Screening tests are performed to identify diseases in asymptomatic individuals to improve outcomes. An ideal screening test is cheap, easy to use, reliable, and valid. Screening test accuracy is evaluated using a 2x2 table to calculate sensitivity, specificity, positive predictive value, and negative predictive value. An example evaluates a diabetic retinopathy screening test and finds it has high sensitivity (96%) and specificity (95%) but low positive predictive value (70%), indicating many false positives.
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Screening is an essential concept in the field of Medicine, specially in Preventive Medicine. This presentation covers the essentials to understand Screening of Diseases.
Evidence based medicine is now focusing on diagnostic tests: how accurate and useful could be ? sensitivity and specificity are no longer the important criteria for a test
5 essential steps for sample size determination in clinical trials slidesharenQuery
In this free webinar hosted by nQuery Researcher & Statistician Eimear Keyes, we map out the 5 essential steps for sample size determination in clinical trials. At each step, Eimear will highlight the important function it plays and how to avoid the errors that will negatively impact your sample size determination and therefore your study.
Watch the Video: https://www.statsols.com/webinar/the-5-essential-steps-for-sample-size-determination
Screening is an essential concept in the field of Medicine, specially in Preventive Medicine. This presentation covers the essentials to understand Screening of Diseases.
Evidence based medicine is now focusing on diagnostic tests: how accurate and useful could be ? sensitivity and specificity are no longer the important criteria for a test
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Sensitivity, specificity and likelihood ratiosChew Keng Sheng
A short tutorial on sensitivity, specificity and likelihood ratios. In this presentation, I demonstrate why likelihood ratios are better parameters compared to sensitivity and specificity in real world setting.
This PPT will enable you to get a comprehensive understanding related to the topic, with examples. Important topic through research point of view. Simple language used, with a slide on distinguish, for better recap of the content.
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Sensitivity, specificity and likelihood ratiosChew Keng Sheng
A short tutorial on sensitivity, specificity and likelihood ratios. In this presentation, I demonstrate why likelihood ratios are better parameters compared to sensitivity and specificity in real world setting.
This PPT will enable you to get a comprehensive understanding related to the topic, with examples. Important topic through research point of view. Simple language used, with a slide on distinguish, for better recap of the content.
Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy
The ppt is a short description about how to ascertain the validity, ie; sensitivity and specificity of a screening test as well as their predictive powers. you can also find the technique to ascertain the best possible screening test through the help of an ROC curve...
Screening for Disease (Epidemiology)
Define screening
Describe the aims and objectives of the screening
Describe the differences between Screening & Diagnostic tests
List the uses of screening
Explain the types of screening, criteria for screening
Discuss the Validity of the screening test
Calculate and interpret the evaluation of the screening test
Disease screening and screening test validityTampiwaChebani
Full lecture covering screening tests and validity testing. Covers topics such as calculation and interpretation of sensitivity, specificity, positive predictive value and negative predictive value of a screening test.
Epidemiological Approaches for Evaluation of diagnostic tests.pptxBhoj Raj Singh
Diagnosis of a disease or a problem is the first step towards solution/ treatment. Clinical Diagnosis or Provisional Diagnosis is the first step in diagnosis and is done after a physical examination of the patient by a clinician. Clinical diagnosis may or may not be true and to reach Final diagnosis Laboratory Investigations using gross and microscopic pathological observations and determining the disease indicators are required. The diagnostic tests may be Non-dichotomous Diagnostic Tests (when continuous values are given by the test in a range starting from sub-normal to above-normal range) and Dichotomous Diagnostic Tests (when results are given either plus or minus, disease or no-disease). To make non- Dichotomous diagnostic test a Dichotomous one you need to establish the cut-off values based on reference values or Gold Standard test readings or with the use of Receiver operator characteristic (ROC) curves, Precision-Recall Curves, Likelihood Ratios, etc., and finally establishing statistical agreement (using Kappa values, Level of Agreement, χ2 Statistics) between the true diagnosis and laboratory diagnosis. Thereafter, the Accuracy, Precision, Bias, Sensitivity, Specificity, Positive Predictive value, and Negative Predictive value, of a diagnostic test are established for use in clinical practice. Diagnostic tests are also used to determine Prevalence (True prevalence, apparent prevalence) and Incidence of the disease to estimate the disease burden so that control measures can be implemented. There are several Phases in the development and use of a diagnostic assay starting from conceptualization of the diagnostic test, development and evaluation to determine flaws in diagnostic test use and Interpretation influencers. This presentation mainly deals with the epidemiological evaluation procedures for diagnostic tests.
