envelopment Analysis (DEA) to train paths, treating them as decision-making units (DMUs). The primary focus is on punctuality as a crucial aspect of service performance, with the goal of enhancing economic infrastructure through timetable scheduling and management. This research innovatively contributes to the field by integrating systems thinking with DEA, thus offering a non-parametric optimization-based method for performance evaluation in complex socio-technical systems (STS) such as railway transportation systems. By considering train paths as DMUs, this approach enables a detailed performance analysis at both aggregate and disaggregate levels, allowing for a more nuanced understanding of the network's operational dynamics. The study considers the Belgium's railway system (INFRABEL) as a case study and leverages a unique dataset comprised of real-world operational data, which includes various indicators of performance such as punctuality, frequency, and cancellations. The findings highlight significant potential for improving the technical efficiency of train paths, which can lead to better utilization of existing resources and enhanced passenger satisfaction without necessitating extensive capital investment. The study's implications extend to railway operations management, suggesting that strategic adjustments to scheduling and the management of train paths can substantially mitigate operational inefficiencies and enhance overall system performance.
Integrating Equation Coding with Residual Networks for Efficient ODE Approximation in Biological Research
Yi, Ziyue (MDPI, 2025-04-27)
Biological research traditionally relies on experimental methods, which can be inefficient and hinder knowledge transfer due to redundant trial-and-error processes and difficulties in standardizing results. The complexity of biological systems, combined with large volumes of data, necessitates precise mathematical models like ordinary differential equations (ODEs) to describe interactions within these systems. However, the practical use of ODE-based models is limited by the need for curated data, making them less accessible for routine research. To overcome these challenges, we introduce LazyNet, a novel machine learning model that integrates logarithmic and exponential functions within a Residual Network (ResNet) to approximate ODEs. LazyNet reduces the complexity of mathematical operations, enabling faster model training with fewer data and lower computational costs. We evaluate LazyNet across several biological applications, including HIV dynamics, gene regulatory networks, and mass spectrometry analysis of small molecules. Our findings show that LazyNet effectively predicts complex biological phenomena, accelerating model development while reducing the need for extensive experimental data. This approach offers a promising advancement in computational biology, enhancing the efficiency and accuracy of biological research.
Predicting Loan Defaults with Machine Learning: A Business Intelligence Approach to Responsible Lending
Coca, Rocio; Jadhav, Rushabh; Patel, Noopur; Quiroz, Evelyn (2025-06-10)
This report presents a comprehensive analysis of loan default prediction using machine learning techniques applied to a curated dataset from a peer-to-peer lending platform. The study aims to enhance credit risk assessment by identifying high-risk loan applicants before loan issuance, thereby improving financial decision-making and promoting responsible lending practices. A subset of 2,468 anonymized loan records, containing borrower demographics, financial information, credit history, and loan status, served as the foundation for model development and evaluation. The research employs exploratory data analysis, principal component analysis, clustering, and supervised learning methods including logistic regression, decision trees, support vector machines (SVM), neural networks, and ensemble models. Key predictors of default, such as loan_percent_income, loan_int_rate, credit_score, and homeownership status, were consistently identified across models. Among the models tested, SVM demonstrated the highest validation AUC, indicating strong generalization capability, while logistic regression and decision trees provided interpretable, threshold-based insights for operational use. Clustering analysis revealed distinct borrower segments with varying risk profiles, offering a strategic advantage for personalized risk mitigation strategies. Despite the dataset’s anonymized nature and modest size, the models achieved strong predictive accuracy and actionable insights. The findings support the integration of machine learning into financial risk assessment pipelines and offer evidence-based recommendations for data driven lending. Ultimately, this study demonstrates how explainable and predictive machine learning models can be applied to real world financial datasets to support credit scoring, reduce default risk, and increase overall economic stability through more informed loan approval processes.
Can LLMs Recommend More Responsible Prompts?
