Prevalence of hearing loss and hearing impairment among small and medium enterprise workers in Selangor, Malaysia

Factories and Machinery (Noise Exposure) Regulation 1989 has always demanded employer to protect workers from noise exposure ≥85 dB. However, noise induced hearing loss (NIHL) has been the highest notifiable occupational health issue among Malaysian workers in 2014. A cross-sectional study aimed to investigate the prevalence of hearing loss (HL) among 146 adult manufacturing workers with noise exposure ≥85 dB was carried out in Selangor. Pure tone audiometry and a validated questionnaire were used to determine the hearing status and information of the respondents. The results showed that the prevalence of HL was 73.3% and the prevalence of hearing impairment was 23.3%. Male workers (63.0%) had higher prevalence of HL than female workers (36.4%). Mean hearing threshold levels of HL respondents were significantly higher than respondents with normal hearing. It was discovered that among workers with employment of more than 10 years, > 80% of them suffered from HL. Association between employment years and hearing condition (normal, non-bilateral and bilateral HL) was statistically significant, χ(4)= 10.51, p=0.033, with Kendall tau-c correlation showing positive and weak (p=0.18) association. Highest HL cases were found at 4 and 6 kHz. World Health Organisation and Factories and Machinery (Noise Exposure) Regulation 1989 classification on degree of hearing impairment showed slight to moderate hearing impairment among the respondents and both standards did not differ much (8.9% vs 6.9%, respectively). As a conclusion, prevalence of hearing loss and hearing impairment among manufacturing workers were high. Age, male sex, monthly salary and employment years were possible influencing factors

Age- and sex-specific physical fitness reference and association with body mass index in Hong Kong Chinese schoolchildren

There is lacking a population-based study on the fitness level of Hong Kong schoolchildren, and it seems that increasing childhood obesity prevalence has shifted the classification of healthy fitness, with ‘underfit’ as normal. This cross-sectional territory study aimed to develop an age- and sex-specific physical fitness reference using a representative sample of children aged 6–17 and to determine the associations with body mass index in schoolchildren. The study analyzed Hong Kong School Physical Fitness Award Scheme data covering grade 1 to grade 12 students’ physical fitness and anthropometric measurements from 2017 to 2018. This reference was established without the impact due to COVID-19. Four aspects of physical fitness tests were measured using a standardized protocol, including (i) upper limb muscle strength, (ii) one-minute sit-up, (iii) sit-and-reach, and (iv) endurance run tests. The generalized additive model for location, scale, and shape was used to construct the reference charts. A Mann–Whitney U test was used to compare the mean differences in age, weight, and height, and a Pearson’s chi-square test was used to examine the distributions of sex groups. A Kruskal–Wallis test was used to compare the group differences in BMI status, followed by the Dunn test for pairwise comparisons. A 5% level of significance was regarded as statistically significant. Data of 119,693 students before the COVID-19 pandemic were included in the analysis. The association between physical fitness level and BMI status varied depending on the test used, and there were significant differences in fitness test scores among BMI groups. The mean test scores of the obese group were lower in most of the tests for both boys and girls, except for handgrip strength. The underweight group outperformed the obese group in push-ups, one-minute sit-ups, and endurance run tests, but not in handgrip strength. In conclusion, a sex- and age-specific physical fitness reference value for Hong Kong Chinese children aged 6 to 17 years old is established, and this study demonstrated a nonlinear relationship between BMI status and physical fitness. The reference will help to identify children with poor physical fitness to offer support and guidance on exercise training. It also serves as a baseline for assessing the impact of the COVID-19 pandemic on Hong Kong students’ physical fitness

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

A Population-Level Comparison of Cancer-Related and Non-Cancer-Related Health Care Costs Using Publicly Available Provincial Administrative Data

Introduction: Costs associated with cancer care are increasing. Cancer costs in the context of other common non-cancer diagnoses have not been extensively studied at the population level. Knowledge from such analyses can inform health care resource allocation and highlight strategies to reduce overall costs. Methods: Using cross-sectional data from publicly available population-level administrative data sources (health insurance claims, physician billing, and hospital discharge abstracts), we calculated incidence-adjusted health care costs (in 2014 Canadian dollars) for cancers and common non-cancer diagnoses in the adult population in a large Canadian province. Subgroup analyses were also performed for various provincial health administrative regions. Results: Total costs related to cancer care amounted to $495 million for the province, of which at least$67 million (14%) was attributable to radiation and chemotherapy. Of the various cancer subtypes, hematologic malignancies were most costly at $70 million, accounting for 14$51 million (10%), and lung cancer, at $44 million (9%). Cancer costs (with and without costs for radiation and chemotherapy) exceeded those for cardiovascular disease, diabetes mellitus, mental health, and trauma (p < 0.001). In addition, the costs of specific cancer subtypes varied by region, but hematologic and lung cancers were typically the most costly no matter the health region. Conclusions: Using provincial administrative data to establish cost trends can help to inform health care allocation and budget decisions, and can facilitate comparisons between provinces

Hashing-based undersampling for large scale histopathology image classification

The early diagnosis of cancer based on histopathology images plays an important role in medical science. Existing techniques generally partition the original histopathology image into small pieces for further classification. However, due to the fact that the number of benign (majority) samples is much larger than that of malignant (minority) samples, the classification is significantly imbalanced which adversely affects classification performance. Undersampling is commonly used to address the class-imbalance problem. However, existing methods are typically time consuming so they are not suitable to handle large-scale and high-dimensional data. In this paper we propose a fast and scalable undersampling method, hashing-based undersampling (HBU), to address class imbalance in large-scale medical image classification. Benign images are hashed and then placed into different buckets according to their locations in the input space. Undersampling is achieved by proportionally selecting benign images from the hash buckets. The HBU method is experimentally evaluated on two real histopathology image datasets, CAMELYON16 and ACDC@LUNGHP, by comparison with existing methods. Experimental results show that the HBU method outperforms six state-of-The-Art methods and is scalable and fast.</p