{"componentChunkName":"component---node-modules-narative-gatsby-theme-novela-src-templates-article-template-tsx","path":"/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know","result":{"data":{"allSite":{"edges":[{"node":{"siteMetadata":{"name":"MIT Civic Data Design Lab"}}}]}},"pageContext":{"article":{"id":"05fc4a31-394b-5eed-9975-130225fcf0e1","slug":"/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know","secret":false,"title":"Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know","author":"Brian Williams","date":"July 31st, 2020","dateForSEO":"2020-07-31T00:00:00.000Z","timeToRead":12,"excerpt":"As of July 26th, there were 4.2 million positive COVID-19 tests in the United States, however, only about 2.2 million of those cases have race data associated. ...How did we get here?","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know\",\n  \"author\": \"Brian Williams\",\n  \"date\": \"2020-07-31T00:00:00.000Z\",\n  \"excerpt\": \"As of July 26th, there were 4.2 million positive COVID-19 tests in the United States, however, only about 2.2 million of those cases have race data associated. ...How did we get here?\",\n  \"tags\": [\"Covid\", \"Data\", \"Health\", \"Race\", \"Visualization\"],\n  \"hero\": \"images/newplot-1-.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Missing Racial Data in COVID-19 Reporting\")), mdx(\"p\", null, \"In the past six months, hospitals, clinics, and medical institutions across the United States have conducted millions of COVID-19 tests. The data from the tests has been made publicly available through multiple avenues for public use. \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://covidtracking.com/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"The Covid Tracking Project\"), \", a volunteer organization launched from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"The Atlantic\"), \", has collected and published metadata to accompany the testing data. One such piece of metadata: race.\"), mdx(\"p\", null, \"When analyzing the reported race data, I\\u2019ve noticed that some states report race data much more consistently and thoroughly than others. This prompted me to dig into why this is, why a testing center does or does not report race, and how the state, county, municipal, and lab policies vary with regards to collecting and reporting race information.\"), mdx(\"p\", null, \"As a part of the CDDL\\u2019s Missing Data Project, this investigation tries to tackle just that: the missing data that is inherent in reported health data. By trying to highlight this missing data, we may be able to better illuminate issues within this larger system.\"), mdx(\"h3\", {\n    \"id\": \"at-first-glance\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"At First glance.\")), mdx(\"p\", null, \"Below is a graphic representing the racial breakdown of positive cases in each U.S. state and territory over time, as data is made available.\"), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"600\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/13.embed?link=false\"\n  }), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notes:\")), mdx(\"p\", null, \"It\\u2019s an \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.plotly.com/~brianwilliams2022/13.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"interactive visualization\"), \"! \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\"), \" (1) Use the options in the top right to start/pause the animation, or even select states to compare percentages over time. You can highlight one racial category (like Asian or white) and see those individual trends in the states over time. This is a very useful feature!\"), mdx(\"p\", null, \"I urge you to pay attention to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category as percentages move over time. \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notice these states and territories in particular:\"), \" North Dakota (ND), New York (NY), Puerto Rico (PR), Texas (TX), Northern Marianas (MP), and Virgin Islands (VI). These regions do a particularly poor job in reporting race in their testing results.\"), mdx(\"p\", null, \"When investigating, I wondered \\u201CWhat\\u2019s the functional difference between the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" designation and the\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \"designation?\\u201D In the context of the other racial and ethnic categories: \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"American Indian or Alaska Native, Asian, Black or African American, Latinx, Native Hawaiian or Other Pacific Islander\"), \", and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"White\"), \", the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category doesn\\u2019t give any description of value more than the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category. And in some states, reported race data seems to switch categories - from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" to \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" - after a certain date. Therefore, I decided \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" will be treated as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" for the purposes of this project. Regardless, it speaks to how \\u201Cmissing data\\u201D is pervasive throughout the healthcare system.\"), mdx(\"h3\", {\n    \"id\": \"the-bigger-picture\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"The Bigger Picture\")), mdx(\"p\", null, \"As of July 26th, there were \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"4.2 million\"), \" positive COVID-19 tests in the United States, however, only about \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"2.2 million\"), \" of those cases have race data associated. The \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category is purposefully \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"not\"), \" considered to be a race designation in this calculation because of how it differs descriptively from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Multiracial\"), \". (2)\"), mdx(\"p\", null, \"In other words, for any given COVID test, you could flip a coin to determine whether the patient\\u2019s race is known. With that level of (un)certainty, I ask: what aren\\u2019t we seeing? What could all this missing racial data mean for real COVID testing results? And how is it impacting our communities?