scholarly journals Lethal Over the Counter Cardioselective Drugs: Urgent Call For Policy Makers

2021 ◽  
Vol 1 (3) ◽  
pp. 47-49
Author(s):  
Joud M. Kossai Enabi ◽  
Abdulmohsen A. Alhumayn ◽  
Hisham Alomari ◽  
Yousef Ibrahim S. Alawad ◽  
Sharafaldeen Bin Nafisah
Keyword(s):  
Public Health ◽  
Saudi Arabia ◽  
Public Health Intervention ◽  
Over The Counter ◽  
Policy Makers ◽  
Suicidal Intent ◽  
Mortality And Morbidity ◽  
Common Presentation ◽  
Life Threatening ◽  
Shed Light

Drug overdose is a common presentation in emergency departments, and overdose of cardioselective agents warrants special attention, given its association with high mortality and morbidity. This study reports a case of cardioselective overdose with suicidal intent. We shed light on the accessibility in Saudi Arabia of these life-threatening drugs, and explore the nature of public health intervention to reduce to reduce the risk of misuse.    

Rethinking Innovation Accounting in Pharmaceutical Regulation

2010 ◽  
Vol 36 (6) ◽  
pp. 791-815 ◽  
Author(s):  
Courtney Davis ◽  
John Abraham
Keyword(s):  
Public Health ◽  
Scientific Evidence ◽  
New Drugs ◽  
Accounting Systems ◽  
Policy Makers ◽  
Pharmaceutical Manufacturers ◽  
Life Threatening ◽  
Patient Groups ◽  
Scientific Expertise ◽  
Regulatory Decisions

The controversy over the prescription drug, alosetron, is examined in order to investigate what is permitted to count as ‘therapeutic advance’ and ‘therapeutic breakthrough’ within pharmaceutical innovation and regulation. It is argued that those official accounting categories can mask very modest efficacy of some drugs by reference to the official techno-scientific evidence, thus leading to questionable acceptance of risks to public health. This is explained by: the drug availability options set by the commercial interests of manufacturers; the FDA management's need to demonstrate rapid approval of therapeutic advances to their budgetary masters, especially in the context of patient demands for access to new drugs; and the increasing capacity of patient groups, sometimes in collaboration with pharmaceutical manufacturers, to challenge techno-scientific expertise and evidence with experiential testimony. It is concluded that regulatory policy-makers need much more sophisticated accounting systems for differentiating between drugs defined as significant therapeutic advances, and between drugs (‘therapeutic breakthroughs’ fast-tracked to treat serious or life-threatening conditions. Contrary to some STS analyses, the desirability of an ascendancy of patients’ anecdotal evidence in regulatory decisions for public health is questioned.

scholarly journals Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Philip J Turk ◽  
Shih-Hsiung Chou ◽  
Marc A Kowalkowski ◽  
Pooja P Palmer ◽  
Jennifer S Priem ◽  
...  
Keyword(s):  
Public Health ◽  
North Carolina ◽  
Public Health Intervention ◽  
Sir Model ◽  
Health Intervention ◽  
Disease Spread ◽  
Local Context ◽  
Imperfect Detection ◽  
Policy Makers ◽  
Spatial Differences

BACKGROUND Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. RESULTS Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

scholarly journals Management of Maxillary Mucormycosis - A Case Report in Bangladesh

2015 ◽  
Vol 27 (1) ◽  
pp. 38-40
Author(s):  
Quazi Billur Rahman ◽  
Muhammad Mizanur Rahaman ◽  
Ashik Abdullah Imon ◽  
Md Atiqul Islam Rabby ◽  
MA Jalil Chowdhury
Keyword(s):  
Risk Factor ◽  
Case Report ◽  
Early Diagnosis ◽  
Severe Infection ◽  
Causative Organism ◽  
Mortality And Morbidity ◽  
Prompt Treatment ◽  
Common Presentation ◽  
Life Threatening ◽  
The Brain

Mucormycosis refers to several different disease caused by infection in the order of mucorales. Rhizopus species are the most common causative organism. Most mucormycosis infections are life-threatening and risk factor such as diabetic ketoacidosis and neutropenia, are present in most cases. Severe infection of the facial sinuses, which may extend into the brain, is the most common presentation. We describe our clinical experience with a case of mucormycosis of the maxilla. Early diagnosis and prompt treatment can significantly reduce the mortality and morbidity of this lethal fungal infection. MedicineToday 2015 Vol.27(1): 38-40

scholarly journals Application of National and Sub-National Indicators to Rank Needs of People with Life-threatening Conditions and Chronic Diseases Before, During, and After a Disaster

2019 ◽  
Vol 34 (s1) ◽  
pp. s47-s47
Author(s):  
Benjamin Ryan ◽  
Joseph Green ◽  
Richard Franklin ◽  
Frederick Burkle
Keyword(s):  
Public Health ◽  
Chronic Diseases ◽  
Risk Evaluation ◽  
Social Protection ◽  
Humanitarian Assistance ◽  
Renal Diseases ◽  
Mortality And Morbidity ◽  
Hazard Warnings ◽  
Life Threatening ◽  
Social Protection Systems