Santana, Vagner; Berger, Sara; Machado, Tiago; de Macedo, Maysa Malfiza; Sanctos, Cassia; Williams, Lemara; Wu, Zhaoqing (ACM, 2025-03-24)
Human-Computer Interaction practitioners have been proposing best practices in user interface design for decades. However, generative Artificial Intelligence (GenAI) brings additional design considerations and currently lacks sufficient user guidance regarding affordances, inputs, and outputs. In this context, we developed a recommender system to promote responsible AI (RAI) practices while people prompt GenAI systems, by recommending addition of sentences based on social values and removal of harmful sentences. We detail a lightweight recommender system designed to be used in prompting-time and compare its recommendations to the ones provided by three base large language models (LLMs) and two LLMs fine-tuned for the task, i.e., recommending inclusion of sentences based on social values and removal of harmful sentences from a given prompt. Results indicate that our approach has the best F1-score balance in terms of recommendations for additions and removal of sentences to promote responsible prompts, while a fine-tuned model obtained the best F1-score for additions, and our approach obtained the best F1-score for removals of harmful sentences. In addition, fine-tuned models improved the objectiveness of responses by reducing the verbosity of generated content in 93% when compared to the content generated by base models. Presented findings contribute to RAI by showing the limits and bias of existing LLMs in terms of recommendations on how to create more responsible prompts and how open-source technologies can fill this gap in prompting-time.
Sectoral Contributions to Primary and Secondary PM_2.5 in Regional Airsheds of India
Kumar, Alok; Imam, Fahad; Dixit, Kuldeep; Chaudhary, Ekta; Sharma, Sumit; Singh, Nimish; Katoch, Varun; Agarwal, Shivang; Ganguly, Dilip; Dey, Sagnik (American Chemical Society, 2025-03-18)
The exceedance of annual ambient fine particulate matter (PM2.5) concentrations above the national air quality standard across a large region in India, extending beyond the urban centers, necessitates an airshed approach for effective air quality management. Using over two decades of satellite-derived PM2.5 concentration data, seasonal wind patterns, and topography, we identified 9–11 major regional airsheds in India and further delineated the local airsheds of nonattainment cities. We separated sectoral contributions to primary and secondary PM2.5 in each airshed using outputs from a chemical transport model for the National Clean Air Program (NCAP) baseline year. In most airsheds, secondary PM2.5 constituted a larger share than primary PM2.5 except for the monsoon season. The domestic sector contributed the most to primary PM2.5 in most airsheds, while transboundary transport, industry, power, and other sources were the major contributors to secondary PM2.5. Our results can be used as a reference to assess progress in reducing ambient PM2.5 levels through the implementation of the NCAP action plan. Our study provides a comprehensive analysis of airsheds in India and underscores the need to control precursor gases, along with primary sources, for effective air pollution mitigation in the context of airsheds.
Care cascades of diabetes and hypertension among late adolescents in India
Malik, Bijaya Kumar; Goyal, Amit Kumar; Maiti, Suraj; Mohanty, Sanjay K. (International Society of Global Health, 2025-03-07)
BACKGROUND: Diabetes and hypertension are the most prevalent morbidities in India and are quickly becoming common among the younger age groups. Adolescents aged 10-19 years, accounting for one-fifth of the country's population, are at an increasing risk of developing these conditions. We aim to examine the prevalence, awareness, treatment, and control (ATC) of diabetes and hypertension among late adolescents (15-19 years) in India. METHODS: We used microdata of 204 346 late adolescents from India's fifth round of the National Family and Health Survey, 2019-21. We defined hypertensive adolescents as those diagnosed with hypertension or those with a systolic blood pressure (BP) measurement of ≥130 mm Hg, diastolic BP measurements of levels ≥80 mm Hg, or those who used medication to lower BP at the time of the survey. Diabetic adolescents were those diagnosed as such by health professionals, those with glucose levels above 140 mg/dL, or those taking any medication to control high blood glucose levels at the time of the survey. We estimated the age-sex-adjusted prevalence of both conditions and their ATC rates, referred to as cascade care. We used the Erreygers' Concentration Index to examine the socioeconomic inequality in cascade care. We used multivariable logistic regression to estimate the average marginal effects while controlling for sociodemographic characteristics. RESULTS: Of 204 346 late adolescents, 27.8% (95% confidence interval (CI) = 27.6, 28.2) had either of the two conditions, with 3.5% (95% CI = 3.4, 3.6) being diabetic and 24.3% (95% CI = 24.0, 24.6) having hypertension. The ATC rate of diabetes was 13.5% (95% CI = 12.4, 14.7), 13.1% (95% CI = 11.9, 14.2), and 12.1% (95% CI = 11.0, 13.3), respectively. For hypertension, the ATC rate was extremely low at 6.2% (95% CI = 5.8, 6.5), 3.5% (95% CI = 3.3, 3.7), and 3.3% (95% CI = 3.1, 3.5), respectively. There was a pro-rich socioeconomic inequality in the prevalence of hypertension and a pro-poor inequality in the prevalence of diabetes among late adolescents. We observed significant variations in both conditions across the regions of India. CONCLUSIONS: The high prevalence and low care cascade levels of diabetes and hypertension among late adolescents in India are concerning. A multipronged strategy that includes screening, diagnosis, and timely interventions at school and home can reduce the burden of hypertension and diabetes among the prospective workforce in India. Sensitising adolescents through school curricula under the New Education Policy (2020) is recommended to reduce the burden of these conditions. We also recommend that longitudinal and intervention studies focussed on this age group be undertaken in the future to help reduce the disease burden.