\"), mdx(\"p\", null, \"From various \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"sources\"), \", we know that the pandemic impacts varying demographics differently, notably, disproportionately impacting Black and Latinx communities. To that end, how accurately can we create solutions or policy decisions to alleviate and support these communities with only 50% certainty of data?\"), mdx(\"p\", null, \"In the below visualization, the known percentages of cases with reported race are plotted against each state\\u2019s testing per capita. (3) The plot is animated to show how each state has progressed over time.\"), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"800\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/11.embed?link=false\"\n  }), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notes:\")), mdx(\"p\", null, \"Another \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://plotly.com/~brianwilliams2022/11.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"interactive visualization\"), \"! \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\"), \" Use the options in the top right to pan around the graph and select states to highlight their path over time. You can also select multiple states with Shift+Select for easy comparisons. Hover over each state bubble for more relevant data. You can press each category name to only view states of that category as well.\"), mdx(\"p\", null, \"The size of each state bubble represents its total positive tests. So a state with more cases will be represented as a larger bubble than a state with fewer cases in this visualization.\"), mdx(\"h3\", {\n    \"id\": \"categories\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Categories\")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"[\", \"Highly Impacted States, Increasing, Decreasing, and Constant]\")), mdx(\"p\", null, \"States are categorized by whether this \\u201Cknown percentage\\u201D factor has increased, decreased, or remained relatively constant from the first time data is available for that state to the latest data available. In order for a state to be considered \\u201Cincreasing,\\u201D it would need to increase by 5 or more percentage points. Similarly, \\u201Cdecreasing\\u201D states are those that decreased by 5 or more percentage points. \\u201CConstant\\u201D states are those that lie in the middle.\"), mdx(\"p\", null, \"For example, if a state\\u2019s \\u201Cknown percentage\\u201D is 69.7% on it\\u2019s earliest date and on it\\u2019s latest date it\\u2019s 85.4%, the state will be placed in the \\u201CIncreasing\\u201D category which is labeled green in the visualization.\"), mdx(\"h3\", {\n    \"id\": \"discussion\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Discussion\")), mdx(\"p\", null, \"The motivation to use testing per capita as a metric rather than absolute state population was because we thought the rate of reporting race could be affected by the total number of tests, which correlates with a state\\u2019s population. Rather, we were interested in comparing states with similar tests per capita as that could correlate better with similar testing practices.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Highly Impacted States\")), mdx(\"p\", null, \"We expected states with large amounts of total positive tests (states like Arizona, California, Florida, Texas, and New York) to have very low amounts of race data reported compared to other states. We suspected that as people flooded into testing locations and the states\\u2019 testing per capita increased, some aspects of the testing process would give way. As testing locations made operational decisions to keep the testing process optimized and safe for patients and medical staff, we thought patient identification would be overlooked from the strain of this increased testing density.\"), mdx(\"p\", null, \"Looking back at this, we weren\\u2019t necessarily wrong! But considering how the state bubbles fluctuate over time and how scattered they are (suggesting little correlation between reported race data and testing density), we understand there are other variables contributing to discrepancies in reported race data other than just the impact of rising testing density.\"), mdx(\"p\", null, \"Nonetheless, here are some interesting numbers:\"), mdx(\"p\", null, \"As of July 26th,\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in those \", mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"five\"), \" highly impacted states: 32.81%\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in every state and territory: 51.75%\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in the \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"non-vulnerable\"), \" states: 66.29%\")), mdx(\"p\", null, \"In Arizona, California, Florida, Texas, and New York, for every 3 cases, only 1 case has race data reported, but in every other region in the United States combined, 2 out of 3 cases have race data reported. Yet, when you combine these two sets, the national average moves to 1 out of 2 cases having race data reported. This means that around \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"half of all COVID-19 cases in the United States\"), \" are in these \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"five, most impacted\"), \" states.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Increasing - Green\")), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Arizona (AZ)\"), \", Connecticut (CT), Delaware (DE), Georgia (GA), Illinois (IL), Louisiana (LA), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Missouri (MO), Nebraska (NE), New Hampshire (NH), New Jersey (NJ), Nevada (NV), Pennsylvania (PA), Virginia (VA), Vermont (VT), and Washington (WA) fall into this category.\"), mdx(\"p\", null, \"Generally, green should represent positive trends as these states are reporting more race data over time.