Introduction:Disasters can damage the essential public health infrastructure and social protection systems required for vulnerable populations. This contributes to indirect mortality and morbidity as high as 70–90%, primarily due to an exacerbation of life-threatening conditions and chronic diseases. Despite this, the traditional focus of public health systems has been on communicable diseases. To address this challenge, disaster and health planners require access to repeatable and measurable methods to rank and prioritize the needs of people with life-threatening and chronic diseases before, during, and after a disaster.Aim:Propose a repeatable and measurable method for ranking and prioritizing the needs of people with life-threatening and chronic diseases before, during, and after a disaster.Methods:The research began with identifying the risk disasters pose to people with life-threatening and chronic diseases. The data gathered was then used to develop indicators and explore the use of DisasterAWARE™ (All-hazard Warnings, Analysis, and Risk Evaluation) to rank and prioritize the needs before, during, and after a disaster.Results:This research found people at greatest risk are those with underlying cardiovascular and respiratory diseases, unstable diabetes, renal diseases, and those undergoing cancer treatment. A sustainable method to help address this problem is to expand the use of DisasterAWARE™ (All-hazard Warnings, Analysis, and Risk Evaluation) to rank and prioritize needs at national and sub-national levels.Discussion:DisasterAWARE™ has been successfully applied to the assessment and prioritization of disaster risk and humanitarian assistance needs in Southeast Asia (ASEAN, Viet Nam), Central America (Guatemala, El Salvador, Honduras, Nicaragua), South America (Peru), and the Caribbean (Jamaica, Dominican Republic). Using the indicators developed through this research, this proven methodology can be seamlessly and easily translated to rank and prioritize the needs of people with life-threatening and chronic diseases before, during, and after a disaster.

scholarly journals Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study

10.2196/19353 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e19353
Author(s):  
Philip J Turk ◽  
Shih-Hsiung Chou ◽  
Marc A Kowalkowski ◽  
Pooja P Palmer ◽  
Jennifer S Priem ◽  
...  
Keyword(s):  
Public Health ◽  
North Carolina ◽  
Public Health Intervention ◽  
Sir Model ◽  
Health Intervention ◽  
Disease Spread ◽  
Local Context ◽  
Imperfect Detection ◽  
Policy Makers ◽  
Spatial Differences

Background Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. Objective The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. Methods Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. Results Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. Conclusions Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

scholarly journals Smarter, faster, better vaccines

2015 ◽  
pp. 7-12
Author(s):  
Evin Allen
Keyword(s):  
Public Health ◽  
Immune Response ◽  
18Th Century ◽  
Public Health Intervention ◽  
The Body ◽  
Influenza Vaccines ◽  
Subsequent Infection ◽  
Mortality And Morbidity ◽  
Late 18Th Century ◽  
Live Bacteria

Since Edward Jenner pioneered modern vaccination in the late 18th century, no other public health intervention has saved as many lives. Vaccines keep us safe and dramatically reduce the mortality and morbidity from many infectious diseases such as measles, diphtheria and influenza. Vaccines are safe, non-viable versions of pathogens (viruses, bacteria) that when administered directs the body into producing an immune response against the pathogen, and it is this that prevents any subsequent infection by the live bacteria or virus. Vaccines are composed of proteins, unlike most medicines that we are prescribed which are chemical molecules produced synthetically by a series of chemical reactions. Chemical molecules are quite small in size and are very stable to the environment; in contrast proteins are large and complex and very unstable. This means that generally we can’t give vaccines as tablets because they would be degraded in the stomach by the same enzymes ...

scholarly journals Over-the-counter antibiotics in Saudi Arabia, an urgent call for policy makers

2017 ◽  
Vol 10 (5) ◽  
pp. 522-526 ◽  
Author(s):  
Sharafaldeen Bin Nafisah ◽  
Salahaldin Bin Nafesa ◽  
Aliyah H. Alamery ◽  
Mazen A. Alhumaid ◽  
Haitham M. AlMuhaidib ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Over The Counter ◽  
Policy Makers

The Affective Politics of Karma among Buddhist Cancer Charities in Vietnam

2020 ◽  
Vol 15 (4) ◽  
pp. 33-62
Author(s):  
Sara Swenson
Keyword(s):  
Public Health ◽  
Global Existence ◽  
Food Production ◽  
Public Health Intervention ◽  
Health Intervention ◽  
Moral Failure ◽  
Cancer Rates ◽  
Charity Work

In this article, I explore how Buddhist charity workers in Vietnam interpret rising cancer rates through understandings of karma. Rather than framing cancer as a primarily physical or medical phenomenon, volunteers state that cancer is a product of collective moral failure. Corruption in public food production is both caused by and perpetuates bad karma, which negatively impacts global existence. Conversely, charity work creates merit, which can improve collective karma and benefit all living beings. I argue that through such interpretations of karma, Buddhist volunteers understand their charity at cancer hospitals as an affective and ethical form of public health intervention.

Critical Review of Data Analytics techniques used in the Expanded Program on Immunization (EPI)

2020 ◽  
Vol 16 ◽  
Author(s):  
Sadaf Qazi ◽  
Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Analytics ◽  
Public Health Intervention ◽  
Cold Chain ◽  
Data Repository ◽  
Quality Of Data ◽  
Mortality And Morbidity ◽  
Healthcare Facilities ◽  
Expanded Program On Immunization

Background: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities. Purpose: In this paper, the existing machine learning based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner. Results: It has been revealed from our review, that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities. Conclusion: We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage at different geographical locations.

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