Displaying all 3 publications

Abstract:
Sort:
  1. Thapa B, Pandey A, Gautum S, Kc S, Chhetri PD, Pokhrel E, et al.
    J Nepal Health Res Counc, 2023 Jul 20;20(4):859-867.
    PMID: 37489668 DOI: 10.33314/jnhrc.v20i4.4172
    BACKGROUND: Dengue is a mosquito-borne viral disease with a wide spectrum of presentations ranging from subclinical disease to severe dengue. Dengue is endemic to the Terai of Nepal. Interestingly, an increasing incidence has been reported from hilly areas like Kathmandu valley. This study explored the clinicopathological profile of dengue infection.

    METHODS: A total of 84 serologically confirmed dengue cases from September to November 2019 at KIST Medical College were recruited in a cross-sectional study after obtaining ethical approval. Dengue was categorized as dengue without warning signs, dengue with warning signs, and severe dengue. Clinicopathological information was recorded in the proforma by reviewing patients' records. A descriptive statistical tool and chi-square test were carried out.

    RESULTS: Out of 84 patients, 76% (64) were dengue without warning signs, 21.4% (18) were dengue with warning signs and 2.4% (2) were severe dengue. About 97.6% (82) presented with fever. During the course of illness, anemia was identified in 38.1% (32), thrombocytopenia in 65.5% (55), hemoconcentration in 6% (5), and leucopenia in 82.1% (69). Similarly, elevated aspartate transaminase and alanine transaminase (ALT) was observed in 67.7% (42) and 53.2% (33) respectively. The severity of dengue on presentation to hospital was significantly associated with thrombocytopenia, leucopenia, and elevated ALT. Similarly, the severity during course of illness in hospital was significantly associated with hemoconcentration, thrombocytopenia, leucopenia, and elevated ALT.

    CONCLUSIONS: Most common presentation of dengue infection was fever. The most common laboratory abnormalities were leucopenia, thrombocytopenia, hemoconcentration, anemia, and elevated liver enzymes. Awareness of these clinical and laboratory parameters is important for the prompt diagnosis, severity estimation, and overall management of dengue infection.

  2. Paneru DP, Adhikari C, Poudel S, Adhikari LM, Neupane D, Bajracharya J, et al.
    Front Public Health, 2022;10:978732.
    PMID: 36589957 DOI: 10.3389/fpubh.2022.978732
    OBJECTIVE: The Social Health Insurance Program (SHIP) shares a major portion of social security, and is also key to Universal Health Coverage (UHC) and health equity. The Government of Nepal launched SHIP in the Fiscal Year 2015/16 for the first phase in three districts, on the principle of financial risk protection through prepayment and risk pooling in health care. Furthermore, the adoption of the program depends on the stakeholders' behaviors, mainly, the beneficiaries and the providers. Therefore, we aimed to explore and assess their perception and experiences regarding various factors acting on SHIP enrollment and adherence.

    METHODS: A cross-sectional, facility-based, concurrent mixed-methods study was carried out in seven health facilities in the Kailali, Baglung, and Ilam districts of Nepal. A total of 822 beneficiaries, sampled using probability proportional to size (PPS), attending health care institutions, were interviewed using a structured questionnaire for quantitative data. A total of seven focus group discussions (FGDs) and 12 in-depth interviews (IDIs), taken purposefully, were conducted with beneficiaries and service providers, using guidelines, respectively. Quantitative data were entered into Epi-data and analyzed with SPSS, MS-Excel, and Epitools, an online statistical calculator. Manual thematic analysis with predefined themes was carried out for qualitative data. Percentage, frequency, mean, and median were used to describe the variables, and the Chi-square test and binary logistic regression were used to infer the findings. We then combined the qualitative data from beneficiaries' and providers' perceptions, and experiences to explore different aspects of health insurance programs as well as to justify the quantitative findings.

    RESULTS AND PROSPECTS: Of a total of 822 respondents (insured-404, uninsured-418), 370 (45%) were men. Families' median income was USD $65.96 (8.30-290.43). The perception of insurance premiums did not differ between the insured and uninsured groups (p = 0.53). Similarly, service utilization (OR = 220.4; 95% CI, 123.3-393.9) and accessibility (OR = 74.4; 95% CI, 42.5-130.6) were found to have high odds among the insured as compared to the uninsured respondents. Qualitative findings showed that the coverage and service quality were poor. Enrollment was gaining momentum despite nearly a one-tenth (9.1%) dropout rate. Moreover, different aspects, including provider-beneficiary communication, benefit packages, barriers, and ways to go, are discussed. Additionally, we also argue for some alternative health insurance schemes and strategies that may have possible implications in our contexts.

    CONCLUSION: Although enrollment is encouraging, adherence is weak, with a considerable dropout rate and poor renewal. Patient management strategies and insurance education are recommended urgently. Furthermore, some alternate schemes and strategies may be considered.

  3. Ali S, Ghatwary N, Jha D, Isik-Polat E, Polat G, Yang C, et al.
    Sci Rep, 2024 Jan 23;14(1):2032.
    PMID: 38263232 DOI: 10.1038/s41598-024-52063-x
    Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links