#i dont understand the imperial metric system | Explore Tumblr Posts and Blogs

saveyoua-seat · 5 years

Text

21 questions

Amazed that my favs @thougtsofadyingatheist and @acomebackstory tagged me! 💖 💖 💖 💖 💖

Rules simply answer the following 21 and tag 21 people (or more pfft 😂 don’t wanna annoy people) you do like to know more!

Name/Nickname: Jessica/Jess.

Sign: Leo, but I dont really relate to it (nor believe in astrology).

Height: 1.57 (metric system) or 5'2" .

Hogwarts House: Gryffindor, but I really really wanted to be a slytherin, its always been my favourite house, mostly because I love Draco Malfoy lol

Last thing I googled: United States map, I was trying to find Minnesota because I was watching a documentary and one of the crimes had happened there.

Last song I listened to: Love Myself by Olivia O'brien

Song stuck in my head: A Whole New World by Zayn and Zhavia Ward, I watched Aladdin and I cant stop singing it 😂

Following: Mostly are Roswell new mexico and Taylor Swifts blogs, and people who have the same interests and political views as me. I dont have a opinion about plenty of things but if there is something that I do have a f**king strong opinion about is politics, so if you support any type of oppression - violence, exploitation, marginalization, powerlessness, or cultural imperialism - GET THE HELL AWAY FROM ME!!

Followers: Basically the same as following, i love them so much 🧡🧡🧡🧡

Do you get asks: lol dont even know what that is. On the few occasions that I get, I cant keep the conversation cause I suck at it. 😂😂

Amount of sleep: I dont like sleeping many hours during the night, cus i dont sleep well, i keep waking up and looking at the window to see if the sun has risen already (hello anxiety) so id say around 5 hours but during the day I feel like I could sleep forever.

Lucky number : I like the number 6 cause is my birthday (august 6) lol

What am I wearing: blue sweatpants, a minnie mouse sweater and flip flops!! (i love flip flops, if i could id wear them everywhere)

Dream trip: I try not to think about it since i dont have enough money, so anywhere would be fine by me, every place has its own beauty 💜

Favorite food: Japonese food!!! Well, tbh, i prefer chocolate and sweets in general.

Instruments: I tried to learn guitar once but I always have been too impacient so I gave up, and I joined my schools choir when i was a kid (8, 9 years old). Id love to have talent for music but its just not in my blood, unfortunately.

Languages: English and Portuguese, I like to think I understand Spanish but I cant speak

Favorite song: It changes everytime! Id say anything by Taylor Swift, Ive been listening to ME! nonstop since its release.

Random fact: Im pretty boring (and Im proud of it) so im gonna say a few non important facts: I love Taylor Swift and her songs so much, its like my safe haven, I hate men but I love Harry Styles, I like Niall Horan very much and i also listen to Lana Del Rey a lot, I lived in Ireland for about 1 year in 2017, London is my favourite city on earth, I love coffee and I even tho I hate men i dont like women either.

Aesthetic: cozy blanket, a cup of coffee, a book, rainy days, dawn, peace, warm, silence.

Tagging those who seem AWESOME but i could never interact with cause social anxiety: @sterek-malec-karamel @imlonelybutfabulous @alcoolatra-de-toddynho @alexmanesairstream @songbirdfly @just-screws-me-up @malextruelove

5 notes · View notes

khalilhumam · 4 years

Text

When Numbers Don’t Speak for Themselves: COVID-19 and Thoughts on How to Measure a Country’s Performance

New Post has been published on http://khalilhumam.com/when-numbers-dont-speak-for-themselves-covid-19-and-thoughts-on-how-to-measure-a-countrys-performance/

When Numbers Don’t Speak for Themselves: COVID-19 and Thoughts on How to Measure a Country’s Performance

table border-collapse: collapse; width: 100%; margin-bottom: 20px; th background-color: #00677F; color: #FFFFFF; padding: 15px 10px; text-align: center; vertical-align: middle; font-weight: bold; tr:nth-child(even) background-color: #ede7de; td text-align: center; padding: 10px; td:first-child, th:first-child text-align: left; Please note this blog is part of a short series on COVID-19 and mortality. Please see here and here for our previous blogs, and here for a link to a recent event. There are multiple data and metrics used to assess a country’s performance in responding to the threat of COVID-19. As seen in a recent interview with President Trump of the United States, the choice of metric can influence whether a country’s response is considered a relative success or failure. In this case the metrics leading to different narratives were per capita death rate–the US looks relatively bad–and case fatality ratio–the US looks much better. In this blog we share our thoughts on how to measure a country’s performance in confronting the pandemic.

Measures of mortality relevant to assessing performance

Table 1 shows the five key mortality estimates required to understand the impact of COVID-19. Our central question is this: Can existing sources of data, when combined with judicious use of modeling, tell us how well a country’s COVID-19 control measures have performed? Can existing sources of data, when combined with judicious use of modeling, tell us how well a country’s COVID-19 control measures have performed?

