On February 29th, 2020 Italy had 655 confirmed cases of COVID-19 while New York did not yet have a confirmed case. However, by March 17th New York had hit the 1000 case mark, with a total of 1,706 cases. Quickly after that date the rate of residents infected with COVID-19 in New York State sharply rose above Italy. In eleven days from hitting the 1,000 case mark New York recorded 52,410 confirmed COVID-19 cases, for a rate of 268 per 100,000 residents. Italy, twenty-eight days past hitting the 1,000 case mark, was still well below that level, with a rate of 153 confirmed COVID-19 cases per 100,000 residents.
The table below shows the amount of days it has taken for cases to double in New York State versus Italy. New York is now doubling every three days while Italy is doubling every four to five days.
Number of Days to Double Cases in NY versus Italy
500 to 1K
1K to 2K
2K to 4K
4K to 8K
8K to 16K
16K to 32K
New York
3d
1d
1d
1d
3d
3d
Italy
2d
2d
4d
3d
4d
5d
Data source: Confirmed COVID-19 cases from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Downloaded March 29th, 2020.
As of February 29, 2020 Italy had reached 1,000 confirmed cases of COVID-19. At this time Japan had 241 cases and the United States had 68 cases. As of March 27, 2020 all three countries were substantially above the 1,000 case mark. In fact, the United States has since overtaken the world for the most confirmed COVID-19 cases. The chart below shows the trends in the number of confirmed cases, starting on the day they each hit the 1,000 case mark.
As you can see in the chart above, the epidemic curve for the United States is much steeper than that of Italy. On the 17th day after hitting the 1,000 case mark the United States already had 121,478 confirmed cases of COVID-19. Italy was only at 27,980 confirmed cases of COVID-19 on their 17th day after hitting the 1,000 case mark. Japan on the other hand, despite having a first confirmed case before the United States, has kept a relatively flat curve, and not shown the exponential growth that many countries have seen.
The table below shows the number of days it has taken for the number of confirmed COVID-19 cases to double in each country, since the 1,000 case mark between the United States and Italy. The amount of time it takes for cases to double in Italy has slowed down recently, while the United States has continued to double cases in under 3 days.
Number of Days to Double Cases in the U.S. versus Italy
Country
1k to 2k
2k to 4k
4k to 8k
8k to 16k
16k to 32k
32k to 64k
U.S
2d
3d
3d
1d
2d
3d
Italy
2d
4d
3d
4d
5d
5d
The United States is a much larger country than Italy, and when you adjust for population size, what you see, shown in the chart below, is that the trend line in the United States for confirmed COVID-19 cases per 100,000 residents is tracking very closely to Italy.
The United States currently has 38 confirmed cases of COVID-19 per 100,000 residents compared to 1 confirmed case of COVID-19 per 100,000 residents in Japan and 153 confirmed cases of COVID-19 per 100,000 residents in Italy.
Data source: Confirmed COVID-19 cases from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Downloaded March 29th, 2020.
As of March 20th, Washington, New York, and California had a total of 11,011 combined confirmed cases, comprising 58% of all known confirmed cases in the U.S. (N= 19,069).
The first known case of COVID-19 in Washington state, and in the U.S., was confirmed on January 21, 2020. By February 1st, California had recorded their first confirmed case, but new confirmed cases were slow to accumulate. On March 2nd, New York confirmed their first case and that’s when the curve started to kick up for Washington, California and New York. Washington hit 500 cases on March 13th, 52 days after the first confirmed case. New York hit 500 cases on March 14th, 11 days after the first confirmed case. California hit 500 cases on March 16th, 44 days after the first confirmed case.
Not only did New York rapidly hit the 500 case mark compared to Washington and California, but cases have continued to rise sharply. The graph below shows the trends in these three U.S. states after they hit the 500 confirmed cases mark.
Trends in confirmed COVID-19 cases per 100 thousand residents after 500 confirmed
Seven days after hitting the 500 cases mark, New York state had 43 confirmed cases of COVID-19 per 100,000 residents, with a total of 8,310 cases. Eight days after hitting the 500 cases mark, Washington state had 20 confirmed cases per 100,000 residents, with a total of 1,524 cases. Five days after hitting the 500 cases mark, California state had 3 confirmed cases per 100,000 residents, with a total of 1,177 cases.
Italy is the European country that many in the U.S. are keeping a close eye on, hoping we don’t follow a similar trajectory for cases. Japan is a country on the other end of the spectrum that has done a great job preventing cases from climbing too steeply. Below is a graph comparing the current trajectories of confirmed cases in the U.S., Italy and Japan as of March 20th, per 100,000 residents, after each country hit the 500 confirmed cases mark.
Trends in confirmed COVID-19 cases in the U.S., Italy and Japan per 100 thousand residents after 500 cases
The United States is tracking very closely to the steep curve of Italy, while the curve of Japan has remained flat. The hope is that the recent state and federal measures to encourage and enforce social distancing will help flatten the curve for the U.S., so the curve looks more like Japan than Italy. Only time will tell, however experts currently predict the trend for the U.S. continuing to match that of Italy seems far more likely.
While the U.S. as a whole still seems like the trend is following Italy, the case for New York state is quite different. The graph below shows how New York State compares to Italy, once each hit the 500 confirmed cases mark.
Trends in confirmed COVID-19 cases in NY vs. Italy per 100 thousand residents after 500 cases
Five days after hitting the 500 confirmed cases mark, New York had 43 confirmed cases per 100,000 residents. Italy did not reach a similar mark until 19 days after they hit the 500 confirmed cases. New York currently has a total of 8,310 cases while Italy has a total of 47,021.
Data source: Confirmed COVID-19 cases from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Downloaded March 20th, 2020.
An article was recently published online by the Journal of the American Medical Association (JAMA) describing the association between income and life expectancy in the United States between 2001 and 2014. The article highlights major differences in life expectancy by income group and that these differences increased from 2001 to 2014. The figure below, an adaptation of Figure 3 from the article, shows that in 2001 the difference in life expectancy among men between those in the lowest income group (mean income = $17,000 per year) versus those in the highest income group (median income = $256,000 per year) was 9 years and by 2014 this difference had increased to 10.6 years. Among women, the difference in life expectancy in 2001 between those in the lowest income group (mean income = $16,000 per year) versus those in the highest income group (median income = $243,000 per year) was 4.3 years and by 2014 this difference had increased to 6 years. From 2001 to 2014 the gap between those in the highest income group and the lowest income group widened by about the same amount for men (1.6 years) and women (1.7 years).
Change in race- and ethnicity-adjusted life expectancy from 2001 to 2014 among men and women by income group
Notes: The data points for this figure were extrapolated from Figure 3 of the Chetty et al article. The quartiles of median income were based on the median household earnings among working individuals. Only the top and bottom quartiles are shown to highlight the gap between these two extreme income groups. Data Source:Figure 3 from Chetty et al. The Association between Income and Life Expectancy in the United States, 2001 to 2014. Published online by JAMA on April 10, 2016.
A colonoscopy is a procedure that uses a colonoscope, a thin, flexible tube, to inspect the inner lining of your rectum and colon for ulcers, polyps, tumors, inflammation or bleeding. While the procedure itself is fairly standard, the cost for the procedure is not. The figure below highlights the differences in the cost of a colonoscopy both within and between the 20 most populous cities in the U.S.
The average cost of a colonoscopy is lowest in Memphis, TN ($1,666) and highest in San Jose, CA ($3,718). That’s a $2,052 difference in the cost of the procedure just based on whether you live in San Jose or Memphis. A city many may assume would be on the more expensive end, New York, is actually on the lower end, with an average cost for a colonoscopy of $2,062.
Differences in the cost of a colonoscopy within and between 20 most populous U.S. cities
What is perhaps more interesting than the difference between cities is the difference found within cities. San Jose not only has the highest average cost for a colonoscopy but also has the greatest difference in cost. The lowest available cost for a colonoscopy in San Jose is $1,924, while the highest cost is $6,618. That’s a $4,694 difference in cost depending on where in San Jose you have the procedure performed.
The figure is interactive. Hover over the colored bars to get the exact costs in each city as of March 11, 2015.
The 2015 women’s world cup just ended and the U.S. came out on top. However, when we look at the social progress of the same countries that competed in this year’s world cup, Norway wins out as the most socially progressive nation. The two teams that competed in the final this year, Japan and the U.S., rank 10th and 11th, respectively.
Social Progress, as measured by the Social Progress Index, measures how well nations meet the needs of its citizens. The Social Progress Index is made up of three domains: basic human needs, foundations of well-being and opportunity. The basic human needs domain consists of nutrition and basic medical care, water and sanitation, shelter and personal safety. The foundations of well-being domain consists of access to basic knowledge, access to information and communications, health and wellness and ecosystem sustainability. The opportunity domain consists of personal rights, personal freedom and choice, tolerance and inclusion and access to advanced education.
In the figure below, the countries competing in this year’s women’s World Cup are plotted along the Y-axis, ranked from top to bottom. The most socially progressive countries (e.g. Norway) appear at the top of the y-axis and the least socially progressive countries (e.g. Nigeria) fall at the bottom. Along the x-axis is the Social Progress Index, which can range from 0 to 100. The minimum for this sample of countries is 43.3 and the maximum is 88.4.
Countries ranked according to the 2015 Social Progress Index
Note: There is not enough publicly available data to calculate the social progress index for Côte d’Ivoire.
Source: 2015 Social Progress Index http://www.socialprogressimperative.org/
Yesterday, for the first time, the Center for Medicare and Medicaid Services (CMS) released data on prescription claims under Medicare part D at the provider-level. This means you can now see the number and cost of drugs prescribed by each individual health care provider (e.g. physician, physician assistant or nurse practitioner). Over the course of 2013, over one million unique providers prescribed $103 billion in prescription drugs.
There are a lot of interesting ways to look at this data, but one quick view is to look at the average cost of a prescription by specialty of the health care provider. The figure below shows that when a hematologist/oncologist prescribes a drug it costs hundreds of dollars more than the average drug prescribed by providers in different specialties. Despite the very high average cost per drug prescribed by hematologists/oncologists, the total cost of prescription drugs prescribed by hematologists/oncologists was $5 billion in 2013 compared to the $23 and $27 billion prescribed by family and internal medicine physicians, respectively, due to the large volume of patients seen by family and internal medicine physicians every year.
Prescription drug claims made in 2013 under Medicare Part D
Notes: For a detailed description of the limitations of this data please see the original press release referenced below. Data Source:Chart 2 from the Center for Medicare and Medicaid Services press release on April 30, 2014.
The first data viz below shows the lifetime risk of being diagnosed with any cancer among blacks and whites in the U.S. by sex. White males have the highest lifetime risk (44.3%), while black women have the lowest risk (36.4%). Black men have the same risk as white women (42.0%).
It is important to note that these estimated lifetime risks take into account competing causes of death. This means that because black men are much more likely to die of homicide than white men, black men may have a lower lifetime risk of cancer because they have a higher risk of dying from other things, like homicide.
Lifetime risk of being diagnosed with cancer
Males and females
Now let’s look at the black vs. white difference in the lifetime risk of a cancer diagnosis by cancer type among men. Along the y-axis in the figure below is cancer type, ordered from top to bottom according to the difference in risk. Melanoma is at the top because the black vs. white difference is -4.66%. This means that black men have a 4.66% less chance of being diagnosed with melanoma in their lifetime compared to white men. Prostate cancer is at the bottom of the y-axis because the difference is +4.92%, meaning that black men have a 4.92% greater chance of being diagnosed with prostate cancer in their lifetime compared to white men. For the majority of cancers, black men and white men have a very similar lifetime risk of diagnosis.
Lifetime risk of being diagnosed with specific cancers
Quantitative difference
Black males vs. white males
If we were to classify a difference in lifetime risk between black and white men of + or - 1% as being roughly the same risk then there would only be four types of cancer, not including all sites, for which the lifetime risk in black vs. white men was different. These four cancers are melanoma, non-Hodgkin lymphoma, prostate and bladder cancer, as shown in the data viz below. The cancers in green are those for which the lifetime risk is lower in black men vs. white men (i.e. <1% different). The cancers in red are those for which the lifetime risk is higher in black men vs. white men (i.e. >1% different). The cancers in grey, which are the majority of the cancer types, are those for which the lifetime risk is roughly the same (i.e. + or - 1%).