screening is defined as the presumptive identification of unrecognized disease or defect by the application of tests, examinations, or other procedures which can be applied rapidly to sort out those who probably have a disease from those who probably do not.
screening does not diagnose a disease but it is done to separate persons who has high probability of developing diseases during the study from apparently well person
Specificity is the ability of a test to give a negative finding when the tested person is truly free of the disease under study. i.e true negative
Sensitivity is the ability of a test to give a positive finding when the tested person truly has the disease under the study. i.e true positive
Diseased individuals with positive screening test are True positive TP
Healthy individual with positive screening test are False positive FP
Diseased individuals with negative findings are False negative FN
Healthy individual with negative screening test are True negative TN
An ideally screening test have few false positives and false negatives as possible
Mass screening: This involves screening of a whole population
Multiple or multiphasic screening: Involves the use of a variety of tests on the same occasion for the same condition
Targeted screening: Involves screening of groups with specific exposures
Case-finding or opportunistic screening: Screening of patients visiting a health care delivery point for some other purpose
Ideally we need this test to identify correctly those with the disease under investigation and to exclude this from all non diseased
The test should give
The purpose of this MPH course unit is to build the capacity (knowledge, skills, and attitudes) of the MPH trainees as future, policy makers, Health Services researchers , planners and health managers at
International, National, regional, district and sub-district levels) who can implement and strengthen health systems in their respective countries
Factors Associated with patients adherence to Tb treatment following COVI-19 ...MtMt37
studies show that, Poor adherence to treatment is one of the major challenges affecting tuberculosis control and account for the major obstacles to treatment management . Uganda had a TB default rate of 11% with a treatment success rate of only 70% among smear positive patients (WHO, 2010), compared with national accepted adherence level of 95% of as per the WHO guidelines. It is on record that, Masaka District has high prevalence of TB known to be associated with HIV/AIDs (NTRL 2016). an institutional based survey established among other factors that, inadequate and irregular supplies of TB drugs, long travel distance by patients, stigma, discrimination and suspension of transport as COVID 19 prevention guideline have contributed to poor adherence of TB patients in Masaka.
Scholarly notes for Environmental and Public Heath Learners in tertiary institutions.As recommended by Dr Tumwebaze Mathias PhD, Bishop Stuart University
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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1. Screening tests
Dr Mathias Tumwebaze PhD
Senior Lecturer. And Consultant
MPH Programs- 2020
DR MT/LECTURE NOTES/MPH
2. Screening
• Screening is performed in order to identify whether people have
a disease for which they currently have no symptoms
• Screening is not performed to diagnose illness. Instead, it aims to
improve the outcomes of those who are affected, by detecting a
disease before its symptoms have developed.
• screening test should be able to detect disease in the period
between the time when it can be detected using a screening test
and the time when symptoms develop.
DR MT/LECTURE NOTES/MPH
3. Con’t screening
• In practice, screening tests are never completely accurate
• There will always be a number of false-positive results (in which the
test indicates that a subject has the infection when in reality they
do not).
• False-negative results can also occur (in which the test indicates that
there is no infection present, when in reality the subject does have
the disease)
• good screening test should keep false-positive and false-negative
results to an absolute minimum.
DR MT/LECTURE NOTES/MPH
4. Screening test
• The screening test itself must be cheap, easy to apply,
acceptable to the public, reliable and valid.
• A test is reliable if it provides consistent results, and
valid if it correctly categorizes people into groups
with and without disease, as measured by its
sensitivity and specificity.
DR MT/LECTURE NOTES/MPH
5. Evaluating the accuracy of
screening tests
• A screening test can be evaluated using a 2x2 table,
Present in table
• how many subjects with a positive result actually have the
disease (true positive) (cell a)
• how many subjects with a positive result do not have the
disease (false positive) (b)
• how many subjects have a positive result (a + b)
• how many subjects have a negative result (c + d)
DR MT/LECTURE NOTES/MPH
6. Con’t evaluation
• how many subjects with a negative result actually have the
disease (false negative) (c)
• how many subjects with a negative result do not have the
disease (true negative) (d)
• how many subjects actually have the disease (a + c)
• how many subjects do not have the disease (b + d)
• the total number of subjects (a + b + c + d).
DR MT/LECTURE NOTES/MPH
7. A 2 x 2 table for evaluating a screening test
Disease
Present
Disease
absent
Total
Positive a
True Positive
b
False Positive
A+b
Negative c
False Negative
d
True Negative
C+d
Total a+c b+d A+b+c+d
DR MT/LECTURE NOTES/MPH
8. Ways to measure the accuracy of a
screening test.