Antimicrobial resistance transmission in the environmental settings through traditional and UV-enabled advanced wastewater treatment plants: a metagenomic insight
Talat, Absar; Bashir, Yasir; Khalil, Nadeem; Brown, Connor L.; Gupta, Dinesh; Khan, Asad U. (2025-03-06)
Background: Municipal wastewater treatment plants (WWTPs) are pivotal reservoirs for antibiotic-resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). Selective pressures from antibiotic residues, co-selection by heavy metals, and conducive environments sustain ARGs, fostering the emergence of ARB. While advancements in WWTP technology have enhanced the removal of inorganic and organic pollutants, assessing ARG and ARB content in treated water remains a gap. This metagenomic study meticulously examines the filtration efficiency of two distinct WWTPs-conventional (WWTPC) and advanced (WWTPA), operating on the same influent characteristics and located at Aligarh, India. Results: The dominance of Proteobacteria or Pseudomonadota, characterized the samples from both WWTPs and carried most ARGs. Acinetobacter johnsonii, a prevailing species, exhibited a diminishing trend with wastewater treatment, yet its persistence and association with antibiotic resistance underscore its adaptive resilience. The total ARG count was reduced in effluents, from 58 ARGs, representing 14 distinct classes of antibiotics in the influent to 46 and 21 in the effluents of WWTPC and WWTPA respectively. However, an overall surge in abundance, particularly influenced by genes such as qacL, blaOXA−900, and rsmA was observed. Numerous clinically significant ARGs, including those against aminoglycosides (AAC(6’)-Ib9, APH(3’’)-Ib, APH(6)-Id), macrolides (EreD, mphE, mphF, mphG, mphN, msrE), lincosamide (lnuG), sulfonamides (sul1, sul2), and beta-lactamases (blaNDM−1), persisted across both conventional and advanced treatment processes. The prevalence of mobile genetic elements and virulence factors in the effluents possess a high risk for ARG dissemination. Conclusions: Advanced technologies are essential for effective ARG and ARB removal. A multidisciplinary approach focused on investigating the intricate association between ARGs, microbiome dynamics, MGEs, and VFs is required to identify robust indicators for filtration efficacy, contributing to optimized WWTP operations and combating ARG proliferation across sectors.
Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks
Duan, Zhangqi; Fernandez, Nigel; Hicks, Alexander; Lan, Andrew (ACM, 2025-03-03)
Open-ended coding tasks, which ask students to construct programs according to certain specifications, are common in computer science education. Student modeling can be challenging since their open-ended nature means that student code can be diverse. Traditional knowledge tracing (KT) models that only analyze response correctness may not fully capture nuances in student knowledge from student code. In this paper, we introduce Test case-Informed Knowledge Tracing for Open-ended Coding (TIKTOC), a framework to simultaneously analyze and predict both open-ended student code and whether the code passes each test case. We augment the existing CodeWorkout dataset with the test cases used for a subset of the open-ended coding questions, and propose a multitask learning KT method to simultaneously analyze and predict 1) whether a student’s code submission passes each test case and 2) the student’s open-ended code, using a large language model as the backbone. We quantitatively show that these methods outperform existing KT methods for coding that only use the overall score a code submission receives. We also qualitatively demonstrate how test case information, combined with open-ended code, helps us gain fine-grained insights into student knowledge.