Table 1. Five mortality estimates, before, during, and after COVID-19

Pre-COVID-19 COVID-19 and post COVID-19, with policies (to address both COVID-19 and non-COVID-19) COVID-19 in the absence of policy or intervention Deaths COVID-19 deaths Non-COVID-19 deaths COVID-19 deaths Non-COVID-19 deaths A B C D E

Pre-COVID-19 Mortality – A

We label deaths that occurred before the outbreak of COVID-19 “A.” Data on these deaths are produced by national governments as part of civil registration systems. However, in most low- and many middle-income countries (LMICs), the data are limited by low completeness and lack of timely reporting and data processing. If countries invest today in the use of the World Health Organization (WHO) international standard death certificate, (ICD-11), and in boosting the civil registration of deaths, they will quickly cease to need to rely on modeled estimates of these deaths. However, for many years the Institute of Health Metrics and Evaluation has modeled the number, and cause, of deaths (by age and sex) in 195 countries to fill this chronic data gap.

Excess Mortality during and after the COVID-19 Pandemic – B and C

The next broad category of deaths (both COVID-19 and non-COVID-19) to examine are those that are occurring now and in the future. These signify how policies have affected the level of infection and mortality. These latter deaths may be measured in the future but by necessity will be modeled today before they have occurred. The deaths in cell B (COVID-19 deaths to date) are reported by national governments and summarized by groups such as the European Center for Disease Prevention and Control (the source used by Our World in Data) or the WHO. We also have predictions of future COVID-19 deaths for a range of policies available to governments to help slow the spread of the virus. Options here include the estimates published by Imperial College London, London School of Hygiene & Tropical Medicine, and (ideally) “local” models where available. Note that some of the early predictions of overall mortality burden of the pandemic have not been updated to include the actual policies adopted, which have evolved over time as countries course-correct their responses. Therefore, the original predictions are of limited utility today. In cell C we have the non-COVID mortality occurring during and after the pandemic, which is challenging to measure where routine systems have not historically provided timely cause-specific or total mortality data, i.e., in most LMICs. We increasingly have modeled program/cause-specific estimates of the potential non-COVID-19 deaths caused by actions taken by governments (that will produce intended but also unintended consequences) and changes in the behavior of individuals (due both to policies but also the fear of the virus). However, unless countries specifically track the total mortality occurring and distinguish it from confirmed and suspected COVID-19 mortality, they will lack comprehensive estimates of these non-COVID-19 deaths. At present there are at least 13 countries, primarily supported by Bloomberg Philanthropies, seeking to address this question through the application of a technical package for rapid mortality surveillance. When listening to policymakers and reading their reports, our major recommendation is caveat lector–reader beware! Each and every metric has its own limitation, and selective use of one over others can present a distorted—or at best incomplete—picture of performance. Of course, part of the problem is that there will be a lag in the reporting of many of these deaths as routine health services disrupted by COVID-19 often prevent future mortality; hence our reliance on models. Similarly, the health consequences of the economic impact of COVID-19 will occur in the future. Nevertheless, we encourage more efforts to attempt to model (and then start and keep tracking) these deaths in order to provide a more complete picture of performance. Without considering the indirect health effects, we do not see how the performance of a country’s response can be fairly assessed (even though most debates on this topic still tend to exclusively focus on the COVID cases and deaths count). It is possible to associate the trajectory of total and cause-specific excess mortality with the introduction of public health and social measures. For some this may be taken as “plausible” evidence of the impact (or lack) of policies. What may never be well understood, however, is whether the virus or the control measures caused excess deaths.

COVID-19 and non-COVID 19 mortality absent control measures – D and E

Attributing excess deaths to either the virus or the unintended consequences of policy requires modeling the counterfactual of what would have happened in the absence of control measures. Let’s call the counterfactual of how many COVID-19 deaths would have happened in the absence of any control measures “D.” Note that the Imperial and London School models failed to consider any spontaneous social distancing in their unmitigated scenarios, and are therefore overestimates of COVID-19 mortality. And finally, how many additional non-COVID-19 cases and deaths would there have been due to behavior changes, and the overcrowding of health systems, and the ensuing disruption to routine health services? Let’s call these deaths “E.” We are not aware of any published estimates of E. Note that these should not be assumed to be A (i.e. zero excess non-COVID-19 deaths).

Uses and limitations of mortality measures

These different estimates can be analyzed in the following ways:

COVID-19 deaths (B) or (D)

Measures: Self-explanatory Limitations/Comments:

Should the total number of deaths be reported, or should they be presented on a per capita basis?

What about the shape of the curve, i.e. is the outbreak (and response) getting better or worse?

Some policymakers cite the unmitigated scenario estimates (D) and compare them to the cumulative number of COVID deaths to date (B) – this provides a false sense of accomplishment as the typical counterfactual of no mitigation is not realistic.