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Decreasing - Red\")), mdx(\"p\", null, \"Alaska (AK), Alabama (AL), Arkansas (AR), Colorado (CO), District of Columbia (DC), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Florida (FL)\"), \", Hawaii (HI), Iowa (IA), Idaho (ID), Indiana (IN), Minnesota (MN), Mississippi (MS), Montana (MT), North Carolina (NC), Oklahoma (OK), Oregon (OR), Rhode Island (RI), South Carolina (SC), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Texas (TX)\"), \", Utah (UT), and Wyoming (WY) fall into this category.\"), mdx(\"p\", null, \"This is a red flag category and some concern should be shown toward data management and reporting around race from these areas as time goes on during the pandemic.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Constant - Neutral\")), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"California (CA)\"), \", Guam (GU), Kansas (KS), Kentucky (KY), Northern Mariana Islands (MP), North Dakota (ND), New Mexico (NM), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"New York (NY)\"), \", Ohio (OH), Puerto Rico (PR), South Dakota (SD), Tennessee (TN), Virgin Islands (VI), Wisconsin (WI), and West Virginia (WV) fall into this category.\"), mdx(\"p\", null, \"Notably, North Dakota, New York, and Puerto Rico do not report \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"any\"), \" race data for their reported cases. This is alarming, and we need better data from these regions.\"), mdx(\"p\", null, \"Here\\u2019s a clear \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.plotly.com/~brianwilliams2022/15.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"visualization\"), \" of what these categories look like on a map. \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\")), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"800\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/15.embed?link=false\"\n  }), mdx(\"h3\", {\n    \"id\": \"interview-insights\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Interview Insights\")), mdx(\"p\", null, \"To get a more qualitative explanation of what is happening in the data, I sought experts and health officials to help me understand why so much race data could be missing and where in the process - from patient arrival to lab collection to data reporting - the missing link could occur. Additionally, I wanted to understand what the process of race designation is and how it is reported accurately.\"), mdx(\"p\", null, \"First, I consulted with MIT Medical Associate Medical Director & Chief of Student Health Shawn Ferullo. He explained that policies and procedures could vary greatly not only from state to state but from institution to institution. For example, Massachusetts General Hospital may have a completely different reporting protocol than MIT Medical, associated with a higher education institution, which has a much more rigidly defined community. But at the end of the day, Ferullo says, \\u201C\", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"what is being reported is what the State mandates to be reported.\"), \"\\u201D Let\\u2019s keep this in mind.\"), mdx(\"p\", null, \"From what I gathered, this is the testing methodology at MIT Medical, broken down into three key areas:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"Clinical Testing: Someone is sick or has symptoms and needs to be monitored, typically done in the office\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"Contact Tracing Testing: a separate waiting area for people who may have been potentially exposed to COVID-19 but may still be healthy\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Asymptomatic Testing:\"), \" Outside testing booths for people who don\\u2019t have symptoms but want testing, low-risk large volume testing is performed here, most similar to the concept of \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"drive-thru\"), \" testing (4)\")), mdx(\"p\", null, \"I\\u2019m most interested in how demographic data is being organized at these pop-up sites.\"), mdx(\"p\", null, \"As Ferullo explained, since MIT Medical uses an online health record system, with demographic data already on file, race data is automatically associated with a patient\\u2019s test, positive or negative. And in practice, this system minimizes the risk of losses of qualitative data even in the presence of high testing stress.\"), mdx(\"p\", null, \"He leaves this closing remark:\"), mdx(\"p\", null, \"\\u201CAs a clinician, so much of the day to day work is so patient-focused, as you can imagine\\u2026 The lab has whatever requirements it has to submit, so a lot of clinicians on the front lines may not even know what data is reported\\u2026 because they\\u2019re tasked with seeing the patient, collecting the test, and all of that. More of these bigger, drive up, population-based testing sites, I wonder how many are scrambling, quite honestly, if some states are just scrambling to get testing that they\\u2019re not as thoughtful with how they are setting up their systems\", \"[\", \"for data collecting]. I hate to think about how many states just don\\u2019t want to know or are intentionally not asking the question\\u201D\", \"[\", \"about race].\"), mdx(\"p\", null, \"Next, an excerpt from an interview with Sarita Shah, who is an epidemiologist at Emory University, studying racial disparities in areas with high rates of COVID-19 and volunteering with Fulton County\\u2019s health department. Notably, Shah sees the data collection problem firsthand. After doing nasal swabs at a drive-up testing site, she later calls those who test positive to fill in personal information, including race. \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"[\", \"self-identification]\"), \" However, even after multiple attempts, the team reaches only about half of these people. Shah says she\\u2019d love to note a person\\u2019s race when they\\u2019re sitting in front of her at the test site, but so far, the forms provided by labs that process the samples don\\u2019t have a place to note it. \\u201CI wish it was something more complex than that,\\u201D she says, \\u201Cbut it\\u2019s not.