Optimizing Schools: An Ethical Analysis of AI Integration in Education
Aina, Adeyemi (2025-01-03)
This case highlights the intersection of technology, society, and ethics, where AI offers transformative opportunities to identify struggling students and enhance their well-being through tailored interventions. However, it also presents risks, including algorithmic bias, data misuse, and a shift away from human-centered education. The Minerva High School case underscores broader ethical challenges in integrating AI into public institutions, particularly those serving vulnerable populations, prompting critical questions about balancing innovation with respect for individual rights and whether technological efficiency should outweigh traditional educational values. This analysis explores these dilemmas through ethical frameworks, offering insights into the responsible deployment of technology in society.
Public health insurance coverage in India before and after PM-JAY: Repeated cross-sectional analysis of nationally representative survey data
Mohanty, Sanjay K.; Upadhyay, Ashish Kumar; Maiti, Suraj; Mishra, Radhe Shyam; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (BMJ, 2023-08-28)
Introduction The provision of non-contributory public health insurance (NPHI) to marginalised populations is a critical step along the path to universal health coverage. We aimed to assess the extent to which Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (PM-JAY) - potentially, the world's largest NPHI programme - has succeeded in raising health insurance coverage of the poorest two-fifths of the population of India. Methods We used nationally representative data from the National Family Health Survey on 633 699 and 601 509 households in 2015-2016 (pre-PM-JAY) and 2019-2021 (mostly, post PM-JAY), respectively. We stratified by urban/rural and estimated NPHI coverage nationally, and by state, district and socioeconomic categories. We decomposed coverage variance between states, districts, and households and measured socioeconomic inequality in coverage. For Uttar Pradesh, we tested whether coverage increased most in districts where PM-JAY had been implemented before the second survey and whether coverage increased most for targeted poorer households in these districts. Results We estimated that NPHI coverage increased by 11.7 percentage points (pp) (95% CI 11.0% to 12.4%) and 8.0 pp (95% CI 7.3% to 8.7%) in rural and urban India, respectively. In rural areas, coverage increased most for targeted households and pro-rich inequality decreased. Geographical inequalities in coverage narrowed. Coverage did not increase more in states that implemented PM-JAY. In Uttar Pradesh, the coverage increase was larger by 3.4 pp (95% CI 0.9% to 6.0%) and 4.2 pp (95% CI 1.2% to 7.1%) in rural and urban areas, respectively, in districts exposed to PM-JAY and the increase was 3.5 pp (95% CI 0.9% to 6.1%) larger for targeted households in these districts. Conclusion The introduction of PM-JAY coincided with increased public health insurance coverage and decreased inequality in coverage. But the gains cannot all be plausibly attributed to PM-JAY, and they are insufficient to reach the goal of universal coverage of the poor.
Healthcare inequity arising from unequal response to need in the older (45+ years) population of India: Analysis of nationally representative data
Mohanty, Sanjay K.; Khan, Junaid; Maiti, Suraj; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (Elsevier, 2024-11-20)
Given the large and growing number of older (45+ years) people in India, inequitable access to healthcare in this population would slow global progress toward universal health coverage. We used a 2017-18 nationally representative sample of this population (n = 53,687) to estimate healthcare inequality and inequity by economic status. We used an extensive battery of indicators in nine health domains, plus age and sex, to adjust for need. We measured economic status by monthly per capita consumption expenditure and used a concentration index to measure inequalities in probabilities of any doctor visit and any hospitalisation within 12 months. We decomposed inequality with a regression method that allowed for economic and urban/rural heterogeneity in partial associations between healthcare and both need and non-need covariates. We used the associations achieved by the richest fifth of urban dwellers to predict a need-justified distribution of healthcare and compared the actual distribution with that benchmark to identify inequity. We found pro-rich inequalities in doctor visits and hospitalisations, which were driven by use of private healthcare. Adjustment for the greater need of poorer individuals revealed pro-rich inequity that exceeded inequality by about 65% and 39% for doctor visits and hospitalisations, respectively. These adjustments would have been substantially smaller, and inequity underestimated, without allowing for use-need heterogeneity, which accounted for 11% of the inequity in doctor visits and was 373% of inequity in hospitalisations. Targeting service coverage on poorer and rural groups, and increasing their access to private providers, would both reduce inequity and raise average coverage in the older population of India.
