Decentralised Local Governance and Poverty Reduction in Post-1991 Ethiopia: A Political Economy Study

"After 1991, Ethiopia has introduced an ethnic federal governance system constituting nine regional states and two autonomous city administrations, Addis Ababa and Dire Dawa. The restructuring of the state seemingly led to the decentralisation of power to the regions and Woreda (district authority) levels local governance structure in 1995 and 2002 respectively. The purpose of this article is to examine the practices of decentralised local governance in Ethiopia in general and the local governance performance at the level of peasant association (Kebele) in particular. The article also analyses the link between the local governance and poverty based on three indicators: decentralisation and self-rule (DSR), local capacity for planning (LCP), and effectiveness of local governance system (ELGS). Data was collected from eight selected Kebeles of three different regional states through household survey, qualitative interviews and focus group discussions. The study shows that while the power and control of the central government is well established, the Kebeles lack the capacity and resources to deliver development. The LCP at Kebele level is weak because of organisational incapacity and institutional constraints related to DSR. The ELGS is also poor since Kebeles do not have any fiscal rights and administrative power for the reasons associated with DSR and LCP. The government has been implementing poverty reduction strategies using productive safety net programmes and farmer training centres. These, however, have not had the desired outcome due to organisational and institutional incapacitation of Kebele administrations." (author's abstract

The persistent adivasi demand for land rights and the forest rights act 2006 in kerala, india

publishedVersio

A Nordic initiative for a more personal and accurate diagnostic pathway for prostate cancer

Non peer reviewe

Prostate Age Gap: An MRI Surrogate Marker of Aging for Prostate Cancer Detection

Background Aging is the most important risk factor for prostate cancer (PC). Imaging techniques can be useful to measure age-related changes associated with the transition to diverse pathological states. However, biomarkers of aging from prostate magnetic resonance imaging (MRI) remain to be explored. Purpose To develop an aging biomarker from prostate MRI and to examine its relationship with clinically significant PC (csPC, Gleason score ≥7) risk occurrence. Study Type Retrospective. Population Four hundred and sixty-eight (65.97 ± 6.91 years) biopsied males, contributing 7243 prostate MRI slices. A deep learning (DL) model was trained on 3223 MRI slices from 81 low-grade PC (Gleason score ≤6) and 131 negative patients, defined as non-csPC. The model was tested on 90 negative, 52 low-grade (142 non-csPC), and 114 csPC patients. Field Strength/Sequence 3-T, axial T2-weighted spin sequence. Assessment Chronological age was defined as the age of the participant at the time of the visit. Prostate-specific antigen (PSA), prostate volume, Gleason, and Prostate Imaging-Reporting and Data System (PI-RADS) scores were also obtained. Manually annotated prostate masks were used to crop the MRI slices, and a DL model was trained with those from non-csPC patients to estimate the age of the patients. Following, we obtained the prostate age gap (PAG) on previously unseen csPC and non-csPC cropped MRI exams. PAG was defined as the estimated model age minus the patient's age. Finally, the relationship between PAG and csPC risk occurrence was assessed through an adjusted multivariate logistic regression by PSA levels, age, prostate volume, and PI-RADS ≥ 3 score. Statistical Tests T-test, Mann–Whitney U test, permutation test, receiver operating characteristics (ROC), area under the curve (AUC), and odds ratio (OR). A P value <0.05 was considered statistically significant. Results After adjusting, there was a significant difference in the odds of csPC (OR = 3.78, 95% confidence interval [CI]: 2.32–6.16). Further, PAG showed a significantly larger bootstrapped AUC to discriminate between csPC and non-csPC than that of adjusted PI-RADS ≥ 3 (AUC = 0.981, 95% CI: 0.975–0.987).publishedVersio

Supportive care needs of men with prostate cancer after hospital discharge: Multi-stakeholder perspectives.

Purpose This study explored the supportive care needs of men with prostate cancer (PCa) after hospital discharge based on the perceptions of multiple stakeholders. Methods Eight semi-structured focus groups and three individual interviews were conducted between September 2019 and January 2020, with 34 participants representing men with PCa, primary and secondary healthcare professionals, and cancer organizations in western Norway. Data was analysed using systematic text condensation. Results Four categories emerged: 1) men with PCa have many information needs which should be optimally provided throughout the cancer care process; 2) various coordination efforts among stakeholders are needed to support men with PCa during follow-up; 3) supportive care resources supplement the healthcare services but knowledge about them is random; and 4) structured healthcare processes are needed to improve the services offered to men with PCa. Variations were described regarding priority, optimal mode and timeliness of supportive care needs, while alignment was concerned with establishing structures within and between stakeholders to improve patient care and coordination. Conclusions Despite alignment among stakeholders’ regarding the necessity for standardization of information and coordination practices, the mixed prioritization of supportive care needs of men with PCa indicate the need for additional individualized and adapted measures.publishedVersio

Men’s perception of information and descriptions of emotional strain in the diagnostic phase of prostate cancer—a qualitative individual interview study

Objective To explore men`s perception of information and their possible emotional strain in the diagnostic phase of prostate cancer. Design, setting, patients A qualitative explorative research design was employed. Data were collected from June to November 2017. The study was set at a urological outpatient clinic at a university hospital in Norway. Semi-structured interviews were conducted with ten men who had been examined for prostate cancer. Interviews were analyzed using Systematic Text Condensation (STC). Results The analysis revealed three themes. The theme ‘Different needs and perceptions of information’ illustrated that information should be personalized. Despite different information needs, insufficient information about prostate cancer may prevent some men from being involved in decisions. The theme, ‘A discovery of not being alone’, indicated that a sense of affinity occurs when men realize the commonality of prostate cancer. Some men benefited from other men’s experiences and knowledge about prostate cancer. The last theme ‘Worries about cancer and mortality’ showed that the emotional strain was affected by men’s knowledge of cancer and the received information. Men expressed conflicting feelings toward prostate cancer that could be difficult to express. Conclusions The findings indicate that men in the diagnostic phase of prostate cancer are not a homogeneous group, but need personalized information. Some men may benefit from other men’s experiences and support. Men’s emotional strain can affect their communication about prostate cancer, which should be acknowledged. Procedures that identify patients’ information needs early on should be an integrated part of the diagnostic phase of prostate cancer.publishedVersio

3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization.publishedVersio

Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection

Traditional deep learning (DL) approaches based on supervised learning paradigms require large amounts of annotated data that are rarely available in the medical domain. Unsupervised Out-of-distribution (OOD) detection is an alternative that requires less annotated data. Further, OOD applications exploit the class skewness commonly present in medical data. Magnetic resonance imaging (MRI) has proven to be useful for prostate cancer (PCa) diagnosis and management, but current DL approaches rely on T2w axial MRI, which suffers from low out-of-plane resolution. We propose a multi-stream approach to accommodate different T2w directions to improve the performance of PCa lesion detection in an OOD approach. We evaluate our approach on a publicly available data-set, obtaining better detection results in terms of AUC when compared to a single direction approach (73.1 vs 82.3). Our results show the potential of OOD approaches for PCa lesion detection based on MRI.Comment: Accepted and presented in ISBI 2023. To be published in Proceeding