\\u201D \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"[\", \"the researcher also lacks an opportunity to identify patients and race data is lost]\"), \" \", \"[\", \"3]\"), mdx(\"p\", null, \"Her experience is not in isolation. Shortly after the interview with Ferullo, I followed up with MIT Medical Lab Director Jonathan Pelletier who confirmed that the template form used at MIT Medical to report testing results - sent directly from the Massachusetts Department of Public Health - has no column or option for reporting race designations. This is especially confusing because the previous data suggests Massachusetts is one of the leading states in which race data is being reported. It raises the question of how this is even possible? And where are they getting their data from if it\\u2019s not required, as Massachusetts historically has done a good job with their race data reporting. But these questions and many more, I do not have the answers to and as I leave them unexplored, I assume other laboratories across the nation are facing similar data issues.\"), mdx(\"p\", null, \"But maybe there\\u2019s hope; an amendment to the Coronavirus Aid, Relief, and Economic Security (CARES) Act passed back on June 4th will require laboratories to include relevant demographic data, such as age and race, on every test. \", \"[\", \"5] However, this is scheduled to go into effect on August 1st, leaving many passionate researchers and relief organizations in the dark about this crucial piece of metadata. I guess we\\u2019ll have to wait and see if this makes a difference in the data.\"), mdx(\"h3\", {\n    \"id\": \"conclusions\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Conclusions\")), mdx(\"p\", null, \"I believe the lack of normalized and uniform systems of reporting demographic data at the local level - individual laboratories, medical institutions, testing locations, communities, and counties - results in these large discrepancies that we see at the state and national level when researchers try to draw conclusions from data aggregates. This needs to change, and fast.\"), mdx(\"p\", null, \"\\u201COne problem that epidemiologists, in particular, have seen with all of this new lab testing sites data (pharmacies, drive-throughs, non-traditional lab settings) is incomplete data,\\u201D Scott Becker of the Association of Public Health Laboratories wrote in an email to NPR. And public health experts say what\\u2019s been needed are detailed breakdowns on how the virus is affecting Black and other marginalized communities. These groups have been hit especially hard, suffering higher case numbers per capita, serious illness, hospitalizations, and death.\\u201D \", \"[\", \"2]\"), mdx(\"p\", null, \"Lastly, accurate information on race is critical in making policy decisions, particularly for civil rights and federal programs like unemployment stimulus packages and eviction moratoriums. Race data is also used to promote equal employment opportunities and to assess racial disparities in health and environmental risks. \", \"[\", \"1] And in this case, laboratory testing data, in conjunction with case reports and other demographic data, provide vital guidance for mitigation and control activities, as well as properly allocating resources for relief. \", \"[\", \"5]\"), mdx(\"p\", null, \"As the country begins to reopen its doors, access to clear and accurate data is essential to communities and leadership as they make data-driven decisions for a phased reopening. For individuals, access to clear representations of real-world data improves feelings of safety, security, and awareness, and even empowers them to take action to support themselves, their families, and their communities. I hope we are able to see real change soon.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Project Notes - Methodology\")), mdx(\"p\", null, \"(1) On some days, a state\\u2019s percentages may add up to more than 100%, we understand these to be inaccuracies in the original data set of which the visualization was made. This could be attributed to either compiling errors or reporting errors from the individual medical institutions\", \"[\", \"labs].\"), mdx(\"p\", null, \"(2) For the purposes of this project, we considered the race designation \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" to be functionally the same as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" and therefore we included all cases reported having a race of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" as unknown. We carry over this adjustment in all calculations made for unknown visualizations.\"), mdx(\"p\", null, \"This first suggestion came about after some manually screening of the CSV file used to generate the visualizations showed some interesting patterns in data management which in shorter words, looked like some cases originally designated as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" were swapped over to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category. As the running totals continued in the spreadsheet, it seemed suspicious and we think this is a better way to account for that uncertainty.\"), mdx(\"p\", null, \"For instance, on May 13th in Iowa, 2878 cases are reported as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" and 232 cases as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \", then on May 17th, 3144 cases are now reported as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" and 0 cases as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \", and from this moment forward, case numbers increase in the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category but remain stagnant in the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category until June 17th. This is only noticeable because of how large a change it is but would be virtually undetectable if an individual laboratory chose to reinterpret data in this way. This seems more like a shift in data management rather than real testing results, and it\\u2019s these shifts that are most interesting to us as we try to interpret the gaps in the data.\"), mdx(\"p\", null, \"(3) What does testing per capita mean?\"), mdx(\"p\", null, \"After much deliberation, we choose to map the \\u201Cknown percentages\\u201D - the percent of positive cases in which race data exists - to testing per capita instead of positive tests because of some confounding variables. Testing per capita represents the total amount of tests performed in that state by the date specified divided by the state\\u2019s population. In short, we were looking for a good way to visualize magnitudes of uncertainty as states increase their number of tests. Do testing strategies change (per state, per district, per county?) as testing density increases, especially looking at race and ethnicity data?\"), mdx(\"p\", null, \"Generally speaking, the rate of infection of a disease multiplied by the total number of tests, should result in the total number of positive tests. Therefore, inherent differences in the rate of infection in separate states may impact positive test results without increasing the total number of tests - a confounding variable that made using total positive tests undesirable.\"), mdx(\"p\", null, \"And by using raw positive tests as an axis label, we would be assuming that increases in testing density are the only cause of increases in positive tests, when this may not be valid; the rate of infection also matters. There are many social regulations and restrictions in states, such as mask mandates or general climate patterns, which can impact the rate of infection. Especially considering the timing of this legislation varies greatly from state to state. Therefore, we thought testing per capita would be a more normalized metric off which to make conclusions.\"), mdx(\"p\", null, \"(4) Drive-thru refers to making tests more available in communities in an easier way, which would increase the volume of testing. 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I’m an aspiring protein engineer, who's actively looking for ways to uplift marginalized communities everywhere. I am a rising junior at MIT, majoring in Biological Engineering and minoring in Black Studies, and am looking forward to working in the Civic Data Design Lab this summer! So far, I have been analyzing and visualizing data reports on racial and ethnic data tied to COVID-19 test results and related deaths. In this project, I’m hoping to explain the confusing (and not so surprising) trends in the data and shed light on the reasons why discrepancies exist between states when reporting race in COVID-19 tests and deaths. Why is there missing data here, and what does this tell us? About testing strategies? About practicality? About policy?  I am passionate about understanding how policy decisions drive economic and social change. As an avid creative, I love to model and write poetry, and I play varsity baseball at MIT.","id":"f542d3ea-8f8f-58ea-8d10-cb0bf7b9a4cd","name":"Brian 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328px"}}}],"basePath":"/","permalink":"https://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know/","slug":"/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know","id":"05fc4a31-394b-5eed-9975-130225fcf0e1","title":"Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know","canonicalUrl":null,"mailchimp":"","next":[{"id":"e439fcc2-bd48-56d1-abfc-d94c3a0dc360","slug":"/h2a:-america's-essential-yet-unknown-program","secret":false,"title":"H2A: America's Essential yet Unknown Program","author":"Evan Denmark","date":"July 31st, 2020","dateForSEO":"2020-07-31T00:00:00.000Z","timeToRead":5,"excerpt":"The first of many posts that dig into America's H2A visa program, how our food supply chain depends on it, and how data can show its potential flaws, especially in the midst of a pandemic.   ","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"H2A: America's Essential yet Unknown Program\",\n  \"author\": \"Evan Denmark\",\n  \"date\": \"2020-07-31T00:00:00.000Z\",\n  \"excerpt\": \"The first of many posts that dig into America's H2A visa program, how our food supply chain depends on it, and how data can show its potential flaws, especially in the midst of a pandemic.   \",\n  \"tags\": [\"Covid\", \"Visualization\", \"Economy\"],\n  \"hero\": \"images/screen-shot-2020-07-28-at-10.16.52-am.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, \"The Mississippi sweet potato planting season recently came to a close. Mike Williamson, a farmer of Williamson Family Farms in Water Valley, Mississippi, only hopes that all he\\u2019ll have the workforce to harvest those crops come October. \"), mdx(\"p\", null, \"\\u201CI\\u2019m not one hundred percent sure that my amigos will be here in the fall. If not, I\\u2019ll be out of business,\\u201D Williamson said.\"), mdx(\"p\", null, \"The \\u201Camigos\\u201D he refers to are his farm workers from Mexico, specifically those who were issued H2A visas\\u2014one of the only visas that has not been suspended per a \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.whitehouse.gov/presidential-actions/proclamation-suspending-entry-aliens-present-risk-u-s-labor-market-following-coronavirus-outbreak/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"June 22 Presidential Proclamation\"), \" because it is \\u201Cessential to the food supply chain.\\u201D\"), mdx(\"p\", null, \"For the last seven years, Williamson has hired H2A visa workers to plant and harvest his crop. This year, he requested 28 workers, but due to personal fears of COVID-19, only 25 made the trip. Fortunately for Williamson, all of his workers are in the 90% of visa holders who have previously held an H2A visa, which means that their visa processing time was streamlined.\"), mdx(\"p\", null, \"In a pre-pandemic time, H2A visa applicants - 92% of whom are from Mexico - would undergo a personal interview at a US Consulate to get their visa approved. 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However, as he noted, \\u201Cthere is no hard rule that \", \"[\", \"applications] have to be held back\\u201D because \\u201Cit depends on the Consulate staff.\\u201D\"), mdx(\"p\", null, \"A world with COVID-19 is also presenting financial obstacles for farmers. Allen Robison, an apple and cherry farmer in Chelan, Washington, said that he is taking daily temperatures of his employees and spending \\u201Cthousands of dollars\\u201D to buy masks and other personal protective equipment.\"), mdx(\"p\", null, \"Despite the logistical hurdles and financial expense caused by the pandemic, the US Department of State and farmers across America are making sure that most H2A visa applicants are able to make it to their final destination. Their efforts speak to the fact that the H2A program is increasingly essential to America\\u2019s food supply chain.\"), mdx(\"p\", null, \"H2A visa holders carried 9% of the country\\u2019s agricultural labor budget in 2019, a number that is up from 6% in 2016 and that has been steadily growing in the past decade.\"), mdx(\"p\", null, \"Created in 1952 (and modified in 1986), the H2A program allows US-based farmers and agricultural firms to hire foreign workers when \\u201Cthere are not sufficient \", \"[\", \"American] workers who are able, willing, qualified, and available, and that the employment of aliens will not adversely affect the wages and working conditions of workers similarly employed in the U.S.\\u201D\"), mdx(\"p\", null, \"Despite posting the positions in newspapers and on job boards in multiple states, Mike Williamson said that he has not received \\u201Ca single American application\\u201D in the last seven years.\"), mdx(\"p\", null, \"Without an American agriculture workforce, the demand for visa-issued foreign workers has increased but comes at no small expense for farmers. 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Source: U.S. Department of State, Bureau of Consular Affairs, “Nonimmigrant Visa Statistics\\\"Source: \",\n    \"title\": \"The H2A program has dramatically increased during both Obama and Trump's tenure in the White House. Source: U.S. Department of State, Bureau of Consular Affairs, “Nonimmigrant Visa Statistics\\\"Source: \",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, \"Since 2016 (the year when the US DOL began producing data with consistent formatting), the labor costs of the H2A program have increased by 66% due to a rise in the amount of labor being performed and significant increases in the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.mobilefarmware.com/support/wams/aewr/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"adverse wage\"), \" - a state-by-state minimum wage to ensure that guest worker wages will not depress the wages of domestic workers in similar occupations. In some states, like Washington, the adverse wage has increased by 24% since 2016.\"), mdx(\"p\", null, \"Additionally, while the number of farms participating in H2A increased by 28%, the total person-hours done by H2A workers increased by 43% and the number of certified jobs by 45%, indicating both that new farms began participating in the program and that returning farms required more farm labor.\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"3031px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"64.88%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,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')\",\n      \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"picture\", {\n    parentName: \"span\"\n  }, \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/d1ad5efd2efeae49433a0e27dbdd71c6/a3bc4/2016v2020.webp 2500w\", \"/static/d1ad5efd2efeae49433a0e27dbdd71c6/f2528/2016v2020.webp 3031w\"],\n    \"sizes\": \"(max-width: 3031px) 100vw, 3031px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/d1ad5efd2efeae49433a0e27dbdd71c6/412e4/2016v2020.png 2500w\", \"/static/d1ad5efd2efeae49433a0e27dbdd71c6/6fab7/2016v2020.png 3031w\"],\n    \"sizes\": \"(max-width: 3031px) 100vw, 3031px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/d1ad5efd2efeae49433a0e27dbdd71c6/6fab7/2016v2020.png\",\n    \"alt\": \"Source:  U.S. Department of Labor, Employment and Training Administration, “H2A Disclosure Data” for years 2016-2019\",\n    \"title\": \"Source:  U.S. Department of Labor, Employment and Training Administration, “H2A Disclosure Data” for years 2016-2019\",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, \"The use and expense of the H2A program have been steadily increasing, yet most Americans are completely unaware of its existence. Our food is the foundation of our health as individuals and, by extension, our society. Without an understanding of the farm to table process, the American food supply chain and agricultural economy at large becomes less resilient to obstacles\\u2026 like a pandemic.\"), mdx(\"p\", null, \"Thus, this post is the first of many CDDL posts which will explore the H2A visa program, its value in America\\u2019s supply chain, and its \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nbcnews.com/specials/h2a-visa-program-for-farmworkers-surging-under-trump-and-labor-violations/index.html?fbclid=IwAR30iL0RLxMBpUfQzh-b4d_Qeu-x_R6rERytYMGeEuG-J6OfR1pvFJ0_7HY\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"failures\"), \" through both data visualizations and extensive interviews to bring our readers and Americans at large a little bit closer to the food they eat.\"), mdx(\"h2\", {\n    \"id\": \"h2a-visually\"\n  }, \"H2A, Visually\"), mdx(\"iframe\", {\n    src: \"https://public.tableau.com/shared/8YXPTJFYZ?:display_count=y&:origin=viz_share_link:showVizHome=no&:embed=true\",\n    width: \"100%\",\n    height: \"600\",\n    allowFullScreen: true\n  }), mdx(\"p\", null, \"This \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://public.tableau.com/views/TheCurrentStateofH2A/Story1?:language=en&:display_count=y&publish=yes&:origin=viz_share_link\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"visualization\"), \" is a Tableau storyboard that highlights some of the basics about the H2A program: where workers are employed by county, which states use the H2A program, and which crops are most affected by H2A.\"), mdx(\"p\", null, \"All of the data used in this graphic come from the US Department of Labor\\u2019s Employment and Training Administration\\u2019s published dataset on H2A visa applications for the years 2016-2019.\"), mdx(\"p\", null, \"The map,  \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Where do H2A visa workers go?\"), \", shows where H2A jobs are being taken in each county in the US. If a county has H2A activity, the size of the circle represents the number of certified H2A jobs in 2019. It is worth noting that this does not show the number of workers going to each county but rather the number of jobs that were given to an H2A visa holder. Although the two may correlate, a single H2A visa holder may have multiple jobs (potentially on the same farm) at different points in the year. Mike Williamson - the Mississippi sweet potato farmer - hires the same 28 workers in May to plant the crop and in September to pick the harvest. For these 28 workers, these are two separate jobs. In the summer months between the jobs, the workers must leave the US.\"), mdx(\"p\", null, \"The second visualization, \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Which states use H2A?\"), \", shows the amount of H2A labor required in each state in 2019. Each visa application contains the job start and end date as well as the expected number of hours worked per week, so this was used to calculate the total number of person-hours required for each state. Interestingly, the top five states - North Carolina, Washington, Florida, California, and Georgia - make up 51% of the entire labor in the country.\"), mdx(\"p\", null, \"Lastly, in \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Which crops are affected by H2A?\"), \", the size of each bubble corresponds to the number of person-hours spent on a given crop in each state. By default, the visualization shows data for the entire country but the state can be adjusted in the dropdown. On each visa application, a farmer must specify the job function or primary crop for the job. Many farmers often put \\u201CGeneral Farm Worker.\\u201D However, the most popular crop for H2A visa holders is tobacco, a crop that supports a large industry but also requires intensive manual labor. It is interesting to see that other crops like corn - America\\u2019s largest crop by total production - require much less H2A labor because harvesting is aided by machinery and, thus, requires fewer person-hours. Therefore, the number of person-hours dedicated to a particular crop is a function of the size of the industryandhow much individual attention is required for crop production.\"), mdx(\"p\", null, \"Sources\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"U.S. Department of Labor, Employment and Training Administration, \\u201CH2A Disclosure Data\\u201D for years 2016-2019\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"U.S. Department of State, Bureau of Consular Affairs, \\u201CNonimmigrant Visa Statistics\\u201D\")));\n}\n;\nMDXContent.isMDXComponent = 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Some have authorized statewide stay-at-home orders, and…","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"COVID-19 State Reopening Policies and Mask Mandates\",\n  \"author\": \"Amy Fang\",\n  \"date\": \"2020-07-24T00:00:00.000Z\",\n  \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n  \"hero\": \"images/statewide-reopening-and-mask-mandate-map-by-week.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, \"All 50 states have been affected uniquely and have reacted differently to COVID-19. Some have authorized statewide stay-at-home orders, and some have authorized statewide mask mandates, but even these policies vary in both scale and time from state to state. Notable news publications, including the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"New York Times\"), \" \", \"[\", \"1],\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"CNN\"), \" \", \"[\", \"2], and the\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Washington Post\"), \" \", \"[\", \"3], have created interactive visualizations illustrating these variations in state reopening statuses, consistently updating in real-time to provide the latest information. 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As our collective understanding of the virus increases over time, guidelines, particularly those for preventative measures, have been ever-evolving. For example, although the first case of COVID-19 in the United States was laboratory-confirmed on January 20, 2020 \", \"[\", \"5], the Center for Disease Control (CDC), our leading national health authority, did not recommend that everyone wear face coverings around others until the beginning of April \", \"[\", \"6, 7]. Until then, it was unconfirmed and unknown that the virus could also be spread by asymptomatic people. In fact, President Trump did not endorse wearing face masks until July 21.\"), mdx(\"p\", null, \"As prominently displayed in the above map, this inevitable lack of clarity around COVID-19 is also reflected in the constant shifting of state reopening policies. By the beginning of June, every state had initiated their reopening plans. By mid-July, \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"20\"), \" states had paused or even reversed upon these original plans. Although individual counties and cities have imposed further restrictions on their residents, these data visualizations look exclusively at reopening policies and mask mandates at the state level.\"), mdx(\"p\", null, \"Although COVID-19 associated death counts can be a rough metric, indicative of where a state stands in successfully combating the virus, it is difficult to completely understand how and why each state makes such decisions. The following data visualizations compare COVID-19 associated death counts with state reopening and mask wearing policies. Despite an interesting graphic, we are incapable of commenting on the level of correlation or causation between these two variables because the spread and control of the virus is a multi-dimensional problem. 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\"/static/1007cf56d7bb5a6425aec64343f13590/1a152/covid-19-associated-death-count-and-statewide-mask-mandate-chart-by-week.png\",\n    \"alt\": \"covid 19 associated death count and statewide mask mandate chart by week\",\n    \"title\": \"COVID-19 Associated Death Count and Statewide Mask Mandate Chart by Week, Source: NYT [1], CNN [4], and CDC [9]\",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"1787px\"\n    }\n  }), \"\\n      \", mdx(\"span\", 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[\"/static/20b4e21bf2ced9d3884eac94d07445f9/c6cb5/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.webp 1787w\"],\n    \"sizes\": \"(max-width: 1787px) 100vw, 1787px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/20b4e21bf2ced9d3884eac94d07445f9/b7458/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.png 1787w\"],\n    \"sizes\": \"(max-width: 1787px) 100vw, 1787px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/20b4e21bf2ced9d3884eac94d07445f9/b7458/covid-19-associated-death-count-up-until-statewide-mask-mandate-graph.png\",\n    \"alt\": \"covid 19 associated death count up until statewide mask mandate graph\",\n    \"title\": \"COVID-19 Associated Death Count until Statewide Mask Mandate Graph, Categorized by Reopening Stage at Time of Mask Mandate, Source: NYT [1], CNN [4], and CDC [9]\",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, \"Thus, from these data visualizations, I draw the following conclusions:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"Statewide mask mandates do not necessarily align with the stages of a state\\u2019s reopening policies. In fact, statewide mask mandates have been initiated when a state has been under a stay-at-home order, while reopening, after being reopened, while pausing reopening, and while reversing reopening.