Out-of-pocket payment and financial risk protection for breast cancer treatment: a prospective study from India
Wadasadawala, Tabassum; Mohanty, Sanjay K.; Sen, Soumendu; Kanala, Tejaswi S.; Maiti, Suraj; Puchali, Namita; Gupta, Sudeep; Sarin, Rajiv; Parmar, Vani (Elsevier, 2024-01-16)
Background: Available data on cost of cancer treatment, out-of-pocket payment and reimbursement are limited in India. We estimated the treatment costs, out-of-pocket payment, and reimbursement in a cohort of breast cancer patients who sought treatment at a publicly funded tertiary cancer care hospital in India. Methods: A prospective longitudinal study was conducted from June 2019 to March 2022 at Tata Memorial Centre (TMC), Mumbai. Data on expenditure during each visit of treatment was collected by a team of trained medical social workers. The primary outcome variables were total cost (TC) of treatment, out-of-pocket payment (OOP), and reimbursement. TC included cost incurred by breast cancer patients during treatment at TMC. OOP was defined as the total cost incurred at TMC less of reimbursement. Reimbursement was any form of financial assistance (cashless or repayment), including social health insurance, private health insurance, employee health schemes, and assistance from charitable trusts, received by the patients for breast cancer treatment. Findings: Of the 500 patients included in the study, 45 discontinued treatment (due to financial or other reasons) and 26 died during treatment. The mean TC of breast cancer treatment was ₹258,095/US$3531 (95% CI: 238,225, 277,934). Direct medical cost (MC) accounted for 56.3% of the TC. Systemic therapy costs (₹50,869/US$696) were higher than radiotherapy (₹33,483/US$458) and surgery costs (₹25,075/US$343). About 74.4% patients availed some form of financial assistance at TMC; 8% patients received full reimbursement. The mean OOP for breast cancer treatment was ₹186,461/US$2551 (95% CI: 167,666, 205,257), accounting for 72.2% of the TC. Social health insurance (SHI) had a reasonable coverage (33.1%), followed by charitable trusts (29.6%), employee health insurance (5.1%), private health insurance (4.4%) and 25.6% had no reimbursement. But SHI covered only 40.1% of the TC of treatment compared to private health insurance that covered as much as 57.1% of it. Both TC and OOP were higher for patients who were younger, belonged to rural areas, had a comorbidity, were diagnosed at an advanced stage, and were from outside Maharashtra. Interpretation: In India, the cost and OOP for breast cancer treatment are high and reimbursement for the treatment flows from multiple sources. Though many of the patients receive some form of reimbursement, it is insufficient to prevent high OOP. Hence both wider insurance coverage as well as higher cap of the insurance packages in the health insurance schemes is suggested. Allowing for the automatic inclusion of cancer treatment in SHI can mitigate the financial burden of cancer patients in India. Funding: This work was funded by an extramural grant from the Women's Cancer Initiative and the Nag Foundation and an intramural grant from the International Institute of Population Sciences, Mumbai.