Does not address COVID-19 mortality in relation to any other factors including, e.g., age of death/year of life lost, morbidity (short- and long-term), cost of control measures.

COVID-19 deaths averted (D – B)

Measures: How many COVID-19 deaths would have occurred in the absence of policy measures? Limitations/Comments:

Initial focus of modelers.

Focus on COVID-19 mortality fails to consider indirect mortality effects of the virus, and the response to the virus, has had and is having.

Again, provides an artificially high number as the typical counterfactual of no mitigation is not realistic.

COVID-19 deaths vs. excess non-COVID-19 deaths (B compared to C)

Measures: Mortality due to COVID-19 versus all other causes of death Limitations/Comments:

No reference to average, historic mortality is given.

Will not tell us how well the country and its range of policy responses performed. Although comparing across countries can highlight relative success, i.e. no or low mortality compared to high mortality.

Low COVID-19 deaths could be because: i) the policies effectively controlled the virus, or ii) perhaps, for a variety of reasons, the numbers were never going to be high (e.g. Africa).

Similarly, low non-COVID-19 estimates could be due to, for example, time lags, or successful efforts to mitigate the effects on routine services.

Excess mortality (B + C) compared to A (typically, the average number of deaths in the previous 5 years, i.e. 2015-2019)

Measures: Excess deaths occurring during the pandemic Limitations/Comments:

Relatively few countries have the civil registration systems in place to confidently track excess mortality.

While there are efforts under-way to strengthen rapid mortality surveillance to provide these data, there has been an over-reliance on modeling.

Fails to capture COVID-19 mortality averted.

Non-COVID-19 “excess mortality”: C compared to A

Measures: Deaths due to causes other than COVID-19 occurring during the pandemic Limitations/Comments:

Often the breakdown between COVID and other is not provided (although where total excess mortality is reported, it can be inferred by subtracting the reported COVID-19 deaths to obtain an estimate of the indirect deaths).

Fails to disentangle the effects of the virus, policies to address the virus, and policies to mitigate the effects of the virus.

Perhaps disentangling the cause of this excess mortality should not be a concern of policymakers; the damage needs to be mitigated no matter how it is caused.

As countries improve the timeliness of cause of death data, it may be increasingly possible to peer into the specific component causes of non-COVID-19 excess deaths and so formulate ameliorative action.

Net health impact: (D - B) compared to (C - E)

Limitations/Comments:

If (D -B) is greater than (C - E), the policies did more good than harm. Conversely, the policies did more harm than good. This is what we have proposed in earlier blogs. But how do we estimate E? And are D and E useful, the right comparators?

In conclusion

At the beginning of this blog, we asked “Can existing sources of data, when combined with judicious use of modeling, provide summary measures capable of quantifying how well a country’s COVID-19 control measures have performed?” The short answer is yes, but we should proceed with caution in doing so. As the COVID-19 pandemic passes its eight-month mark, it’s clear that there has been mixed success with controlling its spread. Countries such as Fiji, South Korea, Thailand, Vietnam, Germany, and New Zealand, in addition to most of the African continent, have been comparatively successful in controlling the outbreaks within their borders. Countries such as the United States, Brazil, and South Africa have yet to “bend the curve;” new confirmed cases are still increasing day after day. Most countries sit somewhere between these two groups of countries. Of course, without the benefit of hindsight, we cannot definitively declare successes and failures. However, it matters how we define success (and failure). Has a country succeeded if it has stopped the spread of the virus leading to very few deaths? What if this “success” has been achieved at the expense of other non-COVID-19 deaths? What, in addition to excess mortality, must we measure or model? Of course, without the benefit of hindsight, we cannot definitively declare successes and failures. However, it matters how we define success (and failure). Has a country succeeded if it has stopped the spread of the virus leading to very few deaths? What if this “success” has been achieved at the expense of other non-COVID-19 deaths? What, in addition to excess mortality, must we measure or model? As some countries begin to relax measures adopted at the peak of the pandemic several months ago, while yet others consider reintroducing lockdowns to whole or subpopulations, we believe that a better understanding of what drives COVID-19 mortality rates across time and space, as well as considering metrics beyond excess mortality, can help decision-makers who are planning the ongoing COVID-19 response. Mortality measurement is part of the picture. It is necessary but not sufficient for answering relevant policy questions pertaining to the health impact of the virus and the response to it. A true focus on the “excess burden” would necessitate the use of disability-adjusted life-years (DALYs) or some other summary measure of population health capable of accounting for all the non-fatal disability caused by the virus. Likewise, for policymakers, health impact data in the absence of information about the cost of policy choices and other core indicators, only tells part of the story. Finally, when listening to policymakers and reading their reports, our major recommendation is caveat lector–reader beware! Each and every metric has its own limitation, and selective use of one over others can present a distorted—or at best incomplete—picture of performance.

0 notes