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"As of June 12th, 24 states have statewide mask mandates. 16 of these states instated their mask mandate within two weeks of their peak (in the case of Texas, their second peak) COVID-19 associated death count.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"The 12 states with the largest total COVID-19 associated death counts, excluding Florida, have statewide mask mandates, 7 of which were instated while the state was under a stay-at-home order. This aligns with the results of a \", mdx(\"a\", _extends({\n    parentName: \"li\"\n  }, {\n    \"href\": \"https://www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%20Stories&pgtype=Homepage\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"New York Times nationwide survey\"), \" \", \"[\", \"10], which qualitatively asked individuals about their mask-wearing preferences, and found that \\u201CPlaces that have experienced a lot of coronavirus, with a few exceptions, show higher-than-usual rates of masking.\\u201D\")), mdx(\"p\", null, \"This same article concludes, \\u201CIn parts of the country without a lot of cases, there are still wide variations in mask use that appear to be driven more by local custom and partisan identification than by specific disease risk. If coronavirus cases end up climbing in those areas, too, masks may start appearing on many more faces.\\u201D\"), mdx(\"p\", null, \"As with everything surrounding the collective status of the United States and COVID-19, looking not only at statewide mask mandates but also at statewide reopening policies, we\\u2019ll have to see how everything plays out.\"), mdx(\"h2\", {\n    \"id\": \"sources\"\n  }, \"Sources\"), mdx(\"p\", null, \"[\", \"1] Lee, Jasmine C., et al. \\u201CSee How All 50 States Are Reopening (and Closing Again).\\u201D The New York Times, The New York Times, 25 Apr. 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html\"), \".\"), mdx(\"p\", null, \"[\", \"2] Elassar, Alaa. \\u201CThis Is Where All 50 States Stand on Reopening.\\u201D CNN, Cable News Network, 26 Apr. 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.cnn.com/interactive/2020/us/states-reopen-coronavirus-trnd/\"), \".\"), mdx(\"p\", null, \"[\", \"3] Gerhart, Ann. \\u201CWhere States Reopened and Cases Spiked after the U.S. Shutdown.\\u201D The Washington Post, WP Company, 20 July 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/\"), \".\"), mdx(\"p\", null, \"[\", \"4] Kim, Allen, et al. \\u201CThese Are the States Requiring People to Wear Masks When out in Public.\\u201D CNN, Cable News Network, 22 July 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.cnn.com/2020/06/19/us/states-face-mask-coronavirus-trnd/index.html\"), \".\"), mdx(\"p\", null, \"[\", \"5] Stokes EK, Zambrano LD, Anderson KN, et al. Coronavirus Disease 2019 Case Surveillance \\u2014 United States, January 22\\u2013May 30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:759\\u2013765. DOI:\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://dx.doi.org/10.15585/mmwr.mm6924e2\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"http://dx.doi.org/10.15585/mmwr.mm6924e2\")), mdx(\"p\", null, \"[\", \"6] Goodnough, Abby, and Knvul Sheikh. \\u201CC.D.C. Weighs Advising Everyone to Wear a Mask.\\u201D The New York Times, The New York Times, 31 Mar. 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.nytimes.com/2020/03/31/health/cdc-masks-coronavirus.html?action=click&module=RelatedLinks&pgtype=Article\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.nytimes.com/2020/03/31/health/cdc-masks-coronavirus.html?action=click&module=RelatedLinks&pgtype=Article\"), \".\"), mdx(\"p\", null, \"[\", \"7] \\u201CSurgeon General Explains Evolution of CDC Face Mask Guidance.\\u201D MSNBC, NBCUniversal News Group, 3 Apr. 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.msnbc.com/msnbc/watch/surgeon-general-explains-evolution-of-cdc-face-mask-guidance-81619013687\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.msnbc.com/msnbc/watch/surgeon-general-explains-evolution-of-cdc-face-mask-guidance-81619013687\"), \".\"), mdx(\"p\", null, \"[\", \"8] Baker, Peter. \\u201CTrump, in a Shift, Endorses Masks and Says Virus Will Get Worse.\\u201D The New York Times, The New York Times, 22 July 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.nytimes.com/2020/07/21/us/politics/trump-coronavirus-masks.html?action=click&module=RelatedLinks&pgtype=Article\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.nytimes.com/2020/07/21/us/politics/trump-coronavirus-masks.html?action=click&module=RelatedLinks&pgtype=Article\"), \".\"), mdx(\"p\", null, \"[\", \"9] National Center for Health Statistics. \\u201CProvisional COVID-19 Death Counts by Week Ending Date and State.\\u201D Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 23 July 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Week-Ending-D/r8kw-7aab.\")), mdx(\"p\", null, \"[\", \"10] Katz, Josh, et al. \\u201CA Detailed Map of Who Is Wearing Masks in the U.S.\\u201D The New York Times, The New York Times, 17 July 2020,\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%2BStories&pgtype=Homepage\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html?action=click&module=Top%2BStories&pgtype=Homepage\"), \".\"));\n}\n;\nMDXContent.isMDXComponent = 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