Nepal Journal of Epidemiology最新文献

Background: The Hematological neoplasms (HN) are a heterogeneous group of cancers that originated in the hematopoietic or lymphoid tissues. There is reduced information published regarding HN mortality in Ecuador. This study aims to present the crude and age-specific mortality rates for HN in the Ecuadorian population.

Methods: We performed a cross-sectional study through the national database of defunctions published by the Ecuadorian National Institute of Statistics and Census, 2019. We used the ICD-10 codes to classify the HN.

Results: During 2019, 1462 deaths were reported, 53.83% were males, 87.96% of mestizo ethnicity, and 78.32% residents in urban areas. The median age was 62 years, with an interquartile range of 34.The crude mortality rate obtained was 8.49 per 100000 inhabitants, and the higher age-specific mortality rates was 43.29 per 100000 inhabitants aged ≥ 60 years, contrasting with the 2.63 per 100000 inhabitants in people aged < 20 years. Considering each ICD-10 group, we found the following rates by 100000 inhabitants; C85 2.04, C91 1.92, C92 1.46, C90 1.11, C83 0.70, C95 0.48, C81 0.38, C84 0.16, C82 0.10, C96 0.05, C93 0.04, C86 and C94 0.02, and C88 0.01.

Conclusion: In Ecuador, during 2019, approximately eight people died due to HN by 100000 inhabitants, affecting mainly people aged ≥ 60 years. The most frequent neoplasms were Non-Hodgkin lymphomas, similar to other reports globally. These results should be analyzed considering some deficiencies in the Ecuadorian health system and the national registry. Therefore, we suggest conducting more studies regarding HN.

Background: The objectives of the study were to identify the psychological impacts of lockdown on medical students due to COVID-19 and to discover the educational perplexities being faced by these students during the lockdown.

Methods: A cross-sectional study was conducted at Sir Seewoosagur Ramgoolam Medical College (SSRMC), Mauritius. Questions were designed after an extensive review of the literature, so as to ensure relevance to meet the objectives of the study.

Results: Out of 700 undergraduate medical students, 663 participated, which equates to a response rate of 95%. 348 (52.5 %) of the students were stationed in their hometown and the remaining 315 (47.5 %) were stationed in Mauritius. 464(70%) of the students suffered from the psychological impacts of lockdown whereas 634(95.6%) of students suffered from the educational impact thereof. Mauritian students suffered a greater educational impact aOR4.236[1.606-11.173]. Psychological impacts aOR 1.280 [0.917-1.789] and educational impacts aOR 2.464 [1.076-5.647] were more prevalent in hometown-based students. Students pursuing their clinical studies had aOR1.219 [ 0.531-2.798] a greater educational impact as compared to preclinical studies.

Conclusion: Lockdown triggered both educational and psychological impacts on medical students. On a psychological basis it was proven that the lockdown induced a feeling of guilt and had a greater psychological impact in pre-clinical students. The COVID-19 situation was simultaneously indicated to be a motivator in the majority of students; however, juxtaposed to this was the fact that various students felt as if they couldn't study at the same level that they were accustomed to due to the uncertainty of the situation.

A mutation is defined as an alteration in the DNA or RNA sequences of a genome which may consequently confer a new phenotypic and or genotypic advantage both increasing the virulence as well as the survival of a virus or pathogen. At this current point in time there are 4 known major variants of the original SARS-CoV-2 virus, namely the English variant (B.1.1.7), the South African variant (B.1.351), Brazilian variants (VOC202101/02 (P.1) and VUI202101/01) and a variant similar to that of the South African variant found in North America (B.1.526), all of which have varying levels of resistance and infectivity. It is evident that the SARS-CoV-2 variants pose an international health risk, the mutations of E484K and N501Y are the two most implicated mutations. E484K being the most concerning as it aids in immune evasion and drastically causes the efficacy of the current vaccines to be reduced by large margins. The most worrisome variant is the South African or B.1.351 which harbors the above mutations. It is of the upmost importance that targeted vaccines are synthesized to ensure that immunized individuals have effective protection against these variants. Until these specific targeted vaccines are synthesized the current vaccines offer little long-term protection, however do confer a level of immunity to stop severe infections. It is thus advised that current vaccination programs should continue in earnest as a degree of protection is conferred.

The Coronavirus disease 2019 (COVID-19) pandemic is caused by rapidly spreading pathogenic virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that affects vast majority of population worldwide. Although, around 80% of the cases had mild infection but still remaining 20% had developed respiratory failure and dysfunction of other organs that necessitate urgent oxygen therapy or specific interventions. Therefore, it is imperative to establish novel prognostic approaches to screen patients at high-risk of developing severe complications. The primary focus of current research for COVID-19 is to discover safe and efficacious vaccine for prevention and effective treatment for better management of the patients to overcome the pandemic. To achieve this goal, it is imperative to have better understanding of the molecular pathways involved in the pathophysiology and progression of severe COVID-19. The surge for reliable diagnostics and therapeutics targets for COVID-19 highlighted the great potential of high-throughput approach like metabolomics which may enable the development of personalized medicine.

Background: Tuberculosis (TB) is one of the major public health problems in Bhutan. Evaluation of treatment outcomes of TB and identification of the risk factors are important components for the success of National TB control program. Therefore, this study was undertaken to assess the TB treatment outcome and factors associated with it in Samtse General Hospital.

Methods: This was a retrospective, cross sectional study using the TB data from Samtse General Hospital from 2008-2019. A univariate and multiple logistic regression was used to check for associations between the outcome and other independent variables.

Results: The study included a total of 634 TB patients. Of this, 44.0% (279) were smear positive TB (PTB+), 36.1% (229) were extra pulmonary TB (EPTB) and 19.9% (126) were smear negative TB (PTB-). During the study period, 56.2% (356) of them completed treatment, 33.3% (211) were declared cured, 0.2% (1) had defaulted, 5.1% (32) died and 5.4% (34) had treatment failure. The mean treatment success rate (TSR) was 89.4% (567). The TSR was highest for EPTB with 96.9% (222/229), followed by PTB- at 88.1% (111/126) and lowest for PTB+ with 83.9% (234/279). Successful treatment outcome was observed in EPTB patients (AOR: 7.3; 95% CI: 2.46-21.36), patients in age 15-28 years (AOR: 3.4; 95% CI: 1.59-7.46) and 29-42 years (AOR: 9.1; 95% CI: 2.44-33.61).

Conclusion: The treatment outcome of TB in Samtse General Hospital is satisfactory and at par with the national level. Since, smear positive TB and elderly patients are prone to develop poor treatment outcome, they need to be monitored and followed up adequately.

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data. This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal's health and education systems to track such developments and apply them locally. Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions. This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI.