This study investigates disparities in healthcare services in the districts of Uttar Pradesh, the most populous and one of the poorest states of India in the health sector. To measure the disparities in healthcare services, a composite index has been computed using Principal Component Analysis in three domains namely, health availability, health amenities and health affordability and 12 representative indicators. On the basis of the results, districts have been clubbed into five categories, such as high, high-medium, medium, medium-low and low according to their composite scores. The study also uses k-means cluster analysis to find out the set of districts which are similar within the group, but they are different between the groups. Key results indicate that the districts of the western region are more developed in comparison to the districts of the eastern region in terms of three indices such as availability, amenities, affordability of healthcare services and overall composite health index. Also, k-means cluster analysis shows that there are many districts which are identical in many respects; however, they are located in different regions of the state. This study may be helpful to understand the poor availability and affordability of healthcare services in the districts of Uttar Pradesh, which should be improved to meet the objectives of the basic-needs approach and the millennium development goals.
BackgroundIdentifying patients with BRCA mutations is clinically important to inform on the potential response to treatment and for risk management of patients and their relatives. However, traditional referral routes may not meet clinical needs, and therefore, mainstreaming cancer genetics has been shown to be effective in some high-income and high health-literacy settings. To date, no study has reported on the feasibility of mainstreaming in low-income and middle-income settings, where the service considerations and health literacy could detrimentally affect the feasibility of mainstreaming.MethodsThe Mainstreaming Genetic Counselling for Ovarian Cancer Patients (MaGiC) study is a prospective, two-arm observational study comparing oncologist-led and genetics-led counselling. This study included 790 multiethnic patients with ovarian cancer from 23 sites in Malaysia. We compared the impact of different method of delivery of genetic counselling on the uptake of genetic testing and assessed the feasibility, knowledge and satisfaction of patients with ovarian cancer.ResultsOncologists were satisfied with the mainstreaming experience, with 95% indicating a desire to incorporate testing into their clinical practice. The uptake of genetic testing was similar in the mainstreaming and genetics arm (80% and 79%, respectively). Patient satisfaction was high, whereas decision conflict and psychological impact were low in both arms of the study. Notably, decisional conflict, although lower than threshold, was higher for the mainstreaming group compared with the genetics arm. Overall, 13.5% of patients had a pathogenic variant in BRCA1 or BRCA2, and there was no difference between psychosocial measures for carriers in both arms.ConclusionThe MaGiC study demonstrates that mainstreaming cancer genetics is feasible in low-resource and middle-resource Asian setting and increased coverage for genetic testing.
BackgroundTreatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response.MethodsRNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n = 28) of a pre-intervention case cohort of patients whom we followed post-treatment.ResultsA blood-based gene expression signature composed of three genes — LDLRAP1, PHF20, and LUC7L3 — is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0·90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.ConclusionWe have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.