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
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
1. Caveats and Comments
1
Overview:
This is my analysis, not Stanford’s. My goal is to understand the trajectory of COVID. It is not confidential and can be freely shared. The R program code is
available at https://github.com/StevenLShafer/COVID19/. The daily analysis are available at https://1drv.ms/u/s!AuOyHP_aTIy7rowrt2AjGpWm_frnEQ?e=KBcNbh.
You are welcome to use the R code on GitHub for any purpose.
I am attempting to keep the analysis and commentary apolitical. I am now including partisan lean as a metric to help understand the epidemic. I occasionally point
out misrepresentations by government officials. I occasionally point out where government recommendations have placed Americans at increasing risk.
I try to provide a daily update in the morning, except Sundays. My analysis my be delayed by my clinical responsibilities as a Stanford anesthesiologist.
There is a lot of information on the figures. If something isn’t clear, please see the explanation on slide 2.
Data sources:
• USA Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv
• USA Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv
• USA Testing and Hospitalization Data: https://raw.githubusercontent.com/COVID19Tracking/covid-tracking-data/master/data/states_daily_4pm_et.csv
• Global Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
• Global Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
• Global Testing Data: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv
• Mobility Data: https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv
• Partisan Lean: MIT Election Data and Science Lab: https://doi.org/10.7910/DVN/VOQCHQ/HEIJCQ
• Ensemble Model: https://github.com/reichlab/covid19-forecast-hub/raw/master/data-processed/COVIDhub-ensemble/2020-xx-xx-COVIDhub-ensemble.csv
Models:
1. Future projections of case numbers are based on the Gompertz function (https://en.wikipedia.org/wiki/Gompertz_function): log 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠𝑒𝑠 =
𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 + 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑐𝑎𝑠𝑒𝑠 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 1 − 𝑒−𝑘 𝑡 . This is a naïve asymptotic model. k is the rate constant, such that log(2) / k = time to 50%
rise. t is the number of days. Wikipedia The Gompertz function is estimated from the last 3 weeks of data for cumulative cases (red dots in the figures).
Deaths are predicted from a log linear regression of deaths over the past 21 days. For the US, and individual states, I am also including the 98% prediction
interval from the COVID-19 Forecast Hub (https://covid19forecasthub.org/).
2. The rate of change in daily cases and deaths is the slope of delta cases / day over the last 14 days, divided by the average number of cases.
Locations
The locations for the modeling are where Pamela and I have family and friends, locations of interest to friends and colleagues, or countries in the news (e.g.,
China, South Korea, Sweden, Brazil) or with significant economic impact on the United States (e.g., Japan, Canada, Mexico). Locations are easy to add.
Stay safe, well, resilient, and kind.
Steve Shafer
steven.shafer@Stanford.edu
2. 2,586,092
152,804
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Actual(points)/Predicted(line)
Phase
Pre-Model
Modeled
Deaths
Tests
USA projection as of 2020-05-27
0
10,000
20,000
30,000
0
2,000
4,000
6,000
Cases/Day
Deaths/Day
Cases: 1,662,302 (32,123) -- Deaths: 98,220 (829) -- Case Mortality: 5.9% -- Daily Change in Cases: -0.5%
Explanation of the Figures
2
Brown dots:
cumulative tests
Red dots: cumulative cases
used to estimate Gompertz
function, presently set to last
3 weeks
Red line: predicted cumulative
cases based on the Gompertz
function estimated from the red
dots
Red number: total cases
on June 30th, based on
the Gompertz function
estimated from the red
dots
Black number: total
Deaths on July 31th,
based on log-linear
regression of the past
21 days
Black line: predicted
cumulative deaths, based
on a log linear regression
of deaths over past 21
days.
Axis for deaths / day, usually
1/10th of the axis for cases /
day on the left side of the
figure.
Green line: linear regression
over 8 days, used to calculate
percent increase / decrease
(see below)
Daily change in cases,
calculated as the slope of the
green line (above left) /
number of new cases
yesterday.
Case mortality:
cumulative deaths
/ cumulative cases.
