Journal Healthcare Treatment Development(JHTD) ISSN : 2799-1148

Evaluation of Different Survival Analysis Models for Nki Breast Cancer Data

2023-09-12T09:40:25+00:00

The goal of this study is to evaluate different Survival Analysis Models in terms of their predictive capabilities, accuracy in determining significant covariates within the data, as well as their respective results compared across standard indices. Highest Concordance Index and Lowest Akaike Information Criterion (AIC) are used as the basis of selecting the ideal Survival Analysis model as a template for the construction of the Survival Prediction model for NKI Breast Cancer Data. 6 Survival Analysis Models were used in this study. For the semi-parametric survival models, Classical Cox, Cox-Lasso, and Cox-Ridge Regressions. For the parametric models, 3 Accelerated Failure Time (AFT) models were implemented. These are: Weibull AFT, Log-logistic AFT, and Log-Normal AFT models. Right-censoring was performed in the data since it has been assumed that there are subjects which were not called back anymore for the entire, 18-year clinical trial where the data was taken from. A proportional hazards test was then performed to find out if the covariates in the data are fit to be modeled using Cox Regression and its derivatives. A test for the distribution on the time of event was also done to find whether it follows a specific distribution or not. This was done to verify the usability of the parametric survival analysis models on the data. It has been found out that in terms of Concordance Index and AIC, the Cox-Ridge Regression model outperforms its 2 other semi-parametric counterparts, having the least AIC of 752.6703 and Highest Concordance Index of 0.7709. As for the other 3 parametric models, Log-Normal AFT outperformed the Weibull AFT and Log-Logistic AFT models by a Concordance Index of 0.780 with a corresponding AIC of 608.822. This result also suggests that the time of event of the subjects is best fitted by Log-Normal Distribution. By comparing the 2, best-performing models, it has been reported that Log-Normal AFT outperforms Cox-Ridge Regressions, therefore suggesting to use this Parametric Survival Analysis Model as the basis for a Survival Prediction model suited for NKI Breast Cancer data.

The Effect of Asthma on the Quality of Life in Children

2023-09-13T04:21:26+00:00

Background: Asthma is one of the most prevalent respiratory disorders, causing considerable morbidity and death among people of all ages. Objective: This paper aims to study and analyze the effect of asthma on the quality of life in children.

Patients and methods: This paper studied and analyzed the effect of asthma on the quality of life in children with ages under 13 years were divided into two groups to patients and controls. The data collected were got on patients with 65 cases from 18th March 2021 to 24th July 2022 in different hospitals in Iraq. The collected data were estimated and designed by the SPSS program.

Discussion: Our study noted that chest tightness, coughing, and difficulty breathing affect the difficulty of recovering from asthma patients, which increases the risk of developing asthma. With regard to complications, our study found that most of the complications were found to be difficult to control in asthmatic patients of the pediatric patients' group compared to the control group.

Conclusion: In conclusion, the current results show that 70% of children with high obesity may be more susceptible to asthma than those with less obesity, than they may be at constant risk during childhood and adolescence. In addition, this study found comorbidities such as allergic rhinitis, atopic dermatitis, and obesity, as well as inappropriate and successful inhalation techniques for multivariate predictors of asthma performed in patients with high obesity over the control group, which had less obesity.

Catastrophic Out-of-Pocket Expenditure on Health: Evidence from the Regions in the Philippines

2023-09-20T05:58:21+00:00

This study estimates Filipino’s catastrophic and impoverishing out-of-pocket (OOP) healthcare expenditures on a regional perspective to measure the effectiveness and progress of Philippines’ healthcare financing system in ensuring population protection from excessive and unequal healthcare costs. Observations are drawn from the most recent household-based survey in the Philippines, 2018 and 2021 Family Income and Expenditure Survey, and grouped by four major regions: National Capital Region, the rest of Luzon, Visayas, and Mindanao region. From 2018 to 2021, results showed that the distribution of catastrophic OOP healthcare expenditures decreases while depth of these payments increases across the four major regions. The highest incidences of these catastrophic payments were found in the rest of Luzon and Visayas regions, while the highest intensities were found in Mindanao region. Despite medicines and pharmaceuticals products dominance on OOP healthcare expenditures, payment on inpatient medical services drives the catastrophic expenditures of households. Aside, deepening poverty caused by healthcare payments were more evident in households residing in Mindanao regions, thus exposing this Filipino families to financial risk and poverty threats. Logistic regression analysis showed that aging households head, employment, and socio-economic status are significant and common factors affecting the likelihood of incurring excessive health expenditures across the regions. The risk of catastrophic and impoverishing effects of healthcare expenditures persists in the country. Low levels of catastrophic OOP healthcare expenditures may indicate that disadvantaged households focused on spending on basic necessities and struggle to afford healthcare due to high costs amid COVID 19, leading to foregone treatment. Thus, continuous implementation of fair and innovative ways of financing the healthcare system to reduce the financial burden of OOP payments on Filipino households is recommended.

Prophylactic Efficacy of Silymarin upon Renal Dysfunction Induced by Copper Oxide Nanoparticle

2023-09-20T07:19:48+00:00

Copper oxide nanoparticles (CuO-NPs) have unique properties encouraged their rapidly growing uses in various fields as biomedical applications and environmental remediation. In this regard, the potential toxicity resulting from repeated exposure increases exponentially, so ensuring the suitability and safety of these nanoparticles for different uses is urgent. This experimental study aims to estimate nephrotoxic effects of CuO-NPs on some renal dysfunction markers along with the possibility attenuated efficacy of silymarin (SLM) in laboratory animals. Twenty four adult male rats were recruited in current study distributed into (4) equal groups. In CON group rats were given distilled water only and considered as control, while rats in CuO-NPs group were poisoned with copper oxide nanoparticles using gastric gavage. In CuO-NPs + SLM group, rats were provided with both CuO-NPs plus silymarin orally. In the SLM group, only silymarin was administered to rats. Studied rats were sacrificed and sera and kidney homogenates were obtained to complete the necessary biochemical and oxidative stress tests. According to the results, rats intoxicated with CuO-NPs recorded deleterious alterations in all studied renal biomarkers levels compared to control rats. However, the data confirmed that the coadministration of SLM with CuO-NPs had positively ameliorated the detrimental changes induced by CuO-NPs. In conclusion, it was suggested that a high dose of copper oxide NPs may cause significant impairment in renal functioning, and silymarin can be considered as a protective agent upon potential nephrotoxicity induced by copper oxide nanoparticle.