Catastrophic health expenditure and distress financing of breast cancer treatment in India: evidence from a longitudinal cohort study
Mohanty, Sanjay K.; Wadasadawala, Tabassum; Sen, Soumendu; Maiti, Suraj; E, Jishna (Springer, 2024-07-23)
Objective: To estimate the catastrophic health expenditure and distress financing of breast cancer treatment in India. Methods: The unit data from a longitudinal survey that followed 500 breast cancer patients treated at Tata Memorial Centre (TMC), Mumbai from June 2019 to March 2022 were used. The catastrophic health expenditure (CHE) was estimated using households' capacity to pay and distress financing as selling assets or borrowing loans to meet cost of treatment. Bivariate and logistic regression models were used for analysis. Findings: The CHE of breast cancer was estimated at 84.2% (95% CI: 80.8,87.9%) and distress financing at 72.4% (95% CI: 67.8,76.6%). Higher prevalence of CHE and distress financing was found among rural, poor, agriculture dependent households and among patients from outside of Maharashtra. About 75% of breast cancer patients had some form of reimbursement but it reduced the incidence of catastrophic health expenditure by only 14%. Nearly 80% of the patients utilised multiple financing sources to meet the cost of treatment. The significant predictors of distress financing were catastrophic health expenditure, type of patient, educational attainment, main income source, health insurance, and state of residence. Conclusion: In India, the CHE and distress financing of breast cancer treatment is very high. Most of the patients who had CHE were more likely to incur distress financing. Inclusion of direct non-medical cost such as accommodation, food and travel of patients and accompanying person in the ambit of reimbursement of breast cancer treatment can reduce the CHE. We suggest that city specific cancer care centre need to be strengthened under the aegis of PM-JAY to cater quality cancer care in their own states of residence.
Compressive and Flexural Strength Characteristics of Paving Stones Produced with Concrete Modified with Polypropylene Waste Chair
Olukanni, E. O.; Oyedepo, O. J.; Arowolo, T. R. (Proceedings of the 2021 Annual Conference of the School of Engineering & Engineering Technology, FUTA, 6th – 8th October, 2021, 2021-10)
The demand for a better performing pavement and the need to convert the ever-growing polymer waste into beneficial use necessitated the need to develop and characterize a polypropylene modified concrete for use in pavement construction. This research focuses on characterizing the strength of concrete produced with polypropylene waste as modifiers for pavement construction. The materials used in this research are fine and coarse aggregates, cement and polypropylene waste chairs (PWC). Tests were performed on the aggregate and fresh concrete to determine their suitability and characteristics for use in concrete for pavement. Two concrete grades 1:2:4 and 1:3:6 was produced into 200 mm, 400 mm and 500 mm long paving stones on which compressive and flexural tests were performed. Results obtained showed that 400 mm 1:2:4 grade concrete has the highest compressive strength of 27.36 N/mm2 at 10% polypropylene composition. The 200 mm 1:2:4 concrete grade paving stone with 10% polyprpopylene composition has the highest flexural strength of 12.90 N/mm2 . It was concluded that the 200 mm long 1:2:4 concrete grade paving stone at 10% polypropylene composition is the best length of paving stone that can give an adequate flexural strength which is the most important requirent in concrete pavement requirement.
Evaluation of Rheological Characteristics of Graphite Modified Bitumen
Oladunjoye, O. O.; Oyedepo, O. J.; Olukanni, E. O.; Akande, S. P. (Proceedings of the 2021 Annual Conference of the School of Engineering & Engineering Technology, FUTA, 6th – 8th October, 2021, 2021-03-06)
The level of performance of asphalt concrete has a close relationship with the properties of bitumen used. This research evaluates the rheological parameters of graphite modified bitumen. Index properties tests were conducted on bitumen and graphite to determine their suitability. Dynamic viscosity and dynamic shear rheometer were conducted on bituminous binder modified with four different proportion of graphite ranging from 2% to 10% by bitumen weight. Dynamic viscosity test was conducted on bitumen and graphite modified bitumen at temperature of 1350C and 1650C using Brookfield Viscometer. The rheological properties are centered on phase angle (δ) and complex shear modulus (G*) which were determined on bitumen and graphite modified bitumen at temperature ranging from 520C – 700C at 10 rad/s frequency using Dynamic Shear Rheometer in accordance with ASTM D7175-15. The storage modulus (G ' ), loss modulus (G") and rutting parameters were then evaluated from phase angle and complex shear modulus. The bitumen and graphite modified bitumen showed that graphite modified bitumen has the highest complex shear modulus and rutting parameter of 8984 (kPa) and 33387 (kPa) at 10% graphite content. The results of viscosity helped to determine the mixing and compaction temperatures. Dynamic shear rheometer test results determined the elastic and viscous behaviour at various temperature. The higher the complex shear modulus and rutting parameter the stiffer the binder will resist deformation and rutting.
Global Perspectives Program 2024 Final Report
Dosumu, Fiyinfunjah Adenike (2024)

Browse

Recent Submissions