Cases / day calculated
from cumulative cases
used to estimate the
Gompertz function
Cases / day calculated
from cumulative cases
not used to estimate the
Gompertz function
Deaths / day,
axis is on the left
Blue line: today
Blue dots: cumulative cases not
used to estimate Gompertz
function
Cumulative cases
(yesterday’s cases)
and cumulative deaths
(yesterday’s deaths)
Axis for cases / day.
Axis for deaths / day
appears to the right.
Geographic
location
Date of analysis,
also shown as
blue vertical line
below
Purple wedge: 98% ensemble
prediction interval from COVID-19
Forecast Hub (USA and US
States only)
9. Average new cases over past 7 days
PER
ISR
ARG
BRA
ESP
COL
USA
IRQ
CHL
HND
BOL
PRY
FRA
LBY
IND
GTM
ROU
ECU
MEX
UKR
DOM
ZAF
BEL
CHE
PHL
MAR
CZE
SAU
VEN
NPL
RUS
NLD
AUT
IRN
PRT
GBR
ITA
GRC
TUR
KGZ
BGR
POL
SLV
AZE
BLR
ETH
SWE
DNK
DEU
BGD
CAN
HUN
SRB
ZMB
SVK
TUN
UZB
IDN
DZA
RWA
JOR
KAZ
NIC
KOR
SEN
USA
None
1 in 20,000
1 in 10,000
1 in 6,667
1 in 5,000
1 in 4,000
1 in 3,333
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Rank
Averagecases/day
Average new cases over past 7 days
Excludes countries with population < 5,000,000
2020-09-02 Summary: 9
12. Average daily deaths over past 7 days
BOL
COL
PER
MEX
ARG
BRA
HND
CHL
USA
ZAF
IRQ
PRY
DOM
ECU
ROU
ISR
IRN
GTM
KAZ
BGR
SAU
SLV
MAR
UKR
ESP
LBY
IND
AUS
RUS
PHL
BLR
ZWE
TUR
IDN
NPL
GRC
POL
PRT
BGD
DZA
AZE
FRA
VEN
NLD
EGY
SRB
ETH
UZB
CAN
NIC
CHE
GBR
SYR
CZE
JPN
SEN
ITA
TUN
ZMB
MDG
HTI
AGO
YEM
AFG
KEN
USA
None
1 in 500,000
1 in 250,000
1 in 166,667
1 in 125,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Rank
Averagedeaths/day
Average daily deaths over past 7 days
Excludes countries with population < 5,000,000
2020-09-02 Summary: 12
14. Change in New Cases per Day
New cases are:
Increasing > +3%
Increasing between +1% and +3%
No Change (-1% to +1%)
Decreasing between -1% and -3%
Decreasing > -3%
New cases by state as of 2020-09-02
2020-09-02 Summary: 14
15. Cases as a Percent of Peak Cases
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Cases as a Percent of Peak Cases
2020-09-02 Summary: 15
16. Change in New Deaths per Day
New deaths are:
Increasing > +0.5%
Increasing between +0.1% and +0.5%
No Change (-0.1% to +0.1%)
Decreasing between -0.1% and -0.5%
Decreasing > -0.5%
New deaths by state as of 2020-09-02
2020-09-02 Summary: 16
17. Deaths as a Percent of Peak Deaths
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Deaths as a Percent of Peak Deaths
2020-09-02 Summary: 17
18. Change in cases vs change in deaths
AL
AK
AZ
AR
CA
CO
CT
DEDC
FL
GA
HI
ID
IL
IN
IAKS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY-6
-3
0
3
6
-5.0 -2.5 0.0 2.5 5.0
Change in cases (%/day)
Changeindeaths(%/day)
Change in cases vs change in deaths over last 14 days as of 2020-09-02
2020-09-02 Summary: 18
19. Total US COVID-19 Cases
CA
TX
FL
NY
GA
IL
AZ
NJ
NC
TN
LA
PA
MA
AL
OH
VA
SC
MI
MD
IN
MO
MS
WI
MN
WA
NV
IA
AR
OK
CO
CT
UT
KY
KS
NE
ID
OR
NM
RI
DE
DC
SD
ND
WV
HI
MT
NH
AK
ME
WY
VT
0
200,000
400,000
600,000
800,000
1 6 11 16 21 26 31 36 41 46 51
Rank
Totalcases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.42, p governor: 0.79. NB: association != causation.