Sensitivity
• This is the proportion of subjects who really have the
disease, and who have been identified as diseased by the test.
• The formula for calculating sensitivity is a/ (a + c)
DR MT/LECTURE NOTES/MPH
9. Specificity
• This is the proportion of subjects who really do not have the disease,
and who have been identified as non-diseased by the test.
• The formula for calculating specificity is d/(b + d).
• Sensitivity and specificity both indicate how accurately the test can
detect whether or not a subject has the disease (this is known as the
test's validity}.
DR MT/LECTURE NOTES/MPH
10. Positive predictive value (PPV)
• This is the probability that a subject with a positive test result
really has the disease.
• The formula for calculating PPV is a/ (a+b)
• Negative predictive value (NPV)
• This is the probability that a subject with a negative test result
really does not have the disease.
• The formula for calculating NPV is d/(c + d}.
DR MT/LECTURE NOTES/MPH
11. Prevalence
• This is the proportion of diseased subjects in a screened
population (also called the pre-test probability), and it is the
probability of having the disease before the screening test is
performed.
• It can be especially useful when evaluating screening tests for
groups of people who may have different prevalences (e.g.
different genders, age groups or ethnic groups).
• The formula for calculating prevalence in screening is (a+c)/
(a+b+c+d)
DR MT/LECTURE NOTES/MPH
12. Example
• Suppose that a new screening test has been
developed for diabetic retinopathy. We carry out a
study to find out how effective it is in a population
of 33750 patients with diabetes, all aged over 55
years. Use data to evaluate the test.
DR MT/LECTURE NOTES/MPH
13. A 2 x 2 table for evaluating a diabetic retinopathy
screening test
Diabetic retinopathy
Disease
Present
Adisease
bsent
Total
Positive (a)
3200
(b)
1400
A+b
4600
Negative (c)
150
(d)
29,000
(C+d)
29150
Total (a+c)
3350
(b+d)
30400
(A+b+c+d)
33750
DR MT/LECTURE NOTES/MPH
14. • Compute the sensitivity of this test
• Interpret your finding
DR MT/LECTURE NOTES/MPH
15. Sensitivity
• Sensitivity = a/(a + c)
= 3200/3350 = 0.9552 = 96%.
• This means that 96% of subjects who actually have
diabetic retinopathy will be correctly identified by the
test.
• This result indicates that only 4% of subjects with
diabetic retinopathy will be wrongly identified as
being disease-free.
DR MT/LECTURE NOTES/MPH
16. Compute specificity of the test,
• What is the specificity of this test.
• What is the interpretation of your finding.
DR MT/LECTURE NOTES/MPH
17. Specificity
• Specificity = d/(b + d}
= 29000/30400 = 0.9539 = 95%.
• This means that 95% of subjects who do not have
diabetic retinopathy will be correctly identified by the
test. This result indicates that only 5% of subjects
without the disease will be wrongly identified as
having
• diabetic retinopathy.
DR MT/LECTURE NOTES/MPH
18. Compute the positive predictive
value of this test
• What is the PPV of this test.
• What is the interpretation of this +PPV
DR MT/LECTURE NOTES/MPH
19. Compute PPV
• Positive predictive value = a/(a + b) =
3200/4600 = 0.6957 = 70%.
• This means that there is a 70% chance that someone
who tests positive does have diabetic retinopathy.
• This is poor, as there is a 30% chance that someone
with a positive test result is actually disease-free.
DR MT/LECTURE NOTES/MPH
20. Compute the Negative predictive
value
• What is the NPV of this test.
• What is the interpretation of this Negative
predicti?ve value
DR MT/LECTURE NOTES/MPH
21. Compute the NPV of the test.
• Negative predictive value = d/(c + d)
= 29000/29150 = 0.9949 = 99%.
• This means that there is a 99% chance that someone
who tests negative does not have diabetic retinopathy.
• This is good, as there is only a 1% chance that
someone with a negative test result will actually have
the disease.
DR MT/LECTURE NOTES/MPH
22. Prevalence
• Prevalence = (a + c)/(a + b + c + d)
= 3350/33 750 = 0.0993 = 10%.
• This means that 10% of the screened population have diabetic
retinopathy.
• We can conclude that although this screening test appears to be
generally
• very good, the disappointing positive predictive value of only
70% would
• limit its overall usefulness.DR MT/LECTURE NOTES/MPH