Lifetime risk of being diagnosed with specific cancers
Qualitative difference
Black males vs. white males
Now let's take a look at the difference in the lifetime risk of a cancer diagnosis, by cancer type, in women. In the data viz below, the y-axis is sorted from top to bottom according to the difference in risk in black vs. whites, with the smallest value at the top and the largest value at the bottom. Black women are less likely, similar to what we saw above with black men, to be diagnosed with melanoma than white women. Black women are also less likely to be diagnosed with breast and lung cancer than white women.
Lifetime risk of being diagnosed with specific cancers
Quantitative difference
Black females vs. white females
If we were to classify a difference in lifetime risk between black and white women of + or - 1% as being roughly the same risk then there would only be three types of cancer, not including all sites, for which the lifetime risk in black vs. white women was different: breast, lung and melanoma (shown below). There are no red bars in this data viz for women, as there were for men. This is because there are no cancers for which black women are more likely to be diagnosed during their lifetime than white women.
Lifetime risk of being diagnosed with specific cancers
Qualitative difference
Black females vs. white females
Overall, what the five data viz in this post highlight is that the overall risk of a lifetime diagnosis of cancer is lower in blacks vs. whites. However, the overall lower risk is really driven by a few cancers, since the lifetime risk is roughly the same for the majority of cancers. Given this overall conclusion, one might assume this translates to a lower, if not similar, overall lifetime risk of death from cancer in blacks vs. whites. To find out if this is true stay tuned for the next post.
Notes: A cancer diagnosis includes both invasive and In situ cancers.
An article published in the New England Journal of Medicine and discussed on Vox, showed that the incidence of thyroid cancer in South Korea has skyrocketed in recent years while mortality from the cancer has remained relatively stable. The figure below shows the relationship between incidence and mortality from thyroid cancer for high-income countries in 2012. While the difference between incidence and mortality from thyroid cancer is extremely large in South Korea, indicating a potential problem of overdiagnosis, many of the other high-income countries, like the United States, are likely to suffer from the same problem.
Incidence and mortality from thyroid cancer in high income countries in 2012
Notes: The y-axis contains a list of 46 high-income countries based on the World Bank classification as of July 1, 2014. Countries are rank ordered based on incidence from high to low.
The next figure shows the relationship over time in the United States between incidence and mortality of thyroid cancer. The incidence of thyroid cancer has been rapidly increasing since 1975 while mortality has remained relatively stable, indicating that like South Korea, the United States likely has an overdiagnosis problem when it comes to thyroid cancer.
Incidence and mortality from thyroid cancer in the U.S., 1975-2010
Notes: Rates are age-adjusted to the 2000 US Standard Population.
Data Source Figure 1: Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No.11 [Internet]. Lyon, France: International Agency for Research on Cancer; 2013. Available from: http://globocan.iarc.fr. Accessed on February 11, 2015. Data Source Figure 2: Incidence data comes from SEER 9 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah and Atlanta) and Mortality data comes from US Mortality Files, NCHS, CDC. All data was downloaded from http://seer.cancer.gov/faststats/ on February 11, 2015.
A recent article published in the American Journal of Infection Control found that only 53.9% of nursing home staff in three states (Florida, Georgia and Wisconsin) had been vaccinated against the flu during the 2011-2012 flu season. Low vaccination rates among nursing home staff are of public health concern as they may put many nursing home residents at risk.
The figure below is a re-design of a figure published in the original article and shows the wide variation in vaccination rates by facility. In one facility in Florida only 15% of nursing home staff were vaccinated against the flu.
Nursing home staff vaccination rates by facility in three states
Notes:Each square represents the estimated proportion of nursing home staff members that received a flu vaccination at each facility. The line represents the 95% confidence interval of the proportion vaccinated. Facilities are ranked based on the proportion of staff vaccinated at each facility and ordered from low to high along the x-axis
Data Source: Re-design of Figure 1. JD Daugherty et al. Influenza vaccination rates and beliefs about vaccination among nursing home employees. American Journal of Infection Control, Volume 43, Issue 2, 2015, 100 – 106.