2020-09-02 Summary: 19
20. Total US COVID-19 Cases
LA
FL
MS
AZ
AL
GA
SC
TN
NV
NY
TX
NJ
IA
RI
AR
DC
IL
MA
CA
ID
DE
MD
NE
UT
NC
ND
SD
OK
CT
KS
VA
MO
IN
MN
WI
NM
MI
KY
PA
OH
CO
WA
AK
MT
WY
OR
HI
WV
NH
ME
VT
None
1 in 100
1 in 50
1 in 33
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalCases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.79, p governor: 0.1. NB: association != causation.
2020-09-02 Summary: 20
21. Average US COVID-19 cases over the past
7 days
IA
SD
ND
AL
MS
TN
MO
GA
AR
KS
OK
SC
TX
HI
FL
NE
ID
NC
IL
KY
NV
LA
IN
MN
CA
WI
MT
UT
VA
OH
MD
WV
AK
RI
MI
DE
DC
AZ
WA
WY
NM
OR
CO
PA
MA
NJ
CT
NY
ME
NH
VT
None
1 in 10,000
1 in 5,000
1 in 3,333
1 in 2,500
1 6 11 16 21 26 31 36 41 46 51
Rank
NewCases/Day
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 cases over the past 7 days
p masks as of July 20, 2020: 0.0066, p governor: 0.0042. NB: association != causation.
2020-09-02 Summary: 21
22. Total US COVID-19 Deaths
NY
NJ
CA
TX
FL
MA
IL
PA
MI
GA
AZ
LA
CT
OH
MD
IN
SC
NC
VA
MS
AL
CO
WA
MN
TN
MO
NV
WI
IA
RI
KY
AR
OK
NM
DC
DE
OR
KS
NH
UT
NE
ID
WV
SD
ND
ME
MT
HI
VT
AK
WY
0
10,000
20,000
30,000
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeaths
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks as of July 20, 2020: 0.062, p governor: 0.23. NB: association != causation.
2020-09-02 Summary: 22
23. Total US COVID-19 Deaths
NJ
NY
MA
CT
LA
RI
DC
MS
AZ
MI
IL
MD
DE
PA
GA
SC
FL
IN
AL
TX
NV
NM
OH
IA
CO
CA
MN
NH
VA
AR
NC
TN
MO
WA
KY
NE
ID
OK
WI
ND
SD
KS
UT
WV
OR
MT
ME
VT
WY
AK
HI
None
1 in 2,000
1 in 1,000
1 in 667
1 in 500
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeaths
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks as of July 20, 2020: 0.029, p governor: 0.26. NB: association != causation.
2020-09-02 Summary: 23
24. Average US COVID-19 deaths over the past
7 days
MS
GA
SC
LA
FL
TX
AR
AZ
NV
AL
ID
TN
WV
IA
CA
OH
OK
NC
NM
MO
HI
MA
VA
KY
RI
IL
MT
IN
MD
ND
OR
MI
MN
AK
PA
KS
WI
WA
NE
SD
UT
NY
CO
DC
NH
CT
DE
NJ
ME
VT
WY
None
1 in 500,000
1 in 250,000
1 in 166,667
1 in 125,000
1 in 100,000
1 6 11 16 21 26 31 36 41 46 51
Rank
Deaths/Day
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 deaths over the past 7 days
p masks as of July 20, 2020: 0.65, p governor: 0.035. NB: association != causation.
2020-09-02 Summary: 24
25. Daily testing trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Dailytestingfrommintomax
Daily testing trends from min to max
Line = Friedman's supersmoother
2020-09-02 Summary: 25
26. Change in daily tests over past 14 days
VT
ME
AK
UT
MA
HI
MI
OH
CT
NC
NH
IL
WV
AR
IN
KS
WY
NY
WA
ND
MT
AL
MS
RI
NJ
KY
IA
OK
SD
MN
DC
NE
PA
MD
CA
OR
MO
CO
VA
ID
DE
NM
GA
TX
WI
AZ
LA
SC
TN
FL
NV
-2.0
0.0
2.0
4.0
6.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeindailytests(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in daily tests over past 14 days
p masks as of July 20, 2020: 0.66, p governor: 0.93. NB: association != causation.
