Implementation of a Nationwide Online Exercise Program in Hemodialysis Patients

Background: Physical inactivity of HD patients was aggravated during the COVID-19 pandemic due to the imposed lockdown and suspension of ongoing intradialytic exercise program (IDE). To address this, we have developed an online exercise program (OLEP). The aim of this study was to analyze its implementation over a 12-weeks period. Method(s): Implementation study based on retrospective analysis using the REAIM framework (reach, effectiveness, adoption, implementation, maintenance). OLEP was proposed to 24 HD units previously offering IDE and included live online exercise sessions (3 times/week) led by 2 exercise physiologists via Zoom. For each RE-AIM dimension specific implementation outcomes were adapted to OLEP. Effectiveness measures included safety (adverse events during exercise sessions) and in-clinic physical function tests (sit-to-stand 5 and 30, 8-foot up and go (8UG), handgrip strength and single leg stance) performed at baseline and 12 weeks in a group of OLEP participants and a group of patients who refused to participate. Result(s): OLEP was adopted by 16 units (66.7%). Among 2063 patients of these units, 313 (15.2%) were eligible. Of those, 84 accepted to participate in OLEP (4.1% reach of all patients). Compared to refusals, OLEP participants had higher female proportion (p=0.009), higher education level (p<0.001), lower lean tissue index and handgrip strength (both p<0.001), and completed less steps/day (p=0.008). Maintenance in OLEP over the 12 weeks was 59.5%, i.e., 40.5% drop-out - of which 65% were voluntary. Implementation fidelity (patient's adherence to exercise sessions) was 73.1+/-18.8%, and implementation dose was 2.2+/-0.6 exercise sessions/week. Effectiveness: OLEP participants improved performance in all physical function measures (p<0.05), except in 8UG (p=0.677), whilst refusals did not (p>0.05);no severe adverse events were reported. Conclusion(s): Our data suggests that an OLEP is realistic, safe and may improve physical function. Therefore, its applicability may subsist beyond the pandemic and be used to complement IDE. However, strategies to increase proficiency to use mobile health technology may be needed to reach more patients.

BAKUNANAYS guidelines for communicating COVID-19 vaccination information to pregnant women in the Philippines and other developing countries

COVID-19 remains a pressing global health disaster, and pregnant women and their unborn child/ren continue to be extremely at risk. In the Philippines, a developing country in Southeast Asia, pregnant women were generally excluded from initial vaccination drives to avoid adverse effects in their offspring, amidst findings from animal studies and post-trial monitoring on the vaccines' safety. In August 2021, the Philippine Obstetrical and Gynecological Society (POGS) and the Department of Health (DOH) released guidelines for the vaccination of pregnant women due to the eventual increase in their mortality during outbreaks of the Delta variant. This perspective presents various forms of scientific communication on COVID-19 vaccination to Filipino pregnant women and forwards recommendations to improve communication in various settings. First, we present three modalities on how information on COVID-19 vaccination is disseminated to pregnant women in the Philippines and discuss their potential impacts on knowledge promotion and actual vaccination uptake, taking into account the Filipino cultural value of "pakikipagkapwa". These include government and doctor-led initiatives, social media posts and comments, and experiences of one of the authors in vaccination drives in rural and remote communities. Findings are used to develop the BAKUNANAYS guidelines, comprised of 10 recommendations for healthcare workers, health agencies, and doctors vaccinating pregnant women in the Philippines and other developing countries, especially those with a similar socio-economic profile and cultural values.