2020-09-02 Summary: 26
28. Percent of Positive COVID Tests
AZ
MS
FL
AL
SC
ID
TX
NV
GA
KS
IA
NE
SD
IN
MO
AR
MD
PA
CO
RI
UT
LA
VA
NC
DE
MA
TN
MN
NJ
OK
WI
CA
ND
KY
IL
OH
NY
WA
WY
DC
OR
CT
HI
MI
NH
NM
MT
WV
ME
AK
VT
0.0
5.0
10.0
15.0
1 6 11 16 21 26 31 36 41 46 51
Rank
PercentofPositiveTests
Masks
No
Yes
Governor
aa
Democratic
Republican
Percent of Positive COVID Tests
p masks as of July 20, 2020: 0.045, p governor: 0.0058. NB: association != causation.
2020-09-02 Summary: 28
29. Positive fraction trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Fractionpositivefrommintomax
Positive fraction trends from min to max
2020-09-02 Summary: 29
30. Change in positive tests over past 14 days
HI
ND
SD
IA
MO
KY
OK
AL
MN
WI
KS
SC
NV
AK
UT
MS
MT
OR
NE
ID
AR
TX
WV
FL
IN
NC
TN
VA
CA
GA
WA
NM
LA
PA
CO
IL
AZ
DE
RI
MD
MI
OH
NH
NJ
DC
WY
NY
VT
MA
CT
ME
-1.0
0.0
1.0
2.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeinpositivetests(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in positive tests over past 14 days
p masks as of July 20, 2020: 0.014, p governor: 0.092. NB: association != causation.
2020-09-02 Summary: 30
31. Change in tests vs change in positive tests
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MTNE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
-1
0
1
2
-2 0 2 4
Change in tests (%/day)
Changeinpositivetests(%/day)
Change in tests vs change in positive tests last 14 days as of 2020-09-02
Size of the state font reflects the number of deaths over the past 7 days.
2020-09-02 Summary: 31
32. Current hospitalizations as a percent of peak
since FebruaryHI
MO
MT
ND
WV
KS
AK
NE
OK
WY
SD
KY
ID
NC
IA
AR
GA
OH
VA
TN
MS
AL
IN
WA
CA
NV
SC
MN
UT
OR
WI
LA
FL
TX
IL
NM
RI
MD
CO
AZ
DE
VT
MI
PA
DC
ME
MA
NJ
NH
CT
NY
0
30
60
90
1 6 11 16 21 26 31 36 41 46 51
Rank
Hospitalizations(%ofpeak)
Masks
No
Yes
Governor
aa
Democratic
Republican
Current hospitalizations as a percent of peak since February
p masks as of July 20, 2020: 0.01, p governor: 0.042. NB: association != causation.
2020-09-02 Summary: 32
33. Hospitalizations trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Hospitalizationsfrommintomax
Hospitalizations trends from min to max
2020-09-02 Summary: 33
34. Change in hospitalizations over past 14
days
DE
HI
MT
SD
ND
NE
IA
CT
KS
WV
MO
NJ
MN
OK
MI
IN
RI
IL
GA
WY
ID
NC
DC
KY
AK
PA
VA
WA
OH
MA
NY
TN
CO
AR
MD
ME
WI
AL
SC
CA
UT
LA
OR
MS
NV
VT
FL
TX
AZ
NM
NH
-5.0
-2.5
0.0
2.5
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeinhospitalizations(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in hospitalizations over past 14 days
p masks as of July 20, 2020: 0.81, p governor: 0.44. NB: association != causation.
2020-09-02 Summary: 34
35. Change in New Cases per Day
Direction
Increasing > +2%
Increasing between +0.5% and +2%
No Change (-0.5% to +0.5%)
Decreasing between -0.5% and -2%
Decreasing > -2%
NA
Trends by county as of 2020-09-02
NA = Inadequate data
2020-09-02 Summary: 35