Supply chain risk management strategies in normal and abnormal times: policymakers' role in reducing generic medicine shortages

Purpose: This paper links supply chain risk management to medicine supply chains to explore the role of policymakers in employing supply chain risk management strategies (SCRMS) to reduce generic medicine shortages. Design/methodology/approach: Using secondary data supplemented with primary data, the authors map and compare seven countries' SCRMS for handling shortage risks in their paracetamol supply chains before and during the first two waves of the COVID-19 pandemic. Findings: Consistent with recent research, the study finds that policymakers had implemented few SCRMS specifically for responding to disruptions caused by COVID-19. However, shortages were largely avoided since multiple strategies for coping with business-as-usual disruptions had been implemented prior to the pandemic. The authors did find that SCRMS implemented during COVID-19 were not always aligned with those implemented pre-pandemic. The authors also found that policymakers played both direct and indirect roles. Research limitations/implications: Combining longitudinal secondary data with interviews sheds light on how, regardless of the level of preparedness during normal times, SCRMS can be leveraged to avert shortages in abnormal times. However, the problem is highly complex, which warrants further research. Practical implications: Supply chain professionals and policymakers in the healthcare sector can use the findings when developing preparedness and response plans. Social implications: The insights developed can help policymakers improve the availability of high-volume generic medicines in (ab)normal times. Originality/value: The authors contribute to prior SCRM research in two ways. First, the authors operationalize SCRMS in the medicine supply chain context in (ab)normal times, thereby opening avenues for future research on SCRM in this context. Second, the authors develop insights on the role policymakers play and how they directly implement and indirectly influence the adoption of SCRMS. Based on the study findings, the authors develop a framework that captures the diverse roles of policymakers in SCRM. © 2022, Emerald Publishing Limited.

Understanding the Effects of Human Factors on the Spread of COVID-19 Using a Neural Network

During the spread of an infectious disease such as COVID-19, the identification of human factors that affect the spread is a really important area of research. These factors directly impact the spread of such a disease and are important in identifying the various regions that are at a higher risk than others. This allows for an optimal distribution of resources according to predicted demand. Traditional infectious modeling techniques are good at representing the spread and can incorporate multiple factors that resemble real-life scenarios. The primary issue here is the identification of relevant variables. In this study, a residual analysis is presented to downsize the dataset available and shortlist the variables classified as absolutely necessary for disease modeling. The performance of different datasets is evaluated using an Artificial Neural Network and regression analysis. The results show that the drop in performance using the reduced dataset is reasonable as it is very difficult to obtain a perfect dataset covering only necessary variables. This approach can be automated in the future as it offers a small dataset containing a few variables against a large dataset with possibly hundreds of variables. © 2020 IEEE.

Identifying Factors in COVID-19 AI Case Predictions

Many machine learning methods are being developed to predict the spread of COVID-19. This paper focuses on the expansion of inputs that may be considered in these models. A correlation matrix is used to identify those variables with the highest correlation to COVID-19 cases. These variables are then used and compared in three methods that predict future cases: a Support Vector Machine Regression (SVR), Multidimensional Regression with Interactions, and the Stepwise Regression method. All three methods predict a rise in cases similar to the actual rise in cases, and importantly, are all able to predict to a certain degree the unexpected dip in cases on the 10th and 11th day of prediction. © 2020 IEEE.

Fasciola hepatica infection in cattle and the use of simulation models for endemic areas.

Fasciolosis is a food-borne disease that causes great distress to a range of hosts, including humans. The objectives of this study were to (1) evaluate the liver damage and carcass weight of cattle naturally infected with Fasciola hepatica from the state of Rio Grande do Sul (RS), Brazil, and to (2) determine the distribution of adult flukes in 12,236 cattle liver from RS. The data from these experiments were used to calculate the overall economic loss due to F. hepatica infection. Eighteen adult Polled Hereford cows were divided into a triclabendazole (TbG) and a F. hepatica-positive group (FhG). For Experiment 1, a generalized linear mixed model revealed a statistical difference in carcass weight (49.8 kg) between TbG and FhG. The Monte Carlo analysis also revealed that the animals' weight differences were due to the disease. For Experiment 2, the prevalence of infected livers was above 16% (1904/12,236), mostly (20.1%) from the south-west region of RS. The Susceptible-infected-recovered (SIR) epidemic model revealed the evolution of the infection using a high infectivity and low recovery rate. Other distinctive scenarios that occur in RS were also established with different rates of infectivity. The economic assessment showed a potential loss of US$45 million to the beef cattle industry of RS, with an overall State cost of US$90.3 million. These novel findings reveal the importance of fasciolosis infection, which can cause a significant health condition and poor animal welfare.